JSON Database

Package validator implements value validations for structs and individual fields based on tags. It can also handle Cross-Field and Cross-Struct validation for nested structs and has the ability to dive into arrays and maps of any type. see more examples https://github.com/go-playground/validator/tree/master/_examples Validator is designed to be thread-safe and used as a singleton instance. It caches information about your struct and validations, in essence only parsing your validation tags once per struct type. Using multiple instances neglects the benefit of caching. The not thread-safe functions are explicitly marked as such in the documentation. Doing things this way is actually the way the standard library does, see the file.Open method here: The authors return type "error" to avoid the issue discussed in the following, where err is always != nil: Validator only InvalidValidationError for bad validation input, nil or ValidationErrors as type error; so, in your code all you need to do is check if the error returned is not nil, and if it's not check if error is InvalidValidationError ( if necessary, most of the time it isn't ) type cast it to type ValidationErrors like so err.(validator.ValidationErrors). Custom Validation functions can be added. Example: Cross-Field Validation can be done via the following tags: If, however, some custom cross-field validation is required, it can be done using a custom validation. Why not just have cross-fields validation tags (i.e. only eqcsfield and not eqfield)? The reason is efficiency. If you want to check a field within the same struct "eqfield" only has to find the field on the same struct (1 level). But, if we used "eqcsfield" it could be multiple levels down. Example: Multiple validators on a field will process in the order defined. Example: Bad Validator definitions are not handled by the library. Example: Baked In Cross-Field validation only compares fields on the same struct. If Cross-Field + Cross-Struct validation is needed you should implement your own custom validator. Comma (",") is the default separator of validation tags. If you wish to have a comma included within the parameter (i.e. excludesall=,) you will need to use the UTF-8 hex representation 0x2C, which is replaced in the code as a comma, so the above will become excludesall=0x2C. Pipe ("|") is the 'or' validation tags deparator. If you wish to have a pipe included within the parameter i.e. excludesall=| you will need to use the UTF-8 hex representation 0x7C, which is replaced in the code as a pipe, so the above will become excludesall=0x7C Here is a list of the current built in validators: Tells the validation to skip this struct field; this is particularly handy in ignoring embedded structs from being validated. (Usage: -) This is the 'or' operator allowing multiple validators to be used and accepted. (Usage: rgb|rgba) <-- this would allow either rgb or rgba colors to be accepted. This can also be combined with 'and' for example ( Usage: omitempty,rgb|rgba) When a field that is a nested struct is encountered, and contains this flag any validation on the nested struct will be run, but none of the nested struct fields will be validated. This is useful if inside of your program you know the struct will be valid, but need to verify it has been assigned. NOTE: only "required" and "omitempty" can be used on a struct itself. Same as structonly tag except that any struct level validations will not run. Allows conditional validation, for example if a field is not set with a value (Determined by the "required" validator) then other validation such as min or max won't run, but if a value is set validation will run. Allows to skip the validation if the value is nil (same as omitempty, but only for the nil-values). This tells the validator to dive into a slice, array or map and validate that level of the slice, array or map with the validation tags that follow. Multidimensional nesting is also supported, each level you wish to dive will require another dive tag. dive has some sub-tags, 'keys' & 'endkeys', please see the Keys & EndKeys section just below. Example #1 Example #2 Keys & EndKeys These are to be used together directly after the dive tag and tells the validator that anything between 'keys' and 'endkeys' applies to the keys of a map and not the values; think of it like the 'dive' tag, but for map keys instead of values. Multidimensional nesting is also supported, each level you wish to validate will require another 'keys' and 'endkeys' tag. These tags are only valid for maps. Example #1 Example #2 This validates that the value is not the data types default zero value. For numbers ensures value is not zero. For strings ensures value is not "". For booleans ensures value is not false. For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value when using WithRequiredStructEnabled. The field under validation must be present and not empty only if all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty unless all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only if any of the other specified fields are present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only if all of the other specified fields are present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Example: The field under validation must be present and not empty only when any of the other specified fields are not present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only when all of the other specified fields are not present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Example: The field under validation must not be present or not empty only if all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must not be present or empty unless all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: This validates that the value is the default value and is almost the opposite of required. For numbers, length will ensure that the value is equal to the parameter given. For strings, it checks that the string length is exactly that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, len will ensure that the value is equal to the duration given in the parameter. For numbers, max will ensure that the value is less than or equal to the parameter given. For strings, it checks that the string length is at most that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, max will ensure that the value is less than or equal to the duration given in the parameter. For numbers, min will ensure that the value is greater or equal to the parameter given. For strings, it checks that the string length is at least that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, min will ensure that the value is greater than or equal to the duration given in the parameter. For strings & numbers, eq will ensure that the value is equal to the parameter given. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, eq will ensure that the value is equal to the duration given in the parameter. For strings & numbers, ne will ensure that the value is not equal to the parameter given. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, ne will ensure that the value is not equal to the duration given in the parameter. For strings, ints, and uints, oneof will ensure that the value is one of the values in the parameter. The parameter should be a list of values separated by whitespace. Values may be strings or numbers. To match strings with spaces in them, include the target string between single quotes. Kind of like an 'enum'. Works the same as oneof but is case insensitive and therefore only accepts strings. For numbers, this will ensure that the value is greater than the parameter given. For strings, it checks that the string length is greater than that number of characters. For slices, arrays and maps it validates the number of items. Example #1 Example #2 (time.Time) For time.Time ensures the time value is greater than time.Now.UTC(). Example #3 (time.Duration) For time.Duration, gt will ensure that the value is greater than the duration given in the parameter. Same as 'min' above. Kept both to make terminology with 'len' easier. Example #1 Example #2 (time.Time) For time.Time ensures the time value is greater than or equal to time.Now.UTC(). Example #3 (time.Duration) For time.Duration, gte will ensure that the value is greater than or equal to the duration given in the parameter. For numbers, this will ensure that the value is less than the parameter given. For strings, it checks that the string length is less than that number of characters. For slices, arrays, and maps it validates the number of items. Example #1 Example #2 (time.Time) For time.Time ensures the time value is less than time.Now.UTC(). Example #3 (time.Duration) For time.Duration, lt will ensure that the value is less than the duration given in the parameter. Same as 'max' above. Kept both to make terminology with 'len' easier. Example #1 Example #2 (time.Time) For time.Time ensures the time value is less than or equal to time.Now.UTC(). Example #3 (time.Duration) For time.Duration, lte will ensure that the value is less than or equal to the duration given in the parameter. This will validate the field value against another fields value either within a struct or passed in field. Example #1: Example #2: Field Equals Another Field (relative) This does the same as eqfield except that it validates the field provided relative to the top level struct. This will validate the field value against another fields value either within a struct or passed in field. Examples: Field Does Not Equal Another Field (relative) This does the same as nefield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as gtfield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as gtefield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as ltfield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as ltefield except that it validates the field provided relative to the top level struct. This does the same as contains except for struct fields. It should only be used with string types. See the behavior of reflect.Value.String() for behavior on other types. This does the same as excludes except for struct fields. It should only be used with string types. See the behavior of reflect.Value.String() for behavior on other types. For arrays & slices, unique will ensure that there are no duplicates. For maps, unique will ensure that there are no duplicate values. For slices of struct, unique will ensure that there are no duplicate values in a field of the struct specified via a parameter. This validates that a string value contains ASCII alpha characters only This validates that a string value contains ASCII alphanumeric characters only This validates that a string value contains unicode alpha characters only This validates that a string value contains unicode alphanumeric characters only This validates that a string value can successfully be parsed into a boolean with strconv.ParseBool This validates that a string value contains number values only. For integers or float it returns true. This validates that a string value contains a basic numeric value. basic excludes exponents etc... for integers or float it returns true. This validates that a string value contains a valid hexadecimal. This validates that a string value contains a valid hex color including hashtag (#) This validates that a string value contains only lowercase characters. An empty string is not a valid lowercase string. This validates that a string value contains only uppercase characters. An empty string is not a valid uppercase string. This validates that a string value contains a valid rgb color This validates that a string value contains a valid rgba color This validates that a string value contains a valid hsl color This validates that a string value contains a valid hsla color This validates that a string value contains a valid E.164 Phone number https://en.wikipedia.org/wiki/E.164 (ex. +1123456789) This validates that a string value contains a valid email This may not conform to all possibilities of any rfc standard, but neither does any email provider accept all possibilities. This validates that a string value is valid JSON This validates that a string value is a valid JWT This validates that a string value contains a valid file path and that the file exists on the machine. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid file path and that the file exists on the machine and is an image. This is done using os.Stat and github.com/gabriel-vasile/mimetype This validates that a string value contains a valid file path but does not validate the existence of that file. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid url This will accept any url the golang request uri accepts but must contain a schema for example http:// or rtmp:// This validates that a string value contains a valid uri This will accept any uri the golang request uri accepts This validates that a string value contains a valid URN according to the RFC 2141 spec. This validates that a string value contains a valid bas324 value. Although an empty string is valid base32 this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 value. Although an empty string is valid base64 this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 URL safe value according the RFC4648 spec. Although an empty string is a valid base64 URL safe value, this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 URL safe value, but without = padding, according the RFC4648 spec, section 3.2. Although an empty string is a valid base64 URL safe value, this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid bitcoin address. The format of the string is checked to ensure it matches one of the three formats P2PKH, P2SH and performs checksum validation. Bitcoin Bech32 Address (segwit) This validates that a string value contains a valid bitcoin Bech32 address as defined by bip-0173 (https://github.com/bitcoin/bips/blob/master/bip-0173.mediawiki) Special thanks to Pieter Wuille for providing reference implementations. This validates that a string value contains a valid ethereum address. The format of the string is checked to ensure it matches the standard Ethereum address format. This validates that a string value contains the substring value. This validates that a string value contains any Unicode code points in the substring value. This validates that a string value contains the supplied rune value. This validates that a string value does not contain the substring value. This validates that a string value does not contain any Unicode code points in the substring value. This validates that a string value does not contain the supplied rune value. This validates that a string value starts with the supplied string value This validates that a string value ends with the supplied string value This validates that a string value does not start with the supplied string value This validates that a string value does not end with the supplied string value This validates that a string value contains a valid isbn10 or isbn13 value. This validates that a string value contains a valid isbn10 value. This validates that a string value contains a valid isbn13 value. This validates that a string value contains a valid UUID. Uppercase UUID values will not pass - use `uuid_rfc4122` instead. This validates that a string value contains a valid version 3 UUID. Uppercase UUID values will not pass - use `uuid3_rfc4122` instead. This validates that a string value contains a valid version 4 UUID. Uppercase UUID values will not pass - use `uuid4_rfc4122` instead. This validates that a string value contains a valid version 5 UUID. Uppercase UUID values will not pass - use `uuid5_rfc4122` instead. This validates that a string value contains a valid ULID value. This validates that a string value contains only ASCII characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains only printable ASCII characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains one or more multibyte characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains a valid DataURI. NOTE: this will also validate that the data portion is valid base64 This validates that a string value contains a valid latitude. This validates that a string value contains a valid longitude. This validates that a string value contains a valid U.S. Social Security Number. This validates that a string value contains a valid IP Address. This validates that a string value contains a valid v4 IP Address. This validates that a string value contains a valid v6 IP Address. This validates that a string value contains a valid CIDR Address. This validates that a string value contains a valid v4 CIDR Address. This validates that a string value contains a valid v6 CIDR Address. This validates that a string value contains a valid resolvable TCP Address. This validates that a string value contains a valid resolvable v4 TCP Address. This validates that a string value contains a valid resolvable v6 TCP Address. This validates that a string value contains a valid resolvable UDP Address. This validates that a string value contains a valid resolvable v4 UDP Address. This validates that a string value contains a valid resolvable v6 UDP Address. This validates that a string value contains a valid resolvable IP Address. This validates that a string value contains a valid resolvable v4 IP Address. This validates that a string value contains a valid resolvable v6 IP Address. This validates that a string value contains a valid Unix Address. This validates that a string value contains a valid MAC Address. Note: See Go's ParseMAC for accepted formats and types: This validates that a string value is a valid Hostname according to RFC 952 https://tools.ietf.org/html/rfc952 This validates that a string value is a valid Hostname according to RFC 1123 https://tools.ietf.org/html/rfc1123 Full Qualified Domain Name (FQDN) This validates that a string value contains a valid FQDN. This validates that a string value appears to be an HTML element tag including those described at https://developer.mozilla.org/en-US/docs/Web/HTML/Element This validates that a string value is a proper character reference in decimal or hexadecimal format This validates that a string value is percent-encoded (URL encoded) according to https://tools.ietf.org/html/rfc3986#section-2.1 This validates that a string value contains a valid directory and that it exists on the machine. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid directory but does not validate the existence of that directory. This is done using os.Stat, which is a platform independent function. It is safest to suffix the string with os.PathSeparator if the directory may not exist at the time of validation. This validates that a string value contains a valid DNS hostname and port that can be used to validate fields typically passed to sockets and connections. This validates that a string value is a valid datetime based on the supplied datetime format. Supplied format must match the official Go time format layout as documented in https://golang.org/pkg/time/ This validates that a string value is a valid country code based on iso3166-1 alpha-2 standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid country code based on iso3166-1 alpha-3 standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid country code based on iso3166-1 alpha-numeric standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid BCP 47 language tag, as parsed by language.Parse. More information on https://pkg.go.dev/golang.org/x/text/language BIC (SWIFT code) This validates that a string value is a valid Business Identifier Code (SWIFT code), defined in ISO 9362. More information on https://www.iso.org/standard/60390.html This validates that a string value is a valid dns RFC 1035 label, defined in RFC 1035. More information on https://datatracker.ietf.org/doc/html/rfc1035 This validates that a string value is a valid time zone based on the time zone database present on the system. Although empty value and Local value are allowed by time.LoadLocation golang function, they are not allowed by this validator. More information on https://golang.org/pkg/time/#LoadLocation This validates that a string value is a valid semver version, defined in Semantic Versioning 2.0.0. More information on https://semver.org/ This validates that a string value is a valid cve id, defined in cve mitre. More information on https://cve.mitre.org/ This validates that a string value contains a valid credit card number using Luhn algorithm. This validates that a string or (u)int value contains a valid checksum using the Luhn algorithm. This validates that a string is a valid 24 character hexadecimal string or valid connection string. Example: This validates that a string value contains a valid cron expression. This validates that a string is valid for use with SpiceDb for the indicated purpose. If no purpose is given, a purpose of 'id' is assumed. Alias Validators and Tags NOTE: When returning an error, the tag returned in "FieldError" will be the alias tag unless the dive tag is part of the alias. Everything after the dive tag is not reported as the alias tag. Also, the "ActualTag" in the before case will be the actual tag within the alias that failed. Here is a list of the current built in alias tags: Validator notes: A collection of validation rules that are frequently needed but are more complex than the ones found in the baked in validators. A non standard validator must be registered manually like you would with your own custom validation functions. Example of registration and use: Here is a list of the current non standard validators: This package panics when bad input is provided, this is by design, bad code like that should not make it to production.

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql. It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jackc/pgx/v4/stdlib to use pgx as a database/sql compatible driver. The primary way of establishing a connection is with `pgx.Connect`. The database connection string can be in URL or DSN format. Both PostgreSQL settings and pgx settings can be specified here. In addition, a config struct can be created by `ParseConfig` and modified before establishing the connection with `ConnectConfig`. `*pgx.Conn` represents a single connection to the database and is not concurrency safe. Use sub-package pgxpool for a concurrency safe connection pool. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. QueryFunc can be used to execute a callback function for every row. This is often easier to use than Query. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. If pgx does cannot natively encode a type and that type is a renamed type (e.g. type MyTime time.Time) pgx will attempt to encode the underlying type. While this is usually desired behavior it can produce surprising behavior if one the underlying type and the renamed type each implement database/sql interfaces and the other implements pgx interfaces. It is recommended that this situation be avoided by implementing pgx interfaces on the renamed type. Row values and composite types are represented as pgtype.Record (https://pkg.go.dev/github.com/jackc/pgtype?tab=doc#Record). It is possible to get values of your custom type by implementing DecodeBinary interface. Decoding into pgtype.Record first can simplify process by avoiding dealing with raw protocol directly. For example: []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin. The Tx returned from Begin also implements the Begin method. This can be used to implement pseudo nested transactions. These are internally implemented with savepoints. Use BeginTx to control the transaction mode. BeginFunc and BeginTxFunc are variants that begin a transaction, execute a function, and commit or rollback the transaction depending on the return value of the function. These can be simpler and less error prone to use. Prepared statements can be manually created with the Prepare method. However, this is rarely necessary because pgx includes an automatic statement cache by default. Queries run through the normal Query, QueryRow, and Exec functions are automatically prepared on first execution and the prepared statement is reused on subsequent executions. See ParseConfig for information on how to customize or disable the statement cache. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. When you already have a typed array using CopyFromSlice can be more convenient. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the `Conn.WaitForNotification` method. It blocks until a notification is received or the context is canceled. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/sirupsen/logrus, go.uber.org/zap, github.com/rs/zerolog, and the testing log are provided in the log directory. pgx is implemented on top of github.com/jackc/pgconn a lower level PostgreSQL driver. The Conn.PgConn() method can be used to access this lower layer. pgx is compatible with PgBouncer in two modes. One is when the connection has a statement cache in "describe" mode. The other is when the connection is using the simple protocol. This can be set with the PreferSimpleProtocol config option.

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql. It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jackc/pgx/stdlib to use pgx as a database/sql compatible driver. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. Connection pool usage is explicit and configurable. In pgx, a connection can be created and managed directly, or a connection pool with a configurable maximum connections can be used. The connection pool offers an after connect hook that allows every connection to be automatically setup before being made available in the connection pool. It delegates methods such as QueryRow to an automatically checked out and released connection so you can avoid manually acquiring and releasing connections when you do not need that level of control. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgx/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. If pgx does cannot natively encode a type and that type is a renamed type (e.g. type MyTime time.Time) pgx will attempt to encode the underlying type. While this is usually desired behavior it can produce suprising behavior if one the underlying type and the renamed type each implement database/sql interfaces and the other implements pgx interfaces. It is recommended that this situation be avoided by implementing pgx interfaces on the renamed type. []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin or BeginEx. The BeginEx variant can create a transaction with a specified isolation level. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the WaitForNotification function. It takes a maximum time to wait for a notification. The pgx ConnConfig struct has a TLSConfig field. If this field is nil, then TLS will be disabled. If it is present, then it will be used to configure the TLS connection. This allows total configuration of the TLS connection. pgx has never explicitly supported Postgres < 9.6's `ssl_renegotiation` option. As of v3.3.0, it doesn't send `ssl_renegotiation: 0` either to support Redshift (https://github.com/jackc/pgx/pull/476). If you need TLS Renegotiation, consider supplying `ConnConfig.TLSConfig` with a non-zero `Renegotiation` value and if it's not the default on your server, set `ssl_renegotiation` via `ConnConfig.RuntimeParams`. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/sirupsen/logrus, and the testing log are provided in the log directory.

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package logr defines a general-purpose logging API and abstract interfaces to back that API. Packages in the Go ecosystem can depend on this package, while callers can implement logging with whatever backend is appropriate. Logging is done using a Logger instance. Logger is a concrete type with methods, which defers the actual logging to a LogSink interface. The main methods of Logger are Info() and Error(). Arguments to Info() and Error() are key/value pairs rather than printf-style formatted strings, emphasizing "structured logging". With Go's standard log package, we might write: With logr's structured logging, we'd write: Errors are much the same. Instead of: We'd write: Info() and Error() are very similar, but they are separate methods so that LogSink implementations can choose to do things like attach additional information (such as stack traces) on calls to Error(). Error() messages are always logged, regardless of the current verbosity. If there is no error instance available, passing nil is valid. Often we want to log information only when the application in "verbose mode". To write log lines that are more verbose, Logger has a V() method. The higher the V-level of a log line, the less critical it is considered. Log-lines with V-levels that are not enabled (as per the LogSink) will not be written. Level V(0) is the default, and logger.V(0).Info() has the same meaning as logger.Info(). Negative V-levels have the same meaning as V(0). Error messages do not have a verbosity level and are always logged. Where we might have written: We can write: Logger instances can have name strings so that all messages logged through that instance have additional context. For example, you might want to add a subsystem name: The WithName() method returns a new Logger, which can be passed to constructors or other functions for further use. Repeated use of WithName() will accumulate name "segments". These name segments will be joined in some way by the LogSink implementation. It is strongly recommended that name segments contain simple identifiers (letters, digits, and hyphen), and do not contain characters that could muddle the log output or confuse the joining operation (e.g. whitespace, commas, periods, slashes, brackets, quotes, etc). Logger instances can store any number of key/value pairs, which will be logged alongside all messages logged through that instance. For example, you might want to create a Logger instance per managed object: With the standard log package, we might write: With logr we'd write: Logger has very few hard rules, with the goal that LogSink implementations might have a lot of freedom to differentiate. There are, however, some things to consider. The log message consists of a constant message attached to the log line. This should generally be a simple description of what's occurring, and should never be a format string. Variable information can then be attached using named values. Keys are arbitrary strings, but should generally be constant values. Values may be any Go value, but how the value is formatted is determined by the LogSink implementation. Logger instances are meant to be passed around by value. Code that receives such a value can call its methods without having to check whether the instance is ready for use. The zero logger (= Logger{}) is identical to Discard() and discards all log entries. Code that receives a Logger by value can simply call it, the methods will never crash. For cases where passing a logger is optional, a pointer to Logger should be used. Keys are not strictly required to conform to any specification or regex, but it is recommended that they: These guidelines help ensure that log data is processed properly regardless of the log implementation. For example, log implementations will try to output JSON data or will store data for later database (e.g. SQL) queries. While users are generally free to use key names of their choice, it's generally best to avoid using the following keys, as they're frequently used by implementations: Implementations are encouraged to make use of these keys to represent the above concepts, when necessary (for example, in a pure-JSON output form, it would be necessary to represent at least message and timestamp as ordinary named values). Implementations may choose to give callers access to the underlying logging implementation. The recommended pattern for this is: Logger grants access to the sink to enable type assertions like this: Custom `With*` functions can be implemented by copying the complete Logger struct and replacing the sink in the copy: Don't use New to construct a new Logger with a LogSink retrieved from an existing Logger. Source code attribution might not work correctly and unexported fields in Logger get lost. Beware that the same LogSink instance may be shared by different logger instances. Calling functions that modify the LogSink will affect all of those.

