Data Validation

Package websocket implements the WebSocket protocol defined in RFC 6455. The Conn type represents a WebSocket connection. A server application calls the Upgrader.Upgrade method from an HTTP request handler to get a *Conn: Call the connection's WriteMessage and ReadMessage methods to send and receive messages as a slice of bytes. This snippet of code shows how to echo messages using these methods: In above snippet of code, p is a []byte and messageType is an int with value websocket.BinaryMessage or websocket.TextMessage. An application can also send and receive messages using the io.WriteCloser and io.Reader interfaces. To send a message, call the connection NextWriter method to get an io.WriteCloser, write the message to the writer and close the writer when done. To receive a message, call the connection NextReader method to get an io.Reader and read until io.EOF is returned. This snippet shows how to echo messages using the NextWriter and NextReader methods: The WebSocket protocol distinguishes between text and binary data messages. Text messages are interpreted as UTF-8 encoded text. The interpretation of binary messages is left to the application. This package uses the TextMessage and BinaryMessage integer constants to identify the two data message types. The ReadMessage and NextReader methods return the type of the received message. The messageType argument to the WriteMessage and NextWriter methods specifies the type of a sent message. It is the application's responsibility to ensure that text messages are valid UTF-8 encoded text. The WebSocket protocol defines three types of control messages: close, ping and pong. Call the connection WriteControl, WriteMessage or NextWriter methods to send a control message to the peer. Connections handle received close messages by calling the handler function set with the SetCloseHandler method and by returning a *CloseError from the NextReader, ReadMessage or the message Read method. The default close handler sends a close message to the peer. Connections handle received ping messages by calling the handler function set with the SetPingHandler method. The default ping handler sends a pong message to the peer. Connections handle received pong messages by calling the handler function set with the SetPongHandler method. The default pong handler does nothing. If an application sends ping messages, then the application should set a pong handler to receive the corresponding pong. The control message handler functions are called from the NextReader, ReadMessage and message reader Read methods. The default close and ping handlers can block these methods for a short time when the handler writes to the connection. The application must read the connection to process close, ping and pong messages sent from the peer. If the application is not otherwise interested in messages from the peer, then the application should start a goroutine to read and discard messages from the peer. A simple example is: Connections support one concurrent reader and one concurrent writer. Applications are responsible for ensuring that no more than one goroutine calls the write methods (NextWriter, SetWriteDeadline, WriteMessage, WriteJSON, EnableWriteCompression, SetCompressionLevel) concurrently and that no more than one goroutine calls the read methods (NextReader, SetReadDeadline, ReadMessage, ReadJSON, SetPongHandler, SetPingHandler) concurrently. The Close and WriteControl methods can be called concurrently with all other methods. Web browsers allow Javascript applications to open a WebSocket connection to any host. It's up to the server to enforce an origin policy using the Origin request header sent by the browser. The Upgrader calls the function specified in the CheckOrigin field to check the origin. If the CheckOrigin function returns false, then the Upgrade method fails the WebSocket handshake with HTTP status 403. If the CheckOrigin field is nil, then the Upgrader uses a safe default: fail the handshake if the Origin request header is present and the Origin host is not equal to the Host request header. The deprecated package-level Upgrade function does not perform origin checking. The application is responsible for checking the Origin header before calling the Upgrade function. Connections buffer network input and output to reduce the number of system calls when reading or writing messages. Write buffers are also used for constructing WebSocket frames. See RFC 6455, Section 5 for a discussion of message framing. A WebSocket frame header is written to the network each time a write buffer is flushed to the network. Decreasing the size of the write buffer can increase the amount of framing overhead on the connection. The buffer sizes in bytes are specified by the ReadBufferSize and WriteBufferSize fields in the Dialer and Upgrader. The Dialer uses a default size of 4096 when a buffer size field is set to zero. The Upgrader reuses buffers created by the HTTP server when a buffer size field is set to zero. The HTTP server buffers have a size of 4096 at the time of this writing. The buffer sizes do not limit the size of a message that can be read or written by a connection. Buffers are held for the lifetime of the connection by default. If the Dialer or Upgrader WriteBufferPool field is set, then a connection holds the write buffer only when writing a message. Applications should tune the buffer sizes to balance memory use and performance. Increasing the buffer size uses more memory, but can reduce the number of system calls to read or write the network. In the case of writing, increasing the buffer size can reduce the number of frame headers written to the network. Some guidelines for setting buffer parameters are: Limit the buffer sizes to the maximum expected message size. Buffers larger than the largest message do not provide any benefit. Depending on the distribution of message sizes, setting the buffer size to a value less than the maximum expected message size can greatly reduce memory use with a small impact on performance. Here's an example: If 99% of the messages are smaller than 256 bytes and the maximum message size is 512 bytes, then a buffer size of 256 bytes will result in 1.01 more system calls than a buffer size of 512 bytes. The memory savings is 50%. A write buffer pool is useful when the application has a modest number writes over a large number of connections. when buffers are pooled, a larger buffer size has a reduced impact on total memory use and has the benefit of reducing system calls and frame overhead. Per message compression extensions (RFC 7692) are experimentally supported by this package in a limited capacity. Setting the EnableCompression option to true in Dialer or Upgrader will attempt to negotiate per message deflate support. If compression was successfully negotiated with the connection's peer, any message received in compressed form will be automatically decompressed. All Read methods will return uncompressed bytes. Per message compression of messages written to a connection can be enabled or disabled by calling the corresponding Conn method: Currently this package does not support compression with "context takeover". This means that messages must be compressed and decompressed in isolation, without retaining sliding window or dictionary state across messages. For more details refer to RFC 7692. Use of compression is experimental and may result in decreased performance.

Package bolt implements a low-level key/value store in pure Go. It supports fully serializable transactions, ACID semantics, and lock-free MVCC with multiple readers and a single writer. Bolt can be used for projects that want a simple data store without the need to add large dependencies such as Postgres or MySQL. Bolt is a single-level, zero-copy, B+tree data store. This means that Bolt is optimized for fast read access and does not require recovery in the event of a system crash. Transactions which have not finished committing will simply be rolled back in the event of a crash. The design of Bolt is based on Howard Chu's LMDB database project. Bolt currently works on Windows, Mac OS X, and Linux. There are only a few types in Bolt: DB, Bucket, Tx, and Cursor. The DB is a collection of buckets and is represented by a single file on disk. A bucket is a collection of unique keys that are associated with values. Transactions provide either read-only or read-write access to the database. Read-only transactions can retrieve key/value pairs and can use Cursors to iterate over the dataset sequentially. Read-write transactions can create and delete buckets and can insert and remove keys. Only one read-write transaction is allowed at a time. The database uses a read-only, memory-mapped data file to ensure that applications cannot corrupt the database, however, this means that keys and values returned from Bolt cannot be changed. Writing to a read-only byte slice will cause Go to panic. Keys and values retrieved from the database are only valid for the life of the transaction. When used outside the transaction, these byte slices can point to different data or can point to invalid memory which will cause a panic.

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 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/v9/_examples 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: rbg|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. 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 slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. 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. 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. 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. 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. Example: 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. 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. 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. 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. 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. 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. 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(). 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(). 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(). 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(). 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 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 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 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 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 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 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 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 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 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 validataes that a string value contains a valid URN according to the RFC 2141 spec. 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 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 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 providng 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 Full validation is blocked by https://github.com/golang/crypto/pull/28 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 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 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. 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 pq is a pure Go Postgres driver for the database/sql package. In most cases clients will use the database/sql package instead of using this package directly. For example: You can also connect to a database using a URL. For example: Similarly to libpq, when establishing a connection using pq you are expected to supply a connection string containing zero or more parameters. A subset of the connection parameters supported by libpq are also supported by pq. Additionally, pq also lets you specify run-time parameters (such as search_path or work_mem) directly in the connection string. This is different from libpq, which does not allow run-time parameters in the connection string, instead requiring you to supply them in the options parameter. For compatibility with libpq, the following special connection parameters are supported: Valid values for sslmode are: See http://www.postgresql.org/docs/current/static/libpq-connect.html#LIBPQ-CONNSTRING for more information about connection string parameters. Use single quotes for values that contain whitespace: A backslash will escape the next character in values: Note that the connection parameter client_encoding (which sets the text encoding for the connection) may be set but must be "UTF8", matching with the same rules as Postgres. It is an error to provide any other value. In addition to the parameters listed above, any run-time parameter that can be set at backend start time can be set in the connection string. For more information, see http://www.postgresql.org/docs/current/static/runtime-config.html. Most environment variables as specified at http://www.postgresql.org/docs/current/static/libpq-envars.html supported by libpq are also supported by pq. If any of the environment variables not supported by pq are set, pq will panic during connection establishment. Environment variables have a lower precedence than explicitly provided connection parameters. The pgpass mechanism as described in http://www.postgresql.org/docs/current/static/libpq-pgpass.html is supported, but on Windows PGPASSFILE must be specified explicitly. database/sql does not dictate any specific format for parameter markers in query strings, and pq uses the Postgres-native ordinal markers, as shown above. The same marker can be reused for the same parameter: pq does not support the LastInsertId() method of the Result type in database/sql. To return the identifier of an INSERT (or UPDATE or DELETE), use the Postgres RETURNING clause with a standard Query or QueryRow call: For more details on RETURNING, see the Postgres documentation: For additional instructions on querying see the documentation for the database/sql package. Parameters pass through driver.DefaultParameterConverter before they are handled by this package. When the binary_parameters connection option is enabled, []byte values are sent directly to the backend as data in binary format. This package returns the following types for values from the PostgreSQL backend: All other types are returned directly from the backend as []byte values in text format. pq may return errors of type *pq.Error which can be interrogated for error details: See the pq.Error type for details. You can perform bulk imports by preparing a statement returned by pq.CopyIn (or pq.CopyInSchema) in an explicit transaction (sql.Tx). The returned statement handle can then be repeatedly "executed" to copy data into the target table. After all data has been processed you should call Exec() once with no arguments to flush all buffered data. Any call to Exec() might return an error which should be handled appropriately, but because of the internal buffering an error returned by Exec() might not be related to the data passed in the call that failed. CopyIn uses COPY FROM internally. It is not possible to COPY outside of an explicit transaction in pq. Usage example: PostgreSQL supports a simple publish/subscribe model over database connections. See http://www.postgresql.org/docs/current/static/sql-notify.html for more information about the general mechanism. To start listening for notifications, you first have to open a new connection to the database by calling NewListener. This connection can not be used for anything other than LISTEN / NOTIFY. Calling Listen will open a "notification channel"; once a notification channel is open, a notification generated on that channel will effect a send on the Listener.Notify channel. A notification channel will remain open until Unlisten is called, though connection loss might result in some notifications being lost. To solve this problem, Listener sends a nil pointer over the Notify channel any time the connection is re-established following a connection loss. The application can get information about the state of the underlying connection by setting an event callback in the call to NewListener. A single Listener can safely be used from concurrent goroutines, which means that there is often no need to create more than one Listener in your application. However, a Listener is always connected to a single database, so you will need to create a new Listener instance for every database you want to receive notifications in. The channel name in both Listen and Unlisten is case sensitive, and can contain any characters legal in an identifier (see http://www.postgresql.org/docs/current/static/sql-syntax-lexical.html#SQL-SYNTAX-IDENTIFIERS for more information). Note that the channel name will be truncated to 63 bytes by the PostgreSQL server. You can find a complete, working example of Listener usage at https://godoc.org/github.com/lib/pq/example/listen. If you need support for Kerberos authentication, add the following to your main package: This package is in a separate module so that users who don't need Kerberos don't have to download unnecessary dependencies. When imported, additional connection string parameters are supported:

Package metric provides the OpenTelemetry API used to measure metrics about source code operation. This API is separate from its implementation so the instrumentation built from it is reusable. See go.opentelemetry.io/otel/sdk/metric for the official OpenTelemetry implementation of this API. All measurements made with this package are made via instruments. These instruments are created by a Meter which itself is created by a MeterProvider. Applications need to accept a MeterProvider implementation as a starting point when instrumenting. This can be done directly, or by using the OpenTelemetry global MeterProvider via GetMeterProvider. Using an appropriately named Meter from the accepted MeterProvider, instrumentation can then be built from the Meter's instruments. Each instrument is designed to make measurements of a particular type. Broadly, all instruments fall into two overlapping logical categories: asynchronous or synchronous, and int64 or float64. All synchronous instruments (Int64Counter, Int64UpDownCounter, Int64Histogram, Float64Counter, Float64UpDownCounter, and Float64Histogram) are used to measure the operation and performance of source code during the source code execution. These instruments only make measurements when the source code they instrument is run. All asynchronous instruments (Int64ObservableCounter, Int64ObservableUpDownCounter, Int64ObservableGauge, Float64ObservableCounter, Float64ObservableUpDownCounter, and Float64ObservableGauge) are used to measure metrics outside of the execution of source code. They are said to make "observations" via a callback function called once every measurement collection cycle. Each instrument is also grouped by the value type it measures. Either int64 or float64. The value being measured will dictate which instrument in these categories to use. Outside of these two broad categories, instruments are described by the function they are designed to serve. All Counters (Int64Counter, Float64Counter, Int64ObservableCounter, and Float64ObservableCounter) are designed to measure values that never decrease in value, but instead only incrementally increase in value. UpDownCounters (Int64UpDownCounter, Float64UpDownCounter, Int64ObservableUpDownCounter, and Float64ObservableUpDownCounter) on the other hand, are designed to measure values that can increase and decrease. When more information needs to be conveyed about all the synchronous measurements made during a collection cycle, a Histogram (Int64Histogram and Float64Histogram) should be used. Finally, when just the most recent measurement needs to be conveyed about an asynchronous measurement, a Gauge (Int64ObservableGauge and Float64ObservableGauge) should be used. See the OpenTelemetry documentation for more information about instruments and their intended use. OpenTelemetry defines an instrument name syntax that restricts what instrument names are allowed. Instrument names should ... To ensure compatibility with observability platforms, all instruments created need to conform to this syntax. Not all implementations of the API will validate these names, it is the callers responsibility to ensure compliance. Measurements are made by recording values and information about the values with an instrument. How these measurements are recorded depends on the instrument. Measurements for synchronous instruments (Int64Counter, Int64UpDownCounter, Int64Histogram, Float64Counter, Float64UpDownCounter, and Float64Histogram) are recorded using the instrument methods directly. All counter instruments have an Add method that is used to measure an increment value, and all histogram instruments have a Record method to measure a data point. Asynchronous instruments (Int64ObservableCounter, Int64ObservableUpDownCounter, Int64ObservableGauge, Float64ObservableCounter, Float64ObservableUpDownCounter, and Float64ObservableGauge) record measurements within a callback function. The callback is registered with the Meter which ensures the callback is called once per collection cycle. A callback can be registered two ways: during the instrument's creation using an option, or later using the RegisterCallback method of the Meter that created the instrument. If the following criteria are met, an option (WithInt64Callback or WithFloat64Callback) can be used during the asynchronous instrument's creation to register a callback (Int64Callback or Float64Callback, respectively): If the criteria are not met, use the RegisterCallback method of the Meter that created the instrument to register a Callback. This package does not conform to the standard Go versioning policy, all of its interfaces may have methods added to them without a package major version bump. This non-standard API evolution could surprise an uninformed implementation author. They could unknowingly build their implementation in a way that would result in a runtime panic for their users that update to the new API. The API is designed to help inform an instrumentation author about this non-standard API evolution. It requires them to choose a default behavior for unimplemented interface methods. There are three behavior choices they can make: All interfaces in this API embed a corresponding interface from go.opentelemetry.io/otel/metric/embedded. If an author wants the default behavior of their implementations to be a compilation failure, signaling to their users they need to update to the latest version of that implementation, they need to embed the corresponding interface from go.opentelemetry.io/otel/metric/embedded in their implementation. For example, If an author wants the default behavior of their implementations to a panic, they need to embed the API interface directly. This is not a recommended behavior as it could lead to publishing packages that contain runtime panics when users update other package that use newer versions of go.opentelemetry.io/otel/metric. Finally, an author can embed another implementation in theirs. The embedded implementation will be used for methods not defined by the author. For example, an author who wants to default to silently dropping the call can use go.opentelemetry.io/otel/metric/noop: It is strongly recommended that authors only embed go.opentelemetry.io/otel/metric/noop if they choose this default behavior. That implementation is the only one OpenTelemetry authors can guarantee will fully implement all the API interfaces when a user updates their API.