Package health provides a generic health checking framework. The health package works expvar style. By importing the package the debug server is getting a "/debug/health" endpoint that returns the current status of the application. If there are no errors, "/debug/health" will return a HTTP 200 status, together with an empty JSON reply "{}". If there are any checks with errors, the JSON reply will include all the failed checks, and the response will be have an HTTP 503 status. A Check can either be run synchronously, or asynchronously. We recommend that most checks are registered as an asynchronous check, so a call to the "/debug/health" endpoint always returns immediately. This pattern is particularly useful for checks that verify upstream connectivity or database status, since they might take a long time to return/timeout. To install health, just import it in your application: You can also (optionally) import "health/api" that will add two convenience endpoints: "/debug/health/down" and "/debug/health/up". These endpoints add "manual" checks that allow the service to quickly be brought in/out of rotation. After importing these packages to your main application, you can start registering checks. The recommended way of registering checks is using a periodic Check. PeriodicChecks run on a certain schedule and asynchronously update the status of the check. This allows CheckStatus to return without blocking on an expensive check. A trivial example of a check that runs every 5 seconds and shuts down our server if the current minute is even, could be added as follows: Alternatively, you can also make use of "RegisterPeriodicThresholdFunc" to implement the exact same check, but add a threshold of failures after which the check will be unhealthy. This is particularly useful for flaky Checks, ensuring some stability of the service when handling them. The lowest-level way to interact with the health package is calling "Register" directly. Register allows you to pass in an arbitrary string and something that implements "Checker" and runs your check. If your method returns an error with nil, it is considered a healthy check, otherwise it will make the health check endpoint "/debug/health" start returning a 503 and list the specific check that failed. Assuming you wish to register a method called "currentMinuteEvenCheck() error" you could do that by doing: CheckFunc is a convenience type that implements Checker. Another way of registering a check could be by using an anonymous function and the convenience method RegisterFunc. An example that makes the status endpoint always return an error: You could also use the health checker mechanism to ensure your application only comes up if certain conditions are met, or to allow the developer to take the service out of rotation immediately. An example that checks database connectivity and immediately takes the server out of rotation on err: You can also use the predefined Checkers that come included with the health package. First, import the checks: After that you can make use of any of the provided checks. An example of using a `FileChecker` to take the application out of rotation if a certain file exists can be done as follows: After registering the check, it is trivial to take an application out of rotation from the console: You could also test the connectivity to a downstream service by using a "HTTPChecker", but ensure that you only mark the test unhealthy if there are a minimum of two failures in a row:

Package gosnowflake is a pure Go Snowflake driver for the database/sql package. Clients can use the database/sql package directly. For example: Use the Open() function to create a database handle with connection parameters: The Go Snowflake Driver supports the following connection syntaxes (or data source name (DSN) formats): where all parameters must be escaped or use Config and DSN to construct a DSN string. For information about account identifiers, see the Snowflake documentation (https://docs.snowflake.com/en/user-guide/admin-account-identifier.html). The following example opens a database handle with the Snowflake account named "my_account" under the organization named "my_organization", where the username is "jsmith", password is "mypassword", database is "mydb", schema is "testschema", and warehouse is "mywh": The connection string (DSN) can contain both connection parameters (described below) and session parameters (https://docs.snowflake.com/en/sql-reference/parameters.html). The following connection parameters are supported: account <string>: Specifies your Snowflake account, where "<string>" is the account identifier assigned to your account by Snowflake. For information about account identifiers, see the Snowflake documentation (https://docs.snowflake.com/en/user-guide/admin-account-identifier.html). If you are using a global URL, then append the connection group and ".global" (e.g. "<account_identifier>-<connection_group>.global"). The account identifier and the connection group are separated by a dash ("-"), as shown above. This parameter is optional if your account identifier is specified after the "@" character in the connection string. region <string>: DEPRECATED. You may specify a region, such as "eu-central-1", with this parameter. However, since this parameter is deprecated, it is best to specify the region as part of the account parameter. For details, see the description of the account parameter. database: Specifies the database to use by default in the client session (can be changed after login). schema: Specifies the database schema to use by default in the client session (can be changed after login). warehouse: Specifies the virtual warehouse to use by default for queries, loading, etc. in the client session (can be changed after login). role: Specifies the role to use by default for accessing Snowflake objects in the client session (can be changed after login). passcode: Specifies the passcode provided by Duo when using multi-factor authentication (MFA) for login. passcodeInPassword: false by default. Set to true if the MFA passcode is embedded in the login password. Appends the MFA passcode to the end of the password. loginTimeout: Specifies the timeout, in seconds, for login. The default is 60 seconds. The login request gives up after the timeout length if the HTTP response is success. requestTimeout: Specifies the timeout, in seconds, for a query to complete. 0 (zero) specifies that the driver should wait indefinitely. The default is 0 seconds. The query request gives up after the timeout length if the HTTP response is success. authenticator: Specifies the authenticator to use for authenticating user credentials: To use the internal Snowflake authenticator, specify snowflake (Default). If you want to cache your MFA logins, use AuthTypeUsernamePasswordMFA authenticator. To authenticate through Okta, specify https://<okta_account_name>.okta.com (URL prefix for Okta). To authenticate using your IDP via a browser, specify externalbrowser. To authenticate via OAuth, specify oauth and provide an OAuth Access Token (see the token parameter below). application: Identifies your application to Snowflake Support. insecureMode: false by default. Set to true to bypass the Online Certificate Status Protocol (OCSP) certificate revocation check. IMPORTANT: Change the default value for testing or emergency situations only. token: a token that can be used to authenticate. Should be used in conjunction with the "oauth" authenticator. client_session_keep_alive: Set to true have a heartbeat in the background every hour to keep the connection alive such that the connection session will never expire. Care should be taken in using this option as it opens up the access forever as long as the process is alive. ocspFailOpen: true by default. Set to false to make OCSP check fail closed mode. validateDefaultParameters: true by default. Set to false to disable checks on existence and privileges check for Database, Schema, Warehouse and Role when setting up the connection tracing: Specifies the logging level to be used. Set to error by default. Valid values are trace, debug, info, print, warning, error, fatal, panic. disableQueryContextCache: disables parsing of query context returned from server and resending it to server as well. Default value is false. clientConfigFile: specifies the location of the client configuration json file. In this file you can configure Easy Logging feature. disableSamlURLCheck: disables the SAML URL check. Default value is false. All other parameters are interpreted as session parameters (https://docs.snowflake.com/en/sql-reference/parameters.html). For example, the TIMESTAMP_OUTPUT_FORMAT session parameter can be set by adding: A complete connection string looks similar to the following: Session-level parameters can also be set by using the SQL command "ALTER SESSION" (https://docs.snowflake.com/en/sql-reference/sql/alter-session.html). Alternatively, use OpenWithConfig() function to create a database handle with the specified Config. # Connection Config You can also connect to your warehouse using the connection config. The dbSql library states that when you want to take advantage of driver-specific connection features that aren’t available in a connection string. Each driver supports its own set of connection properties, often providing ways to customize the connection request specific to the DBMS For example: If you are using this method, you dont need to pass a driver name to specify the driver type in which you are looking to connect. Since the driver name is not needed, you can optionally bypass driver registration on startup. To do this, set `GOSNOWFLAKE_SKIP_REGISTERATION` in your environment. This is useful you wish to register multiple verions of the driver. Note: GOSNOWFLAKE_SKIP_REGISTERATION should not be used if sql.Open() is used as the method to connect to the server, as sql.Open will require registration so it can map the driver name to the driver type, which in this case is "snowflake" and SnowflakeDriver{}. You can load the connnection configuration with .toml file format. With two environment variables SNOWFLAKE_HOME(connections.toml file directory) SNOWFLAKE_DEFAULT_CONNECTION_NAME(DSN name), the driver will search the config file and load the connection. You can find how to use this connection way at ./cmd/tomlfileconnection or Snowflake doc: https://docs.snowflake.com/en/developer-guide/snowflake-cli-v2/connecting/specify-credentials The Go Snowflake Driver honors the environment variables HTTP_PROXY, HTTPS_PROXY and NO_PROXY for the forward proxy setting. NO_PROXY specifies which hostname endings should be allowed to bypass the proxy server, e.g. no_proxy=.amazonaws.com means that Amazon S3 access does not need to go through the proxy. NO_PROXY does not support wildcards. Each value specified should be one of the following: The end of a hostname (or a complete hostname), for example: ".amazonaws.com" or "xy12345.snowflakecomputing.com". An IP address, for example "192.196.1.15". If more than one value is specified, values should be separated by commas, for example: By default, the driver's builtin logger is exposing logrus's FieldLogger and default at INFO level. Users can use SetLogger in driver.go to set a customized logger for gosnowflake package. In order to enable debug logging for the driver, user could use SetLogLevel("debug") in SFLogger interface as shown in demo code at cmd/logger.go. To redirect the logs SFlogger.SetOutput method could do the work. A custom query tag can be set in the context. Each query run with this context will include the custom query tag as metadata that will appear in the Query Tag column in the Query History log. For example: A specific query request ID can be set in the context and will be passed through in place of the default randomized request ID. For example: If you need query ID for your query you have to use raw connection. For queries: ``` ``` For execs: ``` ``` The result of your query can be retrieved by setting the query ID in the WithFetchResultByID context. ``` ``` From 0.5.0, a signal handling responsibility has moved to the applications. If you want to cancel a query/command by Ctrl+C, add a os.Interrupt trap in context to execute methods that can take the context parameter (e.g. QueryContext, ExecContext). See cmd/selectmany.go for the full example. The Go Snowflake Driver now supports the Arrow data format for data transfers between Snowflake and the Golang client. The Arrow data format avoids extra conversions between binary and textual representations of the data. The Arrow data format can improve performance and reduce memory consumption in clients. Snowflake continues to support the JSON data format. The data format is controlled by the session-level parameter GO_QUERY_RESULT_FORMAT. To use JSON format, execute: The valid values for the parameter are: If the user attempts to set the parameter to an invalid value, an error is returned. The parameter name and the parameter value are case-insensitive. This parameter can be set only at the session level. Usage notes: The Arrow data format reduces rounding errors in floating point numbers. You might see slightly different values for floating point numbers when using Arrow format than when using JSON format. In order to take advantage of the increased precision, you must pass in the context.Context object provided by the WithHigherPrecision function when querying. Traditionally, the rows.Scan() method returned a string when a variable of types interface was passed in. Turning on the flag ENABLE_HIGHER_PRECISION via WithHigherPrecision will return the natural, expected data type as well. For some numeric data types, the driver can retrieve larger values when using the Arrow format than when using the JSON format. For example, using Arrow format allows the full range of SQL NUMERIC(38,0) values to be retrieved, while using JSON format allows only values in the range supported by the Golang int64 data type. Users should ensure that Golang variables are declared using the appropriate data type for the full range of values contained in the column. For an example, see below. When using the Arrow format, the driver supports more Golang data types and more ways to convert SQL values to those Golang data types. The table below lists the supported Snowflake SQL data types and the corresponding Golang data types. The columns are: The SQL data type. The default Golang data type that is returned when you use snowflakeRows.Scan() to read data from Arrow data format via an interface{}. The possible Golang data types that can be returned when you use snowflakeRows.Scan() to read data from Arrow data format directly. The default Golang data type that is returned when you use snowflakeRows.Scan() to read data from JSON data format via an interface{}. (All returned values are strings.) The standard Golang data type that is returned when you use snowflakeRows.Scan() to read data from JSON data format directly. Go Data Types for Scan() =================================================================================================================== | ARROW | JSON =================================================================================================================== SQL Data Type | Default Go Data Type | Supported Go Data | Default Go Data Type | Supported Go Data | for Scan() interface{} | Types for Scan() | for Scan() interface{} | Types for Scan() =================================================================================================================== BOOLEAN | bool | string | bool ------------------------------------------------------------------------------------------------------------------- VARCHAR | string | string ------------------------------------------------------------------------------------------------------------------- DOUBLE | float32, float64 [1] , [2] | string | float32, float64 ------------------------------------------------------------------------------------------------------------------- INTEGER that | int, int8, int16, int32, int64 | string | int, int8, int16, fits in int64 | [1] , [2] | | int32, int64 ------------------------------------------------------------------------------------------------------------------- INTEGER that doesn't | int, int8, int16, int32, int64, *big.Int | string | error fit in int64 | [1] , [2] , [3] , [4] | ------------------------------------------------------------------------------------------------------------------- NUMBER(P, S) | float32, float64, *big.Float | string | float32, float64 where S > 0 | [1] , [2] , [3] , [5] | ------------------------------------------------------------------------------------------------------------------- DATE | time.Time | string | time.Time ------------------------------------------------------------------------------------------------------------------- TIME | time.Time | string | time.Time ------------------------------------------------------------------------------------------------------------------- TIMESTAMP_LTZ | time.Time | string | time.Time ------------------------------------------------------------------------------------------------------------------- TIMESTAMP_NTZ | time.Time | string | time.Time ------------------------------------------------------------------------------------------------------------------- TIMESTAMP_TZ | time.Time | string | time.Time ------------------------------------------------------------------------------------------------------------------- BINARY | []byte | string | []byte ------------------------------------------------------------------------------------------------------------------- ARRAY [6] | string / array | string / array ------------------------------------------------------------------------------------------------------------------- OBJECT [6] | string / struct | string / struct ------------------------------------------------------------------------------------------------------------------- VARIANT | string | string ------------------------------------------------------------------------------------------------------------------- MAP | map | map [1] Converting from a higher precision data type to a lower precision data type via the snowflakeRows.Scan() method can lose low bits (lose precision), lose high bits (completely change the value), or result in error. [2] Attempting to convert from a higher precision data type to a lower precision data type via interface{} causes an error. [3] Higher precision data types like *big.Int and *big.Float can be accessed by querying with a context returned by WithHigherPrecision(). [4] You cannot directly Scan() into the alternative data types via snowflakeRows.Scan(), but can convert to those data types by using .Int64()/.String()/.Uint64() methods. For an example, see below. [5] You cannot directly Scan() into the alternative data types via snowflakeRows.Scan(), but can convert to those data types by using .Float32()/.String()/.Float64() methods. For an example, see below. [6] Arrays and objects can be either semistructured or structured, see more info in section below. Note: SQL NULL values are converted to Golang nil values, and vice-versa. Snowflake supports two flavours of "structured data" - semistructured and structured. Semistructured types are variants, objects and arrays without schema. When data is fetched, it's represented as strings and the client is responsible for its interpretation. Example table definition: The data not have any corresponding schema, so values in table may be slightly different. Semistuctured variants, objects and arrays are always represented as strings for scanning: When inserting, a marker indicating correct type must be used, for example: Structured types differentiate from semistructured types by having specific schema. In all rows of the table, values must conform to this schema. Example table definition: To retrieve structured objects, follow these steps: 1. Create a struct implementing sql.Scanner interface, example: a) b) Automatic scan goes through all fields in a struct and read object fields. Struct fields have to be public. Embedded structs have to be pointers. Matching name is built using struct field name with first letter lowercase. Additionally, `sf` tag can be added: - first value is always a name of a field in an SQL object - additionally `ignore` parameter can be passed to omit this field 2. Use WithStructuredTypesEnabled context while querying data. 3. Use it in regular scan: See StructuredObject for all available operations including null support, embedding nested structs, etc. Retrieving array of simple types works exactly the same like normal values - using Scan function. You can use WithMapValuesNullable and WithArrayValuesNullable contexts to handle null values in, respectively, maps and arrays of simple types in the database. In that case, sql null types will be used: If you want to scan array of structs, you have to use a helper function ScanArrayOfScanners: Retrieving structured maps is very similar to retrieving arrays: To bind structured objects use: 1. Create a type which implements a StructuredObjectWriter interface, example: a) b) 2. Use an instance as regular bind. 3. If you need to bind nil value, use special syntax: Binding structured arrays are like any other parameter. The only difference is - if you want to insert empty array (not nil but empty), you have to use: The following example shows how to retrieve very large values using the math/big package. This example retrieves a large INTEGER value to an interface and then extracts a big.Int value from that interface. If the value fits into an int64, then the code also copies the value to a variable of type int64. Note that a context that enables higher precision must be passed in with the query. If the variable named "rows" is known to contain a big.Int, then you can use the following instead of scanning into an interface and then converting to a big.Int: If the variable named "rows" contains a big.Int, then each of the following fails: Similar code and rules also apply to big.Float values. If you are not sure what data type will be returned, you can use code similar to the following to check the data type of the returned value: You can retrieve data in a columnar format similar to the format a server returns, without transposing them to rows. When working with the arrow columnar format in go driver, ArrowBatch structs are used. These are structs mostly corresponding to data chunks received from the backend. They allow for access to specific arrow.Record structs. An ArrowBatch can exist in a state where the underlying data has not yet been loaded. The data is downloaded and translated only on demand. Translation options are retrieved from a context.Context interface, which is either passed from query context or set by the user using WithContext(ctx) method. In order to access them you must use `WithArrowBatches` context, similar to the following: This returns []*ArrowBatch. ArrowBatch functions: GetRowCount(): Returns the number of rows in the ArrowBatch. Note that this returns 0 if the data has not yet been loaded, irrespective of it’s actual size. WithContext(ctx context.Context): Sets the context of the ArrowBatch to the one provided. Note that the context will not retroactively apply to data that has already been downloaded. For example: will produce the same result in records1 and records2, irrespective of the newly provided ctx. Context worth noting are: -WithArrowBatchesTimestampOption -WithHigherPrecision -WithArrowBatchesUtf8Validation described in more detail later. Fetch(): Returns the underlying records as *[]arrow.Record. When this function is called, the ArrowBatch checks whether the underlying data has already been loaded, and downloads it if not. Limitations: How to handle timestamps in Arrow batches: Snowflake returns timestamps natively (from backend to driver) in multiple formats. The Arrow timestamp is an 8-byte data type, which is insufficient to handle the larger date and time ranges used by Snowflake. Also, Snowflake supports 0-9 (nanosecond) digit precision for seconds, while Arrow supports only 3 (millisecond), 6 (microsecond), an 9 (nanosecond) precision. Consequently, Snowflake uses a custom timestamp format in Arrow, which differs on timestamp type and precision. If you want to use timestamps in Arrow batches, you have two options: How to handle invalid UTF-8 characters in Arrow batches: Snowflake previously allowed users to upload data with invalid UTF-8 characters. Consequently, Arrow records containing string columns in Snowflake could include these invalid UTF-8 characters. However, according to the Arrow specifications (https://arrow.apache.org/docs/cpp/api/datatype.html and https://github.com/apache/arrow/blob/a03d957b5b8d0425f9d5b6c98b6ee1efa56a1248/go/arrow/datatype.go#L73-L74), Arrow string columns should only contain UTF-8 characters. To address this issue and prevent potential downstream disruptions, the context WithArrowBatchesUtf8Validation, is introduced. When enabled, this feature iterates through all values in string columns, identifying and replacing any invalid characters with `�`. This ensures that Arrow records conform to the UTF-8 standards, preventing validation failures in downstream services like the Rust Arrow library that impose strict validation checks. How to handle higher precision in Arrow batches: To preserve BigDecimal values within Arrow batches, use WithHigherPrecision. This offers two main benefits: it helps avoid precision loss and defers the conversion to upstream services. Alternatively, without this setting, all non-zero scale numbers will be converted to float64, potentially resulting in loss of precision. Zero-scale numbers (DECIMAL256, DECIMAL128) will be converted to int64, which could lead to overflow. Binding allows a SQL statement to use a value that is stored in a Golang variable. Without binding, a SQL statement specifies values by specifying literals inside the statement. For example, the following statement uses the literal value “42“ in an UPDATE statement: With binding, you can execute a SQL statement that uses a value that is inside a variable. For example: The “?“ inside the “VALUES“ clause specifies that the SQL statement uses the value from a variable. Binding data that involves time zones can require special handling. For details, see the section titled "Timestamps with Time Zones". Version 1.6.23 (and later) of the driver takes advantage of sql.Null types which enables the proper handling of null parameters inside function calls, i.e.: The timestamp nullability had to be achieved by wrapping the sql.NullTime type as the Snowflake provides several date and time types which are mapped to single Go time.Time type: Version 1.3.9 (and later) of the Go Snowflake Driver supports the ability to bind an array variable to a parameter in a SQL INSERT statement. You can use this technique to insert multiple rows in a single batch. As an example, the following code inserts rows into a table that contains integer, float, boolean, and string columns. The example binds arrays to the parameters in the INSERT statement. If the array contains SQL NULL values, use slice []interface{}, which allows Golang nil values. This feature is available in version 1.6.12 (and later) of the driver. For example, For slices []interface{} containing time.Time values, a binding parameter flag is required for the preceding array variable in the Array() function. This feature is available in version 1.6.13 (and later) of the driver. For example, Note: For alternative ways to load data into the Snowflake database (including bulk loading using the COPY command), see Loading Data into Snowflake (https://docs.snowflake.com/en/user-guide-data-load.html). When you use array binding to insert a large number of values, the driver can improve performance by streaming the data (without creating files on the local machine) to a temporary stage for ingestion. The driver automatically does this when the number of values exceeds a threshold (no changes are needed to user code). In order for the driver to send the data to a temporary stage, the user must have the following privilege on the schema: If the user does not have this privilege, the driver falls back to sending the data with the query to the Snowflake database. In addition, the current database and schema for the session must be set. If these are not set, the CREATE TEMPORARY STAGE command executed by the driver can fail with the following error: For alternative ways to load data into the Snowflake database (including bulk loading using the COPY command), see Loading Data into Snowflake (https://docs.snowflake.com/en/user-guide-data-load.html). Go's database/sql package supports the ability to bind a parameter in a SQL statement to a time.Time variable. However, when the client binds data to send to the server, the driver cannot determine the correct Snowflake date/timestamp data type to associate with the binding parameter. For example: To resolve this issue, a binding parameter flag is introduced that associates any subsequent time.Time type to the DATE, TIME, TIMESTAMP_LTZ, TIMESTAMP_NTZ or BINARY data type. The above example could be rewritten as follows: The driver fetches TIMESTAMP_TZ (timestamp with time zone) data using the offset-based Location types, which represent a collection of time offsets in use in a geographical area, such as CET (Central European Time) or UTC (Coordinated Universal Time). The offset-based Location data is generated and cached when a Go Snowflake Driver application starts, and if the given offset is not in the cache, it is generated dynamically. Currently, Snowflake does not support the name-based Location types (e.g. "America/Los_Angeles"). For more information about Location types, see the Go documentation for https://golang.org/pkg/time/#Location. Internally, this feature leverages the []byte data type. As a result, BINARY data cannot be bound without the binding parameter flag. In the following example, sf is an alias for the gosnowflake package: The driver directly downloads a result set from the cloud storage if the size is large. It is required to shift workloads from the Snowflake database to the clients for scale. The download takes place by goroutine named "Chunk Downloader" asynchronously so that the driver can fetch the next result set while the application can consume the current result set. The application may change the number of result set chunk downloader if required. Note this does not help reduce memory footprint by itself. Consider Custom JSON Decoder. Custom JSON Decoder for Parsing Result Set (Experimental) The application may have the driver use a custom JSON decoder that incrementally parses the result set as follows. This option will reduce the memory footprint to half or even quarter, but it can significantly degrade the performance depending on the environment. The test cases running on Travis Ubuntu box show five times less memory footprint while four times slower. Be cautious when using the option. The Go Snowflake Driver supports JWT (JSON Web Token) authentication. To enable this feature, construct the DSN with fields "authenticator=SNOWFLAKE_JWT&privateKey=<your_private_key>", or using a Config structure specifying: The <your_private_key> should be a base64 URL encoded PKCS8 rsa private key string. One way to encode a byte slice to URL base 64 URL format is through the base64.URLEncoding.EncodeToString() function. On the server side, you can alter the public key with the SQL command: The <your_public_key> should be a base64 Standard encoded PKI public key string. One way to encode a byte slice to base 64 Standard format is through the base64.StdEncoding.EncodeToString() function. To generate the valid key pair, you can execute the following commands in the shell: Note: As of February 2020, Golang's official library does not support passcode-encrypted PKCS8 private key. For security purposes, Snowflake highly recommends that you store the passcode-encrypted private key on the disk and decrypt the key in your application using a library you trust. JWT tokens are recreated on each retry and they are valid (`exp` claim) for `jwtTimeout` seconds. Each retry timeout is configured by `jwtClientTimeout`. Retries are limited by total time of `loginTimeout`. The driver allows to authenticate using the external browser. When a connection is created, the driver will open the browser window and ask the user to sign in. To enable this feature, construct the DSN with field "authenticator=EXTERNALBROWSER" or using a Config structure with following Authenticator specified: The external browser authentication implements timeout mechanism. This prevents the driver from hanging interminably when browser window was closed, or not responding. Timeout defaults to 120s and can be changed through setting DSN field "externalBrowserTimeout=240" (time in seconds) or using a Config structure with following ExternalBrowserTimeout specified: This feature is available in version 1.3.8 or later of the driver. By default, Snowflake returns an error for queries issued with multiple statements. This restriction helps protect against SQL Injection attacks (https://en.wikipedia.org/wiki/SQL_injection). The multi-statement feature allows users skip this restriction and execute multiple SQL statements through a single Golang function call. However, this opens up the possibility for SQL injection, so it should be used carefully. The risk can be reduced by specifying the exact number of statements to be executed, which makes it more difficult to inject a statement by appending it. More details are below. The Go Snowflake Driver provides two functions that can execute multiple SQL statements in a single call: To compose a multi-statement query, simply create a string that contains all the queries, separated by semicolons, in the order in which the statements should be executed. To protect against SQL Injection attacks while using the multi-statement feature, pass a Context that specifies the number of statements in the string. For example: When multiple queries are executed by a single call to QueryContext(), multiple result sets are returned. After you process the first result set, get the next result set (for the next SQL statement) by calling NextResultSet(). The following pseudo-code shows how to process multiple result sets: The function db.ExecContext() returns a single result, which is the sum of the number of rows changed by each individual statement. For example, if your multi-statement query executed two UPDATE statements, each of which updated 10 rows, then the result returned would be 20. Individual row counts for individual statements are not available. The following code shows how to retrieve the result of a multi-statement query executed through db.ExecContext(): Note: Because a multi-statement ExecContext() returns a single value, you cannot detect offsetting errors. For example, suppose you expected the return value to be 20 because you expected each UPDATE statement to update 10 rows. If one UPDATE statement updated 15 rows and the other UPDATE statement updated only 5 rows, the total would still be 20. You would see no indication that the UPDATES had not functioned as expected. The ExecContext() function does not return an error if passed a query (e.g. a SELECT statement). However, it still returns only a single value, not a result set, so using it to execute queries (or a mix of queries and non-query statements) is impractical. The QueryContext() function does not return an error if passed non-query statements (e.g. DML). The function returns a result set for each statement, whether or not the statement is a query. For each non-query statement, the result set contains a single row that contains a single column; the value is the number of rows changed by the statement. If you want to execute a mix of query and non-query statements (e.g. a mix of SELECT and DML statements) in a multi-statement query, use QueryContext(). You can retrieve the result sets for the queries, and you can retrieve or ignore the row counts for the non-query statements. Note: PUT statements are not supported for multi-statement queries. If a SQL statement passed to ExecQuery() or QueryContext() fails to compile or execute, that statement is aborted, and subsequent statements are not executed. Any statements prior to the aborted statement are unaffected. For example, if the statements below are run as one multi-statement query, the multi-statement query fails on the third statement, and an exception is thrown. If you then query the contents of the table named "test", the values 1 and 2 would be present. When using the QueryContext() and ExecContext() functions, golang code can check for errors the usual way. For example: Preparing statements and using bind variables are also not supported for multi-statement queries. The Go Snowflake Driver supports asynchronous execution of SQL statements. Asynchronous execution allows you to start executing a statement and then retrieve the result later without being blocked while waiting. While waiting for the result of a SQL statement, you can perform other tasks, including executing other SQL statements. Most of the steps to execute an asynchronous query are the same as the steps to execute a synchronous query. However, there is an additional step, which is that you must call the WithAsyncMode() function to update your Context object to specify that asynchronous mode is enabled. In the code below, the call to "WithAsyncMode()" is specific to asynchronous mode. The rest of the code is compatible with both asynchronous mode and synchronous mode. The function db.QueryContext() returns an object of type snowflakeRows regardless of whether the query is synchronous or asynchronous. However: The call to the Next() function of snowflakeRows is always synchronous (i.e. blocking). If the query has not yet completed and the snowflakeRows object (named "rows" in this example) has not been filled in yet, then rows.Next() waits until the result set has been filled in. More generally, calls to any Golang SQL API function implemented in snowflakeRows or snowflakeResult are blocking calls, and wait if results are not yet available. (Examples of other synchronous calls include: snowflakeRows.Err(), snowflakeRows.Columns(), snowflakeRows.columnTypes(), snowflakeRows.Scan(), and snowflakeResult.RowsAffected().) Because the example code above executes only one query and no other activity, there is no significant difference in behavior between asynchronous and synchronous behavior. The differences become significant if, for example, you want to perform some other activity after the query starts and before it completes. The example code below starts a query, which run in the background, and then retrieves the results later. This example uses small SELECT statements that do not retrieve enough data to require asynchronous handling. However, the technique works for larger data sets, and for situations where the programmer might want to do other work after starting the queries and before retrieving the results. For a more elaborative example please see cmd/async/async.go The Go Snowflake Driver supports the PUT and GET commands. The PUT command copies a file from a local computer (the computer where the Golang client is running) to a stage on the cloud platform. The GET command copies data files from a stage on the cloud platform to a local computer. See the following for information on the syntax and supported parameters: Using PUT: The following example shows how to run a PUT command by passing a string to the db.Query() function: "<local_file>" should include the file path as well as the name. Snowflake recommends using an absolute path rather than a relative path. For example: Different client platforms (e.g. linux, Windows) have different path name conventions. Ensure that you specify path names appropriately. This is particularly important on Windows, which uses the backslash character as both an escape character and as a separator in path names. To send information from a stream (rather than a file) use code similar to the code below. (The ReplaceAll() function is needed on Windows to handle backslashes in the path to the file.) Note: PUT statements are not supported for multi-statement queries. Using GET: The following example shows how to run a GET command by passing a string to the db.Query() function: "<local_file>" should include the file path as well as the name. Snowflake recommends using an absolute path rather than a relative path. For example: To download a file into an in-memory stream (rather than a file) use code similar to the code below. Note: GET statements are not supported for multi-statement queries. Specifying temporary directory for encryption and compression: Putting and getting requires compression and/or encryption, which is done in the OS temporary directory. If you cannot use default temporary directory for your OS or you want to specify it yourself, you can use "tmpDirPath" DSN parameter. Remember, to encode slashes. Example: Using custom configuration for PUT/GET: If you want to override some default configuration options, you can use `WithFileTransferOptions` context. There are multiple config parameters including progress bars or compression.

Package goworker is a Resque-compatible, Go-based background worker. It allows you to push jobs into a queue using an expressive language like Ruby while harnessing the efficiency and concurrency of Go to minimize job latency and cost. goworker workers can run alongside Ruby Resque clients so that you can keep all but your most resource-intensive jobs in Ruby. To create a worker, write a function matching the signature and register it using Here is a simple worker that prints its arguments: To create workers that share a database pool or other resources, use a closure to share variables. goworker worker functions receive the queue they are serving and a slice of interfaces. To use them as parameters to other functions, use Go type assertions to convert them into usable types. For testing, it is helpful to use the redis-cli program to insert jobs onto the Redis queue: will insert 100 jobs for the MyClass worker onto the myqueue queue. It is equivalent to: After building your workers, you will have an executable that you can run which will automatically poll a Redis server and call your workers as jobs arrive. There are several flags which control the operation of the goworker client. -queues="comma,delimited,queues" — This is the only required flag. The recommended practice is to separate your Resque workers from your goworkers with different queues. Otherwise, Resque worker classes that have no goworker analog will cause the goworker process to fail the jobs. Because of this, there is no default queue, nor is there a way to select all queues (à la Resque's * queue). Queues are processed in the order they are specififed. If you have multiple queues you can assign them weights. A queue with a weight of 2 will be checked twice as often as a queue with a weight of 1: -queues='high=2,low=1'. -interval=5.0 — Specifies the wait period between polling if no job was in the queue the last time one was requested. -concurrency=25 — Specifies the number of concurrently executing workers. This number can be as low as 1 or rather comfortably as high as 100,000, and should be tuned to your workflow and the availability of outside resources. -connections=2 — Specifies the maximum number of Redis connections that goworker will consume between the poller and all workers. There is not much performance gain over two and a slight penalty when using only one. This is configurable in case you need to keep connection counts low for cloud Redis providers who limit plans on maxclients. -uri=redis://localhost:6379/ — Specifies the URI of the Redis database from which goworker polls for jobs. Accepts URIs of the format redis://user:pass@host:port/db or unix:///path/to/redis.sock. The flag may also be set by the environment variable $($REDIS_PROVIDER) or $REDIS_URL. E.g. set $REDIS_PROVIDER to REDISTOGO_URL on Heroku to let the Redis To Go add-on configure the Redis database. -namespace=resque: — Specifies the namespace from which goworker retrieves jobs and stores stats on workers. -exit-on-complete=false — Exits goworker when there are no jobs left in the queue. This is helpful in conjunction with the time command to benchmark different configurations. -use-number=false — Uses json.Number when decoding numbers in the job payloads. This will avoid issues that occur when goworker and the json package decode large numbers as floats, which then get encoded in scientific notation, losing pecision. This will default to true soon. You can also configure your own flags for use within your workers. Be sure to set them before calling goworker.Main(). It is okay to call flags.Parse() before calling goworker.Main() if you need to do additional processing on your flags. To stop goworker, send a QUIT, TERM, or INT signal to the process. This will immediately stop job polling. There can be up to $CONCURRENCY jobs currently running, which will continue to run until they are finished. Like Resque, goworker makes no guarantees about the safety of jobs in the event of process shutdown. Workers must be both idempotent and tolerant to loss of the job in the event of failure. If the process is killed with a KILL or by a system failure, there may be one job that is currently in the poller's buffer that will be lost without any representation in either the queue or the worker variable. If you are running Goworker on a system like Heroku, which sends a TERM to signal a process that it needs to stop, ten seconds later sends a KILL to force the process to stop, your jobs must finish within 10 seconds or they may be lost. Jobs will be recoverable from the Redis database under as a JSON object with keys queue, run_at, and payload, but the process is manual. Additionally, there is no guarantee that the job in Redis under the worker key has not finished, if the process is killed before goworker can flush the update to Redis.

Package monkit is a flexible code instrumenting and data collection library. I'm going to try and sell you as fast as I can on this library. Example usage We've got tools that capture distribution information (including quantiles) about int64, float64, and bool types. We have tools that capture data about events (we've got meters for deltas, rates, etc). We have rich tools for capturing information about tasks and functions, and literally anything that can generate a name and a number. Almost just as importantly, the amount of boilerplate and code you have to write to get these features is very minimal. Data that's hard to measure probably won't get measured. This data can be collected and sent to Graphite (http://graphite.wikidot.com/) or any other time-series database. Here's a selection of live stats from one of our storage nodes: This library generates call graphs of your live process for you. These call graphs aren't created through sampling. They're full pictures of all of the interesting functions you've annotated, along with quantile information about their successes, failures, how often they panic, return an error (if so instrumented), how many are currently running, etc. The data can be returned in dot format, in json, in text, and can be about just the functions that are currently executing, or all the functions the monitoring system has ever seen. Here's another example of one of our production nodes: https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/callgraph2.png This library generates trace graphs of your live process for you directly, without requiring standing up some tracing system such as Zipkin (though you can do that too). Inspired by Google's Dapper (http://research.google.com/pubs/pub36356.html) and Twitter's Zipkin (http://zipkin.io), we have process-internal trace graphs, triggerable by a number of different methods. You get this trace information for free whenever you use Go contexts (https://blog.golang.org/context) and function monitoring. The output formats are svg and json. Additionally, the library supports trace observation plugins, and we've written a plugin that sends this data to Zipkin (http://github.com/spacemonkeygo/monkit-zipkin). https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/trace.png Before our crazy Go rewrite of everything (https://www.spacemonkey.com/blog/posts/go-space-monkey) (and before we had even seen Google's Dapper paper), we were a Python shop, and all of our "interesting" functions were decorated with a helper that collected timing information and sent it to Graphite. When we transliterated to Go, we wanted to preserve that functionality, so the first version of our monitoring package was born. Over time it started to get janky, especially as we found Zipkin and started adding tracing functionality to it. We rewrote all of our Go code to use Google contexts, and then realized we could get call graph information. We decided a refactor and then an all-out rethinking of our monitoring package was best, and so now we have this library. Sometimes you really want callstack contextual information without having to pass arguments through everything on the call stack. In other languages, many people implement this with thread-local storage. Example: let's say you have written a big system that responds to user requests. All of your libraries log using your log library. During initial development everything is easy to debug, since there's low user load, but now you've scaled and there's OVER TEN USERS and it's kind of hard to tell what log lines were caused by what. Wouldn't it be nice to add request ids to all of the log lines kicked off by that request? Then you could grep for all log lines caused by a specific request id. Geez, it would suck to have to pass all contextual debugging information through all of your callsites. Google solved this problem by always passing a context.Context interface through from call to call. A Context is basically just a mapping of arbitrary keys to arbitrary values that users can add new values for. This way if you decide to add a request context, you can add it to your Context and then all callsites that decend from that place will have the new data in their contexts. It is admittedly very verbose to add contexts to every function call. Painfully so. I hope to write more about it in the future, but Google also wrote up their thoughts about it (https://blog.golang.org/context), which you can go read. For now, just swallow your disgust and let's keep moving. Let's make a super simple Varnish (https://www.varnish-cache.org/) clone. Open up gedit! (Okay just kidding, open whatever text editor you want.) For this motivating program, we won't even add the caching, though there's comments for where to add it if you'd like. For now, let's just make a barebones system that will proxy HTTP requests. We'll call it VLite, but maybe we should call it VReallyLite. Run and build this and open localhost:8080 in your browser. If you use the default proxy target, it should inform you that the world hasn't been destroyed yet. The first thing you'll want to do is add the small amount of boilerplate to make the instrumentation we're going to add to your process observable later. Import the basic monkit packages: and then register environmental statistics and kick off a goroutine in your main method to serve debug requests: Rebuild, and then check out localhost:9000/stats (or localhost:9000/stats/json, if you prefer) in your browser! Remember what I said about Google's contexts (https://blog.golang.org/context)? It might seem a bit overkill for such a small project, but it's time to add them. To help out here, I've created a library that constructs contexts for you for incoming HTTP requests. Nothing that's about to happen requires my webhelp library (https://godoc.org/github.com/jtolds/webhelp), but here is the code now refactored to receive and pass contexts through our two per-request calls. You can create a new context for a request however you want. One reason to use something like webhelp is that the cancelation feature of Contexts is hooked up to the HTTP request getting canceled. Let's start to get statistics about how many requests we receive! First, this package (main) will need to get a monitoring Scope. Add this global definition right after all your imports, much like you'd create a logger with many logging libraries: Now, make the error return value of HandleHTTP named (so, (err error)), and add this defer line as the very first instruction of HandleHTTP: Let's also add the same line (albeit modified for the lack of error) to Proxy, replacing &err with nil: You should now have something like: We'll unpack what's going on here, but for now: For this new funcs dataset, if you want a graph, you can download a dot graph at localhost:9000/funcs/dot and json information from localhost:9000/funcs/json. You should see something like: with a similar report for the Proxy method, or a graph like: https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/handlehttp.png This data reports the overall callgraph of execution for known traces, along with how many of each function are currently running, the most running concurrently (the highwater), how many were successful along with quantile timing information, how many errors there were (with quantile timing information if applicable), and how many panics there were. Since the Proxy method isn't capturing a returned err value, and since HandleHTTP always returns nil, this example won't ever have failures. If you're wondering about the success count being higher than you expected, keep in mind your browser probably requested a favicon.ico. Cool, eh? How it works is an interesting line of code - there's three function calls. If you look at the Go spec, all of the function calls will run at the time the function starts except for the very last one. The first function call, mon.Task(), creates or looks up a wrapper around a Func. You could get this yourself by requesting mon.Func() inside of the appropriate function or mon.FuncNamed(). Both mon.Task() and mon.Func() are inspecting runtime.Caller to determine the name of the function. Because this is a heavy operation, you can actually store the result of mon.Task() and reuse it somehow else if you prefer, so instead of you could instead use which is more performant every time after the first time. runtime.Caller only gets called once. Careful! Don't use the same myFuncMon in different functions unless you want to screw up your statistics! The second function call starts all the various stop watches and bookkeeping to keep track of the function. It also mutates the context pointer it's given to extend the context with information about what current span (in Zipkin parlance) is active. Notably, you *can* pass nil for the context if you really don't want a context. You just lose callgraph information. The last function call stops all the stop watches ad makes a note of any observed errors or panics (it repanics after observing them). Turns out, we don't even need to change our program anymore to get rich tracing information! Open your browser and go to localhost:9000/trace/svg?regex=HandleHTTP. It won't load, and in fact, it's waiting for you to open another tab and refresh localhost:8080 again. Once you retrigger the actual application behavior, the trace regex will capture a trace starting on the first function that matches the supplied regex, and return an svg. Go back to your first tab, and you should see a relatively uninteresting but super promising svg. Let's make the trace more interesting. Add a to your HandleHTTP method, rebuild, and restart. Load localhost:8080, then start a new request to your trace URL, then reload localhost:8080 again. Flip back to your trace, and you should see that the Proxy method only takes a portion of the time of HandleHTTP! https://cdn.rawgit.com/spacemonkeygo/monkit/master/images/trace.svg There's multiple ways to select a trace. You can select by regex using the preselect method (default), which first evaluates the regex on all known functions for sanity checking. Sometimes, however, the function you want to trace may not yet be known to monkit, in which case you'll want to turn preselection off. You may have a bad regex, or you may be in this case if you get the error "Bad Request: regex preselect matches 0 functions." Another way to select a trace is by providing a trace id, which we'll get to next! Make sure to check out what the addition of the time.Sleep call did to the other reports. It's easy to write plugins for monkit! Check out our first one that exports data to Zipkin (http://zipkin.io/)'s Scribe API: https://github.com/spacemonkeygo/monkit-zipkin We plan to have more (for HTrace, OpenTracing, etc, etc), soon!

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jack/pgx/stdlib to use pgx as a database/sql compatible driver. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. Connection pool usage is explicit and configurable. In pgx, a connection can be created and managed directly, or a connection pool with a configurable maximum connections can be used. Also, the connection pool offers an after connect hook that allows every connection to be automatically setup before being made available in the connection pool. This is especially useful to ensure all connections have the same prepared statements available or to change any other connection settings. It delegates Query, QueryRow, Exec, and Begin functions to an automatically checked out and released connection so you can avoid manually acquiring and releasing connections when you do not need that level of control. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is Null* types that have a data field and a valid field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. pgx includes an Hstore type and a NullHstore type. Hstore is simply a map[string]string and is preferred when the hstore contains no nulls. NullHstore follows the Null* pattern and supports null values. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. Support can be added for additional types like point, hstore, numeric, etc. that do not have direct mappings in Go by the types implementing ScannerPgx and Encoder. Custom types can support text or binary formats. Binary format can provide a large performance increase. The natural place for deciding the format for a value would be in ScannerPgx as it is responsible for decoding the returned data. However, that is impossible as the query has already been sent by the time the ScannerPgx is invoked. The solution to this is the global DefaultTypeFormats. If a custom type prefers binary format it should register it there. Note that the type is referred to by name, not by OID. This is because custom PostgreSQL types like hstore will have different OIDs on different servers. When pgx establishes a connection it queries the pg_type table for all types. It then matches the names in DefaultTypeFormats with the returned OIDs and stores it in Conn.PgTypes. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. In like manner, a *[]byte passed to Scan will be filled with the raw bytes returned by PostgreSQL. This can be especially useful for reading varchar, text, json, and jsonb values directly into a []byte and avoiding the type conversion from string. Transactions are started by calling Begin or BeginIso. The BeginIso variant creates a transaction with a specified isolation level. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the WaitForNotification function. It takes a maximum time to wait for a notification. The pgx ConnConfig struct has a TLSConfig field. If this field is nil, then TLS will be disabled. If it is present, then it will be used to configure the TLS connection. This allows total configuration of the TLS connection. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. The log15 package (http://gopkg.in/inconshreveable/log15.v2) satisfies this interface and it is simple to define adapters for other loggers. Set LogLevel to control logging verbosity.

Package config provides convenient access methods to configuration stored as JSON or YAML. Let's start with a simple YAML file config.yml: We can parse it using ParseYaml(), which will return a *Config instance on success: An equivalent JSON configuration could be built using ParseJson(): From now, we can retrieve configuration values using a path in dotted notation: Besides String(), other types can be fetched directly: Bool(), Float64(), Int(), Map() and List(). All these methods will return an error if the path doesn't exist, or the value doesn't match or can't be converted to the requested type. A nested configuration can be fetched using Get(). Here we get a new *Config instance with a subset of the configuration: Then the inner values are fetched relatively to the subset: For lists, the dotted path must use an index to refer to a specific value. To retrieve the information from a user stored in the configuration above: JSON or YAML strings can be created calling the appropriate Render*() functions. Here's how we render a configuration like the one used in these examples: This results in a configuration string to be stored in a file or database. For more more convenience it can parse OS environment variables and command line arguments. We can also specify the order of parsing: In case of OS environment all existing at the moment of parsing keys will be scanned in OS environment, but in uppercase and the separator will be `_` instead of a `.`. If EnvPrefix() is used the given prefix will be used to lookup the environment variable, e.g PREFIX_FOO_BAR will set foo.bar. In case of flags separator will be `-`. In case of command line arguments possible to use regular dot notation syntax for all keys. For see existing keys we can run application with `-h`. We can use unsafe method to get value: There is unsafe methods, like regular, but wuth prefix `U`.