`grpc_middleware` is a collection of gRPC middleware packages: interceptors, helpers and tools. gRPC is a fantastic RPC middleware, which sees a lot of adoption in the Golang world. However, the upstream gRPC codebase is relatively bare bones. This package, and most of its child packages provides commonly needed middleware for gRPC: client-side interceptors for retires, server-side interceptors for input validation and auth, functions for chaining said interceptors, metadata convenience methods and more. By default, gRPC doesn't allow one to have more than one interceptor either on the client nor on the server side. `grpc_middleware` provides convenient chaining methods Simple way of turning a multiple interceptors into a single interceptor. Here's an example for server chaining: These interceptors will be executed from left to right: logging, monitoring and auth. Here's an example for client side chaining: These interceptors will be executed from left to right: monitoring and then retry logic. The retry interceptor will call every interceptor that follows it whenever when a retry happens. Implementing your own interceptor is pretty trivial: there are interfaces for that. But the interesting bit exposing common data to handlers (and other middleware), similarly to HTTP Middleware design. For example, you may want to pass the identity of the caller from the auth interceptor all the way to the handling function. For example, a client side interceptor example for auth looks like: Unfortunately, it's not as easy for streaming RPCs. These have the `context.Context` embedded within the `grpc.ServerStream` object. To pass values through context, a wrapper (`WrappedServerStream`) is needed. For example:

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. Why not a better error message? Because this library intends for you to handle your own error messages. Why should I handle my own errors? Many reasons. We built an internationalized application and needed to know the field, and what validation failed so we could provide a localized error. 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 returns 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 type cast it to type ValidationErrors like so err.(validator.ValidationErrors). Custom 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 default separator of validation tags. 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: rbg|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 usefull if inside of you 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. Is a special tag without a validation function attached. It is used when a field is a Pointer, Interface or Invalid and you wish to validate that it exists. Example: want to ensure a bool exists if you define the bool as a pointer and use exists it will ensure there is a value; couldn't use required as it would fail when the bool was false. exists will fail is the value is a Pointer, Interface or Invalid and is nil. 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. 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. 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 slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. For numbers, max 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. 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. 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. 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. 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. 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(). 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(). 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(). 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(). 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 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 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 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 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 validates that a string value contains alpha characters only This validates that a string value contains alphanumeric characters only This validates that a string value contains a basic numeric value. basic excludes exponents etc... 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 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 email This may not conform to all possibilities of any rfc standard, but neither does any email provider accept all posibilities. 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 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 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 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. This validates that a string value contains a valid version 3 UUID. This validates that a string value contains a valid version 4 UUID. This validates that a string value contains a valid version 5 UUID. 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 Adress. This validates that a string value contains a valid v4 IP Adress. This validates that a string value contains a valid v6 IP Adress. This validates that a string value contains a valid CIDR Adress. This validates that a string value contains a valid v4 CIDR Adress. This validates that a string value contains a valid v6 CIDR Adress. This validates that a string value contains a valid resolvable TCP Adress. This validates that a string value contains a valid resolvable v4 TCP Adress. This validates that a string value contains a valid resolvable v6 TCP Adress. This validates that a string value contains a valid resolvable UDP Adress. This validates that a string value contains a valid resolvable v4 UDP Adress. This validates that a string value contains a valid resolvable v6 UDP Adress. This validates that a string value contains a valid resolvable IP Adress. This validates that a string value contains a valid resolvable v4 IP Adress. This validates that a string value contains a valid resolvable v6 IP Adress. This validates that a string value contains a valid Unix Adress. This validates that a string value contains a valid MAC Adress. Note: See Go's ParseMAC for accepted formats and types: 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: This package panics when bad input is provided, this is by design, bad code like that should not make it to production.

package bbolt implements a low-level key/value store in pure Go. It supports fully serializable transactions, ACID semantics, and lock-free MVCC with multiple readers and a single writer. Bolt can be used for projects that want a simple data store without the need to add large dependencies such as Postgres or MySQL. Bolt is a single-level, zero-copy, B+tree data store. This means that Bolt is optimized for fast read access and does not require recovery in the event of a system crash. Transactions which have not finished committing will simply be rolled back in the event of a crash. The design of Bolt is based on Howard Chu's LMDB database project. Bolt currently works on Windows, Mac OS X, and Linux. There are only a few types in Bolt: DB, Bucket, Tx, and Cursor. The DB is a collection of buckets and is represented by a single file on disk. A bucket is a collection of unique keys that are associated with values. Transactions provide either read-only or read-write access to the database. Read-only transactions can retrieve key/value pairs and can use Cursors to iterate over the dataset sequentially. Read-write transactions can create and delete buckets and can insert and remove keys. Only one read-write transaction is allowed at a time. The database uses a read-only, memory-mapped data file to ensure that applications cannot corrupt the database, however, this means that keys and values returned from Bolt cannot be changed. Writing to a read-only byte slice will cause Go to panic. Keys and values retrieved from the database are only valid for the life of the transaction. When used outside the transaction, these byte slices can point to different data or can point to invalid memory which will cause a panic.

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/v9/_examples 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: rbg|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. 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 slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. 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. 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. 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. 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. Example: 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. 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. 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. 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. 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. 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. 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(). 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(). 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(). 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(). 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 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 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 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 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 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 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 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 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 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 validataes that a string value contains a valid URN according to the RFC 2141 spec. 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 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 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 providng 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 Full validation is blocked by https://github.com/golang/crypto/pull/28 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 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 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. 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 toml implements decoding and encoding of TOML files. This package supports TOML v1.0.0, as specified at https://toml.io The github.com/BurntSushi/toml/cmd/tomlv package implements a TOML validator, and can be used to verify if TOML document is valid. It can also be used to print the type of each key. Example StrictDecoding shows how to detect if there are keys in the TOML document that weren't decoded into the value given. This is useful for returning an error to the user if they've included extraneous fields in their configuration. Example UnmarshalTOML shows how to implement a struct type that knows how to unmarshal itself. The struct must take full responsibility for mapping the values passed into the struct. The method may be used with interfaces in a struct in cases where the actual type is not known until the data is examined. Example Unmarshaler shows how to decode TOML strings into your own custom data type.

Package gocql implements a fast and robust Cassandra driver for the Go programming language. Pass a list of initial node IP addresses to NewCluster to create a new cluster configuration: Port can be specified as part of the address, the above is equivalent to: It is recommended to use the value set in the Cassandra config for broadcast_address or listen_address, an IP address not a domain name. This is because events from Cassandra will use the configured IP address, which is used to index connected hosts. If the domain name specified resolves to more than 1 IP address then the driver may connect multiple times to the same host, and will not mark the node being down or up from events. Then you can customize more options (see ClusterConfig): The driver tries to automatically detect the protocol version to use if not set, but you might want to set the protocol version explicitly, as it's not defined which version will be used in certain situations (for example during upgrade of the cluster when some of the nodes support different set of protocol versions than other nodes). The driver advertises the module name and version in the STARTUP message, so servers are able to detect the version. If you use replace directive in go.mod, the driver will send information about the replacement module instead. When ready, create a session from the configuration. Don't forget to Close the session once you are done with it: CQL protocol uses a SASL-based authentication mechanism and so consists of an exchange of server challenges and client response pairs. The details of the exchanged messages depend on the authenticator used. To use authentication, set ClusterConfig.Authenticator or ClusterConfig.AuthProvider. PasswordAuthenticator is provided to use for username/password authentication: It is possible to secure traffic between the client and server with TLS. To use TLS, set the ClusterConfig.SslOpts field. SslOptions embeds *tls.Config so you can set that directly. There are also helpers to load keys/certificates from files. Warning: Due to historical reasons, the SslOptions is insecure by default, so you need to set EnableHostVerification to true if no Config is set. Most users should set SslOptions.Config to a *tls.Config. SslOptions and Config.InsecureSkipVerify interact as follows: For example: To route queries to local DC first, use DCAwareRoundRobinPolicy. For example, if the datacenter you want to primarily connect is called dc1 (as configured in the database): The driver can route queries to nodes that hold data replicas based on partition key (preferring local DC). Note that TokenAwareHostPolicy can take options such as gocql.ShuffleReplicas and gocql.NonLocalReplicasFallback. We recommend running with a token aware host policy in production for maximum performance. The driver can only use token-aware routing for queries where all partition key columns are query parameters. For example, instead of use The DCAwareRoundRobinPolicy can be replaced with RackAwareRoundRobinPolicy, which takes two parameters, datacenter and rack. Instead of dividing hosts with two tiers (local datacenter and remote datacenters) it divides hosts into three (the local rack, the rest of the local datacenter, and everything else). RackAwareRoundRobinPolicy can be combined with TokenAwareHostPolicy in the same way as DCAwareRoundRobinPolicy. Create queries with Session.Query. Query values must not be reused between different executions and must not be modified after starting execution of the query. To execute a query without reading results, use Query.Exec: Single row can be read by calling Query.Scan: Multiple rows can be read using Iter.Scanner: See Example for complete example. The driver automatically prepares DML queries (SELECT/INSERT/UPDATE/DELETE/BATCH statements) and maintains a cache of prepared statements. CQL protocol does not support preparing other query types. When using CQL protocol >= 4, it is possible to use gocql.UnsetValue as the bound value of a column. This will cause the database to ignore writing the column. The main advantage is the ability to keep the same prepared statement even when you don't want to update some fields, where before you needed to make another prepared statement. Session is safe to use from multiple goroutines, so to execute multiple concurrent queries, just execute them from several worker goroutines. Gocql provides synchronously-looking API (as recommended for Go APIs) and the queries are executed asynchronously at the protocol level. Null values are are unmarshalled as zero value of the type. If you need to distinguish for example between text column being null and empty string, you can unmarshal into *string variable instead of string. See Example_nulls for full example. The driver reuses backing memory of slices when unmarshalling. This is an optimization so that a buffer does not need to be allocated for every processed row. However, you need to be careful when storing the slices to other memory structures. When you want to save the data for later use, pass a new slice every time. A common pattern is to declare the slice variable within the scanner loop: The driver supports paging of results with automatic prefetch, see ClusterConfig.PageSize, Session.SetPrefetch, Query.PageSize, and Query.Prefetch. It is also possible to control the paging manually with Query.PageState (this disables automatic prefetch). Manual paging is useful if you want to store the page state externally, for example in a URL to allow users browse pages in a result. You might want to sign/encrypt the paging state when exposing it externally since it contains data from primary keys. Paging state is specific to the CQL protocol version and the exact query used. It is meant as opaque state that should not be modified. If you send paging state from different query or protocol version, then the behaviour is not defined (you might get unexpected results or an error from the server). For example, do not send paging state returned by node using protocol version 3 to a node using protocol version 4. Also, when using protocol version 4, paging state between Cassandra 2.2 and 3.0 is incompatible (https://issues.apache.org/jira/browse/CASSANDRA-10880). The driver does not check whether the paging state is from the same protocol version/statement. You might want to validate yourself as this could be a problem if you store paging state externally. For example, if you store paging state in a URL, the URLs might become broken when you upgrade your cluster. Call Query.PageState(nil) to fetch just the first page of the query results. Pass the page state returned by Iter.PageState to Query.PageState of a subsequent query to get the next page. If the length of slice returned by Iter.PageState is zero, there are no more pages available (or an error occurred). Using too low values of PageSize will negatively affect performance, a value below 100 is probably too low. While Cassandra returns exactly PageSize items (except for last page) in a page currently, the protocol authors explicitly reserved the right to return smaller or larger amount of items in a page for performance reasons, so don't rely on the page having the exact count of items. See Example_paging for an example of manual paging. There are certain situations when you don't know the list of columns in advance, mainly when the query is supplied by the user. Iter.Columns, Iter.RowData, Iter.MapScan and Iter.SliceMap can be used to handle this case. See Example_dynamicColumns. The CQL protocol supports sending batches of DML statements (INSERT/UPDATE/DELETE) and so does gocql. Use Session.NewBatch to create a new batch and then fill-in details of individual queries. Then execute the batch with Session.ExecuteBatch. Logged batches ensure atomicity, either all or none of the operations in the batch will succeed, but they have overhead to ensure this property. Unlogged batches don't have the overhead of logged batches, but don't guarantee atomicity. Updates of counters are handled specially by Cassandra so batches of counter updates have to use CounterBatch type. A counter batch can only contain statements to update counters. For unlogged batches it is recommended to send only single-partition batches (i.e. all statements in the batch should involve only a single partition). Multi-partition batch needs to be split by the coordinator node and re-sent to correct nodes. With single-partition batches you can send the batch directly to the node for the partition without incurring the additional network hop. It is also possible to pass entire BEGIN BATCH .. APPLY BATCH statement to Query.Exec. There are differences how those are executed. BEGIN BATCH statement passed to Query.Exec is prepared as a whole in a single statement. Session.ExecuteBatch prepares individual statements in the batch. If you have variable-length batches using the same statement, using Session.ExecuteBatch is more efficient. See Example_batch for an example. Query.ScanCAS or Query.MapScanCAS can be used to execute a single-statement lightweight transaction (an INSERT/UPDATE .. IF statement) and reading its result. See example for Query.MapScanCAS. Multiple-statement lightweight transactions can be executed as a logged batch that contains at least one conditional statement. All the conditions must return true for the batch to be applied. You can use Session.ExecuteBatchCAS and Session.MapExecuteBatchCAS when executing the batch to learn about the result of the LWT. See example for Session.MapExecuteBatchCAS. Queries can be marked as idempotent. Marking the query as idempotent tells the driver that the query can be executed multiple times without affecting its result. Non-idempotent queries are not eligible for retrying nor speculative execution. Idempotent queries are retried in case of errors based on the configured RetryPolicy. Queries can be retried even before they fail by setting a SpeculativeExecutionPolicy. The policy can cause the driver to retry on a different node if the query is taking longer than a specified delay even before the driver receives an error or timeout from the server. When a query is speculatively executed, the original execution is still executing. The two parallel executions of the query race to return a result, the first received result will be returned. UDTs can be mapped (un)marshaled from/to map[string]interface{} a Go struct (or a type implementing UDTUnmarshaler, UDTMarshaler, Unmarshaler or Marshaler interfaces). For structs, cql tag can be used to specify the CQL field name to be mapped to a struct field: See Example_userDefinedTypesMap, Example_userDefinedTypesStruct, ExampleUDTMarshaler, ExampleUDTUnmarshaler. It is possible to provide observer implementations that could be used to gather metrics: CQL protocol also supports tracing of queries. When enabled, the database will write information about internal events that happened during execution of the query. You can use Query.Trace to request tracing and receive the session ID that the database used to store the trace information in system_traces.sessions and system_traces.events tables. NewTraceWriter returns an implementation of Tracer that writes the events to a writer. Gathering trace information might be essential for debugging and optimizing queries, but writing traces has overhead, so this feature should not be used on production systems with very high load unless you know what you are doing. Example_batch demonstrates how to execute a batch of statements. Example_dynamicColumns demonstrates how to handle dynamic column list. Example_marshalerUnmarshaler demonstrates how to implement a Marshaler and Unmarshaler. Example_nulls demonstrates how to distinguish between null and zero value when needed. Null values are unmarshalled as zero value of the type. If you need to distinguish for example between text column being null and empty string, you can unmarshal into *string field. Example_paging demonstrates how to manually fetch pages and use page state. See also package documentation about paging. Example_set demonstrates how to use sets. Example_userDefinedTypesMap demonstrates how to work with user-defined types as maps. See also Example_userDefinedTypesStruct and examples for UDTMarshaler and UDTUnmarshaler if you want to map to structs. Example_userDefinedTypesStruct demonstrates how to work with user-defined types as structs. See also examples for UDTMarshaler and UDTUnmarshaler if you need more control/better performance.