Package kivik provides a generic interface to CouchDB or CouchDB-like databases. The kivik package must be used in conjunction with a database driver. The officially supported drivers are: The Filesystem and Memory drivers are also available, but in early stages of development, and so many features do not yet work: The kivik driver system is modeled after the standard library's `sql` and `sql/driver` packages, although the client API is completely different due to the different database models implemented by SQL and NoSQL databases such as CouchDB. couchDB stores JSON, so Kivik translates Go data structures to and from JSON as necessary. The conversion between Go data types and JSON, and vice versa, is handled automatically according to the rules and behavior described in the documentationf or the standard library's `encoding/json` package (https://golang.org/pkg/encoding/json). One would be well-advised to become familiar with using `json` struct field tags (https://golang.org/pkg/encoding/json/#Marshal) when working with JSON documents. Most Kivik methods take `context.Context` as their first argument. This allows the cancellation of blocking operations in the case that the result is no longer needed. A typical use case for a web application would be to cancel a Kivik request if the remote HTTP client ahs disconnected, rednering the results of the query irrelevant. To learn more about Go's contexts, read the `context` package documentation (https://golang.org/pkg/context/) and read the Go blog post "Go Concurrency Patterns: Context" (https://blog.golang.org/context) for example code. If in doubt, you can pass `context.TODO()` as the context variable. Example: Kivik returns errors that embed an HTTP status code. In most cases, this is the HTTP status code returned by the server. The embedded HTTP status code may be accessed easily using the StatusCode() method, or with a type assertion to `interface { StatusCode() int }`. Example: Any error that does not conform to this interface will be assumed to represent a http.StatusInternalServerError status code. For common usage, authentication should be as simple as including the authentication credentials in the connection DSN. For example: This will connect to `localhost` on port 5984, using the username `admin` and the password `abc123`. When connecting to CouchDB (as in the above example), this will use cookie auth (https://docs.couchdb.org/en/stable/api/server/authn.html?highlight=cookie%20auth#cookie-authentication). Depending on which driver you use, there may be other ways to authenticate, as well. At the moment, the CouchDB driver is the only official driver which offers additional authentication methods. Please refer to the CouchDB package documentation for details (https://pkg.go.dev/github.com/go-kivik/couchdb/v3). With a client handle in hand, you can create a database handle with the DB() method to interact with a specific database.

Package jet is a complete solution for efficient and high performance database access, consisting of type-safe SQL builder with code generation and automatic query result data mapping. Jet currently supports PostgreSQL, MySQL, MariaDB and SQLite. Future releases will add support for additional databases. Use the command bellow to add jet as a dependency into go.mod project: Jet generator can be installed in one of the following ways: (Go1.16+) Install jet generator using go install: go install github.com/go-jet/jet/v2/cmd/jet@latest Install jet generator to GOPATH/bin folder: cd $GOPATH/src/ && GO111MODULE=off go get -u github.com/go-jet/jet/cmd/jet Install jet generator into specific folder: git clone https://github.com/go-jet/jet.git cd jet && go build -o dir_path ./cmd/jet Make sure that the destination folder is added to the PATH environment variable. Jet requires already defined database schema(with tables, enums etc), so that jet generator can generate SQL Builder and Model files. File generation is very fast, and can be added as every pre-build step. Sample command: Before we can write SQL queries in Go, we need to import generated SQL builder and model types: To write postgres SQL queries we import: Then we can write the SQL query: Now we can run the statement and store the result into desired destination: We can print a statement to see SQL query and arguments sent to postgres server: Output: If we print destination as json, we'll get: Detail info about all statements, features and use cases can be found at project wiki page - https://github.com/go-jet/jet/wiki.

Package kivik provides a generic interface to CouchDB or CouchDB-like databases. The kivik package must be used in conjunction with a database driver. The officially supported drivers are: The Filesystem and Memory drivers are also available, but in early stages of development, and so many features do not yet work: The kivik driver system is modeled after the standard library's `sql` and `sql/driver` packages, although the client API is completely different due to the different database models implemented by SQL and NoSQL databases such as CouchDB. The most methods, including those on Client and DB are safe to call concurrently, unless otherwise noted. CouchDB stores JSON, so Kivik translates Go data structures to and from JSON as necessary. The conversion from Go data types to JSON, and vice versa, is handled automatically according to the rules and behavior described in the documentation for the standard library's encoding/json package. Most client and database methods take optional arguments of the type Option. Multiple options may be passed, and latter options take precedence over earlier ones, in case of a conflict. Params and Param can be used to set options that are generally converted to URL query parameters. Different backend drivers may also provide their own unique options with driver-specific effects. Consult your driver's documentation for specifics. Kivik returns errors that embed an HTTP status code. In most cases, this is the HTTP status code returned by the server. The embedded HTTP status code may be accessed easily using the HTTPStatus() method, or with a type assertion to `interface { HTTPStatus() int }`. Example: Any error that does not conform to this interface will be assumed to represent a http.StatusInternalServerError status code. For common usage, authentication should be as simple as including the authentication credentials in the connection DSN. For example: This will connect to `localhost` on port 5984, using the username `admin` and the password `abc123`. When connecting to CouchDB (as in the above example), this will use cookie auth. Depending on which driver you use, there may be other ways to authenticate, as well. At the moment, the CouchDB driver is the only official driver which offers additional authentication methods. Please refer to the CouchDB package documentation for details. With a client handle in hand, you can create a database handle with the DB() method to interact with a specific database.

Package golangNeo4jBoltDriver implements a driver for the Neo4J Bolt Protocol. The driver is compatible with Golang's sql.driver interface, but aims to implement a more complete featureset in line with what Neo4J and Bolt provides. As such, there are multiple interfaces the user can choose from. It's highly recommended that the user use the Neo4J-specific interfaces as they are more flexible and efficient than the provided sql.driver compatible methods. The interface tries to be consistent throughout. The sql.driver interfaces are standard, but the Neo4J-specific ones contain a naming convention of either "Neo" or "Pipeline". The "Neo" ones are the basic interfaces for making queries to Neo4j and it's expected that these would be used the most. The "Pipeline" ones are to support Bolt's pipelining features. Pipelines allow the user to send Neo4j many queries at once and have them executed by the database concurrently. This is useful if you have a bunch of queries that aren't necessarily dependant on one another, and you want to get better performance. The internal APIs will also pipeline statements where it is able to reliably do so, but by manually using the pipelining feature you can maximize your throughput. The API provides connection pooling using the `NewDriverPool` method. This allows you to pass it the maximum number of open connections to be used in the pool. Once this limit is hit, any new clients will have to wait for a connection to become available again. The sql driver is registered as "neo4j-bolt". The sql.driver interface is much more limited than what bolt and neo4j supports. In some cases, concessions were made in order to make that interface work with the neo4j way of doing things. The main instance of this is the marshalling of objects to/from the sql.driver.Value interface. In order to support object types that aren't supported by this interface, the internal encoding package is used to marshal these objects to byte strings. This ultimately makes for a less efficient and more 'clunky' implementation. A glaring instance of this is passing parameters. Neo4j expects named parameters but the driver interface can only really support positional parameters. To get around this, the user must create a map[string]interface{} of their parameters and marshal it to a driver.Value using the encoding.Marshal function. Similarly, the user must unmarshal data returned from the queries using the encoding.Unmarshal function, then use type assertions to retrieve the proper type. In most cases the driver will return the data from neo as the proper go-specific types. For integers they always come back as int64 and floats always come back as float64. This is for the convenience of the user and acts similarly to go's JSON interface. This prevents the user from having to use reflection to get these values. Internally, the types are always transmitted over the wire with as few bytes as possible. There are also cases where no go-specific type matches the returned values, such as when you query for a node, relationship, or path. The driver exposes specific structs which represent this data in the 'structures.graph' package. There are 4 types - Node, Relationship, UnboundRelationship, and Path. The driver returns interface{} objects which must have their types properly asserted to get the data out. There are some limitations to the types of collections the driver supports. Specifically, maps should always be of type map[string]interface{} and lists should always be of type []interface{}. It doesn't seem that the Bolt protocol supports uint64 either, so the biggest number it can send right now is the int64 max. The URL format is: `bolt://(user):(password)@(host):(port)` Schema must be `bolt`. User and password is only necessary if you are authenticating. TLS is supported by using query parameters on the connection string, like so: `bolt://host:port?tls=true&tls_no_verify=false` The supported query params are: * timeout - the number of seconds to set the connection timeout to. Defaults to 60 seconds. * tls - Set to 'true' or '1' if you want to use TLS encryption * tls_no_verify - Set to 'true' or '1' if you want to accept any server certificate (for testing, not secure) * tls_ca_cert_file - path to a custom ca cert for a self-signed TLS cert * tls_cert_file - path to a cert file for this client (need to verify this is processed by Neo4j) * tls_key_file - path to a key file for this client (need to verify this is processed by Neo4j) Errors returned from the API support wrapping, so if you receive an error from the library, it might be wrapping other errors. You can get the innermost error by using the `InnerMost` method. Failure messages from Neo4J are reported, along with their metadata, as an error. In order to get the failure message metadata from a wrapped error, you can do so by calling `err.(*errors.Error).InnerMost().(messages.FailureMessage).Metadata` If there is an error with the database connection, you should get a sql/driver ErrBadConn as per the best practice recommendations of the Golang SQL Driver. However, this error may be wrapped, so you might have to call `InnerMost` to get it, as specified above.

Package scribble is a tiny JSON database

Package restlayer is an API framework heavily inspired by the excellent Python Eve (http://python-eve.org/). It helps you create a comprehensive, customizable, and secure REST (graph) API on top of pluggable backend storages with no boiler plate code so can focus on your business logic. Implemented as a net/http middleware, it plays well with other middleware like CORS (http://github.com/rs/cors) and is net/context aware thanks to xhandler. REST Layer is an opinionated framework. Unlike many API frameworks, you don’t directly control the routing and you don’t have to write handlers. You just define resources and sub-resources with a schema, the framework automatically figures out what routes to generate behind the scene. You don’t have to take care of the HTTP headers and response, JSON encoding, etc. either. REST layer handles HTTP conditional requests, caching, integrity checking for you. A powerful and extensible validation engine make sure that data comes pre-validated to your custom storage handlers. Generic resource handlers for MongoDB (http://github.com/rs/rest-layer-mongo), ElasticSearch (http://github.com/rs/rest-layer-es) and other databases are also available so you have few to no code to write to make the whole system work. Moreover, REST Layer let you create a graph API by linking resources between them. Thanks to its advanced field selection syntax (and coming support of GraphQL), you can gather resources and their dependencies in a single request, saving you from costly network roundtrips. REST Layer is composed of several sub-packages: See https://github.com/rs/rest-layer/blob/master/README.md for full REST Layer documentation.

Kallax is a PostgreSQL typesafe ORM for the Go language. Kallax aims to provide a way of programmatically write queries and interact with a PostgreSQL database without having to write a single line of SQL, use strings to refer to columns and use values of any type in queries. For that reason, the first priority of kallax is to provide type safety to the data access layer. Another of the goals of kallax is make sure all models are, first and foremost, Go structs without having to use database-specific types such as, for example, `sql.NullInt64`. Support for arrays of all basic Go types and all JSON and arrays operators is provided as well.

Package monkit is a flexible code instrumenting and data collection library. I'm going to try and sell you as fast as I can on this library. Example usage We've got tools that capture distribution information (including quantiles) about int64, float64, and bool types. We have tools that capture data about events (we've got meters for deltas, rates, etc). We have rich tools for capturing information about tasks and functions, and literally anything that can generate a name and a number. Almost just as importantly, the amount of boilerplate and code you have to write to get these features is very minimal. Data that's hard to measure probably won't get measured. This data can be collected and sent to Graphite (http://graphite.wikidot.com/) or any other time-series database. Here's a selection of live stats from one of our storage nodes: This library generates call graphs of your live process for you. These call graphs aren't created through sampling. They're full pictures of all of the interesting functions you've annotated, along with quantile information about their successes, failures, how often they panic, return an error (if so instrumented), how many are currently running, etc. The data can be returned in dot format, in json, in text, and can be about just the functions that are currently executing, or all the functions the monitoring system has ever seen. Here's another example of one of our production nodes: https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/callgraph2.png This library generates trace graphs of your live process for you directly, without requiring standing up some tracing system such as Zipkin (though you can do that too). Inspired by Google's Dapper (http://research.google.com/pubs/pub36356.html) and Twitter's Zipkin (http://zipkin.io), we have process-internal trace graphs, triggerable by a number of different methods. You get this trace information for free whenever you use Go contexts (https://blog.golang.org/context) and function monitoring. The output formats are svg and json. Additionally, the library supports trace observation plugins, and we've written a plugin that sends this data to Zipkin (http://github.com/spacemonkeygo/monkit-zipkin). https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/trace.png Before our crazy Go rewrite of everything (https://www.spacemonkey.com/blog/posts/go-space-monkey) (and before we had even seen Google's Dapper paper), we were a Python shop, and all of our "interesting" functions were decorated with a helper that collected timing information and sent it to Graphite. When we transliterated to Go, we wanted to preserve that functionality, so the first version of our monitoring package was born. Over time it started to get janky, especially as we found Zipkin and started adding tracing functionality to it. We rewrote all of our Go code to use Google contexts, and then realized we could get call graph information. We decided a refactor and then an all-out rethinking of our monitoring package was best, and so now we have this library. Sometimes you really want callstack contextual information without having to pass arguments through everything on the call stack. In other languages, many people implement this with thread-local storage. Example: let's say you have written a big system that responds to user requests. All of your libraries log using your log library. During initial development everything is easy to debug, since there's low user load, but now you've scaled and there's OVER TEN USERS and it's kind of hard to tell what log lines were caused by what. Wouldn't it be nice to add request ids to all of the log lines kicked off by that request? Then you could grep for all log lines caused by a specific request id. Geez, it would suck to have to pass all contextual debugging information through all of your callsites. Google solved this problem by always passing a context.Context interface through from call to call. A Context is basically just a mapping of arbitrary keys to arbitrary values that users can add new values for. This way if you decide to add a request context, you can add it to your Context and then all callsites that decend from that place will have the new data in their contexts. It is admittedly very verbose to add contexts to every function call. Painfully so. I hope to write more about it in the future, but Google also wrote up their thoughts about it (https://blog.golang.org/context), which you can go read. For now, just swallow your disgust and let's keep moving. Let's make a super simple Varnish (https://www.varnish-cache.org/) clone. Open up gedit! (Okay just kidding, open whatever text editor you want.) For this motivating program, we won't even add the caching, though there's comments for where to add it if you'd like. For now, let's just make a barebones system that will proxy HTTP requests. We'll call it VLite, but maybe we should call it VReallyLite. Run and build this and open localhost:8080 in your browser. If you use the default proxy target, it should inform you that the world hasn't been destroyed yet. The first thing you'll want to do is add the small amount of boilerplate to make the instrumentation we're going to add to your process observable later. Import the basic monkit packages: and then register environmental statistics and kick off a goroutine in your main method to serve debug requests: Rebuild, and then check out localhost:9000/stats (or localhost:9000/stats/json, if you prefer) in your browser! Remember what I said about Google's contexts (https://blog.golang.org/context)? It might seem a bit overkill for such a small project, but it's time to add them. To help out here, I've created a library that constructs contexts for you for incoming HTTP requests. Nothing that's about to happen requires my webhelp library (https://godoc.org/github.com/jtolds/webhelp), but here is the code now refactored to receive and pass contexts through our two per-request calls. You can create a new context for a request however you want. One reason to use something like webhelp is that the cancelation feature of Contexts is hooked up to the HTTP request getting canceled. Let's start to get statistics about how many requests we receive! First, this package (main) will need to get a monitoring Scope. Add this global definition right after all your imports, much like you'd create a logger with many logging libraries: Now, make the error return value of HandleHTTP named (so, (err error)), and add this defer line as the very first instruction of HandleHTTP: Let's also add the same line (albeit modified for the lack of error) to Proxy, replacing &err with nil: You should now have something like: We'll unpack what's going on here, but for now: For this new funcs dataset, if you want a graph, you can download a dot graph at localhost:9000/funcs/dot and json information from localhost:9000/funcs/json. You should see something like: with a similar report for the Proxy method, or a graph like: https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/handlehttp.png This data reports the overall callgraph of execution for known traces, along with how many of each function are currently running, the most running concurrently (the highwater), how many were successful along with quantile timing information, how many errors there were (with quantile timing information if applicable), and how many panics there were. Since the Proxy method isn't capturing a returned err value, and since HandleHTTP always returns nil, this example won't ever have failures. If you're wondering about the success count being higher than you expected, keep in mind your browser probably requested a favicon.ico. Cool, eh? How it works is an interesting line of code - there's three function calls. If you look at the Go spec, all of the function calls will run at the time the function starts except for the very last one. The first function call, mon.Task(), creates or looks up a wrapper around a Func. You could get this yourself by requesting mon.Func() inside of the appropriate function or mon.FuncNamed(). Both mon.Task() and mon.Func() are inspecting runtime.Caller to determine the name of the function. Because this is a heavy operation, you can actually store the result of mon.Task() and reuse it somehow else if you prefer, so instead of you could instead use which is more performant every time after the first time. runtime.Caller only gets called once. Careful! Don't use the same myFuncMon in different functions unless you want to screw up your statistics! The second function call starts all the various stop watches and bookkeeping to keep track of the function. It also mutates the context pointer it's given to extend the context with information about what current span (in Zipkin parlance) is active. Notably, you *can* pass nil for the context if you really don't want a context. You just lose callgraph information. The last function call stops all the stop watches ad makes a note of any observed errors or panics (it repanics after observing them). Turns out, we don't even need to change our program anymore to get rich tracing information! Open your browser and go to localhost:9000/trace/svg?regex=HandleHTTP. It won't load, and in fact, it's waiting for you to open another tab and refresh localhost:8080 again. Once you retrigger the actual application behavior, the trace regex will capture a trace starting on the first function that matches the supplied regex, and return an svg. Go back to your first tab, and you should see a relatively uninteresting but super promising svg. Let's make the trace more interesting. Add a to your HandleHTTP method, rebuild, and restart. Load localhost:8080, then start a new request to your trace URL, then reload localhost:8080 again. Flip back to your trace, and you should see that the Proxy method only takes a portion of the time of HandleHTTP! https://cdn.rawgit.com/spacemonkeygo/monkit/master/images/trace.svg There's multiple ways to select a trace. You can select by regex using the preselect method (default), which first evaluates the regex on all known functions for sanity checking. Sometimes, however, the function you want to trace may not yet be known to monkit, in which case you'll want to turn preselection off. You may have a bad regex, or you may be in this case if you get the error "Bad Request: regex preselect matches 0 functions." Another way to select a trace is by providing a trace id, which we'll get to next! Make sure to check out what the addition of the time.Sleep call did to the other reports. It's easy to write plugins for monkit! Check out our first one that exports data to Zipkin (http://zipkin.io/)'s Scribe API: https://github.com/spacemonkeygo/monkit-zipkin We plan to have more (for HTrace, OpenTracing, etc, etc), soon!

Package esquery provides a non-obtrusive, idiomatic and easy-to-use query and aggregation builder for the official Go client (https://github.com/elastic/go-elasticsearch) for the ElasticSearch database (https://www.elastic.co/products/elasticsearch). esquery alleviates the need to use extremely nested maps (map[string]interface{}) and serializing queries to JSON manually. It also helps eliminating common mistakes such as misspelling query types, as everything is statically typed. Using `esquery` can make your code much easier to write, read and maintain, and significantly reduce the amount of code you write. esquery provides a method chaining-style API for building and executing queries and aggregations. It does not wrap the official Go client nor does it require you to change your existing code in order to integrate the library. Queries can be directly built with `esquery`, and executed by passing an `*elasticsearch.Client` instance (with optional search parameters). Results are returned as-is from the official client (e.g. `*esapi.Response` objects). Getting started is extremely simple: esquery currently supports version 7 of the ElasticSearch Go client. The library cannot currently generate "short queries". For example, whereas ElasticSearch can accept this: { "query": { "term": { "user": "Kimchy" } } } The library will always generate this: This is also true for queries such as "bool", where fields like "must" can either receive one query object, or an array of query objects. `esquery` will generate an array even if there's only one query object.

Package scribble is a tiny JSON database

Package docdbelastic provides the API client, operations, and parameter types for Amazon DocumentDB Elastic Clusters. Amazon DocumentDB elastic-clusters support workloads with millions of reads/writes per second and petabytes of storage capacity. Amazon DocumentDB elastic clusters also simplify how developers interact with Amazon DocumentDB elastic-clusters by eliminating the need to choose, manage or upgrade instances. Amazon DocumentDB elastic-clusters were created to: provide a solution for customers looking for a database that provides virtually limitless scale with rich query capabilities and MongoDB API compatibility. give customers higher connection limits, and to reduce downtime from patching. continue investing in a cloud-native, elastic, and class leading architecture for JSON workloads.

Package ccgo translates C to Go source code. This v3 package is obsolete. Please use current ccgo/v4: Invocation 2021-12-23: v3.13.0 add clang support. To compile the resulting Go programs the package modernc.org/libc has to be installed. CCGO_CPP selects which command is used by the C front end to obtain target configuration. Defaults to `cpp`. Ignored when --load-config <path> is used. TARGET_GOARCH selects the GOARCH of the resulting Go code. Defaults to $GOARCH or runtime.GOARCH if $GOARCH is not set. Ignored when --load-config <path> is used. TARGET_GOOS selects the GOOS of the resulting Go code. Defaults to $GOOS or runtime.GOOS if $GOOS is not set. Ignored when --load-config <path> is used. To compile for the host invoke something like To cross compile set TARGET_GOARCH and/or TARGET_GOOS, not GOARCH/GOOS. Cross compile depends on availability of C stdlib headers for the target platform as well on the set of predefined macros for the target platform. For example, to cross compile on a Linux host, targeting windows/amd64, it's necessary to have mingw64 installed in $PATH. Then invoke something like Only files with extension .c, .h or .json are recognized as input files. A .json file is interpreted as a compile database. All other command line arguments following the .json file are interpreted as items that should be found in the database and included in the output file. Each item should be on object file (.o) or static archive (.a) or a command (no extension). Command line options requiring an argument. -Dfoo Equals `#define foo 1`. -Dfoo=bar Equals `#define foo bar`. -Ipath Add path to the list of include files search path. The option is a capital letter I (India), not a lowercase letter l (Lima). -limport-path The package at <import-path> must have been produced without using the -nocapi option, ie. the package must have a proper capi_$GOOS_$GOARCH.go file. The option is a lowercase letter l (Lima), not a capital letter I (India). -Ufoo Equals `#undef foo`. -compiledb name When this option appears anywhere, most preceding options are ignored and all following command line arguments are interpreted as a command with arguments that will be executed to produce the compilation database. For example: This will execute `make -DFOO -w` and attempts to extract the compile and archive commands. Only POSIX operating systems are supported. The supported build system must output information about entering directories that is compatible with GNU make. The only compilers supported are `gcc` and `clang`. The only archiver supported is `ar`. Format specification: https://clang.llvm.org/docs/JSONCompilationDatabase.html Note: This option produces also information about libraries created with `ar cr` and include it in the json file, which is above the specification. -crt-import-path path Unless disabled by the -nostdlib option, every produced Go file imports the C runtime library. Default is `modernc.org/libc`. -export-defines "" Export C numeric/string defines as Go constants by capitalizing the first letter of the define's name. -export-defines prefix Export C numeric/string defines as Go constants by prefixing the define's name with `prefix`. Name conflicts are resolved by adding a numeric suffix. -export-enums "" Export C enum constants as Go constants by capitalizing the first letter of the enum constant name. -export-enums prefix Export C enum constants as Go constants by prefixing the enum constant name with `prefix`. Name conflicts are resolved by adding a numeric suffix. -export-externs "" Export C extern definitions as Go definitions by capitalizing the first letter of the definition name. -export-externs prefix Export C extern definitions as Go definitions by prefixing the definition name with `prefix`. Name conflicts are resolved by adding a numeric suffix. -export-fields "" Export C struct fields as Go fields by capitalizing the first letter of the field name. -export-fields prefix Export C struct fields as Go fields by prefixing the field name with `prefix`. Name conflicts are resolved by adding a numeric suffix. -export-structs "" Export tagged C struct/union types as Go types by capitalizing the first letter of the tag name. -export-structs prefix Export tagged C struct/union types as Go types by prefixing the tag name with `prefix`. Name conflicts are resolved by adding a numeric suffix. -export-typedefs "" Export C typedefs as Go types by capitalizing the first letter of the typedef name. -export-structs prefix Export C typedefs as as Go types by prefixing the typedef name with `prefix`. Name conflicts are resolved by adding a numeric suffix. -static-locals-prefix prefix Prefix C static local declarators names with 'prefix'. -host-config-cmd command This option has the same effect as setting `CCGO_CPP=command`. -host-config-opts comma-separated-list The separated items of the list are added to the invocation of the configuration command. -pkgname name Set the resulting Go package name to 'name'. Defaults to `main`. -script filename Ccgo does not yet have a concept of object files. All C files that are needed for producing the resulting Go file have to be compiled together and "linked" in memory. There are some problems with this approach, one of them is the situation when foo.c has to be compiled using, for example `-Dbar=42` and "linked" with baz.c that needs to be compiled with `-Dbar=314`. Or `bar` must not defined at all for baz.c, etc. A script in a named file is a CSV file. It is opened like this (error handling omitted): The first field of every record in the CSV file is the directory to use. The remaining fields are the arguments of the ccgo command. This way different C files can be translated using different options. The CSV file may look something like: -volatile comma-separated-list The separated items of the list are added to the list of file scope extern variables the will be accessed atomically, like if their C declarator used the 'volatile' type specifier. Currently only C scalar types of size 4 and 8 bytes are supported. Other types/sizes will ignore both the volatile specifier and the -volatile option. -save-config path This option copies every header included during compilation or compile database generation to a file under the path argument. Additionally the host configuration, ie. predefined macros, include search paths, os and architecture is stored in path/config.json. When this option is used, no Go code is generated, meaning no link phase occurs and thus the memory consumption should stay low. Passing an empty string as an argument of -save-config is the same as if the option is not present at all. Possibly useful when the option set is generated in code. This option is ignored when -compiledb <path> is used. --load-config path Note that this option must have the double dash prefix to distinguish it from -lfoo, the [traditional] short form of `-l foo`. This option configures the compiler using path/config.json. The include paths are adjusted to be relative to path. For example: Assume on machine A the default C preprocessor reports a system include search path "/usr/include". Running ccgo on A with -save-config /tmp/foo to compile foo.c that #includes <stdlib.h>, which is found in /usr/include/stdlib.h on the host results in Assume /tmp/foo from machine A will be recursively copied to machine B, that may run a different operating system and/or architecture. Let the copy be for example in /tmp/bar. Using --load-config /tmp/bar will instruct ccgo to configure its preprocessor with a system include path /tmp/bar/usr/include and thus use the original machine A stdlib.h found there. When the --load-config is used, no host configuration from a machine B cross C preprocessor/compiler is needed to transpile the foo.c source on machine B as if the compiler would be running on machine A. The particular usefulness of this mechanism is for transpiling big projects for 32 bit architectures. There the lack if ccgo having an object format and thus linking everything in RAM can need too much memory for the system to handle. The way around this is possibly to run something like on machine A, transfer path/* to machine B and run the link phase there with eg. Note that the C sources for the project must be in the same path on both machines because the compile database stores absolute paths. It might be convenient to put the sources in path/src, the config in path/config, for example, and transfer the [archive of] path/ to the same directory on the second machine. That also solves the issue when ./configure generates files and the result differs per operating system or architecture. Passing an empty string as an argument of -load-config is the same as if the option is not present at all. Possibly useful when the option set is generated in code. These command line options don't take arguments. -E When this option is present the compiler does not produce any Go files and instead prints the preprocessor output to stdout. -all-errors Normally only the first 10 or so errors are shown. With this option the compiler will show all errors. -header Using this option suppresses producing of any function definitions. This is possibly useful for producing Go files from C header files. Including function signatures with -header. -func-sig Add this option to include fucntion signature when compiling headers (using -header). -nostdinc This option disables the default C include search paths. -nostdlib This option disables importing of the runtime library by the resulting Go code. -trace-pinning This option will print the positions and names of local declarators that are being pinned. -version Ignore all other options, print version and exit. -verbose-compiledb Enable verbose output when -compiledb is present. -ignore-undefined This option tells the linker to not insist on finding definitions for declarators that are not implicitly declared and used - but not defined. This might be useful when the intent is to define the missing function in Go functions manually. Name conflict resolution for such declarator names may or may not be applied. -ignore-unsupported-alignment This option tells the compiler to not complain about alignments that Go cannot support. -trace-included-files This option outputs the path names of all included files. This option is ignored when -compiledb <path> is used. There may exist other options not listed above. Those should be considered temporary and/or unsupported and may be removed without notice. Alternatively, they may eventually get promoted to "documented" options.

Package sqlz (pronounced "sequelize") is an un-opinionated, un-obtrusive SQL query builder for Go projects, based on github.com/jmoiron/sqlx. As opposed to other query builders, sqlz does not mean to bridge the gap between different SQL servers and implementations by providing a unified interface. Instead, it aims to support an extended SQL syntax that may be implementation-specific. For example, if you wish to use PostgreSQL-specific features such as JSON operators and upsert statements, sqlz means to support these without caring if the underlying database backend really is PostgreSQL. In other words, sqlz builds whatever queries you want it to build. sqlz is easy to integrate into existing code, as it does not require you to create your database connections through the sqlz API; in fact, it doesn't supply one. You can either use your existing `*sql.DB` connection or an `*sqlx.DB` connection, so you can start writing new queries with sqlz without having to modify any existing code. sqlz leverages sqlx for easy loading of query results. Please make sure you are familiar with how sqlx works in order to understand how row scanning is performed. You may need to add `db` struct tags to your Go structures. sqlz provides a comfortable API for running queries in a transaction, and will automatically commit or rollback the transaction as necessary.

Package restlayer is an API framework heavily inspired by the excellent Python Eve (http://python-eve.org/). It helps you create a comprehensive, customizable, and secure REST (graph) API on top of pluggable backend storages with no boiler plate code so can focus on your business logic. Implemented as a net/http middleware, it plays well with other middleware like CORS (http://github.com/rs/cors) and is net/context aware thanks to xhandler. REST Layer is an opinionated framework. Unlike many API frameworks, you don’t directly control the routing and you don’t have to write handlers. You just define resources and sub-resources with a schema, the framework automatically figures out what routes to generate behind the scene. You don’t have to take care of the HTTP headers and response, JSON encoding, etc. either. REST layer handles HTTP conditional requests, caching, integrity checking for you. A powerful and extensible validation engine make sure that data comes pre-validated to your custom storage handlers. Generic resource handlers for MongoDB (http://github.com/clarify/rested/storers/mongo) and other databases are also available so you have few to no code to write to make the whole system work. Moreover, REST Layer let you create a graph API by linking resources between them. Thanks to its advanced field selection syntax, you can gather resources and their dependencies in a single request, saving you from costly network roundtrips. REST Layer is composed of several sub-packages: See https://github.com/clarify/rested/blob/master/README.md for full REST Layer documentation.

Package edgedb is the official Go driver for EdgeDB. Additionally, github.com/edgedb/edgedb-go/cmd/edgeql-go is a code generator that generates go functions from edgeql files. Typical client usage looks like this: We recommend using environment variables for connection parameters. See the client connection docs for more information. You may also connect to a database using a DSN: Or you can use Option fields. edgedb never returns underlying errors directly. If you are checking for things like context expiration use errors.Is() or errors.As(). Most errors returned by the edgedb package will satisfy the edgedb.Error interface which has methods for introspecting. The following list shows the marshal/unmarshal mapping between EdgeDB types and go types: Note that EdgeDB's std::duration type is represented in int64 microseconds while go's time.Duration type is int64 nanoseconds. It is incorrect to cast one directly to the other. Shape fields that are not required must use optional types for receiving query results. The edgedb.Optional struct can be embedded to make structs optional. Not all types listed above are valid query parameters. To pass a slice of scalar values use array in your query. EdgeDB doesn't currently support using sets as parameters. Nested structures are also not directly allowed but you can use json instead. By default EdgeDB will ignore embedded structs when marshaling/unmarshaling. To treat an embedded struct's fields as part of the parent struct's fields, tag the embedded struct with `edgedb:"$inline"`. Interfaces for user defined marshaler/unmarshalers are documented in the internal/marshal package. Link properties are treated as fields in the linked to struct, and the @ is omitted from the field's tag.

Package dbsql implements the go driver to Databricks SQL Clients should use the database/sql package in conjunction with the driver: Use sql.Open() to create a database handle via a data source name string: The DSN format is: Supported optional connection parameters can be specified in param=value and include: Supported optional session parameters can be specified in param=value and include: Use sql.OpenDB() to create a database handle via a new connector object created with dbsql.NewConnector(): Supported functional options include: Cancelling a query via context cancellation or timeout is supported. Use the driverctx package under driverctx/ctx.go to add CorrelationId and ConnId to the context. CorrelationId and ConnId makes it convenient to parse and create metrics in logging. **Connection Id** Internal id to track what happens under a connection. Connections can be reused so this would track across queries. **Query Id** Internal id to track what happens under a query. Useful because the same query can be used with multiple connections. **Correlation Id** External id, such as request ID, to track what happens under a request. Useful to track multiple connections in the same request. Use the logger package under logger.go to set up logging (from zerolog). By default, logging level is `warn`. If you want to disable logging, use `disabled`. The user can also utilize Track() and Duration() to custom log the elapsed time of anything tracked. The result log may look like this: Use the driverctx package under driverctx/ctx.go to add callbacks to the query context to receive the connection id and query id. Passing parameters to a query is supported when run against servers with version DBR 14.1. For complex types, you can specify the SQL type using the dbsql.Parameter type field. If this field is set, the value field MUST be set to a string. The Go driver now supports staging operations. In order to use a staging operation, you first must update the context with a list of folders that you are allowing the driver to access. After doing so, you can execute staging operations using this context using the exec context. There are three error types exposed via dbsql/errors Each type has a corresponding sentinel value which can be used with errors.Is() to determine if one of the types is present in an error chain. Example usage: See the documentation for dbsql/errors for more information. The driver supports the ability to retrieve Apache Arrow record batches. To work with record batches it is necessary to use sql.Conn.Raw() to access the underlying driver connection to retrieve a driver.Rows instance. The driver exposes two public interfaces for working with record batches from the rows sub-package: The driver.Rows instance retrieved using Conn.Raw() can be converted to a Databricks Rows instance via a type assertion, then use GetArrowBatches() to retrieve a batch iterator. If the ArrowBatchIterator is not closed it will leak resources, such as the underlying connection. Calling code must call Release() on records returned by DBSQLArrowBatchIterator.Next(). Example usage: ================================== Databricks Type --> Golang Type ================================== BOOLEAN --> bool TINYINT --> int8 SMALLINT --> int16 INT --> int32 BIGINT --> int64 FLOAT --> float32 DOUBLE --> float64 VOID --> nil STRING --> string DATE --> time.Time TIMESTAMP --> time.Time DECIMAL(p,s) --> sql.RawBytes BINARY --> sql.RawBytes ARRAY<elementType> --> sql.RawBytes STRUCT --> sql.RawBytes MAP<keyType, valueType> --> sql.RawBytes INTERVAL (year-month) --> string INTERVAL (day-time) --> string For ARRAY, STRUCT, and MAP types, sql.Scan can cast sql.RawBytes to JSON string, which can be unmarshalled to Golang arrays, maps, and structs. For example: May generate the following row:

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql. It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jackc/pgx/stdlib to use pgx as a database/sql compatible driver. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. Connection pool usage is explicit and configurable. In pgx, a connection can be created and managed directly, or a connection pool with a configurable maximum connections can be used. The connection pool offers an after connect hook that allows every connection to be automatically setup before being made available in the connection pool. It delegates methods such as QueryRow to an automatically checked out and released connection so you can avoid manually acquiring and releasing connections when you do not need that level of control. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgx/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. If pgx does cannot natively encode a type and that type is a renamed type (e.g. type MyTime time.Time) pgx will attempt to encode the underlying type. While this is usually desired behavior it can produce suprising behavior if one the underlying type and the renamed type each implement database/sql interfaces and the other implements pgx interfaces. It is recommended that this situation be avoided by implementing pgx interfaces on the renamed type. []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin or BeginEx. The BeginEx variant can create a transaction with a specified isolation level. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the WaitForNotification function. It takes a maximum time to wait for a notification. The pgx ConnConfig struct has a TLSConfig field. If this field is nil, then TLS will be disabled. If it is present, then it will be used to configure the TLS connection. This allows total configuration of the TLS connection. pgx has never explicitly supported Postgres < 9.6's `ssl_renegotiation` option. As of v3.3.0, it doesn't send `ssl_renegotiation: 0` either to support Redshift (https://github.com/jackc/pgx/pull/476). If you need TLS Renegotiation, consider supplying `ConnConfig.TLSConfig` with a non-zero `Renegotiation` value and if it's not the default on your server, set `ssl_renegotiation` via `ConnConfig.RuntimeParams`. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/sirupsen/logrus, and the testing log are provided in the log directory.

Package validator implements value validations for structs and individual fields based on tags. It can also handle Cross-Field and Cross-Struct validation for nested structs and has the ability to dive into arrays and maps of any type. see more examples https://github.com/go-playground/validator/tree/master/_examples Validator is designed to be thread-safe and used as a singleton instance. It caches information about your struct and validations, in essence only parsing your validation tags once per struct type. Using multiple instances neglects the benefit of caching. The not thread-safe functions are explicitly marked as such in the documentation. Doing things this way is actually the way the standard library does, see the file.Open method here: The authors return type "error" to avoid the issue discussed in the following, where err is always != nil: Validator only InvalidValidationError for bad validation input, nil or ValidationErrors as type error; so, in your code all you need to do is check if the error returned is not nil, and if it's not check if error is InvalidValidationError ( if necessary, most of the time it isn't ) type cast it to type ValidationErrors like so err.(validator.ValidationErrors). Custom Validation functions can be added. Example: Cross-Field Validation can be done via the following tags: If, however, some custom cross-field validation is required, it can be done using a custom validation. Why not just have cross-fields validation tags (i.e. only eqcsfield and not eqfield)? The reason is efficiency. If you want to check a field within the same struct "eqfield" only has to find the field on the same struct (1 level). But, if we used "eqcsfield" it could be multiple levels down. Example: Multiple validators on a field will process in the order defined. Example: Bad Validator definitions are not handled by the library. Example: Baked In Cross-Field validation only compares fields on the same struct. If Cross-Field + Cross-Struct validation is needed you should implement your own custom validator. Comma (",") is the default separator of validation tags. If you wish to have a comma included within the parameter (i.e. excludesall=,) you will need to use the UTF-8 hex representation 0x2C, which is replaced in the code as a comma, so the above will become excludesall=0x2C. Pipe ("|") is the 'or' validation tags deparator. If you wish to have a pipe included within the parameter i.e. excludesall=| you will need to use the UTF-8 hex representation 0x7C, which is replaced in the code as a pipe, so the above will become excludesall=0x7C Here is a list of the current built in validators: Tells the validation to skip this struct field; this is particularly handy in ignoring embedded structs from being validated. (Usage: -) This is the 'or' operator allowing multiple validators to be used and accepted. (Usage: rgb|rgba) <-- this would allow either rgb or rgba colors to be accepted. This can also be combined with 'and' for example ( Usage: omitempty,rgb|rgba) When a field that is a nested struct is encountered, and contains this flag any validation on the nested struct will be run, but none of the nested struct fields will be validated. This is useful if inside of your program you know the struct will be valid, but need to verify it has been assigned. NOTE: only "required" and "omitempty" can be used on a struct itself. Same as structonly tag except that any struct level validations will not run. Allows conditional validation, for example if a field is not set with a value (Determined by the "required" validator) then other validation such as min or max won't run, but if a value is set validation will run. Allows to skip the validation if the value is nil (same as omitempty, but only for the nil-values). This tells the validator to dive into a slice, array or map and validate that level of the slice, array or map with the validation tags that follow. Multidimensional nesting is also supported, each level you wish to dive will require another dive tag. dive has some sub-tags, 'keys' & 'endkeys', please see the Keys & EndKeys section just below. Example #1 Example #2 Keys & EndKeys These are to be used together directly after the dive tag and tells the validator that anything between 'keys' and 'endkeys' applies to the keys of a map and not the values; think of it like the 'dive' tag, but for map keys instead of values. Multidimensional nesting is also supported, each level you wish to validate will require another 'keys' and 'endkeys' tag. These tags are only valid for maps. Example #1 Example #2 This validates that the value is not the data types default zero value. For numbers ensures value is not zero. For strings ensures value is not "". For booleans ensures value is not false. For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value when using WithRequiredStructEnabled. The field under validation must be present and not empty only if all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty unless all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only if any of the other specified fields are present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only if all of the other specified fields are present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Example: The field under validation must be present and not empty only when any of the other specified fields are not present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only when all of the other specified fields are not present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Example: The field under validation must not be present or not empty only if all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must not be present or empty unless all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: This validates that the value is the default value and is almost the opposite of required. For numbers, length will ensure that the value is equal to the parameter given. For strings, it checks that the string length is exactly that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, len will ensure that the value is equal to the duration given in the parameter. For numbers, max will ensure that the value is less than or equal to the parameter given. For strings, it checks that the string length is at most that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, max will ensure that the value is less than or equal to the duration given in the parameter. For numbers, min will ensure that the value is greater or equal to the parameter given. For strings, it checks that the string length is at least that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, min will ensure that the value is greater than or equal to the duration given in the parameter. For strings & numbers, eq will ensure that the value is equal to the parameter given. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, eq will ensure that the value is equal to the duration given in the parameter. For strings & numbers, ne will ensure that the value is not equal to the parameter given. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, ne will ensure that the value is not equal to the duration given in the parameter. For strings, ints, and uints, oneof will ensure that the value is one of the values in the parameter. The parameter should be a list of values separated by whitespace. Values may be strings or numbers. To match strings with spaces in them, include the target string between single quotes. For numbers, this will ensure that the value is greater than the parameter given. For strings, it checks that the string length is greater than that number of characters. For slices, arrays and maps it validates the number of items. Example #1 Example #2 (time.Time) For time.Time ensures the time value is greater than time.Now.UTC(). Example #3 (time.Duration) For time.Duration, gt will ensure that the value is greater than the duration given in the parameter. Same as 'min' above. Kept both to make terminology with 'len' easier. Example #1 Example #2 (time.Time) For time.Time ensures the time value is greater than or equal to time.Now.UTC(). Example #3 (time.Duration) For time.Duration, gte will ensure that the value is greater than or equal to the duration given in the parameter. For numbers, this will ensure that the value is less than the parameter given. For strings, it checks that the string length is less than that number of characters. For slices, arrays, and maps it validates the number of items. Example #1 Example #2 (time.Time) For time.Time ensures the time value is less than time.Now.UTC(). Example #3 (time.Duration) For time.Duration, lt will ensure that the value is less than the duration given in the parameter. Same as 'max' above. Kept both to make terminology with 'len' easier. Example #1 Example #2 (time.Time) For time.Time ensures the time value is less than or equal to time.Now.UTC(). Example #3 (time.Duration) For time.Duration, lte will ensure that the value is less than or equal to the duration given in the parameter. This will validate the field value against another fields value either within a struct or passed in field. Example #1: Example #2: Field Equals Another Field (relative) This does the same as eqfield except that it validates the field provided relative to the top level struct. This will validate the field value against another fields value either within a struct or passed in field. Examples: Field Does Not Equal Another Field (relative) This does the same as nefield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as gtfield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as gtefield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as ltfield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as ltefield except that it validates the field provided relative to the top level struct. This does the same as contains except for struct fields. It should only be used with string types. See the behavior of reflect.Value.String() for behavior on other types. This does the same as excludes except for struct fields. It should only be used with string types. See the behavior of reflect.Value.String() for behavior on other types. For arrays & slices, unique will ensure that there are no duplicates. For maps, unique will ensure that there are no duplicate values. For slices of struct, unique will ensure that there are no duplicate values in a field of the struct specified via a parameter. This validates that a string value contains ASCII alpha characters only This validates that a string value contains ASCII alphanumeric characters only This validates that a string value contains unicode alpha characters only This validates that a string value contains unicode alphanumeric characters only This validates that a string value can successfully be parsed into a boolean with strconv.ParseBool This validates that a string value contains number values only. For integers or float it returns true. This validates that a string value contains a basic numeric value. basic excludes exponents etc... for integers or float it returns true. This validates that a string value contains a valid hexadecimal. This validates that a string value contains a valid hex color including hashtag (#) This validates that a string value contains only lowercase characters. An empty string is not a valid lowercase string. This validates that a string value contains only uppercase characters. An empty string is not a valid uppercase string. This validates that a string value contains a valid rgb color This validates that a string value contains a valid rgba color This validates that a string value contains a valid hsl color This validates that a string value contains a valid hsla color This validates that a string value contains a valid E.164 Phone number https://en.wikipedia.org/wiki/E.164 (ex. +1123456789) This validates that a string value contains a valid email This may not conform to all possibilities of any rfc standard, but neither does any email provider accept all possibilities. This validates that a string value is valid JSON This validates that a string value is a valid JWT This validates that a string value contains a valid file path and that the file exists on the machine. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid file path and that the file exists on the machine and is an image. This is done using os.Stat and github.com/gabriel-vasile/mimetype This validates that a string value contains a valid file path but does not validate the existence of that file. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid url This will accept any url the golang request uri accepts but must contain a schema for example http:// or rtmp:// This validates that a string value contains a valid uri This will accept any uri the golang request uri accepts This validates that a string value contains a valid URN according to the RFC 2141 spec. This validates that a string value contains a valid bas324 value. Although an empty string is valid base32 this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 value. Although an empty string is valid base64 this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 URL safe value according the RFC4648 spec. Although an empty string is a valid base64 URL safe value, this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 URL safe value, but without = padding, according the RFC4648 spec, section 3.2. Although an empty string is a valid base64 URL safe value, this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid bitcoin address. The format of the string is checked to ensure it matches one of the three formats P2PKH, P2SH and performs checksum validation. Bitcoin Bech32 Address (segwit) This validates that a string value contains a valid bitcoin Bech32 address as defined by bip-0173 (https://github.com/bitcoin/bips/blob/master/bip-0173.mediawiki) Special thanks to Pieter Wuille for providing reference implementations. This validates that a string value contains a valid ethereum address. The format of the string is checked to ensure it matches the standard Ethereum address format. This validates that a string value contains the substring value. This validates that a string value contains any Unicode code points in the substring value. This validates that a string value contains the supplied rune value. This validates that a string value does not contain the substring value. This validates that a string value does not contain any Unicode code points in the substring value. This validates that a string value does not contain the supplied rune value. This validates that a string value starts with the supplied string value This validates that a string value ends with the supplied string value This validates that a string value does not start with the supplied string value This validates that a string value does not end with the supplied string value This validates that a string value contains a valid isbn10 or isbn13 value. This validates that a string value contains a valid isbn10 value. This validates that a string value contains a valid isbn13 value. This validates that a string value contains a valid UUID. Uppercase UUID values will not pass - use `uuid_rfc4122` instead. This validates that a string value contains a valid version 3 UUID. Uppercase UUID values will not pass - use `uuid3_rfc4122` instead. This validates that a string value contains a valid version 4 UUID. Uppercase UUID values will not pass - use `uuid4_rfc4122` instead. This validates that a string value contains a valid version 5 UUID. Uppercase UUID values will not pass - use `uuid5_rfc4122` instead. This validates that a string value contains a valid ULID value. This validates that a string value contains only ASCII characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains only printable ASCII characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains one or more multibyte characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains a valid DataURI. NOTE: this will also validate that the data portion is valid base64 This validates that a string value contains a valid latitude. This validates that a string value contains a valid longitude. This validates that a string value contains a valid U.S. Social Security Number. This validates that a string value contains a valid IP Address. This validates that a string value contains a valid v4 IP Address. This validates that a string value contains a valid v6 IP Address. This validates that a string value contains a valid CIDR Address. This validates that a string value contains a valid v4 CIDR Address. This validates that a string value contains a valid v6 CIDR Address. This validates that a string value contains a valid resolvable TCP Address. This validates that a string value contains a valid resolvable v4 TCP Address. This validates that a string value contains a valid resolvable v6 TCP Address. This validates that a string value contains a valid resolvable UDP Address. This validates that a string value contains a valid resolvable v4 UDP Address. This validates that a string value contains a valid resolvable v6 UDP Address. This validates that a string value contains a valid resolvable IP Address. This validates that a string value contains a valid resolvable v4 IP Address. This validates that a string value contains a valid resolvable v6 IP Address. This validates that a string value contains a valid Unix Address. This validates that a string value contains a valid MAC Address. Note: See Go's ParseMAC for accepted formats and types: This validates that a string value is a valid Hostname according to RFC 952 https://tools.ietf.org/html/rfc952 This validates that a string value is a valid Hostname according to RFC 1123 https://tools.ietf.org/html/rfc1123 Full Qualified Domain Name (FQDN) This validates that a string value contains a valid FQDN. This validates that a string value appears to be an HTML element tag including those described at https://developer.mozilla.org/en-US/docs/Web/HTML/Element This validates that a string value is a proper character reference in decimal or hexadecimal format This validates that a string value is percent-encoded (URL encoded) according to https://tools.ietf.org/html/rfc3986#section-2.1 This validates that a string value contains a valid directory and that it exists on the machine. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid directory but does not validate the existence of that directory. This is done using os.Stat, which is a platform independent function. It is safest to suffix the string with os.PathSeparator if the directory may not exist at the time of validation. This validates that a string value contains a valid DNS hostname and port that can be used to validate fields typically passed to sockets and connections. This validates that a string value is a valid datetime based on the supplied datetime format. Supplied format must match the official Go time format layout as documented in https://golang.org/pkg/time/ This validates that a string value is a valid country code based on iso3166-1 alpha-2 standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid country code based on iso3166-1 alpha-3 standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid country code based on iso3166-1 alpha-numeric standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid BCP 47 language tag, as parsed by language.Parse. More information on https://pkg.go.dev/golang.org/x/text/language BIC (SWIFT code) This validates that a string value is a valid Business Identifier Code (SWIFT code), defined in ISO 9362. More information on https://www.iso.org/standard/60390.html This validates that a string value is a valid dns RFC 1035 label, defined in RFC 1035. More information on https://datatracker.ietf.org/doc/html/rfc1035 This validates that a string value is a valid time zone based on the time zone database present on the system. Although empty value and Local value are allowed by time.LoadLocation golang function, they are not allowed by this validator. More information on https://golang.org/pkg/time/#LoadLocation This validates that a string value is a valid semver version, defined in Semantic Versioning 2.0.0. More information on https://semver.org/ This validates that a string value is a valid cve id, defined in cve mitre. More information on https://cve.mitre.org/ This validates that a string value contains a valid credit card number using Luhn algorithm. This validates that a string or (u)int value contains a valid checksum using the Luhn algorithm. This validates that a string is a valid 24 character hexadecimal string or valid connection string. Example: This validates that a string value contains a valid cron expression. This validates that a string is valid for use with SpiceDb for the indicated purpose. If no purpose is given, a purpose of 'id' is assumed. Alias Validators and Tags NOTE: When returning an error, the tag returned in "FieldError" will be the alias tag unless the dive tag is part of the alias. Everything after the dive tag is not reported as the alias tag. Also, the "ActualTag" in the before case will be the actual tag within the alias that failed. Here is a list of the current built in alias tags: Validator notes: A collection of validation rules that are frequently needed but are more complex than the ones found in the baked in validators. A non standard validator must be registered manually like you would with your own custom validation functions. Example of registration and use: Here is a list of the current non standard validators: This package panics when bad input is provided, this is by design, bad code like that should not make it to production.