package bbolt implements a low-level key/value store in pure Go. It supports fully serializable transactions, ACID semantics, and lock-free MVCC with multiple readers and a single writer. Bolt can be used for projects that want a simple data store without the need to add large dependencies such as Postgres or MySQL. Bolt is a single-level, zero-copy, B+tree data store. This means that Bolt is optimized for fast read access and does not require recovery in the event of a system crash. Transactions which have not finished committing will simply be rolled back in the event of a crash. The design of Bolt is based on Howard Chu's LMDB database project. Bolt currently works on Windows, Mac OS X, and Linux. There are only a few types in Bolt: DB, Bucket, Tx, and Cursor. The DB is a collection of buckets and is represented by a single file on disk. A bucket is a collection of unique keys that are associated with values. Transactions provide either read-only or read-write access to the database. Read-only transactions can retrieve key/value pairs and can use Cursors to iterate over the dataset sequentially. Read-write transactions can create and delete buckets and can insert and remove keys. Only one read-write transaction is allowed at a time. The database uses a read-only, memory-mapped data file to ensure that applications cannot corrupt the database, however, this means that keys and values returned from Bolt cannot be changed. Writing to a read-only byte slice will cause Go to panic. Keys and values retrieved from the database are only valid for the life of the transaction. When used outside the transaction, these byte slices can point to different data or can point to invalid memory which will cause a panic.

package bbolt implements a low-level key/value store in pure Go. It supports fully serializable transactions, ACID semantics, and lock-free MVCC with multiple readers and a single writer. Bolt can be used for projects that want a simple data store without the need to add large dependencies such as Postgres or MySQL. Bolt is a single-level, zero-copy, B+tree data store. This means that Bolt is optimized for fast read access and does not require recovery in the event of a system crash. Transactions which have not finished committing will simply be rolled back in the event of a crash. The design of Bolt is based on Howard Chu's LMDB database project. Bolt currently works on Windows, Mac OS X, and Linux. There are only a few types in Bolt: DB, Bucket, Tx, and Cursor. The DB is a collection of buckets and is represented by a single file on disk. A bucket is a collection of unique keys that are associated with values. Transactions provide either read-only or read-write access to the database. Read-only transactions can retrieve key/value pairs and can use Cursors to iterate over the dataset sequentially. Read-write transactions can create and delete buckets and can insert and remove keys. Only one read-write transaction is allowed at a time. The database uses a read-only, memory-mapped data file to ensure that applications cannot corrupt the database, however, this means that keys and values returned from Bolt cannot be changed. Writing to a read-only byte slice will cause Go to panic. Keys and values retrieved from the database are only valid for the life of the transaction. When used outside the transaction, these byte slices can point to different data or can point to invalid memory which will cause a panic.

Package middleware `middleware` is a collection of gRPC middleware packages: interceptors, helpers and tools. gRPC is a fantastic RPC middleware, which sees a lot of adoption in the Golang world. However, the upstream gRPC codebase is relatively bare bones. This package, and most of its child packages provides commonly needed middleware for gRPC: client-side interceptors for retires, server-side interceptors for input validation and auth, functions for chaining said interceptors, metadata convenience methods and more. Simple way of turning a multiple interceptors into a single interceptor. Here's an example for server chaining: These interceptors will be executed from left to right: logging, monitoring and auth. Here's an example for client side chaining: These interceptors will be executed from left to right: monitoring and then retry logic. The retry interceptor will call every interceptor that follows it whenever when a retry happens. Implementing your own interceptor is pretty trivial: there are interfaces for that. But the interesting bit exposing common data to handlers (and other middleware), similarly to HTTP Middleware design. For example, you may want to pass the identity of the caller from the auth interceptor all the way to the handling function. For example, a client side interceptor example for auth looks like: Unfortunately, it's not as easy for streaming RPCs. These have the `context.Context` embedded within the `grpc.ServerStream` object. To pass values through context, a wrapper (`WrappedServerStream`) is needed. For example:

Package acceptencoding provides customizations associated with Accept Encoding Header. The Go HTTP client automatically supports accept-encoding and content-encoding gzip by default. This default behavior is not desired by the SDK, and prevents validating the response body's checksum. To prevent this the SDK must manually control usage of content-encoding gzip. To control content-encoding, the SDK must always set the `Accept-Encoding` header to a value. This prevents the HTTP client from using gzip automatically. When gzip is enabled on the API client, the SDK's customization will control decompressing the gzip data in order to not break the checksum validation. When gzip is disabled, the API client will disable gzip, preventing the HTTP client's default behavior. An `EnableAcceptEncodingGzip` option may or may not be present depending on the client using the below middleware. The option if present can be used to enable auto decompressing gzip by the SDK.

Package sessions provides cookie and filesystem sessions and infrastructure for custom session backends. The key features are: Let's start with an example that shows the sessions API in a nutshell: First we initialize a session store calling NewCookieStore() and passing a secret key used to authenticate the session. Inside the handler, we call store.Get() to retrieve an existing session or a new one. Then we set some session values in session.Values, which is a map[interface{}]interface{}. And finally we call session.Save() to save the session in the response. Note that in production code, we should check for errors when calling session.Save(r, w), and either display an error message or otherwise handle it. Save must be called before writing to the response, otherwise the session cookie will not be sent to the client. That's all you need to know for the basic usage. Let's take a look at other options, starting with flash messages. Flash messages are session values that last until read. The term appeared with Ruby On Rails a few years back. When we request a flash message, it is removed from the session. To add a flash, call session.AddFlash(), and to get all flashes, call session.Flashes(). Here is an example: Flash messages are useful to set information to be read after a redirection, like after form submissions. There may also be cases where you want to store a complex datatype within a session, such as a struct. Sessions are serialised using the encoding/gob package, so it is easy to register new datatypes for storage in sessions: As it's not possible to pass a raw type as a parameter to a function, gob.Register() relies on us passing it a value of the desired type. In the example above we've passed it a pointer to a struct and a pointer to a custom type representing a map[string]interface. (We could have passed non-pointer values if we wished.) This will then allow us to serialise/deserialise values of those types to and from our sessions. Note that because session values are stored in a map[string]interface{}, there's a need to type-assert data when retrieving it. We'll use the Person struct we registered above: By default, session cookies last for a month. This is probably too long for some cases, but it is easy to change this and other attributes during runtime. Sessions can be configured individually or the store can be configured and then all sessions saved using it will use that configuration. We access session.Options or store.Options to set a new configuration. The fields are basically a subset of http.Cookie fields. Let's change the maximum age of a session to one week: Sometimes we may want to change authentication and/or encryption keys without breaking existing sessions. The CookieStore supports key rotation, and to use it you just need to set multiple authentication and encryption keys, in pairs, to be tested in order: New sessions will be saved using the first pair. Old sessions can still be read because the first pair will fail, and the second will be tested. This makes it easy to "rotate" secret keys and still be able to validate existing sessions. Note: for all pairs the encryption key is optional; set it to nil or omit it and and encryption won't be used. Multiple sessions can be used in the same request, even with different session backends. When this happens, calling Save() on each session individually would be cumbersome, so we have a way to save all sessions at once: it's sessions.Save(). Here's an example: This is possible because when we call Get() from a session store, it adds the session to a common registry. Save() uses it to save all registered sessions.

Package validation provides configurable and extensible rules for validating data of various types.

Package validation provides configurable and extensible rules for validating data of various types.

Package bloom provides data structures and methods for creating Bloom filters. A Bloom filter is a representation of a set of _n_ items, where the main requirement is to make membership queries; _i.e._, whether an item is a member of a set. A Bloom filter has two parameters: _m_, a maximum size (typically a reasonably large multiple of the cardinality of the set to represent) and _k_, the number of hashing functions on elements of the set. (The actual hashing functions are important, too, but this is not a parameter for this implementation). A Bloom filter is backed by a BitSet; a key is represented in the filter by setting the bits at each value of the hashing functions (modulo _m_). Set membership is done by _testing_ whether the bits at each value of the hashing functions (again, modulo _m_) are set. If so, the item is in the set. If the item is actually in the set, a Bloom filter will never fail (the true positive rate is 1.0); but it is susceptible to false positives. The art is to choose _k_ and _m_ correctly. In this implementation, the hashing functions used is murmurhash, a non-cryptographic hashing function. This implementation accepts keys for setting as testing as []byte. Thus, to add a string item, "Love": Similarly, to test if "Love" is in bloom: For numeric data, I recommend that you look into the binary/encoding library. But, for example, to add a uint32 to the filter: Finally, there is a method to estimate the false positive rate of a Bloom filter with _m_ bits and _k_ hashing functions for a set of size _n_: You can use it to validate the computed m, k parameters: or You would expect ActualfpRate to be close to the desired fp in these cases. The EstimateFalsePositiveRate function creates a temporary Bloom filter. It is also relatively expensive and only meant for validation.

Package saml contains a partial implementation of the SAML standard in golang. SAML is a standard for identity federation, i.e. either allowing a third party to authenticate your users or allowing third parties to rely on us to authenticate their users. In SAML parlance an Identity Provider (IDP) is a service that knows how to authenticate users. A Service Provider (SP) is a service that delegates authentication to an IDP. If you are building a service where users log in with someone else's credentials, then you are a Service Provider. This package supports implementing both service providers and identity providers. The core package contains the implementation of SAML. The package samlsp provides helper middleware suitable for use in Service Provider applications. The package samlidp provides a rudimentary IDP service that is useful for testing or as a starting point for other integrations. Version 0.4.0 introduces a few breaking changes to the _samlsp_ package in order to make the package more extensible, and to clean up the interfaces a bit. The default behavior remains the same, but you can now provide interface implementations of _RequestTracker_ (which tracks pending requests), _Session_ (which handles maintaining a session) and _OnError_ which handles reporting errors. Public fields of _samlsp.Middleware_ have changed, so some usages may require adjustment. See [issue 231](https://github.com/crewjam/saml/issues/231) for details. The option to provide an IDP metadata URL has been deprecated. Instead, we recommend that you use the `FetchMetadata()` function, or fetch the metadata yourself and use the new `ParseMetadata()` function, and pass the metadata in _samlsp.Options.IDPMetadata_. Similarly, the _HTTPClient_ field is now deprecated because it was only used for fetching metdata, which is no longer directly implemented. The fields that manage how cookies are set are deprecated as well. To customize how cookies are managed, provide custom implementation of _RequestTracker_ and/or _Session_, perhaps by extending the default implementations. The deprecated fields have not been removed from the Options structure, but will be in future. In particular we have deprecated the following fields in _samlsp.Options_: - `Logger` - This was used to emit errors while validating, which is an anti-pattern. - `IDPMetadataURL` - Instead use `FetchMetadata()` - `HTTPClient` - Instead pass httpClient to FetchMetadata - `CookieMaxAge` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieName` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieDomain` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieDomain` - Instead assign a custom CookieRequestTracker or CookieSessionProvider Let us assume we have a simple web application to protect. We'll modify this application so it uses SAML to authenticate users. ```golang package main import ( ) ``` Each service provider must have an self-signed X.509 key pair established. You can generate your own with something like this: We will use `samlsp.Middleware` to wrap the endpoint we want to protect. Middleware provides both an `http.Handler` to serve the SAML specific URLs and a set of wrappers to require the user to be logged in. We also provide the URL where the service provider can fetch the metadata from the IDP at startup. In our case, we'll use [samltest.id](https://samltest.id/), an identity provider designed for testing. ```golang package main import ( ) ``` Next we'll have to register our service provider with the identity provider to establish trust from the service provider to the IDP. For [samltest.id](https://samltest.id/), you can do something like: Navigate to https://samltest.id/upload.php and upload the file you fetched. Now you should be able to authenticate. The flow should look like this: 1. You browse to `localhost:8000/hello` 1. The middleware redirects you to `https://samltest.id/idp/profile/SAML2/Redirect/SSO` 1. samltest.id prompts you for a username and password. 1. samltest.id returns you an HTML document which contains an HTML form setup to POST to `localhost:8000/saml/acs`. The form is automatically submitted if you have javascript enabled. 1. The local service validates the response, issues a session cookie, and redirects you to the original URL, `localhost:8000/hello`. 1. This time when `localhost:8000/hello` is requested there is a valid session and so the main content is served. Please see `example/idp/` for a substantially complete example of how to use the library and helpers to be an identity provider. The SAML standard is huge and complex with many dark corners and strange, unused features. This package implements the most commonly used subset of these features required to provide a single sign on experience. The package supports at least the subset of SAML known as [interoperable SAML](http://saml2int.org). This package supports the Web SSO profile. Message flows from the service provider to the IDP are supported using the HTTP Redirect binding and the HTTP POST binding. Message flows from the IDP to the service provider are supported via the HTTP POST binding. The package can produce signed SAML assertions, and can validate both signed and encrypted SAML assertions. It does not support signed or encrypted requests. The _RelayState_ parameter allows you to pass user state information across the authentication flow. The most common use for this is to allow a user to request a deep link into your site, be redirected through the SAML login flow, and upon successful completion, be directed to the originally requested link, rather than the root. Unfortunately, _RelayState_ is less useful than it could be. Firstly, it is not authenticated, so anything you supply must be signed to avoid XSS or CSRF. Secondly, it is limited to 80 bytes in length, which precludes signing. (See section 3.6.3.1 of SAMLProfiles.) The SAML specification is a collection of PDFs (sadly): - [SAMLCore](http://docs.oasis-open.org/security/saml/v2.0/saml-core-2.0-os.pdf) defines data types. - [SAMLBindings](http://docs.oasis-open.org/security/saml/v2.0/saml-bindings-2.0-os.pdf) defines the details of the HTTP requests in play. - [SAMLProfiles](http://docs.oasis-open.org/security/saml/v2.0/saml-profiles-2.0-os.pdf) describes data flows. - [SAMLConformance](http://docs.oasis-open.org/security/saml/v2.0/saml-conformance-2.0-os.pdf) includes a support matrix for various parts of the protocol. [SAMLtest](https://samltest.id/) is a testing ground for SAML service and identity providers. Please do not report security issues in the issue tracker. Rather, please contact me directly at ross@kndr.org ([PGP Key `78B6038B3B9DFB88`](https://keybase.io/crewjam)).