Package validator implements value validations for structs and individual fields based on tags. It can also handle Cross-Field and Cross-Struct validation for nested structs and has the ability to dive into arrays and maps of any type. see more examples https://github.com/go-playground/validator/tree/master/_examples Validator is designed to be thread-safe and used as a singleton instance. It caches information about your struct and validations, in essence only parsing your validation tags once per struct type. Using multiple instances neglects the benefit of caching. The not thread-safe functions are explicitly marked as such in the documentation. Doing things this way is actually the way the standard library does, see the file.Open method here: The authors return type "error" to avoid the issue discussed in the following, where err is always != nil: Validator only InvalidValidationError for bad validation input, nil or ValidationErrors as type error; so, in your code all you need to do is check if the error returned is not nil, and if it's not check if error is InvalidValidationError ( if necessary, most of the time it isn't ) type cast it to type ValidationErrors like so err.(validator.ValidationErrors). Custom Validation functions can be added. Example: Cross-Field Validation can be done via the following tags: If, however, some custom cross-field validation is required, it can be done using a custom validation. Why not just have cross-fields validation tags (i.e. only eqcsfield and not eqfield)? The reason is efficiency. If you want to check a field within the same struct "eqfield" only has to find the field on the same struct (1 level). But, if we used "eqcsfield" it could be multiple levels down. Example: Multiple validators on a field will process in the order defined. Example: Bad Validator definitions are not handled by the library. Example: Baked In Cross-Field validation only compares fields on the same struct. If Cross-Field + Cross-Struct validation is needed you should implement your own custom validator. Comma (",") is the default separator of validation tags. If you wish to have a comma included within the parameter (i.e. excludesall=,) you will need to use the UTF-8 hex representation 0x2C, which is replaced in the code as a comma, so the above will become excludesall=0x2C. Pipe ("|") is the 'or' validation tags deparator. If you wish to have a pipe included within the parameter i.e. excludesall=| you will need to use the UTF-8 hex representation 0x7C, which is replaced in the code as a pipe, so the above will become excludesall=0x7C Here is a list of the current built in validators: Tells the validation to skip this struct field; this is particularly handy in ignoring embedded structs from being validated. (Usage: -) This is the 'or' operator allowing multiple validators to be used and accepted. (Usage: rgb|rgba) <-- this would allow either rgb or rgba colors to be accepted. This can also be combined with 'and' for example ( Usage: omitempty,rgb|rgba) When a field that is a nested struct is encountered, and contains this flag any validation on the nested struct will be run, but none of the nested struct fields will be validated. This is useful if inside of your program you know the struct will be valid, but need to verify it has been assigned. NOTE: only "required" and "omitempty" can be used on a struct itself. Same as structonly tag except that any struct level validations will not run. Allows conditional validation, for example if a field is not set with a value (Determined by the "required" validator) then other validation such as min or max won't run, but if a value is set validation will run. Allows to skip the validation if the value is nil (same as omitempty, but only for the nil-values). This tells the validator to dive into a slice, array or map and validate that level of the slice, array or map with the validation tags that follow. Multidimensional nesting is also supported, each level you wish to dive will require another dive tag. dive has some sub-tags, 'keys' & 'endkeys', please see the Keys & EndKeys section just below. Example #1 Example #2 Keys & EndKeys These are to be used together directly after the dive tag and tells the validator that anything between 'keys' and 'endkeys' applies to the keys of a map and not the values; think of it like the 'dive' tag, but for map keys instead of values. Multidimensional nesting is also supported, each level you wish to validate will require another 'keys' and 'endkeys' tag. These tags are only valid for maps. Example #1 Example #2 This validates that the value is not the data types default zero value. For numbers ensures value is not zero. For strings ensures value is not "". For booleans ensures value is not false. For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value when using WithRequiredStructEnabled. The field under validation must be present and not empty only if all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty unless all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only if any of the other specified fields are present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only if all of the other specified fields are present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Example: The field under validation must be present and not empty only when any of the other specified fields are not present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must be present and not empty only when all of the other specified fields are not present. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Example: The field under validation must not be present or not empty only if all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: The field under validation must not be present or empty unless all the other specified fields are equal to the value following the specified field. For strings ensures value is not "". For slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For structs ensures value is not the zero value. Examples: This validates that the value is the default value and is almost the opposite of required. For numbers, length will ensure that the value is equal to the parameter given. For strings, it checks that the string length is exactly that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, len will ensure that the value is equal to the duration given in the parameter. For numbers, max will ensure that the value is less than or equal to the parameter given. For strings, it checks that the string length is at most that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, max will ensure that the value is less than or equal to the duration given in the parameter. For numbers, min will ensure that the value is greater or equal to the parameter given. For strings, it checks that the string length is at least that number of characters. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, min will ensure that the value is greater than or equal to the duration given in the parameter. For strings & numbers, eq will ensure that the value is equal to the parameter given. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, eq will ensure that the value is equal to the duration given in the parameter. For strings & numbers, ne will ensure that the value is not equal to the parameter given. For slices, arrays, and maps, validates the number of items. Example #1 Example #2 (time.Duration) For time.Duration, ne will ensure that the value is not equal to the duration given in the parameter. For strings, ints, and uints, oneof will ensure that the value is one of the values in the parameter. The parameter should be a list of values separated by whitespace. Values may be strings or numbers. To match strings with spaces in them, include the target string between single quotes. For numbers, this will ensure that the value is greater than the parameter given. For strings, it checks that the string length is greater than that number of characters. For slices, arrays and maps it validates the number of items. Example #1 Example #2 (time.Time) For time.Time ensures the time value is greater than time.Now.UTC(). Example #3 (time.Duration) For time.Duration, gt will ensure that the value is greater than the duration given in the parameter. Same as 'min' above. Kept both to make terminology with 'len' easier. Example #1 Example #2 (time.Time) For time.Time ensures the time value is greater than or equal to time.Now.UTC(). Example #3 (time.Duration) For time.Duration, gte will ensure that the value is greater than or equal to the duration given in the parameter. For numbers, this will ensure that the value is less than the parameter given. For strings, it checks that the string length is less than that number of characters. For slices, arrays, and maps it validates the number of items. Example #1 Example #2 (time.Time) For time.Time ensures the time value is less than time.Now.UTC(). Example #3 (time.Duration) For time.Duration, lt will ensure that the value is less than the duration given in the parameter. Same as 'max' above. Kept both to make terminology with 'len' easier. Example #1 Example #2 (time.Time) For time.Time ensures the time value is less than or equal to time.Now.UTC(). Example #3 (time.Duration) For time.Duration, lte will ensure that the value is less than or equal to the duration given in the parameter. This will validate the field value against another fields value either within a struct or passed in field. Example #1: Example #2: Field Equals Another Field (relative) This does the same as eqfield except that it validates the field provided relative to the top level struct. This will validate the field value against another fields value either within a struct or passed in field. Examples: Field Does Not Equal Another Field (relative) This does the same as nefield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as gtfield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as gtefield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as ltfield except that it validates the field provided relative to the top level struct. Only valid for Numbers, time.Duration and time.Time types, this will validate the field value against another fields value either within a struct or passed in field. usage examples are for validation of a Start and End date: Example #1: Example #2: This does the same as ltefield except that it validates the field provided relative to the top level struct. This does the same as contains except for struct fields. It should only be used with string types. See the behavior of reflect.Value.String() for behavior on other types. This does the same as excludes except for struct fields. It should only be used with string types. See the behavior of reflect.Value.String() for behavior on other types. For arrays & slices, unique will ensure that there are no duplicates. For maps, unique will ensure that there are no duplicate values. For slices of struct, unique will ensure that there are no duplicate values in a field of the struct specified via a parameter. This validates that a string value contains ASCII alpha characters only This validates that a string value contains ASCII alphanumeric characters only This validates that a string value contains unicode alpha characters only This validates that a string value contains unicode alphanumeric characters only This validates that a string value can successfully be parsed into a boolean with strconv.ParseBool This validates that a string value contains number values only. For integers or float it returns true. This validates that a string value contains a basic numeric value. basic excludes exponents etc... for integers or float it returns true. This validates that a string value contains a valid hexadecimal. This validates that a string value contains a valid hex color including hashtag (#) This validates that a string value contains only lowercase characters. An empty string is not a valid lowercase string. This validates that a string value contains only uppercase characters. An empty string is not a valid uppercase string. This validates that a string value contains a valid rgb color This validates that a string value contains a valid rgba color This validates that a string value contains a valid hsl color This validates that a string value contains a valid hsla color This validates that a string value contains a valid E.164 Phone number https://en.wikipedia.org/wiki/E.164 (ex. +1123456789) This validates that a string value contains a valid email This may not conform to all possibilities of any rfc standard, but neither does any email provider accept all possibilities. This validates that a string value is valid JSON This validates that a string value is a valid JWT This validates that a string value contains a valid file path and that the file exists on the machine. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid file path and that the file exists on the machine and is an image. This is done using os.Stat and github.com/gabriel-vasile/mimetype This validates that a string value contains a valid file path but does not validate the existence of that file. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid url This will accept any url the golang request uri accepts but must contain a schema for example http:// or rtmp:// This validates that a string value contains a valid uri This will accept any uri the golang request uri accepts This validates that a string value contains a valid URN according to the RFC 2141 spec. This validates that a string value contains a valid bas324 value. Although an empty string is valid base32 this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 value. Although an empty string is valid base64 this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 URL safe value according the RFC4648 spec. Although an empty string is a valid base64 URL safe value, this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid base64 URL safe value, but without = padding, according the RFC4648 spec, section 3.2. Although an empty string is a valid base64 URL safe value, this will report an empty string as an error, if you wish to accept an empty string as valid you can use this with the omitempty tag. This validates that a string value contains a valid bitcoin address. The format of the string is checked to ensure it matches one of the three formats P2PKH, P2SH and performs checksum validation. Bitcoin Bech32 Address (segwit) This validates that a string value contains a valid bitcoin Bech32 address as defined by bip-0173 (https://github.com/bitcoin/bips/blob/master/bip-0173.mediawiki) Special thanks to Pieter Wuille for providing reference implementations. This validates that a string value contains a valid ethereum address. The format of the string is checked to ensure it matches the standard Ethereum address format. This validates that a string value contains the substring value. This validates that a string value contains any Unicode code points in the substring value. This validates that a string value contains the supplied rune value. This validates that a string value does not contain the substring value. This validates that a string value does not contain any Unicode code points in the substring value. This validates that a string value does not contain the supplied rune value. This validates that a string value starts with the supplied string value This validates that a string value ends with the supplied string value This validates that a string value does not start with the supplied string value This validates that a string value does not end with the supplied string value This validates that a string value contains a valid isbn10 or isbn13 value. This validates that a string value contains a valid isbn10 value. This validates that a string value contains a valid isbn13 value. This validates that a string value contains a valid UUID. Uppercase UUID values will not pass - use `uuid_rfc4122` instead. This validates that a string value contains a valid version 3 UUID. Uppercase UUID values will not pass - use `uuid3_rfc4122` instead. This validates that a string value contains a valid version 4 UUID. Uppercase UUID values will not pass - use `uuid4_rfc4122` instead. This validates that a string value contains a valid version 5 UUID. Uppercase UUID values will not pass - use `uuid5_rfc4122` instead. This validates that a string value contains a valid ULID value. This validates that a string value contains only ASCII characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains only printable ASCII characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains one or more multibyte characters. NOTE: if the string is blank, this validates as true. This validates that a string value contains a valid DataURI. NOTE: this will also validate that the data portion is valid base64 This validates that a string value contains a valid latitude. This validates that a string value contains a valid longitude. This validates that a string value contains a valid U.S. Social Security Number. This validates that a string value contains a valid IP Address. This validates that a string value contains a valid v4 IP Address. This validates that a string value contains a valid v6 IP Address. This validates that a string value contains a valid CIDR Address. This validates that a string value contains a valid v4 CIDR Address. This validates that a string value contains a valid v6 CIDR Address. This validates that a string value contains a valid resolvable TCP Address. This validates that a string value contains a valid resolvable v4 TCP Address. This validates that a string value contains a valid resolvable v6 TCP Address. This validates that a string value contains a valid resolvable UDP Address. This validates that a string value contains a valid resolvable v4 UDP Address. This validates that a string value contains a valid resolvable v6 UDP Address. This validates that a string value contains a valid resolvable IP Address. This validates that a string value contains a valid resolvable v4 IP Address. This validates that a string value contains a valid resolvable v6 IP Address. This validates that a string value contains a valid Unix Address. This validates that a string value contains a valid MAC Address. Note: See Go's ParseMAC for accepted formats and types: This validates that a string value is a valid Hostname according to RFC 952 https://tools.ietf.org/html/rfc952 This validates that a string value is a valid Hostname according to RFC 1123 https://tools.ietf.org/html/rfc1123 Full Qualified Domain Name (FQDN) This validates that a string value contains a valid FQDN. This validates that a string value appears to be an HTML element tag including those described at https://developer.mozilla.org/en-US/docs/Web/HTML/Element This validates that a string value is a proper character reference in decimal or hexadecimal format This validates that a string value is percent-encoded (URL encoded) according to https://tools.ietf.org/html/rfc3986#section-2.1 This validates that a string value contains a valid directory and that it exists on the machine. This is done using os.Stat, which is a platform independent function. This validates that a string value contains a valid directory but does not validate the existence of that directory. This is done using os.Stat, which is a platform independent function. It is safest to suffix the string with os.PathSeparator if the directory may not exist at the time of validation. This validates that a string value contains a valid DNS hostname and port that can be used to validate fields typically passed to sockets and connections. This validates that a string value is a valid datetime based on the supplied datetime format. Supplied format must match the official Go time format layout as documented in https://golang.org/pkg/time/ This validates that a string value is a valid country code based on iso3166-1 alpha-2 standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid country code based on iso3166-1 alpha-3 standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid country code based on iso3166-1 alpha-numeric standard. see: https://www.iso.org/iso-3166-country-codes.html This validates that a string value is a valid BCP 47 language tag, as parsed by language.Parse. More information on https://pkg.go.dev/golang.org/x/text/language BIC (SWIFT code) This validates that a string value is a valid Business Identifier Code (SWIFT code), defined in ISO 9362. More information on https://www.iso.org/standard/60390.html This validates that a string value is a valid dns RFC 1035 label, defined in RFC 1035. More information on https://datatracker.ietf.org/doc/html/rfc1035 This validates that a string value is a valid time zone based on the time zone database present on the system. Although empty value and Local value are allowed by time.LoadLocation golang function, they are not allowed by this validator. More information on https://golang.org/pkg/time/#LoadLocation This validates that a string value is a valid semver version, defined in Semantic Versioning 2.0.0. More information on https://semver.org/ This validates that a string value is a valid cve id, defined in cve mitre. More information on https://cve.mitre.org/ This validates that a string value contains a valid credit card number using Luhn algorithm. This validates that a string or (u)int value contains a valid checksum using the Luhn algorithm. This validates that a string is a valid 24 character hexadecimal string or valid connection string. Example: This validates that a string value contains a valid cron expression. This validates that a string is valid for use with SpiceDb for the indicated purpose. If no purpose is given, a purpose of 'id' is assumed. Alias Validators and Tags NOTE: When returning an error, the tag returned in "FieldError" will be the alias tag unless the dive tag is part of the alias. Everything after the dive tag is not reported as the alias tag. Also, the "ActualTag" in the before case will be the actual tag within the alias that failed. Here is a list of the current built in alias tags: Validator notes: A collection of validation rules that are frequently needed but are more complex than the ones found in the baked in validators. A non standard validator must be registered manually like you would with your own custom validation functions. Example of registration and use: Here is a list of the current non standard validators: This package panics when bad input is provided, this is by design, bad code like that should not make it to production.

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql. It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jackc/pgx/stdlib to use pgx as a database/sql compatible driver. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. Connection pool usage is explicit and configurable. In pgx, a connection can be created and managed directly, or a connection pool with a configurable maximum connections can be used. The connection pool offers an after connect hook that allows every connection to be automatically setup before being made available in the connection pool. It delegates methods such as QueryRow to an automatically checked out and released connection so you can avoid manually acquiring and releasing connections when you do not need that level of control. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgx/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. If pgx does cannot natively encode a type and that type is a renamed type (e.g. type MyTime time.Time) pgx will attempt to encode the underlying type. While this is usually desired behavior it can produce suprising behavior if one the underlying type and the renamed type each implement database/sql interfaces and the other implements pgx interfaces. It is recommended that this situation be avoided by implementing pgx interfaces on the renamed type. []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin or BeginEx. The BeginEx variant can create a transaction with a specified isolation level. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the WaitForNotification function. It takes a maximum time to wait for a notification. The pgx ConnConfig struct has a TLSConfig field. If this field is nil, then TLS will be disabled. If it is present, then it will be used to configure the TLS connection. This allows total configuration of the TLS connection. pgx has never explicitly supported Postgres < 9.6's `ssl_renegotiation` option. As of v3.3.0, it doesn't send `ssl_renegotiation: 0` either to support Redshift (https://github.com/jackc/pgx/pull/476). If you need TLS Renegotiation, consider supplying `ConnConfig.TLSConfig` with a non-zero `Renegotiation` value and if it's not the default on your server, set `ssl_renegotiation` via `ConnConfig.RuntimeParams`. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/sirupsen/logrus, and the testing log are provided in the log directory.