Package toml provides facilities for decoding and encoding TOML configuration files via reflection. There is also support for delaying decoding with the Primitive type, and querying the set of keys in a TOML document with the MetaData type. The specification implemented: https://github.com/toml-lang/toml The sub-command github.com/BurntSushi/toml/cmd/tomlv can be used to verify whether a file is a valid TOML document. It can also be used to print the type of each key in a TOML document. There are two important types of tests used for this package. The first is contained inside '*_test.go' files and uses the standard Go unit testing framework. These tests are primarily devoted to holistically testing the decoder and encoder. The second type of testing is used to verify the implementation's adherence to the TOML specification. These tests have been factored into their own project: https://github.com/BurntSushi/toml-test The reason the tests are in a separate project is so that they can be used by any implementation of TOML. Namely, it is language agnostic. Example StrictDecoding shows how to detect whether there are keys in the TOML document that weren't decoded into the value given. This is useful for returning an error to the user if they've included extraneous fields in their configuration. Example UnmarshalTOML shows how to implement a struct type that knows how to unmarshal itself. The struct must take full responsibility for mapping the values passed into the struct. The method may be used with interfaces in a struct in cases where the actual type is not known until the data is examined. Example Unmarshaler shows how to decode TOML strings into your own custom data type.

Package memguard implements a secure software enclave for the storage of sensitive information in memory. There are two main container objects exposed in this API. Enclave objects encrypt data and store the ciphertext whereas LockedBuffers are more like guarded memory allocations. There is a limit on the maximum number of LockedBuffer objects that can exist at any one time, imposed by the system's mlock limits. There is no limit on Enclaves. The general workflow is to store sensitive information in Enclaves when it is not immediately needed and decrypt it when and where it is. After use, the LockedBuffer should be destroyed. If you need access to the data inside a LockedBuffer in a type not covered by any methods provided by this API, you can type-cast the allocation's memory to whatever type you want. This is of course an unsafe operation and so care must be taken to ensure that the cast is valid and does not result in memory unsafety. Further examples of code and interesting use-cases can be found in the examples subpackage. Several functions exist to make the mass purging of data very easy. It is recommended to make use of them when appropriate. Core dumps are disabled by default. If you absolutely require them, you can enable them by using unix.Setrlimit to set RLIMIT_CORE to an appropriate value.

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 codepipeline provides the API client, operations, and parameter types for AWS CodePipeline. This is the CodePipeline API Reference. This guide provides descriptions of the actions and data types for CodePipeline. Some functionality for your pipeline can only be configured through the API. For more information, see the CodePipeline User Guide. You can use the CodePipeline API to work with pipelines, stages, actions, and transitions. Pipelines are models of automated release processes. Each pipeline is uniquely named, and consists of stages, actions, and transitions. You can work with pipelines by calling: CreatePipeline DeletePipeline GetPipeline GetPipelineExecution GetPipelineState ListActionExecutions ListPipelines ListPipelineExecutions StartPipelineExecution StopPipelineExecution UpdatePipeline Pipelines include stages. Each stage contains one or more actions that must complete before the next stage begins. A stage results in success or failure. If a stage fails, the pipeline stops at that stage and remains stopped until either a new version of an artifact appears in the source location, or a user takes action to rerun the most recent artifact through the pipeline. You can call GetPipelineState, which displays the status of a pipeline, including the status of stages in the pipeline, or GetPipeline, which returns the entire structure of the pipeline, including the stages of that pipeline. For more information about the structure of stages and actions, see CodePipeline Pipeline Structure Reference. Pipeline stages include actions that are categorized into categories such as source or build actions performed in a stage of a pipeline. For example, you can use a source action to import artifacts into a pipeline from a source such as Amazon S3. Like stages, you do not work with actions directly in most cases, but you do define and interact with actions when working with pipeline operations such as CreatePipelineand GetPipelineState. Valid action categories are: Source Build Test Deploy Approval Invoke Compute Pipelines also include transitions, which allow the transition of artifacts from one stage to the next in a pipeline after the actions in one stage complete. You can work with transitions by calling: DisableStageTransition EnableStageTransition For third-party integrators or developers who want to create their own integrations with CodePipeline, the expected sequence varies from the standard API user. To integrate with CodePipeline, developers need to work with the following items: Jobs, which are instances of an action. For example, a job for a source action might import a revision of an artifact from a source. You can work with jobs by calling: AcknowledgeJob GetJobDetails PollForJobs PutJobFailureResult PutJobSuccessResult Third party jobs, which are instances of an action created by a partner action and integrated into CodePipeline. Partner actions are created by members of the Amazon Web Services Partner Network. You can work with third party jobs by calling: AcknowledgeThirdPartyJob GetThirdPartyJobDetails PollForThirdPartyJobs PutThirdPartyJobFailureResult PutThirdPartyJobSuccessResult

Package appconfig provides the API client, operations, and parameter types for Amazon AppConfig. AppConfig feature flags and dynamic configurations help software builders quickly and securely adjust application behavior in production environments without full code deployments. AppConfig speeds up software release frequency, improves application resiliency, and helps you address emergent issues more quickly. With feature flags, you can gradually release new capabilities to users and measure the impact of those changes before fully deploying the new capabilities to all users. With operational flags and dynamic configurations, you can update block lists, allow lists, throttling limits, logging verbosity, and perform other operational tuning to quickly respond to issues in production environments. AppConfig is a capability of Amazon Web Services Systems Manager. Despite the fact that application configuration content can vary greatly from application to application, AppConfig supports the following use cases, which cover a broad spectrum of customer needs: Feature flags and toggles - Safely release new capabilities to your customers in a controlled environment. Instantly roll back changes if you experience a problem. Application tuning - Carefully introduce application changes while testing the impact of those changes with users in production environments. Allow list or block list - Control access to premium features or instantly block specific users without deploying new code. Centralized configuration storage - Keep your configuration data organized and consistent across all of your workloads. You can use AppConfig to deploy configuration data stored in the AppConfig hosted configuration store, Secrets Manager, Systems Manager, Parameter Store, or Amazon S3. This section provides a high-level description of how AppConfig works and how you get started. 1. Identify configuration values in code you want to manage in the cloud Before you start creating AppConfig artifacts, we recommend you identify configuration data in your code that you want to dynamically manage using AppConfig. Good examples include feature flags or toggles, allow and block lists, logging verbosity, service limits, and throttling rules, to name a few. If your configuration data already exists in the cloud, you can take advantage of AppConfig validation, deployment, and extension features to further streamline configuration data management. 2. Create an application namespace To create a namespace, you create an AppConfig artifact called an application. An application is simply an organizational construct like a folder. 3. Create environments For each AppConfig application, you define one or more environments. An environment is a logical grouping of targets, such as applications in a Beta or Production environment, Lambda functions, or containers. You can also define environments for application subcomponents, such as the Web , Mobile , and Back-end . You can configure Amazon CloudWatch alarms for each environment. The system monitors alarms during a configuration deployment. If an alarm is triggered, the system rolls back the configuration. 4. Create a configuration profile A configuration profile includes, among other things, a URI that enables AppConfig to locate your configuration data in its stored location and a profile type. AppConfig supports two configuration profile types: feature flags and freeform configurations. Feature flag configuration profiles store their data in the AppConfig hosted configuration store and the URI is simply hosted . For freeform configuration profiles, you can store your data in the AppConfig hosted configuration store or any Amazon Web Services service that integrates with AppConfig, as described in Creating a free form configuration profilein the the AppConfig User Guide. A configuration profile can also include optional validators to ensure your configuration data is syntactically and semantically correct. AppConfig performs a check using the validators when you start a deployment. If any errors are detected, the deployment rolls back to the previous configuration data. 5. Deploy configuration data When you create a new deployment, you specify the following: An application ID A configuration profile ID A configuration version An environment ID where you want to deploy the configuration data A deployment strategy ID that defines how fast you want the changes to take effect When you call the StartDeployment API action, AppConfig performs the following tasks: Retrieves the configuration data from the underlying data store by using the location URI in the configuration profile. Verifies the configuration data is syntactically and semantically correct by using the validators you specified when you created your configuration profile. Caches a copy of the data so it is ready to be retrieved by your application. This cached copy is called the deployed data. 6. Retrieve the configuration You can configure AppConfig Agent as a local host and have the agent poll AppConfig for configuration updates. The agent calls the StartConfigurationSessionand GetLatestConfiguration API actions and caches your configuration data locally. To retrieve the data, your application makes an HTTP call to the localhost server. AppConfig Agent supports several use cases, as described in Simplified retrieval methodsin the the AppConfig User Guide. If AppConfig Agent isn't supported for your use case, you can configure your application to poll AppConfig for configuration updates by directly calling the StartConfigurationSession and GetLatestConfigurationAPI actions. This reference is intended to be used with the AppConfig User Guide.

go-update allows a program to update itself by replacing its executable file with a new version. It provides the flexibility to implement different updating user experiences like auto-updating, or manual user-initiated updates. It also boasts advanced features like binary patching and code signing verification. Updating your program to a new version is as easy as: You may also choose to update from other data sources such as a file or an io.Reader: Binary diff updates are supported and easy to use: You should also verify the checksum of new updates as well as verify the digital signature of an update. Note that even when you choose to apply a patch, the checksum is verified against the complete update after that patch has been applied. Updating arbitrary files is also supported. You may update files which are not the currently running program: Truly secure updates use code signing to verify that the update was issued by a trusted party. To do this, you'll need to generate a public/private key pair. You can do this with openssl, or the equinox.io client (https://equinox.io/client) can easily generate one for you: Once you have your key pair, you can instruct your program to validate its updates with the public key: Once you've configured your program this way, it will disallow all updates unless they are properly signed. You must now pass in the signature to verify with: To perform an update, the process must be able to read its executable file and to write to the directory that contains its executable file. It can be useful to check whether the process has the necessary permissions to perform an update before trying to apply one. Use the CanUpdate call to provide a useful message to the user if the update can't proceed without elevated permissions: Although exceedingly unlikely, the update operation itself is not atomic and can fail in such a way that a user's computer is left in an inconsistent state. If that happens, go-update attempts to recover to leave the system in a good state. If the recovery step fails (even more unlikely), a second error, referred to as "errRecover" will be non-nil so that you may inform your users of the bad news. You should handle this case as shown here: Sub-package check contains the client functionality for a simple protocol for negotiating whether a new update is available, where it is, and the metadata needed for verifying it. Sub-package download contains functionality for downloading from an HTTP endpoint while outputting a progress meter and supports resuming partial downloads.

Package graphql-go-tools is library to create GraphQL services using the go programming language. GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. Source: https://graphql.org This library is intended to be a set of low level building blocks to write high performance and secure GraphQL applications. Use cases could range from writing layer seven GraphQL proxies, firewalls, caches etc.. You would usually not use this library to write a GraphQL server yourself but to build tools for the GraphQL ecosystem. To achieve this goal the library has zero dependencies at its core functionality. It has a full implementation of the GraphQL AST and supports lexing, parsing, validation, normalization, introspection, query planning as well as query execution etc. With the execution package it's possible to write a fully functional GraphQL server that is capable to mediate between various protocols and formats. In it's current state you can use the following DataSources to resolve fields: - Static data (embed static data into a schema to extend a field in a simple way) - HTTP JSON APIs (combine multiple Restful APIs into one single GraphQL Endpoint, nesting is possible) - GraphQL APIs (you can combine multiple GraphQL APIs into one single GraphQL Endpoint, nesting is possible) - Webassembly/WASM Lambdas (e.g. resolve a field using a Rust lambda) If you're looking for a ready to use solution that has all this functionality packaged as a Gateway have a look at: https://github.com/jensneuse/graphql-gateway Created by Jens Neuse

Package acmpca provides the API client, operations, and parameter types for AWS Certificate Manager Private Certificate Authority. This is the Amazon Web Services Private Certificate Authority API Reference. It provides descriptions, syntax, and usage examples for each of the actions and data types involved in creating and managing a private certificate authority (CA) for your organization. The documentation for each action shows the API request parameters and the JSON response. Alternatively, you can use one of the Amazon Web Services SDKs to access an API that is tailored to the programming language or platform that you prefer. For more information, see Amazon Web Services SDKs. Each Amazon Web Services Private CA API operation has a quota that determines the number of times the operation can be called per second. Amazon Web Services Private CA throttles API requests at different rates depending on the operation. Throttling means that Amazon Web Services Private CA rejects an otherwise valid request because the request exceeds the operation's quota for the number of requests per second. When a request is throttled, Amazon Web Services Private CA returns a ThrottlingExceptionerror. Amazon Web Services Private CA does not guarantee a minimum request rate for APIs. To see an up-to-date list of your Amazon Web Services Private CA quotas, or to request a quota increase, log into your Amazon Web Services account and visit the Service Quotasconsole.

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/v9/_examples 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: rbg|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. 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 slices, maps, pointers, interfaces, channels and functions ensures the value is not nil. 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. 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. 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. 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. Example: 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. 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. 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. 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. 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. 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. 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(). 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(). 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(). 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(). 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 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 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 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 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 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 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 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 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 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 validataes that a string value contains a valid URN according to the RFC 2141 spec. 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 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 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 providng 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 Full validation is blocked by https://github.com/golang/crypto/pull/28 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 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 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. 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.

`grpc_middleware` is a collection of gRPC middleware packages: interceptors, helpers and tools. gRPC is a fantastic RPC middleware, which sees a lot of adoption in the Golang world. However, the upstream gRPC codebase is relatively bare bones. This package, and most of its child packages provides commonly needed middleware for gRPC: client-side interceptors for retires, server-side interceptors for input validation and auth, functions for chaining said interceptors, metadata convenience methods and more. By default, gRPC doesn't allow one to have more than one interceptor either on the client nor on the server side. `grpc_middleware` provides convenient chaining methods Simple way of turning a multiple interceptors into a single interceptor. Here's an example for server chaining: These interceptors will be executed from left to right: logging, monitoring and auth. Here's an example for client side chaining: These interceptors will be executed from left to right: monitoring and then retry logic. The retry interceptor will call every interceptor that follows it whenever when a retry happens. Implementing your own interceptor is pretty trivial: there are interfaces for that. But the interesting bit exposing common data to handlers (and other middleware), similarly to HTTP Middleware design. For example, you may want to pass the identity of the caller from the auth interceptor all the way to the handling function. For example, a client side interceptor example for auth looks like: Unfortunately, it's not as easy for streaming RPCs. These have the `context.Context` embedded within the `grpc.ServerStream` object. To pass values through context, a wrapper (`WrappedServerStream`) is needed. For example:

Package websocket implements the WebSocket protocol defined in RFC 6455. The Conn type represents a WebSocket connection. A server application calls the Upgrader.Upgrade method from an HTTP request handler to get a *Conn: Call the connection's WriteMessage and ReadMessage methods to send and receive messages as a slice of bytes. This snippet of code shows how to echo messages using these methods: In above snippet of code, p is a []byte and messageType is an int with value websocket.BinaryMessage or websocket.TextMessage. An application can also send and receive messages using the io.WriteCloser and io.Reader interfaces. To send a message, call the connection NextWriter method to get an io.WriteCloser, write the message to the writer and close the writer when done. To receive a message, call the connection NextReader method to get an io.Reader and read until io.EOF is returned. This snippet shows how to echo messages using the NextWriter and NextReader methods: The WebSocket protocol distinguishes between text and binary data messages. Text messages are interpreted as UTF-8 encoded text. The interpretation of binary messages is left to the application. This package uses the TextMessage and BinaryMessage integer constants to identify the two data message types. The ReadMessage and NextReader methods return the type of the received message. The messageType argument to the WriteMessage and NextWriter methods specifies the type of a sent message. It is the application's responsibility to ensure that text messages are valid UTF-8 encoded text. The WebSocket protocol defines three types of control messages: close, ping and pong. Call the connection WriteControl, WriteMessage or NextWriter methods to send a control message to the peer. Connections handle received close messages by calling the handler function set with the SetCloseHandler method and by returning a *CloseError from the NextReader, ReadMessage or the message Read method. The default close handler sends a close message to the peer. Connections handle received ping messages by calling the handler function set with the SetPingHandler method. The default ping handler sends a pong message to the peer. Connections handle received pong messages by calling the handler function set with the SetPongHandler method. The default pong handler does nothing. If an application sends ping messages, then the application should set a pong handler to receive the corresponding pong. The control message handler functions are called from the NextReader, ReadMessage and message reader Read methods. The default close and ping handlers can block these methods for a short time when the handler writes to the connection. The application must read the connection to process close, ping and pong messages sent from the peer. If the application is not otherwise interested in messages from the peer, then the application should start a goroutine to read and discard messages from the peer. A simple example is: Connections support one concurrent reader and one concurrent writer. Applications are responsible for ensuring that no more than one goroutine calls the write methods (NextWriter, SetWriteDeadline, WriteMessage, WriteJSON, EnableWriteCompression, SetCompressionLevel) concurrently and that no more than one goroutine calls the read methods (NextReader, SetReadDeadline, ReadMessage, ReadJSON, SetPongHandler, SetPingHandler) concurrently. The Close and WriteControl methods can be called concurrently with all other methods. Web browsers allow Javascript applications to open a WebSocket connection to any host. It's up to the server to enforce an origin policy using the Origin request header sent by the browser. The Upgrader calls the function specified in the CheckOrigin field to check the origin. If the CheckOrigin function returns false, then the Upgrade method fails the WebSocket handshake with HTTP status 403. If the CheckOrigin field is nil, then the Upgrader uses a safe default: fail the handshake if the Origin request header is present and the Origin host is not equal to the Host request header. The deprecated package-level Upgrade function does not perform origin checking. The application is responsible for checking the Origin header before calling the Upgrade function. Per message compression extensions (RFC 7692) are experimentally supported by this package in a limited capacity. Setting the EnableCompression option to true in Dialer or Upgrader will attempt to negotiate per message deflate support. If compression was successfully negotiated with the connection's peer, any message received in compressed form will be automatically decompressed. All Read methods will return uncompressed bytes. Per message compression of messages written to a connection can be enabled or disabled by calling the corresponding Conn method: Currently this package does not support compression with "context takeover". This means that messages must be compressed and decompressed in isolation, without retaining sliding window or dictionary state across messages. For more details refer to RFC 7692. Use of compression is experimental and may result in decreased performance.

Package set provides both threadsafe and non-threadsafe implementations of a generic set data structure. In the threadsafe set, safety encompasses all operations on one set. Operations on multiple sets are consistent in that the elements of each set used was valid at exactly one point in time between the start and the end of the operation.