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql. It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jackc/pgx/stdlib to use pgx as a database/sql compatible driver. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. Connection pool usage is explicit and configurable. In pgx, a connection can be created and managed directly, or a connection pool with a configurable maximum connections can be used. The connection pool offers an after connect hook that allows every connection to be automatically setup before being made available in the connection pool. It delegates methods such as QueryRow to an automatically checked out and released connection so you can avoid manually acquiring and releasing connections when you do not need that level of control. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgx/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. If pgx does cannot natively encode a type and that type is a renamed type (e.g. type MyTime time.Time) pgx will attempt to encode the underlying type. While this is usually desired behavior it can produce suprising behavior if one the underlying type and the renamed type each implement database/sql interfaces and the other implements pgx interfaces. It is recommended that this situation be avoided by implementing pgx interfaces on the renamed type. []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin or BeginEx. The BeginEx variant can create a transaction with a specified isolation level. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the WaitForNotification function. It takes a maximum time to wait for a notification. The pgx ConnConfig struct has a TLSConfig field. If this field is nil, then TLS will be disabled. If it is present, then it will be used to configure the TLS connection. This allows total configuration of the TLS connection. pgx has never explicitly supported Postgres < 9.6's `ssl_renegotiation` option. As of v3.3.0, it doesn't send `ssl_renegotiation: 0` either to support Redshift (https://github.com/jackc/pgx/pull/476). If you need TLS Renegotiation, consider supplying `ConnConfig.TLSConfig` with a non-zero `Renegotiation` value and if it's not the default on your server, set `ssl_renegotiation` via `ConnConfig.RuntimeParams`. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/sirupsen/logrus, and the testing log are provided in the log directory.

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql. It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jackc/pgx/stdlib to use pgx as a database/sql compatible driver. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. Connection pool usage is explicit and configurable. In pgx, a connection can be created and managed directly, or a connection pool with a configurable maximum connections can be used. The connection pool offers an after connect hook that allows every connection to be automatically setup before being made available in the connection pool. It delegates methods such as QueryRow to an automatically checked out and released connection so you can avoid manually acquiring and releasing connections when you do not need that level of control. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgx/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. If pgx does cannot natively encode a type and that type is a renamed type (e.g. type MyTime time.Time) pgx will attempt to encode the underlying type. While this is usually desired behavior it can produce suprising behavior if one the underlying type and the renamed type each implement database/sql interfaces and the other implements pgx interfaces. It is recommended that this situation be avoided by implementing pgx interfaces on the renamed type. []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin or BeginEx. The BeginEx variant can create a transaction with a specified isolation level. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the WaitForNotification function. It takes a maximum time to wait for a notification. The pgx ConnConfig struct has a TLSConfig field. If this field is nil, then TLS will be disabled. If it is present, then it will be used to configure the TLS connection. This allows total configuration of the TLS connection. pgx has never explicitly supported Postgres < 9.6's `ssl_renegotiation` option. As of v3.3.0, it doesn't send `ssl_renegotiation: 0` either to support Redshift (https://github.com/jackc/pgx/pull/476). If you need TLS Renegotiation, consider supplying `ConnConfig.TLSConfig` with a non-zero `Renegotiation` value and if it's not the default on your server, set `ssl_renegotiation` via `ConnConfig.RuntimeParams`. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/sirupsen/logrus, and the testing log are provided in the log directory.

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql. It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jackc/pgx/v4/stdlib to use pgx as a database/sql compatible driver. The primary way of establishing a connection is with `pgx.Connect`. The database connection string can be in URL or DSN format. Both PostgreSQL settings and pgx settings can be specified here. In addition, a config struct can be created by `ParseConfig` and modified before establishing the connection with `ConnectConfig`. `*pgx.Conn` represents a single connection to the database and is not concurrency safe. Use sub-package pgxpool for a concurrency safe connection pool. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. QueryFunc can be used to execute a callback function for every row. This is often easier to use than Query. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. If pgx does cannot natively encode a type and that type is a renamed type (e.g. type MyTime time.Time) pgx will attempt to encode the underlying type. While this is usually desired behavior it can produce surprising behavior if one the underlying type and the renamed type each implement database/sql interfaces and the other implements pgx interfaces. It is recommended that this situation be avoided by implementing pgx interfaces on the renamed type. Row values and composite types are represented as pgtype.Record (https://pkg.go.dev/github.com/jackc/pgtype?tab=doc#Record). It is possible to get values of your custom type by implementing DecodeBinary interface. Decoding into pgtype.Record first can simplify process by avoiding dealing with raw protocol directly. For example: []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin. The Tx returned from Begin also implements the Begin method. This can be used to implement pseudo nested transactions. These are internally implemented with savepoints. Use BeginTx to control the transaction mode. BeginFunc and BeginTxFunc are variants that begin a transaction, execute a function, and commit or rollback the transaction depending on the return value of the function. These can be simpler and less error prone to use. Prepared statements can be manually created with the Prepare method. However, this is rarely necessary because pgx includes an automatic statement cache by default. Queries run through the normal Query, QueryRow, and Exec functions are automatically prepared on first execution and the prepared statement is reused on subsequent executions. See ParseConfig for information on how to customize or disable the statement cache. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. When you already have a typed array using CopyFromSlice can be more convenient. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the `Conn.WaitForNotification` method. It blocks until a notification is received or the context is canceled. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/sirupsen/logrus, go.uber.org/zap, github.com/rs/zerolog, and the testing log are provided in the log directory. pgx is implemented on top of github.com/jackc/pgconn a lower level PostgreSQL driver. The Conn.PgConn() method can be used to access this lower layer. pgx is compatible with PgBouncer in two modes. One is when the connection has a statement cache in "describe" mode. The other is when the connection is using the simple protocol. This can be set with the PreferSimpleProtocol config option.

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql. It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jackc/pgx/stdlib to use pgx as a database/sql compatible driver. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. Connection pool usage is explicit and configurable. In pgx, a connection can be created and managed directly, or a connection pool with a configurable maximum connections can be used. The connection pool offers an after connect hook that allows every connection to be automatically setup before being made available in the connection pool. It delegates methods such as QueryRow to an automatically checked out and released connection so you can avoid manually acquiring and releasing connections when you do not need that level of control. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgx/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. If pgx does cannot natively encode a type and that type is a renamed type (e.g. type MyTime time.Time) pgx will attempt to encode the underlying type. While this is usually desired behavior it can produce suprising behavior if one the underlying type and the renamed type each implement database/sql interfaces and the other implements pgx interfaces. It is recommended that this situation be avoided by implementing pgx interfaces on the renamed type. []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin or BeginEx. The BeginEx variant can create a transaction with a specified isolation level. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the WaitForNotification function. It takes a maximum time to wait for a notification. The pgx ConnConfig struct has a TLSConfig field. If this field is nil, then TLS will be disabled. If it is present, then it will be used to configure the TLS connection. This allows total configuration of the TLS connection. pgx has never explicitly supported Postgres < 9.6's `ssl_renegotiation` option. As of v3.3.0, it doesn't send `ssl_renegotiation: 0` either to support Redshift (https://github.com/jackc/pgx/pull/476). If you need TLS Renegotiation, consider supplying `ConnConfig.TLSConfig` with a non-zero `Renegotiation` value and if it's not the default on your server, set `ssl_renegotiation` via `ConnConfig.RuntimeParams`. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/sirupsen/logrus, and the testing log are provided in the log directory.

Package goworker is a Resque-compatible, Go-based background worker. It allows you to push jobs into a queue using an expressive language like Ruby while harnessing the efficiency and concurrency of Go to minimize job latency and cost. goworker workers can run alongside Ruby Resque clients so that you can keep all but your most resource-intensive jobs in Ruby. To create a worker, write a function matching the signature and register it using Here is a simple worker that prints its arguments: To create workers that share a database pool or other resources, use a closure to share variables. goworker worker functions receive the queue they are serving and a slice of interfaces. To use them as parameters to other functions, use Go type assertions to convert them into usable types. For testing, it is helpful to use the redis-cli program to insert jobs onto the Redis queue: will insert 100 jobs for the MyClass worker onto the myqueue queue. It is equivalent to: After building your workers, you will have an executable that you can run which will automatically poll a Redis server and call your workers as jobs arrive. There are several flags which control the operation of the goworker client. -queues="comma,delimited,queues" — This is the only required flag. The recommended practice is to separate your Resque workers from your goworkers with different queues. Otherwise, Resque worker classes that have no goworker analog will cause the goworker process to fail the jobs. Because of this, there is no default queue, nor is there a way to select all queues (à la Resque's * queue). Queues are processed in the order they are specififed. If you have multiple queues you can assign them weights. A queue with a weight of 2 will be checked twice as often as a queue with a weight of 1: -queues='high=2,low=1'. -interval=5.0 — Specifies the wait period between polling if no job was in the queue the last time one was requested. -concurrency=25 — Specifies the number of concurrently executing workers. This number can be as low as 1 or rather comfortably as high as 100,000, and should be tuned to your workflow and the availability of outside resources. -connections=2 — Specifies the maximum number of Redis connections that goworker will consume between the poller and all workers. There is not much performance gain over two and a slight penalty when using only one. This is configurable in case you need to keep connection counts low for cloud Redis providers who limit plans on maxclients. -uri=redis://localhost:6379/ — Specifies the URI of the Redis database from which goworker polls for jobs. Accepts URIs of the format redis://user:pass@host:port/db or unix:///path/to/redis.sock. The flag may also be set by the environment variable $($REDIS_PROVIDER) or $REDIS_URL. E.g. set $REDIS_PROVIDER to REDISTOGO_URL on Heroku to let the Redis To Go add-on configure the Redis database. -namespace=resque: — Specifies the namespace from which goworker retrieves jobs and stores stats on workers. -exit-on-complete=false — Exits goworker when there are no jobs left in the queue. This is helpful in conjunction with the time command to benchmark different configurations. -use-number=false — Uses json.Number when decoding numbers in the job payloads. This will avoid issues that occur when goworker and the json package decode large numbers as floats, which then get encoded in scientific notation, losing pecision. This will default to true soon. You can also configure your own flags for use within your workers. Be sure to set them before calling goworker.Main(). It is okay to call flags.Parse() before calling goworker.Main() if you need to do additional processing on your flags. To stop goworker, send a QUIT, TERM, or INT signal to the process. This will immediately stop job polling. There can be up to $CONCURRENCY jobs currently running, which will continue to run until they are finished. Like Resque, goworker makes no guarantees about the safety of jobs in the event of process shutdown. Workers must be both idempotent and tolerant to loss of the job in the event of failure. If the process is killed with a KILL or by a system failure, there may be one job that is currently in the poller's buffer that will be lost without any representation in either the queue or the worker variable. If you are running Goworker on a system like Heroku, which sends a TERM to signal a process that it needs to stop, ten seconds later sends a KILL to force the process to stop, your jobs must finish within 10 seconds or they may be lost. Jobs will be recoverable from the Redis database under as a JSON object with keys queue, run_at, and payload, but the process is manual. Additionally, there is no guarantee that the job in Redis under the worker key has not finished, if the process is killed before goworker can flush the update to Redis.

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are can be easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy function. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Kallax is a PostgreSQL typesafe ORM for the Go language. Kallax aims to provide a way of programmatically write queries and interact with a PostgreSQL database without having to write a single line of SQL, use strings to refer to columns and use values of any type in queries. For that reason, the first priority of kallax is to provide type safety to the data access layer. Another of the goals of kallax is make sure all models are, first and foremost, Go structs without having to use database-specific types such as, for example, `sql.NullInt64. Support for arrays of all basic Go types and all JSON and arrays operators is provided as well.

Kallax is a PostgreSQL typesafe ORM for the Go language. Kallax aims to provide a way of programmatically write queries and interact with a PostgreSQL database without having to write a single line of SQL, use strings to refer to columns and use values of any type in queries. For that reason, the first priority of kallax is to provide type safety to the data access layer. Another of the goals of kallax is make sure all models are, first and foremost, Go structs without having to use database-specific types such as, for example, `sql.NullInt64`. Support for arrays of all basic Go types and all JSON and arrays operators is provided as well.