Package ql implements a pure Go embedded SQL database engine. QL is a member of the SQL family of languages. It is less complex and less powerful than SQL (whichever specification SQL is considered to be). 2018-08-02: Release v1.2.0 adds initial support for Go modules. 2017-01-10: Release v1.1.0 fixes some bugs and adds a configurable WAL headroom. 2016-07-29: Release v1.0.6 enables alternatively using = instead of == for equality operation. 2016-07-11: Release v1.0.5 undoes vendoring of lldb. QL now uses stable lldb (github.com/cznic/lldb). 2016-07-06: Release v1.0.4 fixes a panic when closing the WAL file. 2016-04-03: Release v1.0.3 fixes a data race. 2016-03-23: Release v1.0.2 vendors github.com/cznic/exp/lldb and github.com/camlistore/go4/lock. 2016-03-17: Release v1.0.1 adjusts for latest goyacc. Parser error messages are improved and changed, but their exact form is not considered a API change. 2016-03-05: The current version has been tagged v1.0.0. 2015-06-15: To improve compatibility with other SQL implementations, the count built-in aggregate function now accepts * as its argument. 2015-05-29: The execution planner was rewritten from scratch. It should use indices in all places where they were used before plus in some additional situations. It is possible to investigate the plan using the newly added EXPLAIN statement. The QL tool is handy for such analysis. If the planner would have used an index, but no such exists, the plan includes hints in form of copy/paste ready CREATE INDEX statements. The planner is still quite simple and a lot of work on it is yet ahead. You can help this process by filling an issue with a schema and query which fails to use an index or indices when it should, in your opinion. Bonus points for including output of `ql 'explain <query>'`. 2015-05-09: The grammar of the CREATE INDEX statement now accepts an expression list instead of a single expression, which was further limited to just a column name or the built-in id(). As a side effect, composite indices are now functional. However, the values in the expression-list style index are not yet used by other statements or the statement/query planner. The composite index is useful while having UNIQUE clause to check for semantically duplicate rows before they get added to the table or when such a row is mutated using the UPDATE statement and the expression-list style index tuple of the row is thus recomputed. 2015-05-02: The Schema field of table __Table now correctly reflects any column constraints and/or defaults. Also, the (*DB).Info method now has that information provided in new ColumInfo fields NotNull, Constraint and Default. 2015-04-20: Added support for {LEFT,RIGHT,FULL} [OUTER] JOIN. 2015-04-18: Column definitions can now have constraints and defaults. Details are discussed in the "Constraints and defaults" chapter below the CREATE TABLE statement documentation. 2015-03-06: New built-in functions formatFloat and formatInt. Thanks urandom! (https://github.com/urandom) 2015-02-16: IN predicate now accepts a SELECT statement. See the updated "Predicates" section. 2015-01-17: Logical operators || and && have now alternative spellings: OR and AND (case insensitive). AND was a keyword before, but OR is a new one. This can possibly break existing queries. For the record, it's a good idea to not use any name appearing in, for example, [7] in your queries as the list of QL's keywords may expand for gaining better compatibility with existing SQL "standards". 2015-01-12: ACID guarantees were tightened at the cost of performance in some cases. The write collecting window mechanism, a formerly used implementation detail, was removed. Inserting rows one by one in a transaction is now slow. I mean very slow. Try to avoid inserting single rows in a transaction. Instead, whenever possible, perform batch updates of tens to, say thousands of rows in a single transaction. See also: http://www.sqlite.org/faq.html#q19, the discussed synchronization principles involved are the same as for QL, modulo minor details. Note: A side effect is that closing a DB before exiting an application, both for the Go API and through database/sql driver, is no more required, strictly speaking. Beware that exiting an application while there is an open (uncommitted) transaction in progress means losing the transaction data. However, the DB will not become corrupted because of not closing it. Nor that was the case before, but formerly failing to close a DB could have resulted in losing the data of the last transaction. 2014-09-21: id() now optionally accepts a single argument - a table name. 2014-09-01: Added the DB.Flush() method and the LIKE pattern matching predicate. 2014-08-08: The built in functions max and min now accept also time values. Thanks opennota! (https://github.com/opennota) 2014-06-05: RecordSet interface extended by new methods FirstRow and Rows. 2014-06-02: Indices on id() are now used by SELECT statements. 2014-05-07: Introduction of Marshal, Schema, Unmarshal. 2014-04-15: Added optional IF NOT EXISTS clause to CREATE INDEX and optional IF EXISTS clause to DROP INDEX. 2014-04-12: The column Unique in the virtual table __Index was renamed to IsUnique because the old name is a keyword. Unfortunately, this is a breaking change, sorry. 2014-04-11: Introduction of LIMIT, OFFSET. 2014-04-10: Introduction of query rewriting. 2014-04-07: Introduction of indices. QL imports zappy[8], a block-based compressor, which speeds up its performance by using a C version of the compression/decompression algorithms. If a CGO-free (pure Go) version of QL, or an app using QL, is required, please include 'purego' in the -tags option of go {build,get,install}. For example: If zappy was installed before installing QL, it might be necessary to rebuild zappy first (or rebuild QL with all its dependencies using the -a option): The syntax is specified using Extended Backus-Naur Form (EBNF) Lower-case production names are used to identify lexical tokens. Non-terminals are in CamelCase. Lexical tokens are enclosed in double quotes "" or back quotes “. The form a … b represents the set of characters from a through b as alternatives. The horizontal ellipsis … is also used elsewhere in the spec to informally denote various enumerations or code snippets that are not further specified. QL source code is Unicode text encoded in UTF-8. The text is not canonicalized, so a single accented code point is distinct from the same character constructed from combining an accent and a letter; those are treated as two code points. For simplicity, this document will use the unqualified term character to refer to a Unicode code point in the source text. Each code point is distinct; for instance, upper and lower case letters are different characters. Implementation restriction: For compatibility with other tools, the parser may disallow the NUL character (U+0000) in the statement. Implementation restriction: A byte order mark is disallowed anywhere in QL statements. The following terms are used to denote specific character classes The underscore character _ (U+005F) is considered a letter. Lexical elements are comments, tokens, identifiers, keywords, operators and delimiters, integer, floating-point, imaginary, rune and string literals and QL parameters. Line comments start with the character sequence // or -- and stop at the end of the line. A line comment acts like a space. General comments start with the character sequence /* and continue through the character sequence */. A general comment acts like a space. Comments do not nest. Tokens form the vocabulary of QL. There are four classes: identifiers, keywords, operators and delimiters, and literals. White space, formed from spaces (U+0020), horizontal tabs (U+0009), carriage returns (U+000D), and newlines (U+000A), is ignored except as it separates tokens that would otherwise combine into a single token. The formal grammar uses semicolons ";" as separators of QL statements. A single QL statement or the last QL statement in a list of statements can have an optional semicolon terminator. (Actually a separator from the following empty statement.) Identifiers name entities such as tables or record set columns. An identifier is a sequence of one or more letters and digits. The first character in an identifier must be a letter. For example No identifiers are predeclared, however note that no keyword can be used as an identifier. Identifiers starting with two underscores are used for meta data virtual tables names. For forward compatibility, users should generally avoid using any identifiers starting with two underscores. For example The following keywords are reserved and may not be used as identifiers. Keywords are not case sensitive. The following character sequences represent operators, delimiters, and other special tokens Operators consisting of more than one character are referred to by names in the rest of the documentation An integer literal is a sequence of digits representing an integer constant. An optional prefix sets a non-decimal base: 0 for octal, 0x or 0X for hexadecimal. In hexadecimal literals, letters a-f and A-F represent values 10 through 15. For example A floating-point literal is a decimal representation of a floating-point constant. It has an integer part, a decimal point, a fractional part, and an exponent part. The integer and fractional part comprise decimal digits; the exponent part is an e or E followed by an optionally signed decimal exponent. One of the integer part or the fractional part may be elided; one of the decimal point or the exponent may be elided. For example An imaginary literal is a decimal representation of the imaginary part of a complex constant. It consists of a floating-point literal or decimal integer followed by the lower-case letter i. For example A rune literal represents a rune constant, an integer value identifying a Unicode code point. A rune literal is expressed as one or more characters enclosed in single quotes. Within the quotes, any character may appear except single quote and newline. A single quoted character represents the Unicode value of the character itself, while multi-character sequences beginning with a backslash encode values in various formats. The simplest form represents the single character within the quotes; since QL statements are Unicode characters encoded in UTF-8, multiple UTF-8-encoded bytes may represent a single integer value. For instance, the literal 'a' holds a single byte representing a literal a, Unicode U+0061, value 0x61, while 'ä' holds two bytes (0xc3 0xa4) representing a literal a-dieresis, U+00E4, value 0xe4. Several backslash escapes allow arbitrary values to be encoded as ASCII text. There are four ways to represent the integer value as a numeric constant: \x followed by exactly two hexadecimal digits; \u followed by exactly four hexadecimal digits; \U followed by exactly eight hexadecimal digits, and a plain backslash \ followed by exactly three octal digits. In each case the value of the literal is the value represented by the digits in the corresponding base. Although these representations all result in an integer, they have different valid ranges. Octal escapes must represent a value between 0 and 255 inclusive. Hexadecimal escapes satisfy this condition by construction. The escapes \u and \U represent Unicode code points so within them some values are illegal, in particular those above 0x10FFFF and surrogate halves. After a backslash, certain single-character escapes represent special values All other sequences starting with a backslash are illegal inside rune literals. For example A string literal represents a string constant obtained from concatenating a sequence of characters. There are two forms: raw string literals and interpreted string literals. Raw string literals are character sequences between back quotes “. Within the quotes, any character is legal except back quote. The value of a raw string literal is the string composed of the uninterpreted (implicitly UTF-8-encoded) characters between the quotes; in particular, backslashes have no special meaning and the string may contain newlines. Carriage returns inside raw string literals are discarded from the raw string value. Interpreted string literals are character sequences between double quotes "". The text between the quotes, which may not contain newlines, forms the value of the literal, with backslash escapes interpreted as they are in rune literals (except that \' is illegal and \" is legal), with the same restrictions. The three-digit octal (\nnn) and two-digit hexadecimal (\xnn) escapes represent individual bytes of the resulting string; all other escapes represent the (possibly multi-byte) UTF-8 encoding of individual characters. Thus inside a string literal \377 and \xFF represent a single byte of value 0xFF=255, while ÿ, \u00FF, \U000000FF and \xc3\xbf represent the two bytes 0xc3 0xbf of the UTF-8 encoding of character U+00FF. For example These examples all represent the same string If the statement source represents a character as two code points, such as a combining form involving an accent and a letter, the result will be an error if placed in a rune literal (it is not a single code point), and will appear as two code points if placed in a string literal. Literals are assigned their values from the respective text representation at "compile" (parse) time. QL parameters provide the same functionality as literals, but their value is assigned at execution time from an expression list passed to DB.Run or DB.Execute. Using '?' or '$' is completely equivalent. For example Keywords 'false' and 'true' (not case sensitive) represent the two possible constant values of type bool (also not case sensitive). Keyword 'NULL' (not case sensitive) represents an untyped constant which is assignable to any type. NULL is distinct from any other value of any type. A type determines the set of values and operations specific to values of that type. A type is specified by a type name. Named instances of the boolean, numeric, and string types are keywords. The names are not case sensitive. Note: The blob type is exchanged between the back end and the API as []byte. On 32 bit platforms this limits the size which the implementation can handle to 2G. A boolean type represents the set of Boolean truth values denoted by the predeclared constants true and false. The predeclared boolean type is bool. A duration type represents the elapsed time between two instants as an int64 nanosecond count. The representation limits the largest representable duration to approximately 290 years. A numeric type represents sets of integer or floating-point values. The predeclared architecture-independent numeric types are The value of an n-bit integer is n bits wide and represented using two's complement arithmetic. Conversions are required when different numeric types are mixed in an expression or assignment. A string type represents the set of string values. A string value is a (possibly empty) sequence of bytes. The case insensitive keyword for the string type is 'string'. The length of a string (its size in bytes) can be discovered using the built-in function len. A time type represents an instant in time with nanosecond precision. Each time has associated with it a location, consulted when computing the presentation form of the time. The following functions are implicitly declared An expression specifies the computation of a value by applying operators and functions to operands. Operands denote the elementary values in an expression. An operand may be a literal, a (possibly qualified) identifier denoting a constant or a function or a table/record set column, or a parenthesized expression. A qualified identifier is an identifier qualified with a table/record set name prefix. For example Primary expression are the operands for unary and binary expressions. For example A primary expression of the form denotes the element of a string indexed by x. Its type is byte. The value x is called the index. The following rules apply - The index x must be of integer type except bigint or duration; it is in range if 0 <= x < len(s), otherwise it is out of range. - A constant index must be non-negative and representable by a value of type int. - A constant index must be in range if the string a is a literal. - If x is out of range at run time, a run-time error occurs. - s[x] is the byte at index x and the type of s[x] is byte. If s is NULL or x is NULL then the result is NULL. Otherwise s[x] is illegal. For a string, the primary expression constructs a substring. The indices low and high select which elements appear in the result. The result has indices starting at 0 and length equal to high - low. For convenience, any of the indices may be omitted. A missing low index defaults to zero; a missing high index defaults to the length of the sliced operand The indices low and high are in range if 0 <= low <= high <= len(a), otherwise they are out of range. A constant index must be non-negative and representable by a value of type int. If both indices are constant, they must satisfy low <= high. If the indices are out of range at run time, a run-time error occurs. Integer values of type bigint or duration cannot be used as indices. If s is NULL the result is NULL. If low or high is not omitted and is NULL then the result is NULL. Given an identifier f denoting a predeclared function, calls f with arguments a1, a2, … an. Arguments are evaluated before the function is called. The type of the expression is the result type of f. In a function call, the function value and arguments are evaluated in the usual order. After they are evaluated, the parameters of the call are passed by value to the function and the called function begins execution. The return value of the function is passed by value when the function returns. Calling an undefined function causes a compile-time error. Operators combine operands into expressions. Comparisons are discussed elsewhere. For other binary operators, the operand types must be identical unless the operation involves shifts or untyped constants. For operations involving constants only, see the section on constant expressions. Except for shift operations, if one operand is an untyped constant and the other operand is not, the constant is converted to the type of the other operand. The right operand in a shift expression must have unsigned integer type or be an untyped constant that can be converted to unsigned integer type. If the left operand of a non-constant shift expression is an untyped constant, the type of the constant is what it would be if the shift expression were replaced by its left operand alone. Expressions of the form yield a boolean value true if expr2, a regular expression, matches expr1 (see also [6]). Both expression must be of type string. If any one of the expressions is NULL the result is NULL. Predicates are special form expressions having a boolean result type. Expressions of the form are equivalent, including NULL handling, to The types of involved expressions must be comparable as defined in "Comparison operators". Another form of the IN predicate creates the expression list from a result of a SelectStmt. The SelectStmt must select only one column. The produced expression list is resource limited by the memory available to the process. NULL values produced by the SelectStmt are ignored, but if all records of the SelectStmt are NULL the predicate yields NULL. The select statement is evaluated only once. If the type of expr is not the same as the type of the field returned by the SelectStmt then the set operation yields false. The type of the column returned by the SelectStmt must be one of the simple (non blob-like) types: Expressions of the form are equivalent, including NULL handling, to The types of involved expressions must be ordered as defined in "Comparison operators". Expressions of the form yield a boolean value true if expr does not have a specific type (case A) or if expr has a specific type (case B). In other cases the result is a boolean value false. Unary operators have the highest precedence. There are five precedence levels for binary operators. Multiplication operators bind strongest, followed by addition operators, comparison operators, && (logical AND), and finally || (logical OR) Binary operators of the same precedence associate from left to right. For instance, x / y * z is the same as (x / y) * z. Note that the operator precedence is reflected explicitly by the grammar. Arithmetic operators apply to numeric values and yield a result of the same type as the first operand. The four standard arithmetic operators (+, -, *, /) apply to integer, rational, floating-point, and complex types; + also applies to strings; +,- also applies to times. All other arithmetic operators apply to integers only. sum integers, rationals, floats, complex values, strings difference integers, rationals, floats, complex values, times product integers, rationals, floats, complex values / quotient integers, rationals, floats, complex values % remainder integers & bitwise AND integers | bitwise OR integers ^ bitwise XOR integers &^ bit clear (AND NOT) integers << left shift integer << unsigned integer >> right shift integer >> unsigned integer Strings can be concatenated using the + operator String addition creates a new string by concatenating the operands. A value of type duration can be added to or subtracted from a value of type time. Times can subtracted from each other producing a value of type duration. For two integer values x and y, the integer quotient q = x / y and remainder r = x % y satisfy the following relationships with x / y truncated towards zero ("truncated division"). As an exception to this rule, if the dividend x is the most negative value for the int type of x, the quotient q = x / -1 is equal to x (and r = 0). If the divisor is a constant expression, it must not be zero. If the divisor is zero at run time, a run-time error occurs. If the dividend is non-negative and the divisor is a constant power of 2, the division may be replaced by a right shift, and computing the remainder may be replaced by a bitwise AND operation The shift operators shift the left operand by the shift count specified by the right operand. They implement arithmetic shifts if the left operand is a signed integer and logical shifts if it is an unsigned integer. There is no upper limit on the shift count. Shifts behave as if the left operand is shifted n times by 1 for a shift count of n. As a result, x << 1 is the same as x*2 and x >> 1 is the same as x/2 but truncated towards negative infinity. For integer operands, the unary operators +, -, and ^ are defined as follows For floating-point and complex numbers, +x is the same as x, while -x is the negation of x. The result of a floating-point or complex division by zero is not specified beyond the IEEE-754 standard; whether a run-time error occurs is implementation-specific. Whenever any operand of any arithmetic operation, unary or binary, is NULL, as well as in the case of the string concatenating operation, the result is NULL. For unsigned integer values, the operations +, -, *, and << are computed modulo 2n, where n is the bit width of the unsigned integer's type. Loosely speaking, these unsigned integer operations discard high bits upon overflow, and expressions may rely on “wrap around”. For signed integers with a finite bit width, the operations +, -, *, and << may legally overflow and the resulting value exists and is deterministically defined by the signed integer representation, the operation, and its operands. No exception is raised as a result of overflow. An evaluator may not optimize an expression under the assumption that overflow does not occur. For instance, it may not assume that x < x + 1 is always true. Integers of type bigint and rationals do not overflow but their handling is limited by the memory resources available to the program. Comparison operators compare two operands and yield a boolean value. In any comparison, the first operand must be of same type as is the second operand, or vice versa. The equality operators == and != apply to operands that are comparable. The ordering operators <, <=, >, and >= apply to operands that are ordered. These terms and the result of the comparisons are defined as follows - Boolean values are comparable. Two boolean values are equal if they are either both true or both false. - Complex values are comparable. Two complex values u and v are equal if both real(u) == real(v) and imag(u) == imag(v). - Integer values are comparable and ordered, in the usual way. Note that durations are integers. - Floating point values are comparable and ordered, as defined by the IEEE-754 standard. - Rational values are comparable and ordered, in the usual way. - String and Blob values are comparable and ordered, lexically byte-wise. - Time values are comparable and ordered. Whenever any operand of any comparison operation is NULL, the result is NULL. Note that slices are always of type string. Logical operators apply to boolean values and yield a boolean result. The right operand is evaluated conditionally. The truth tables for logical operations with NULL values Conversions are expressions of the form T(x) where T is a type and x is an expression that can be converted to type T. A constant value x can be converted to type T in any of these cases: - x is representable by a value of type T. - x is a floating-point constant, T is a floating-point type, and x is representable by a value of type T after rounding using IEEE 754 round-to-even rules. The constant T(x) is the rounded value. - x is an integer constant and T is a string type. The same rule as for non-constant x applies in this case. Converting a constant yields a typed constant as result. A non-constant value x can be converted to type T in any of these cases: - x has type T. - x's type and T are both integer or floating point types. - x's type and T are both complex types. - x is an integer, except bigint or duration, and T is a string type. Specific rules apply to (non-constant) conversions between numeric types or to and from a string type. These conversions may change the representation of x and incur a run-time cost. All other conversions only change the type but not the representation of x. A conversion of NULL to any type yields NULL. For the conversion of non-constant numeric values, the following rules apply 1. When converting between integer types, if the value is a signed integer, it is sign extended to implicit infinite precision; otherwise it is zero extended. It is then truncated to fit in the result type's size. For example, if v == uint16(0x10F0), then uint32(int8(v)) == 0xFFFFFFF0. The conversion always yields a valid value; there is no indication of overflow. 2. When converting a floating-point number to an integer, the fraction is discarded (truncation towards zero). 3. When converting an integer or floating-point number to a floating-point type, or a complex number to another complex type, the result value is rounded to the precision specified by the destination type. For instance, the value of a variable x of type float32 may be stored using additional precision beyond that of an IEEE-754 32-bit number, but float32(x) represents the result of rounding x's value to 32-bit precision. Similarly, x + 0.1 may use more than 32 bits of precision, but float32(x + 0.1) does not. In all non-constant conversions involving floating-point or complex values, if the result type cannot represent the value the conversion succeeds but the result value is implementation-dependent. 1. Converting a signed or unsigned integer value to a string type yields a string containing the UTF-8 representation of the integer. Values outside the range of valid Unicode code points are converted to "\uFFFD". 2. Converting a blob to a string type yields a string whose successive bytes are the elements of the blob. 3. Converting a value of a string type to a blob yields a blob whose successive elements are the bytes of the string. 4. Converting a value of a bigint type to a string yields a string containing the decimal decimal representation of the integer. 5. Converting a value of a string type to a bigint yields a bigint value containing the integer represented by the string value. A prefix of “0x” or “0X” selects base 16; the “0” prefix selects base 8, and a “0b” or “0B” prefix selects base 2. Otherwise the value is interpreted in base 10. An error occurs if the string value is not in any valid format. 6. Converting a value of a rational type to a string yields a string containing the decimal decimal representation of the rational in the form "a/b" (even if b == 1). 7. Converting a value of a string type to a bigrat yields a bigrat value containing the rational represented by the string value. The string can be given as a fraction "a/b" or as a floating-point number optionally followed by an exponent. An error occurs if the string value is not in any valid format. 8. Converting a value of a duration type to a string returns a string representing the duration in the form "72h3m0.5s". Leading zero units are omitted. As a special case, durations less than one second format using a smaller unit (milli-, micro-, or nanoseconds) to ensure that the leading digit is non-zero. The zero duration formats as 0, with no unit. 9. Converting a string value to a duration yields a duration represented by the string. A duration string is a possibly signed sequence of decimal numbers, each with optional fraction and a unit suffix, such as "300ms", "-1.5h" or "2h45m". Valid time units are "ns", "us" (or "µs"), "ms", "s", "m", "h". 10. Converting a time value to a string returns the time formatted using the format string When evaluating the operands of an expression or of function calls, operations are evaluated in lexical left-to-right order. For example, in the evaluation of the function calls and evaluation of c happen in the order h(), i(), j(), c. Floating-point operations within a single expression are evaluated according to the associativity of the operators. Explicit parentheses affect the evaluation by overriding the default associativity. In the expression x + (y + z) the addition y + z is performed before adding x. Statements control execution. The empty statement does nothing. Alter table statements modify existing tables. With the ADD clause it adds a new column to the table. The column must not exist. With the DROP clause it removes an existing column from a table. The column must exist and it must be not the only (last) column of the table. IOW, there cannot be a table with no columns. For example When adding a column to a table with existing data, the constraint clause of the ColumnDef cannot be used. Adding a constrained column to an empty table is fine. Begin transactions statements introduce a new transaction level. Every transaction level must be eventually balanced by exactly one of COMMIT or ROLLBACK statements. Note that when a transaction is roll-backed because of a statement failure then no explicit balancing of the respective BEGIN TRANSACTION is statement is required nor permitted. Failure to properly balance any opened transaction level may cause dead locks and/or lose of data updated in the uppermost opened but never properly closed transaction level. For example A database cannot be updated (mutated) outside of a transaction. Statements requiring a transaction A database is effectively read only outside of a transaction. Statements not requiring a transaction The commit statement closes the innermost transaction nesting level. If that's the outermost level then the updates to the DB made by the transaction are atomically made persistent. For example Create index statements create new indices. Index is a named projection of ordered values of a table column to the respective records. As a special case the id() of the record can be indexed. Index name must not be the same as any of the existing tables and it also cannot be the same as of any column name of the table the index is on. For example Now certain SELECT statements may use the indices to speed up joins and/or to speed up record set filtering when the WHERE clause is used; or the indices might be used to improve the performance when the ORDER BY clause is present. The UNIQUE modifier requires the indexed values tuple to be index-wise unique or have all values NULL. The optional IF NOT EXISTS clause makes the statement a no operation if the index already exists. A simple index consists of only one expression which must be either a column name or the built-in id(). A more complex and more general index is one that consists of more than one expression or its single expression does not qualify as a simple index. In this case the type of all expressions in the list must be one of the non blob-like types. Note: Blob-like types are blob, bigint, bigrat, time and duration. Create table statements create new tables. A column definition declares the column name and type. Table names and column names are case sensitive. Neither a table or an index of the same name may exist in the DB. For example The optional IF NOT EXISTS clause makes the statement a no operation if the table already exists. The optional constraint clause has two forms. The first one is found in many SQL dialects. This form prevents the data in column DepartmentName to be NULL. The second form allows an arbitrary boolean expression to be used to validate the column. If the value of the expression is true then the validation succeeded. If the value of the expression is false or NULL then the validation fails. If the value of the expression is not of type bool an error occurs. The optional DEFAULT clause is an expression which, if present, is substituted instead of a NULL value when the colum is assigned a value. Note that the constraint and/or default expressions may refer to other columns by name: When a table row is inserted by the INSERT INTO statement or when a table row is updated by the UPDATE statement, the order of operations is as follows: 1. The new values of the affected columns are set and the values of all the row columns become the named values which can be referred to in default expressions evaluated in step 2. 2. If any row column value is NULL and the DEFAULT clause is present in the column's definition, the default expression is evaluated and its value is set as the respective column value. 3. The values, potentially updated, of row columns become the named values which can be referred to in constraint expressions evaluated during step 4. 4. All row columns which definition has the constraint clause present will have that constraint checked. If any constraint violation is detected, the overall operation fails and no changes to the table are made. Delete from statements remove rows from a table, which must exist. For example If the WHERE clause is not present then all rows are removed and the statement is equivalent to the TRUNCATE TABLE statement. Drop index statements remove indices from the DB. The index must exist. For example The optional IF EXISTS clause makes the statement a no operation if the index does not exist. Drop table statements remove tables from the DB. The table must exist. For example The optional IF EXISTS clause makes the statement a no operation if the table does not exist. Insert into statements insert new rows into tables. New rows come from literal data, if using the VALUES clause, or are a result of select statement. In the later case the select statement is fully evaluated before the insertion of any rows is performed, allowing to insert values calculated from the same table rows are to be inserted into. If the ColumnNameList part is omitted then the number of values inserted in the row must be the same as are columns in the table. If the ColumnNameList part is present then the number of values per row must be same as the same number of column names. All other columns of the record are set to NULL. The type of the value assigned to a column must be the same as is the column's type or the value must be NULL. For example If any of the columns of the table were defined using the optional constraints clause or the optional defaults clause then those are processed on a per row basis. The details are discussed in the "Constraints and defaults" chapter below the CREATE TABLE statement documentation. Explain statement produces a recordset consisting of lines of text which describe the execution plan of a statement, if any. For example, the QL tool treats the explain statement specially and outputs the joined lines: The explanation may aid in uderstanding how a statement/query would be executed and if indices are used as expected - or which indices may possibly improve the statement performance. The create index statements above were directly copy/pasted in the terminal from the suggestions provided by the filter recordset pipeline part returned by the explain statement. If the statement has nothing special in its plan, the result is the original statement. To get an explanation of the select statement of the IN predicate, use the EXPLAIN statement with that particular select statement. The rollback statement closes the innermost transaction nesting level discarding any updates to the DB made by it. If that's the outermost level then the effects on the DB are as if the transaction never happened. For example The (temporary) record set from the last statement is returned and can be processed by the client. In this case the rollback is the same as 'DROP TABLE tmp;' but it can be a more complex operation. Select from statements produce recordsets. The optional DISTINCT modifier ensures all rows in the result recordset are unique. Either all of the resulting fields are returned ('*') or only those named in FieldList. RecordSetList is a list of table names or parenthesized select statements, optionally (re)named using the AS clause. The result can be filtered using a WhereClause and orderd by the OrderBy clause. For example If Recordset is a nested, parenthesized SelectStmt then it must be given a name using the AS clause if its field are to be accessible in expressions. A field is an named expression. Identifiers, not used as a type in conversion or a function name in the Call clause, denote names of (other) fields, values of which should be used in the expression. The expression can be named using the AS clause. If the AS clause is not present and the expression consists solely of a field name, then that field name is used as the name of the resulting field. Otherwise the field is unnamed. For example The SELECT statement can optionally enumerate the desired/resulting fields in a list. No two identical field names can appear in the list. When more than one record set is used in the FROM clause record set list, the result record set field names are rewritten to be qualified using the record set names. If a particular record set doesn't have a name, its respective fields became unnamed. The optional JOIN clause, for example is mostly equal to except that the rows from a which, when they appear in the cross join, never made expr to evaluate to true, are combined with a virtual row from b, containing all nulls, and added to the result set. For the RIGHT JOIN variant the discussed rules are used for rows from b not satisfying expr == true and the virtual, all-null row "comes" from a. The FULL JOIN adds the respective rows which would be otherwise provided by the separate executions of the LEFT JOIN and RIGHT JOIN variants. For more thorough OUTER JOIN discussion please see the Wikipedia article at [10]. Resultins rows of a SELECT statement can be optionally ordered by the ORDER BY clause. Collating proceeds by considering the expressions in the expression list left to right until a collating order is determined. Any possibly remaining expressions are not evaluated. All of the expression values must yield an ordered type or NULL. Ordered types are defined in "Comparison operators". Collating of elements having a NULL value is different compared to what the comparison operators yield in expression evaluation (NULL result instead of a boolean value). Below, T denotes a non NULL value of any QL type. NULL collates before any non NULL value (is considered smaller than T). Two NULLs have no collating order (are considered equal). The WHERE clause restricts records considered by some statements, like SELECT FROM, DELETE FROM, or UPDATE. It is an error if the expression evaluates to a non null value of non bool type. Another form of the WHERE clause is an existence predicate of a parenthesized select statement. The EXISTS form evaluates to true if the parenthesized SELECT statement produces a non empty record set. The NOT EXISTS form evaluates to true if the parenthesized SELECT statement produces an empty record set. The parenthesized SELECT statement is evaluated only once (TODO issue #159). The GROUP BY clause is used to project rows having common values into a smaller set of rows. For example Using the GROUP BY without any aggregate functions in the selected fields is in certain cases equal to using the DISTINCT modifier. The last two examples above produce the same resultsets. The optional OFFSET clause allows to ignore first N records. For example The above will produce only rows 11, 12, ... of the record set, if they exist. The value of the expression must a non negative integer, but not bigint or duration. The optional LIMIT clause allows to ignore all but first N records. For example The above will return at most the first 10 records of the record set. The value of the expression must a non negative integer, but not bigint or duration. The LIMIT and OFFSET clauses can be combined. For example Considering table t has, say 10 records, the above will produce only records 4 - 8. After returning record #8, no more result rows/records are computed. 1. The FROM clause is evaluated, producing a Cartesian product of its source record sets (tables or nested SELECT statements). 2. If present, the JOIN cluase is evaluated on the result set of the previous evaluation and the recordset specified by the JOIN clause. (... JOIN Recordset ON ...) 3. If present, the WHERE clause is evaluated on the result set of the previous evaluation. 4. If present, the GROUP BY clause is evaluated on the result set of the previous evaluation(s). 5. The SELECT field expressions are evaluated on the result set of the previous evaluation(s). 6. If present, the DISTINCT modifier is evaluated on the result set of the previous evaluation(s). 7. If present, the ORDER BY clause is evaluated on the result set of the previous evaluation(s). 8. If present, the OFFSET clause is evaluated on the result set of the previous evaluation(s). The offset expression is evaluated once for the first record produced by the previous evaluations. 9. If present, the LIMIT clause is evaluated on the result set of the previous evaluation(s). The limit expression is evaluated once for the first record produced by the previous evaluations. Truncate table statements remove all records from a table. The table must exist. For example Update statements change values of fields in rows of a table. For example Note: The SET clause is optional. If any of the columns of the table were defined using the optional constraints clause or the optional defaults clause then those are processed on a per row basis. The details are discussed in the "Constraints and defaults" chapter below the CREATE TABLE statement documentation. To allow to query for DB meta data, there exist specially named tables, some of them being virtual. Note: Virtual system tables may have fake table-wise unique but meaningless and unstable record IDs. Do not apply the built-in id() to any system table. The table __Table lists all tables in the DB. The schema is The Schema column returns the statement to (re)create table Name. This table is virtual. The table __Colum lists all columns of all tables in the DB. The schema is The Ordinal column defines the 1-based index of the column in the record. This table is virtual. The table __Colum2 lists all columns of all tables in the DB which have the constraint NOT NULL or which have a constraint expression defined or which have a default expression defined. The schema is It's possible to obtain a consolidated recordset for all properties of all DB columns using The Name column is the column name in TableName. The table __Index lists all indices in the DB. The schema is The IsUnique columns reflects if the index was created using the optional UNIQUE clause. This table is virtual. Built-in functions are predeclared. The built-in aggregate function avg returns the average of values of an expression. Avg ignores NULL values, but returns NULL if all values of a column are NULL or if avg is applied to an empty record set. The column values must be of a numeric type. The built-in function contains returns true if substr is within s. If any argument to contains is NULL the result is NULL. The built-in aggregate function count returns how many times an expression has a non NULL values or the number of rows in a record set. Note: count() returns 0 for an empty record set. For example Date returns the time corresponding to in the appropriate zone for that time in the given location. The month, day, hour, min, sec, and nsec values may be outside their usual ranges and will be normalized during the conversion. For example, October 32 converts to November 1. A daylight savings time transition skips or repeats times. For example, in the United States, March 13, 2011 2:15am never occurred, while November 6, 2011 1:15am occurred twice. In such cases, the choice of time zone, and therefore the time, is not well-defined. Date returns a time that is correct in one of the two zones involved in the transition, but it does not guarantee which. A location maps time instants to the zone in use at that time. Typically, the location represents the collection of time offsets in use in a geographical area, such as "CEST" and "CET" for central Europe. "local" represents the system's local time zone. "UTC" represents Universal Coordinated Time (UTC). The month specifies a month of the year (January = 1, ...). If any argument to date is NULL the result is NULL. The built-in function day returns the day of the month specified by t. If the argument to day is NULL the result is NULL. The built-in function formatTime returns a textual representation of the time value formatted according to layout, which defines the format by showing how the reference time, would be displayed if it were the value; it serves as an example of the desired output. The same display rules will then be applied to the time value. If any argument to formatTime is NULL the result is NULL. NOTE: The string value of the time zone, like "CET" or "ACDT", is dependent on the time zone of the machine the function is run on. For example, if the t value is in "CET", but the machine is in "ACDT", instead of "CET" the result is "+0100". This is the same what Go (time.Time).String() returns and in fact formatTime directly calls t.String(). returns on a machine in the CET time zone, but may return on a machine in the ACDT zone. The time value is in both cases the same so its ordering and comparing is correct. Only the display value can differ. The built-in functions formatFloat and formatInt format numbers to strings using go's number format functions in the `strconv` package. For all three functions, only the first argument is mandatory. The default values of the rest are shown in the examples. If the first argument is NULL, the result is NULL. returns returns returns Unlike the `strconv` equivalent, the formatInt function handles all integer types, both signed and unsigned. The built-in function hasPrefix tests whether the string s begins with prefix. If any argument to hasPrefix is NULL the result is NULL. The built-in function hasSuffix tests whether the string s ends with suffix. If any argument to hasSuffix is NULL the result is NULL. The built-in function hour returns the hour within the day specified by t, in the range [0, 23]. If the argument to hour is NULL the result is NULL. The built-in function hours returns the duration as a floating point number of hours. If the argument to hours is NULL the result is NULL. The built-in function id takes zero or one arguments. If no argument is provided, id() returns a table-unique automatically assigned numeric identifier of type int. Ids of deleted records are not reused unless the DB becomes completely empty (has no tables). For example If id() without arguments is called for a row which is not a table record then the result value is NULL. For example If id() has one argument it must be a table name of a table in a cross join. For example The built-in function len takes a string argument and returns the lentgh of the string in bytes. The expression len(s) is constant if s is a string constant. If the argument to len is NULL the result is NULL. The built-in aggregate function max returns the largest value of an expression in a record set. Max ignores NULL values, but returns NULL if all values of a column are NULL or if max is applied to an empty record set. The expression values must be of an ordered type. For example The built-in aggregate function min returns the smallest value of an expression in a record set. Min ignores NULL values, but returns NULL if all values of a column are NULL or if min is applied to an empty record set. For example The column values must be of an ordered type. The built-in function minute returns the minute offset within the hour specified by t, in the range [0, 59]. If the argument to minute is NULL the result is NULL. The built-in function minutes returns the duration as a floating point number of minutes. If the argument to minutes is NULL the result is NULL. The built-in function month returns the month of the year specified by t (January = 1, ...). If the argument to month is NULL the result is NULL. The built-in function nanosecond returns the nanosecond offset within the second specified by t, in the range [0, 999999999]. If the argument to nanosecond is NULL the result is NULL. The built-in function nanoseconds returns the duration as an integer nanosecond count. If the argument to nanoseconds is NULL the result is NULL. The built-in function now returns the current local time. The built-in function parseTime parses a formatted string and returns the time value it represents. The layout defines the format by showing how the reference time, would be interpreted if it were the value; it serves as an example of the input format. The same interpretation will then be made to the input string. Elements omitted from the value are assumed to be zero or, when zero is impossible, one, so parsing "3:04pm" returns the time corresponding to Jan 1, year 0, 15:04:00 UTC (note that because the year is 0, this time is before the zero Time). Years must be in the range 0000..9999. The day of the week is checked for syntax but it is otherwise ignored. In the absence of a time zone indicator, parseTime returns a time in UTC. When parsing a time with a zone offset like -0700, if the offset corresponds to a time zone used by the current location, then parseTime uses that location and zone in the returned time. Otherwise it records the time as being in a fabricated location with time fixed at the given zone offset. When parsing a time with a zone abbreviation like MST, if the zone abbreviation has a defined offset in the current location, then that offset is used. The zone abbreviation "UTC" is recognized as UTC regardless of location. If the zone abbreviation is unknown, Parse records the time as being in a fabricated location with the given zone abbreviation and a zero offset. This choice means that such a time can be parses and reformatted with the same layout losslessly, but the exact instant used in the representation will differ by the actual zone offset. To avoid such problems, prefer time layouts that use a numeric zone offset. If any argument to parseTime is NULL the result is NULL. The built-in function second returns the second offset within the minute specified by t, in the range [0, 59]. If the argument to second is NULL the result is NULL. The built-in function seconds returns the duration as a floating point number of seconds. If the argument to seconds is NULL the result is NULL. The built-in function since returns the time elapsed since t. It is shorthand for now()-t. If the argument to since is NULL the result is NULL. The built-in aggregate function sum returns the sum of values of an expression for all rows of a record set. Sum ignores NULL values, but returns NULL if all values of a column are NULL or if sum is applied to an empty record set. The column values must be of a numeric type. The built-in function timeIn returns t with the location information set to loc. For discussion of the loc argument please see date(). If any argument to timeIn is NULL the result is NULL. The built-in function weekday returns the day of the week specified by t. Sunday == 0, Monday == 1, ... If the argument to weekday is NULL the result is NULL. The built-in function year returns the year in which t occurs. If the argument to year is NULL the result is NULL. The built-in function yearDay returns the day of the year specified by t, in the range [1,365] for non-leap years, and [1,366] in leap years. If the argument to yearDay is NULL the result is NULL. Three functions assemble and disassemble complex numbers. The built-in function complex constructs a complex value from a floating-point real and imaginary part, while real and imag extract the real and imaginary parts of a complex value. The type of the arguments and return value correspond. For complex, the two arguments must be of the same floating-point type and the return type is the complex type with the corresponding floating-point constituents: complex64 for float32, complex128 for float64. The real and imag functions together form the inverse, so for a complex value z, z == complex(real(z), imag(z)). If the operands of these functions are all constants, the return value is a constant. If any argument to any of complex, real, imag functions is NULL the result is NULL. For the numeric types, the following sizes are guaranteed Portions of this specification page are modifications based on work[2] created and shared by Google[3] and used according to terms described in the Creative Commons 3.0 Attribution License[4]. This specification is licensed under the Creative Commons Attribution 3.0 License, and code is licensed under a BSD license[5]. Links from the above documentation This section is not part of the specification. WARNING: The implementation of indices is new and it surely needs more time to become mature. Indices are used currently used only by the WHERE clause. The following expression patterns of 'WHERE expression' are recognized and trigger index use. The relOp is one of the relation operators <, <=, ==, >=, >. For the equality operator both operands must be of comparable types. For all other operators both operands must be of ordered types. The constant expression is a compile time constant expression. Some constant folding is still a TODO. Parameter is a QL parameter ($1 etc.). Consider tables t and u, both with an indexed field f. The WHERE expression doesn't comply with the above simple detected cases. However, such query is now automatically rewritten to which will use both of the indices. The impact of using the indices can be substantial (cf. BenchmarkCrossJoin*) if the resulting rows have low "selectivity", ie. only few rows from both tables are selected by the respective WHERE filtering. Note: Existing QL DBs can be used and indices can be added to them. However, once any indices are present in the DB, the old QL versions cannot work with such DB anymore. Running a benchmark with -v (-test.v) outputs information about the scale used to report records/s and a brief description of the benchmark. For example Running the full suite of benchmarks takes a lot of time. Use the -timeout flag to avoid them being killed after the default time limit (10 minutes).