Command mox is a modern, secure, full-featured, open source mail server for low-maintenance self-hosted email. Mox is started with the "serve" subcommand, but mox also has many other subcommands. Many of those commands talk to a running mox instance, through the ctl file in the data directory. Specify the configuration file (that holds the path to the data directory) through the -config flag or MOXCONF environment variable. Commands that don't talk to a running mox instance are often for testing/debugging email functionality. For example for parsing an email message, or looking up SPF/DKIM/DMARC records. Below is the usage information as printed by the command when started without any parameters. Followed by the help and usage information for each command. Start mox, serving SMTP/IMAP/HTTPS. Incoming email is accepted over SMTP. Email can be retrieved by users using IMAP. HTTP listeners are started for the admin/account web interfaces, and for automated TLS configuration. Missing essential TLS certificates are immediately requested, other TLS certificates are requested on demand. Only implemented on unix systems, not Windows. Quickstart generates configuration files and prints instructions to quickly set up a mox instance. Quickstart writes configuration files, prints initial admin and account passwords, DNS records you should create. If you run it on Linux it writes a systemd service file and prints commands to enable and start mox as service. The user or uid is optional, defaults to "mox", and is the user or uid/gid mox will run as after initialization. Quickstart assumes mox will run on the machine you run quickstart on and uses its host name and public IPs. On many systems the hostname is not a fully qualified domain name, but only the first dns "label", e.g. "mail" in case of "mail.example.org". If so, quickstart does a reverse DNS lookup to find the hostname, and as fallback uses the label plus the domain of the email address you specified. Use flag -hostname to explicitly specify the hostname mox will run on. Mox is by far easiest to operate if you let it listen on port 443 (HTTPS) and 80 (HTTP). TLS will be fully automatic with ACME with Let's Encrypt. You can run mox along with an existing webserver, but because of MTA-STS and autoconfig, you'll need to forward HTTPS traffic for two domains to mox. Run "mox quickstart -existing-webserver ..." to generate configuration files and instructions for configuring mox along with an existing webserver. But please first consider configuring mox on port 443. It can itself serve domains with HTTP/HTTPS, including with automatic TLS with ACME, is easily configured through both configuration files and admin web interface, and can act as a reverse proxy (and static file server for that matter), so you can forward traffic to your existing backend applications. Look for "WebHandlers:" in the output of "mox config describe-domains" and see the output of "mox config example webhandlers". Shut mox down, giving connections maximum 3 seconds to stop before closing them. While shutting down, new IMAP and SMTP connections will get a status response indicating temporary unavailability. Existing connections will get a 3 second period to finish their transaction and shut down. Under normal circumstances, only IMAP has long-living connections, with the IDLE command to get notified of new mail deliveries. Set new password an account. The password is read from stdin. Secrets derived from the password, but not the password itself, are stored in the account database. The stored secrets are for authentication with: scram-sha-256, scram-sha-1, cram-md5, plain text (bcrypt hash). The parameter is an account name, as configured under Accounts in domains.conf and as present in the data/accounts/ directory, not a configured email address for an account. Set a new admin password, for the web interface. The password is read from stdin. Its bcrypt hash is stored in a file named "adminpasswd" in the configuration directory. Print the log levels, or set a new default log level, or a level for the given package. By default, a single log level applies to all logging in mox. But for each "pkg", an overriding log level can be configured. Examples of packages: smtpserver, smtpclient, queue, imapserver, spf, dkim, dmarc, junk, message, etc. Specify a pkg and an empty level to clear the configured level for a package. Valid labels: error, info, debug, trace, traceauth, tracedata. List hold rules for the delivery queue. Messages submitted to the queue that match a hold rule will be marked as on hold and not scheduled for delivery. Add hold rule for the delivery queue. Add a hold rule to mark matching newly submitted messages as on hold. Set the matching rules with the flags. Don't specify any flags to match all submitted messages. Remove hold rule for the delivery queue. Remove a hold rule by its id. List matching messages in the delivery queue. Prints the message with its ID, last and next delivery attempts, last error. Mark matching messages on hold. Messages that are on hold are not delivered until marked as off hold again, or otherwise handled by the admin. Mark matching messages off hold. Once off hold, messages can be delivered according to their current next delivery attempt. See the "queue schedule" command. Change next delivery attempt for matching messages. The next delivery attempt is adjusted by the duration parameter. If the -now flag is set, the new delivery attempt is set to the duration added to the current time, instead of added to the current scheduled time. Schedule immediate delivery with "mox queue schedule -now 0". Set transport for matching messages. By default, the routing rules determine how a message is delivered. The default and common case is direct delivery with SMTP. Messages can get a previously configured transport assigned to use for delivery, e.g. using submission to another mail server or with connections over a SOCKS proxy. Set TLS requirements for delivery of matching messages. Value "yes" is handled as if the RequireTLS extension was specified during submission. Value "no" is handled as if the message has a header "TLS-Required: No". This header is not added by the queue. If messages without this header are relayed through other mail servers they will apply their own default TLS policy. Value "default" is the default behaviour, currently for unverified opportunistic TLS. Fail delivery of matching messages, delivering DSNs. Failing a message is handled similar to how delivery is given up after all delivery attempts failed. The DSN (delivery status notification) message contains a line saying the message was canceled by the admin. Remove matching messages from the queue. Dangerous operation, this completely removes the message. If you want to store the message, use "queue dump" before removing. Dump a message from the queue. The message is printed to stdout and is in standard internet mail format. List matching messages in the retired queue. Prints messages with their ID and results. Print a message from the retired queue. Prints a JSON representation of the information from the retired queue. Print addresses in suppression list. Add address to suppression list for account. Remove address from suppression list for account. Check if address is present in suppression list, for any or specific account. List matching webhooks in the queue. Prints list of webhooks, their IDs and basic information. Change next delivery attempt for matching webhooks. The next delivery attempt is adjusted by the duration parameter. If the -now flag is set, the new delivery attempt is set to the duration added to the current time, instead of added to the current scheduled time. Schedule immediate delivery with "mox queue schedule -now 0". Fail delivery of matching webhooks. Print details of a webhook from the queue. The webhook is printed to stdout as JSON. List matching webhooks in the retired queue. Prints list of retired webhooks, their IDs and basic information. Print details of a webhook from the retired queue. The retired webhook is printed to stdout as JSON. Import a maildir into an account. The mbox/maildir archive is accessed and imported by the running mox process, so it must have access to the archive files. The default suggested systemd service file isolates mox from most of the file system, with only the "data/" directory accessible, so you may want to put the mbox/maildir archive files in a directory like "data/import/" to make it available to mox. By default, messages will train the junk filter based on their flags and, if "automatic junk flags" configuration is set, based on mailbox naming. If the destination mailbox is the Sent mailbox, the recipients of the messages are added to the message metadata, causing later incoming messages from these recipients to be accepted, unless other reputation signals prevent that. Users can also import mailboxes/messages through the account web page by uploading a zip or tgz file with mbox and/or maildirs. Messages are imported even if already present. Importing messages twice will result in duplicate messages. Mailbox flags, like "seen", "answered", will be imported. An optional dovecot-keywords file can specify additional flags, like Forwarded/Junk/NotJunk. Import an mbox into an account. Using mbox is not recommended, maildir is a better defined format. The mbox/maildir archive is accessed and imported by the running mox process, so it must have access to the archive files. The default suggested systemd service file isolates mox from most of the file system, with only the "data/" directory accessible, so you may want to put the mbox/maildir archive files in a directory like "data/import/" to make it available to mox. By default, messages will train the junk filter based on their flags and, if "automatic junk flags" configuration is set, based on mailbox naming. If the destination mailbox is the Sent mailbox, the recipients of the messages are added to the message metadata, causing later incoming messages from these recipients to be accepted, unless other reputation signals prevent that. Users can also import mailboxes/messages through the account web page by uploading a zip or tgz file with mbox and/or maildirs. Messages are imported even if already present. Importing messages twice will result in duplicate messages. Export one or all mailboxes from an account in maildir format. Export bypasses a running mox instance. It opens the account mailbox/message database file directly. This may block if a running mox instance also has the database open, e.g. for IMAP connections. To export from a running instance, use the accounts web page or webmail. Export messages from one or all mailboxes in an account in mbox format. Using mbox is not recommended. Maildir is a better format. Export bypasses a running mox instance. It opens the account mailbox/message database file directly. This may block if a running mox instance also has the database open, e.g. for IMAP connections. To export from a running instance, use the accounts web page or webmail. For mbox export, "mboxrd" is used where message lines starting with the magic "From " string are escaped by prepending a >. All ">*From " are escaped, otherwise reconstructing the original could lose a ">". Start a local SMTP/IMAP server that accepts all messages, useful when testing/developing software that sends email. Localserve starts mox with a configuration suitable for local email-related software development/testing. It listens for SMTP/Submission(s), IMAP(s) and HTTP(s), on the regular port numbers + 1000. Data is stored in the system user's configuration directory under "mox-localserve", e.g. $HOME/.config/mox-localserve/ on linux, but can be overridden with the -dir flag. If the directory does not yet exist, it is automatically initialized with configuration files, an account with email address mox@localhost and password moxmoxmox, and a newly generated self-signed TLS certificate. Incoming messages are delivered as normal, falling back to accepting and delivering to the mox account for unknown addresses. Submitted messages are added to the queue, which delivers by ignoring the destination servers, always connecting to itself instead. Recipient addresses with the following localpart suffixes are handled specially: - "temperror": fail with a temporary error code - "permerror": fail with a permanent error code - [45][0-9][0-9]: fail with the specific error code - "timeout": no response (for an hour) If the localpart begins with "mailfrom" or "rcptto", the error is returned during those commands instead of during "data". Prints help about matching commands. If multiple commands match, they are listed along with the first line of their help text. If a single command matches, its usage and full help text is printed. Creates a backup of the data directory. Backup creates consistent snapshots of the databases and message files and copies other files in the data directory. Empty directories are not copied. These files can then be stored elsewhere for long-term storage, or used to fall back to should an upgrade fail. Simply copying files in the data directory while mox is running can result in unusable database files. Message files never change (they are read-only, though can be removed) and are hard-linked so they don't consume additional space. If hardlinking fails, for example when the backup destination directory is on a different file system, a regular copy is made. Using a destination directory like "data/tmp/backup" increases the odds hardlinking succeeds: the default systemd service file specifically mounts the data directory, causing attempts to hardlink outside it to fail with an error about cross-device linking. All files in the data directory that aren't recognized (i.e. other than known database files, message files, an acme directory, the "tmp" directory, etc), are stored, but with a warning. Remove files in the destination directory before doing another backup. The backup command will not overwrite files, but print and return errors. Exit code 0 indicates the backup was successful. A clean successful backup does not print any output, but may print warnings. Use the -verbose flag for details, including timing. To restore a backup, first shut down mox, move away the old data directory and move an earlier backed up directory in its place, run "mox verifydata", possibly with the "-fix" option, and restart mox. After the restore, you may also want to run "mox bumpuidvalidity" for each account for which messages in a mailbox changed, to force IMAP clients to synchronize mailbox state. Before upgrading, to check if the upgrade will likely succeed, first make a backup, then use the new mox binary to run "mox verifydata" on the backup. This can change the backup files (e.g. upgrade database files, move away unrecognized message files), so you should make a new backup before actually upgrading. Verify the contents of a data directory, typically of a backup. Verifydata checks all database files to see if they are valid BoltDB/bstore databases. It checks that all messages in the database have a corresponding on-disk message file and there are no unrecognized files. If option -fix is specified, unrecognized message files are moved away. This may be needed after a restore, because messages enqueued or delivered in the future may get those message sequence numbers assigned and writing the message file would fail. Consistency of message/mailbox UID, UIDNEXT and UIDVALIDITY is verified as well. Because verifydata opens the database files, schema upgrades may automatically be applied. This can happen if you use a new mox release. It is useful to run "mox verifydata" with a new binary before attempting an upgrade, but only on a copy of the database files, as made with "mox backup". Before upgrading, make a new backup again since "mox verifydata" may have upgraded the database files, possibly making them potentially no longer readable by the previous version. Print licenses of mox source code and dependencies. Parses and validates the configuration files. If valid, the command exits with status 0. If not valid, all errors encountered are printed. Check the DNS records with the configuration for the domain, and print any errors/warnings. Prints annotated DNS records as zone file that should be created for the domain. The zone file can be imported into existing DNS software. You should review the DNS records, especially if your domain previously/currently has email configured. Prints an annotated empty configuration for use as domains.conf. The domains configuration file contains the domains and their configuration, and accounts and their configuration. This includes the configured email addresses. The mox admin web interface, and the mox command line interface, can make changes to this file. Mox automatically reloads this file when it changes. Like the static configuration, the example domains.conf printed by this command needs modifications to make it valid. Prints an annotated empty configuration for use as mox.conf. The static configuration file cannot be reloaded while mox is running. Mox has to be restarted for changes to the static configuration file to take effect. This configuration file needs modifications to make it valid. For example, it may contain unfinished list items. Add an account with an email address and reload the configuration. Email can be delivered to this address/account. A password has to be configured explicitly, see the setaccountpassword command. Remove an account and reload the configuration. Email addresses for this account will also be removed, and incoming email for these addresses will be rejected. All data for the account will be removed. Adds an address to an account and reloads the configuration. If address starts with a @ (i.e. a missing localpart), this is a catchall address for the domain. Remove an address and reload the configuration. Incoming email for this address will be rejected after removing an address. Adds a new domain to the configuration and reloads the configuration. The account is used for the postmaster mailboxes the domain, including as DMARC and TLS reporting. Localpart is the "username" at the domain for this account. If must be set if and only if account does not yet exist. Remove a domain from the configuration and reload the configuration. This is a dangerous operation. Incoming email delivery for this domain will be rejected. List aliases for domain. Print settings and members of alias. Add new alias with one or more addresses. Update alias configuration. Remove alias. Add addresses to alias. Remove addresses from alias. Describe configuration for mox when invoked as sendmail. Prints a systemd unit service file for mox. This is the same file as generated using quickstart. If the systemd service file has changed with a newer version of mox, use this command to generate an up to date version. Ensure host private keys exist for TLS listeners with ACME. In mox.conf, each listener can have TLS configured. Long-lived private key files can be specified, which will be used when requesting ACME certificates. Configuring these private keys makes it feasible to publish DANE TLSA records for the corresponding public keys in DNS, protected with DNSSEC, allowing TLS certificate verification without depending on a list of Certificate Authorities (CAs). Previous versions of mox did not pre-generate private keys for use with ACME certificates, but would generate private keys on-demand. By explicitly configuring private keys, they will not change automatedly with new certificates, and the DNS TLSA records stay valid. This command looks for listeners in mox.conf with TLS with ACME configured. For each missing host private key (of type rsa-2048 and ecdsa-p256) a key is written to config/hostkeys/. If a certificate exists in the ACME "cache", its private key is copied. Otherwise a new private key is generated. Snippets for manually updating/editing mox.conf are printed. After running this command, and updating mox.conf, run "mox config dnsrecords" for a domain and create the TLSA DNS records it suggests to enable DANE. List available config examples, or print a specific example. Check if a newer version of mox is available. A single DNS TXT lookup to _updates.xmox.nl tells if a new version is available. If so, a changelog is fetched from https://updates.xmox.nl, and the individual entries verified with a builtin public key. The changelog is printed. Turn an ID from a Received header into a cid, for looking up in logs. A cid is essentially a connection counter initialized when mox starts. Each log line contains a cid. Received headers added by mox contain a unique ID that can be decrypted to a cid by admin of a mox instance only. Print the configuration for email clients for a domain. Sending email is typically not done on the SMTP port 25, but on submission ports 465 (with TLS) and 587 (without initial TLS, but usually added to the connection with STARTTLS). For IMAP, the port with TLS is 993 and without is 143. Without TLS/STARTTLS, passwords are sent in clear text, which should only be configured over otherwise secured connections, like a VPN. Dial the address using TLS with certificate verification using DANE. Data is copied between connection and stdin/stdout until either side closes the connection. Connect to MX server for domain using STARTTLS verified with DANE. If no destination host is specified, regular delivery logic is used to find the hosts to attempt delivery too. This involves following CNAMEs for the domain, looking up MX records, and possibly falling back to the domain name itself as host. If a destination host is specified, that is the only candidate host considered for dialing. With a list of destinations gathered, each is dialed until a successful SMTP session verified with DANE has been initialized, including EHLO and STARTTLS commands. Once connected, data is copied between connection and stdin/stdout, until either side closes the connection. This command follows the same logic as delivery attempts made from the queue, sharing most of its code. Print TLSA record for given certificate/key and parameters. Valid values: - usage: pkix-ta (0), pkix-ee (1), dane-ta (2), dane-ee (3) - selector: cert (0), spki (1) - matchtype: full (0), sha2-256 (1), sha2-512 (2) Common DANE TLSA record parameters are: dane-ee spki sha2-256, or 3 1 1, followed by a sha2-256 hash of the DER-encoded "SPKI" (subject public key info) from the certificate. An example DNS zone file entry: The first usable information from the pem file is used to compose the TLSA record. In case of selector "cert", a certificate is required. Otherwise the "subject public key info" (spki) of the first certificate or public or private key (pkcs#8, pkcs#1 or ec private key) is used. Lookup DNS name of given type. Lookup always prints whether the response was DNSSEC-protected. Examples: mox dns lookup ptr 1.1.1.1 mox dns lookup mx xmox.nl mox dns lookup txt _dmarc.xmox.nl. mox dns lookup tlsa _25._tcp.xmox.nl Generate a new ed25519 key for use with DKIM. Ed25519 keys are much smaller than RSA keys of comparable cryptographic strength. This is convenient because of maximum DNS message sizes. At the time of writing, not many mail servers appear to support ed25519 DKIM keys though, so it is recommended to sign messages with both RSA and ed25519 keys. Generate a new 2048 bit RSA private key for use with DKIM. The generated file is in PEM format, and has a comment it is generated for use with DKIM, by mox. Lookup and print the DKIM record for the selector at the domain. Print a DKIM DNS TXT record with the public key derived from the private key read from stdin. The DNS should be configured as a TXT record at $selector._domainkey.$domain. Verify the DKIM signatures in a message and print the results. The message is parsed, and the DKIM-Signature headers are validated. Validation of older messages may fail because the DNS records have been removed or changed by now, or because the signature header may have specified an expiration time that was passed. Sign a message, adding DKIM-Signature headers based on the domain in the From header. The message is parsed, the domain looked up in the configuration files, and DKIM-Signature headers generated. The message is printed with the DKIM-Signature headers prepended. Lookup dmarc policy for domain, a DNS TXT record at _dmarc.<domain>, validate and print it. Parse a DMARC report from an email message, and print its extracted details. DMARC reports are periodically mailed, if requested in the DMARC DNS record of a domain. Reports are sent by mail servers that received messages with our domain in a From header. This may or may not be legatimate email. DMARC reports contain summaries of evaluations of DMARC and DKIM/SPF, which can help understand email deliverability problems. Parse an email message and evaluate it against the DMARC policy of the domain in the From-header. mailfromaddress and helodomain are used for SPF validation. If both are empty, SPF validation is skipped. mailfromaddress should be the address used as MAIL FROM in the SMTP session. For DSN messages, that address may be empty. The helo domain was specified at the beginning of the SMTP transaction that delivered the message. These values can be found in message headers. For each reporting address in the domain's DMARC record, check if it has opted into receiving reports (if needed). A DMARC record can request reports about DMARC evaluations to be sent to an email/http address. If the organizational domains of that of the DMARC record and that of the report destination address do not match, the destination address must opt-in to receiving DMARC reports by creating a DMARC record at <dmarcdomain>._report._dmarc.<reportdestdomain>. Test if IP is in the DNS blocklist of the zone, e.g. bl.spamcop.net. If the IP is in the blocklist, an explanation is printed. This is typically a URL with more information. Check the health of the DNS blocklist represented by zone, e.g. bl.spamcop.net. The health of a DNS blocklist can be checked by querying for 127.0.0.1 and 127.0.0.2. The second must and the first must not be present. Lookup the MTASTS record and policy for the domain. MTA-STS is a mechanism for a domain to specify if it requires TLS connections for delivering email. If a domain has a valid MTA-STS DNS TXT record at _mta-sts.<domain> it signals it implements MTA-STS. A policy can then be fetched at https://mta-sts.<domain>/.well-known/mta-sts.txt. The policy specifies the mode (enforce, testing, none), which MX servers support TLS and should be used, and how long the policy can be cached. Recreate and retrain the junk filter for the account. Useful after having made changes to the junk filter configuration, or if the implementation has changed. Sendmail is a drop-in replacement for /usr/sbin/sendmail to deliver emails sent by unix processes like cron. If invoked as "sendmail", it will act as sendmail for sending messages. Its intention is to let processes like cron send emails. Messages are submitted to an actual mail server over SMTP. The destination mail server and credentials are configured in /etc/moxsubmit.conf, see mox config describe-sendmail. The From message header is rewritten to the configured address. When the addressee appears to be a local user, because without @, the message is sent to the configured default address. If submitting an email fails, it is added to a directory moxsubmit.failures in the user's home directory. Most flags are ignored to fake compatibility with other sendmail implementations. A single recipient or the -t flag with a To-header is required. With the -t flag, Cc and Bcc headers are not handled specially, so Bcc is not removed and the addresses do not receive the email. /etc/moxsubmit.conf should be group-readable and not readable by others and this binary should be setgid that group: Check the status of IP for the policy published in DNS for the domain. IPs may be allowed to send for a domain, or disallowed, and several shades in between. If not allowed, an explanation may be provided by the policy. If so, the explanation is printed. The SPF mechanism that matched (if any) is also printed. Lookup the SPF record for the domain and print it. Parse the record as SPF record. If valid, nothing is printed. Lookup the TLSRPT record for the domain. A TLSRPT record typically contains an email address where reports about TLS connectivity should be sent. Mail servers attempting delivery to our domain should attempt to use TLS. TLSRPT lets them report how many connection successfully used TLS, and how what kind of errors occurred otherwise. Parse and print the TLSRPT in the message. The report is printed in formatted JSON. Prints this mox version. Lists available methods, prints request/response parameters for method, or calls a method with a request read from standard input. List available examples, or print a specific example. Change the IMAP UID validity of the mailbox, causing IMAP clients to refetch messages. This can be useful after manually repairing metadata about the account/mailbox. Opens account database file directly. Ensure mox does not have the account open, or is not running. Reassign UIDs in one mailbox or all mailboxes in an account and bump UID validity, causing IMAP clients to refetch messages. Opens account database file directly. Ensure mox does not have the account open, or is not running. Fix inconsistent UIDVALIDITY and UIDNEXT in messages/mailboxes/account. The next UID to use for a message in a mailbox should always be higher than any existing message UID in the mailbox. If it is not, the mailbox UIDNEXT is updated. Each mailbox has a UIDVALIDITY sequence number, which should always be lower than the per-account next UIDVALIDITY to use. If it is not, the account next UIDVALIDITY is updated. Opens account database file directly. Ensure mox does not have the account open, or is not running. Ensure message sizes in the database matching the sum of the message prefix length and on-disk file size. Messages with an inconsistent size are also parsed again. If an inconsistency is found, you should probably also run "mox bumpuidvalidity" on the mailboxes or entire account to force IMAP clients to refetch messages. Parse all messages in the account or all accounts again. Can be useful after upgrading mox with improved message parsing. Messages are parsed in batches, so other access to the mailboxes/messages are not blocked while reparsing all messages. Ensure messages in the database have a pre-parsed MIME form in the database. Recalculate message counts for all mailboxes in the account, and total message size for quota. When a message is added to/removed from a mailbox, or when message flags change, the total, unread, unseen and deleted messages are accounted, the total size of the mailbox, and the total message size for the account. In case of a bug in this accounting, the numbers could become incorrect. This command will find, fix and print them. Parse message, print JSON representation. Reassign message threads. For all accounts, or optionally only the specified account. Threading for all messages in an account is first reset, and new base subject and normalized message-id saved with the message. Then all messages are evaluated and matched against their parents/ancestors. Messages are matched based on the References header, with a fall-back to an In-Reply-To header, and if neither is present/valid, based only on base subject. A References header typically points to multiple previous messages in a hierarchy. From oldest ancestor to most recent parent. An In-Reply-To header would have only a message-id of the parent message. A message is only linked to a parent/ancestor if their base subject is the same. This ensures unrelated replies, with a new subject, are placed in their own thread. The base subject is lower cased, has whitespace collapsed to a single space, and some components removed: leading "Re:", "Fwd:", "Fw:", or bracketed tag (that mailing lists often add, e.g. "[listname]"), trailing "(fwd)", or enclosing "[fwd: ...]". Messages are linked to all their ancestors. If an intermediate parent/ancestor message is deleted in the future, the message can still be linked to the earlier ancestors. If the direct parent already wasn't available while matching, this is stored as the message having a "missing link" to its stored ancestors.

ClickHouse-Bulk Simple Yandex ClickHouse (https://clickhouse.yandex/) insert collector. It collect requests and send to ClickHouse servers. - Group n requests and send to any of ClickHouse server - Sending collected data by interval - Tested with VALUES, TabSeparated formats - Supports many servers to send - Supports query in query parameters and in body - Supports other query parameters like username, password, database - - Supports basic authentication For example: INSERT INTO table3 (c1, c2, c3) VALUES ('v1', 'v2', 'v3') INSERT INTO table3 (c1, c2, c3) VALUES ('v4', 'v5', 'v6') sends as INSERT INTO table3 (c1, c2, c3) VALUES ('v1', 'v2', 'v3')('v4', 'v5', 'v6') - -config - config file (json); default _config.json_ `./clickhouse-bulk` and send queries to :8124 For better performance words FORMAT and VALUES must be uppercase.

Package gosnowflake is a pure Go Snowflake driver for the database/sql package. Clients can use the database/sql package directly. For example: Use Open to create a database handle with connection parameters: The Go Snowflake Driver supports the following connection syntaxes (or data source name formats): where all parameters must be escaped or use `Config` and `DSN` to construct a DSN string. The following example opens a database handle with the Snowflake account myaccount where the username is jsmith, password is mypassword, database is mydb, schema is testschema, and warehouse is mywh: The following connection parameters are supported: account <string>: Specifies the name of your Snowflake account, where string is the name assigned to your account by Snowflake. In the URL you received from Snowflake, your account name is the first segment in the domain (e.g. abc123 in https://abc123.snowflakecomputing.com). This parameter is optional if your account is specified after the @ character. If you are not on us-west-2 region or AWS deployment, then append the region after the account name, e.g. “<account>.<region>”. If you are not on AWS deployment, then append not only the region, but also the platform, e.g., “<account>.<region>.<platform>”. Account, region, and platform should be separated by a period (“.”), as shown above. If you are using a global url, then append connection group and "global", e.g., "account-<connection_group>.global". Account and connection group are separated by a dash ("-"), as shown above. region <string>: DEPRECATED. You may specify a region, such as “eu-central-1”, with this parameter. However, since this parameter is deprecated, it is best to specify the region as part of the account parameter. For details, see the description of the account parameter. database: Specifies the database to use by default in the client session (can be changed after login). schema: Specifies the database schema to use by default in the client session (can be changed after login). warehouse: Specifies the virtual warehouse to use by default for queries, loading, etc. in the client session (can be changed after login). role: Specifies the role to use by default for accessing Snowflake objects in the client session (can be changed after login). passcode: Specifies the passcode provided by Duo when using MFA for login. passcodeInPassword: false by default. Set to true if the MFA passcode is embedded in the login password. Appends the MFA passcode to the end of the password. loginTimeout: Specifies the timeout, in seconds, for login. The default is 60 seconds. The login request gives up after the timeout length if the HTTP response is success. authenticator: Specifies the authenticator to use for authenticating user credentials: To use the internal Snowflake authenticator, specify snowflake (Default). To authenticate through Okta, specify https://<okta_account_name>.okta.com (URL prefix for Okta). To authenticate using your IDP via a browser, specify externalbrowser. To authenticate via OAuth, specify oauth and provide an OAuth Access Token (see the token parameter below). application: Identifies your application to Snowflake Support. insecureMode: false by default. Set to true to bypass the Online Certificate Status Protocol (OCSP) certificate revocation check. IMPORTANT: Change the default value for testing or emergency situations only. token: a token that can be used to authenticate. Should be used in conjunction with the "oauth" authenticator. client_session_keep_alive: Set to true have a heartbeat in the background every hour to keep the connection alive such that the connection session will never expire. Care should be taken in using this option as it opens up the access forever as long as the process is alive. ocspFailOpen: true by default. Set to false to make OCSP check fail closed mode. validateDefaultParameters: true by default. Set to false to disable checks on existence and privileges check for Database, Schema, Warehouse and Role when setting up the connection All other parameters are taken as session parameters. For example, TIMESTAMP_OUTPUT_FORMAT session parameter can be set by adding: The Go Snowflake Driver honors the environment variables HTTP_PROXY, HTTPS_PROXY and NO_PROXY for the forward proxy setting. NO_PROXY specifies which hostname endings should be allowed to bypass the proxy server, e.g. :code:`no_proxy=.amazonaws.com` means that AWS S3 access does not need to go through the proxy. NO_PROXY does not support wildcards. Each value specified should be one of the following: The end of a hostname (or a complete hostname), for example: ".amazonaws.com" or "xy12345.snowflakecomputing.com". An IP address, for example "192.196.1.15". If more than one value is specified, values should be separated by commas, for example: By default, the driver's builtin logger is NOP; no output is generated. This is intentional for those applications that use the same set of logger parameters not to conflict with glog, which is incorporated in the driver logging framework. In order to enable debug logging for the driver, add a build tag sfdebug to the go tool command lines, for example: For tests, run the test command with the tag along with glog parameters. For example, the following command will generate all acitivty logs in the standard error. Likewise, if you build your application with the tag, you may specify the same set of glog parameters. To get the logs for a specific module, use the -vmodule option. For example, to retrieve the driver.go and connection.go module logs: Note: If your request retrieves no logs, call db.Close() or glog.flush() to flush the glog buffer. Note: The logger may be changed in the future for better logging. Currently if the applications use the same parameters as glog, you cannot collect both application and driver logs at the same time. From 0.5.0, a signal handling responsibility has moved to the applications. If you want to cancel a query/command by Ctrl+C, add a os.Interrupt trap in context to execute methods that can take the context parameter, e.g., QueryContext, ExecContext. See cmd/selectmany.go for the full example. Queries return SQL column type information in the ColumnType type. The DatabaseTypeName method returns the following strings representing Snowflake data types: Go's database/sql package limits Go's data types to the following for binding and fetching: Fetching data isn't an issue since the database data type is provided along with the data so the Go Snowflake Driver can translate Snowflake data types to Go native data types. When the client binds data to send to the server, however, the driver cannot determine the date/timestamp data types to associate with binding parameters. For example: To resolve this issue, a binding parameter flag is introduced that associates any subsequent time.Time type to the DATE, TIME, TIMESTAMP_LTZ, TIMESTAMP_NTZ or BINARY data type. The above example could be rewritten as follows: The driver fetches TIMESTAMP_TZ (timestamp with time zone) data using the offset-based Location types, which represent a collection of time offsets in use in a geographical area, such as CET (Central European Time) or UTC (Coordinated Universal Time). The offset-based Location data is generated and cached when a Go Snowflake Driver application starts, and if the given offset is not in the cache, it is generated dynamically. Currently, Snowflake doesn't support the name-based Location types, e.g., America/Los_Angeles. For more information about Location types, see the Go documentation for https://golang.org/pkg/time/#Location. Internally, this feature leverages the []byte data type. As a result, BINARY data cannot be bound without the binding parameter flag. In the following example, sf is an alias for the gosnowflake package: The driver directly downloads a result set from the cloud storage if the size is large. It is required to shift workloads from the Snowflake database to the clients for scale. The download takes place by goroutine named "Chunk Downloader" asynchronously so that the driver can fetch the next result set while the application can consume the current result set. The application may change the number of result set chunk downloader if required. Note this doesn't help reduce memory footprint by itself. Consider Custom JSON Decoder. Experimental: Custom JSON Decoder for parsing Result Set The application may have the driver use a custom JSON decoder that incrementally parses the result set as follows. This option will reduce the memory footprint to half or even quarter, but it can significantly degrade the performance depending on the environment. The test cases running on Travis Ubuntu box show five times less memory footprint while four times slower. Be cautious when using the option. (Private Preview) JWT authentication ** Not recommended for production use until GA Now JWT token is supported when compiling with a golang version of 1.10 or higher. Binary compiled with lower version of golang would return an error at runtime when users try to use JWT authentication feature. To enable this feature, one can construct DSN with fields "authenticator=SNOWFLAKE_JWT&privateKey=<your_private_key>", or using Config structure specifying: The <your_private_key> should be a base64 URL encoded PKCS8 rsa private key string. One way to encode a byte slice to URL base 64 URL format is through base64.URLEncoding.EncodeToString() function. On the server side, one can alter the public key with the SQL command: The <your_public_key> should be a base64 Standard encoded PKI public key string. One way to encode a byte slice to base 64 Standard format is through base64.StdEncoding.EncodeToString() function. To generate the valid key pair, one can do the following command on the shell script: GET and PUT operations are unsupported.