Package gcs provides an API for building and using a Golomb-coded set filter. A Golomb-coded set is a probabilistic data structure used similarly to a Bloom filter. A filter uses constant-size overhead plus on average n+2 bits per item added to the filter, where 2^-n is the desired false positive (collision) probability. GCS filters are a mechanism for storing and transmitting per-block filters. The usage is intended to be the inverse of Bloom filters: a consensus-validating full node commits to a single filter for every block and serves the filter to SPV clients that match against the filter locally to determine if the block is potentially relevant. The suggested collision probability for Decred use is 2^-20.

Pact Go enables consumer driven contract testing, providing a mock service and DSL for the consumer project, and interaction playback and verification for the service provider project. Consumer side Pact testing is an isolated test that ensures a given component is able to collaborate with another (remote) component. Pact will automatically start a Mock server in the background that will act as the collaborators' test double. This implies that any interactions expected on the Mock server will be validated, meaning a test will fail if all interactions were not completed, or if unexpected interactions were found: A typical consumer-side test would look something like this: If this test completed successfully, a Pact file should have been written to ./pacts/my_consumer-my_provider.json containing all of the interactions expected to occur between the Consumer and Provider. In addition to verbatim value matching, you have 3 useful matching functions in the `dsl` package that can increase expressiveness and reduce brittle test cases. Here is a complex example that shows how all 3 terms can be used together: This example will result in a response body from the mock server that looks like: See the examples in the dsl package and the matcher tests (https://github.com/pact-foundation/pact-go/blob/master/dsl/matcher_test.go) for more matching examples. NOTE: You will need to use valid Ruby regular expressions (http://ruby-doc.org/core-2.1.5/Regexp.html) and double escape backslashes. Read more about flexible matching (https://github.com/pact-foundation/pact-ruby/wiki/Regular-expressions-and-type-matching-with-Pact. Provider side Pact testing, involves verifying that the contract - the Pact file - can be satisfied by the Provider. A typical Provider side test would like something like: The `VerifyProvider` will handle all verifications, treating them as subtests and giving you granular test reporting. If you don't like this behaviour, you may call `VerifyProviderRaw` directly and handle the errors manually. Note that `PactURLs` may be a list of local pact files or remote based urls (possibly from a Pact Broker - http://docs.pact.io/documentation/sharings_pacts.html). Pact reads the specified pact files (from remote or local sources) and replays the interactions against a running Provider. If all of the interactions are met we can say that both sides of the contract are satisfied and the test passes. When validating a Provider, you have 3 options to provide the Pact files: 1. Use "PactURLs" to specify the exact set of pacts to be replayed: Options 2 and 3 are particularly useful when you want to validate that your Provider is able to meet the contracts of what's in Production and also the latest in development. See this [article](http://rea.tech/enter-the-pact-matrix-or-how-to-decouple-the-release-cycles-of-your-microservices/) for more on this strategy. Each interaction in a pact should be verified in isolation, with no context maintained from the previous interactions. So how do you test a request that requires data to exist on the provider? Provider states are how you achieve this using Pact. Provider states also allow the consumer to make the same request with different expected responses (e.g. different response codes, or the same resource with a different subset of data). States are configured on the consumer side when you issue a dsl.Given() clause with a corresponding request/response pair. Configuring the provider is a little more involved, and (currently) requires running an API endpoint to configure any [provider states](http://docs.pact.io/documentation/provider_states.html) during the verification process. The option you must provide to the dsl.VerifyRequest is: An example route using the standard Go http package might look like this: See the examples or read more at http://docs.pact.io/documentation/provider_states.html. See the Pact Broker (http://docs.pact.io/documentation/sharings_pacts.html) documentation for more details on the Broker and this article (http://rea.tech/enter-the-pact-matrix-or-how-to-decouple-the-release-cycles-of-your-microservices/) on how to make it work for you. Publishing using Go code: Publishing from the CLI: Use a cURL request like the following to PUT the pact to the right location, specifying your consumer name, provider name and consumer version. The following flags are required to use basic authentication when publishing or retrieving Pact files to/from a Pact Broker: Pact Go uses a simple log utility (logutils - https://github.com/hashicorp/logutils) to filter log messages. The CLI already contains flags to manage this, should you want to control log level in your tests, you can set it like so:

Package kadm provides a helper Kafka admin client around a *kgo.Client. This package is meant to cover the common use cases for dropping into an "admin" like interface for Kafka. As with any admin client, this package must make opinionated decisions on what to provide and what to hide. The underlying Kafka protocol gives more detailed information in responses, or allows more fine tuning in requests, but most of the time, these details are unnecessary. By virtue of making opinionated decisions, this package cannot satisfy every need for requests and responses. If you need more control than this admin client provides, you can use the kmsg package directly. This package contains a lot of types, but the main two types type to know are Client and ShardErrors. Every other type is used for inputs or outputs to methods on the client. The Client type is a simple small wrapper around a *kgo.Client that exists solely to namespace methods. The ShardErrors type is a bit more complicated. When issuing requests, under the hood some of these requests actually need to be mapped to brokers and split, issuing different pieces of the input request to different brokers. The *kgo.Client handles this all internally, but (if using RequestSharded as directed), returns each response to each of these split requests individually. Each response can fail or be successful. This package goes one step further and merges these failures into one meta failure, ShardErrors. Any function that returns ShardErrors is documented as such, and if a function returns a non-nil ShardErrors, it is possible that the returned data is actually valid and usable. If you care to, you can log / react to the partial failures and continue using the partial successful result. This is in contrast to other clients, which either require to to request individual brokers directly, or they completely hide individual failures, or they completely fail on any individual failure. For methods that list or describe things, this package often completely fails responses on auth failures. If you use a method that accepts two topics, one that you are authorized to and one that you are not, you will not receive a partial successful response. Instead, you will receive an AuthError. Methods that do *not* fail on auth errors are explicitly documented as such. Users may often find it easy to work with lists of topics or partitions. Rather than needing to build deeply nested maps directly, this package has a few helper types that are worth knowing: These types are meant to be easy to build and use, and can be used as the starting point for other types. Many functions in this package are variadic and return either a map or a list of responses, and you may only use one element as input and are only interested in one element of output. This package provides the following functions to help: The intended use case of these is something like `kadm.AnyE(kadm.CreateTopics(..., "my-one-topic"))`, such that you can immediately get the response for the one topic you are creating.

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. disableOCSPChecks: 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. insecureMode: deprecated. Use disableOCSPChecks instead. 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. If you want to define S3 client logging, override S3LoggingMode variable using configuration: https://pkg.go.dev/github.com/aws/aws-sdk-go-v2/aws#ClientLogMode Example: 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. WHen using NUMBERs with non zero scale, the value is returned as an integer type and a scale is provided in record metadata. Example. When we have a 123.45 value that comes from NUMBER(9, 4), it will be represented as 1234500 with scale equal to 4. It is a client responsibility to interpret it correctly. Also - see limitations section above. 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 capnp is a Cap'n Proto library for Go. https://capnproto.org/ Read the Getting Started guide for a tutorial on how to use this package. https://github.com/capnproto/go-capnproto2/wiki/Getting-Started capnpc-go provides the compiler backend for capnp. capnpc-go requires two annotations for all files: package and import. package is needed to know what package to place at the head of the generated file and what identifier to use when referring to the type from another package. import should be the fully qualified import path and is used to generate import statement from other packages and to detect when two types are in the same package. For example: For adding documentation comments to the generated code, there's the doc annotation. This annotation adds the comment to a struct, enum or field so that godoc will pick it up. For example: In Cap'n Proto, the unit of communication is a message. A message consists of one or more segments -- contiguous blocks of memory. This allows large messages to be split up and loaded independently or lazily. Typically you will use one segment per message. Logically, a message is organized in a tree of objects, with the root always being a struct (as opposed to a list or primitive). Messages can be read from and written to a stream. The Message and Segment types are the main types that application code will use from this package. The Message type has methods for marshaling and unmarshaling its segments to the wire format. If the application needs to read or write from a stream, it should use the Encoder and Decoder types. The type for a generic reference to a Cap'n Proto object is Ptr. A Ptr can refer to a struct, a list, or an interface. Ptr, Struct, List, and Interface (the pointer types) have value semantics and refer to data in a single segment. All of the pointer types have a notion of "valid". An invalid pointer will return the default value from any accessor and panic when any setter is called. In previous versions of this package, the Pointer interface was used instead of the Ptr struct. This interface and functions that use it are now deprecated. See https://github.com/capnproto/go-capnproto2/wiki/New-Ptr-Type for details about this API change. Data accessors and setters (i.e. struct primitive fields and list elements) do not return errors, but pointer accessors and setters do. There are a few reasons that a read or write of a pointer can fail, but the most common are bad pointers or allocation failures. For accessors, an invalid object will be returned in case of an error. Since Go doesn't have generics, wrapper types provide type safety on lists. This package provides lists of basic types, and capnpc-go generates list wrappers for named types. However, if you need to use deeper nesting of lists (e.g. List(List(UInt8))), you will need to use a PointerList and wrap the elements. For the following schema: capnpc-go will generate: For each group a typedef is created with a different method set for just the groups fields: generates the following: That way the following may be used to access a field in a group: Note that group accessors just convert the type and so have no overhead. Named unions are treated as a group with an inner unnamed union. Unnamed unions generate an enum Type_Which and a corresponding Which() function: generates the following: Which() should be checked before using the getters, and the default case must always be handled. Setters for single values will set the union discriminator as well as set the value. For voids in unions, there is a void setter that just sets the discriminator. For example: generates the following: Similarly, for groups in unions, there is a group setter that just sets the discriminator. This must be called before the group getter can be used to set values. For example: and in usage: capnpc-go generates enum values as constants. For example in the capnp file: In the generated capnp.go file: In addition an enum.String() function is generated that will convert the constants to a string for debugging or logging purposes. By default, the enum name is used as the tag value, but the tags can be customized with a $Go.tag or $Go.notag annotation. For example: In the generated go file: capnpc-go generates type-safe Client wrappers for interfaces. For parameter lists and result lists, structs are generated as described above with the names Interface_method_Params and Interface_method_Results, unless a single struct type is used. For example, for this interface: capnpc-go generates the following Go code (along with the structs Calculator_evaluate_Params and Calculator_evaluate_Results): capnpc-go also generates code to implement the interface: Since a single capability may want to implement many interfaces, you can use multiple *_Methods functions to build a single slice to send to NewServer. An example of combining the client/server code to communicate with a locally implemented Calculator: A note about message ordering: when implementing a server method, you are responsible for acknowledging delivery of a method call. Failure to do so can cause deadlocks. See the server.Ack function for more details.

Package validate is a generic go data validate, filtering library. Source code and other details for the project are available at GitHub:

Package validation provides configurable and extensible rules for validating data of various types.

Package gcs provides an API for building and using a Golomb-coded set filter. A Golomb-Coded Set (GCS) is a space-efficient probabilistic data structure that is used to test set membership with a tunable false positive rate while simultaneously preventing false negatives. In other words, items that are in the set will always match, but items that are not in the set will also sometimes match with the chosen false positive rate. This package currently implements two different versions for backwards compatibility. Version 1 is deprecated and therefore should no longer be used. Version 2 is the GCS variation that follows the specification details in DCP0005: https://github.com/decred/dcps/blob/master/dcp-0005/dcp-0005.mediawiki#golomb-coded-sets. Version 2 sets do not permit empty items (data of zero length) to be added and are parameterized by the following: * A parameter `B` that defines the remainder code bit size * A parameter `M` that defines the false positive rate as `1/M` * A key for the SipHash-2-4 function * The items to include in the set Errors returned by this package are of type gcs.Error. This allows the caller to programmatically determine the specific error by examining the ErrorCode field of the type asserted gcs.Error while still providing rich error messages with contextual information. A convenience function named IsErrorCode is also provided to allow callers to easily check for a specific error code. See ErrorCode in the package documentation for a full list. GCS is used as a mechanism for storing, transmitting, and committing to per-block filters. Consensus-validating full nodes commit to a single filter for every block and serve the filter to SPV clients that match against the filter locally to determine if the block is potentially relevant. The required parameters for Decred are defined by the blockcf2 package. For more details, see the the Block Filters section of DCP0005: https://github.com/decred/dcps/blob/master/dcp-0005/dcp-0005.mediawiki#block-filters

Package ojg is a collection of JSON tools including a validators, parsers, a full JSONPath implementation, data conversion utilities, and a simple type assembler. Most of the tools are designed for simple types although used in complex ways. Simple types in this context are data objects composed of these types. Package oj contains functions and types for parsing JSON as well as support for building simple types. Included in the oj package are: Package gen provides type safe generic types. They are type safe in that array and objects can only be constructed of other types in the package. The basic types are: The collection types are Array and Object. All the types implement the Node interface which is a relatively simple interface defined primarily to restrict what can be in the collection types. The Node interface should not be used to define new generic types. Also included in the package are a builder and parser that behave like the parser and builder in the oj package except for gen types. Package jp provides JSONPath implementation that operations on simple go types, generic (gen package), and public struct with public members. Get, set, and delete operations can be evaluated on data. When needed reflection is used to follow a path. The alt package contains functions and types for altering values. It includes functions for: The asm package provides a means of building JSON or the corresponding simple types based on a JSON script represented by the Plan type. The oj command is a general purpose tool for processing JSON documents. Features include reformatting JSON, colorizing JSON, extracting parts of a JSON document, and filtering. JSONPath is used for both extracting and filtering.

Package capnp is a Cap'n Proto library for Go. https://capnproto.org/ Read the Getting Started guide for a tutorial on how to use this package. https://github.com/capnproto/go-capnproto2/wiki/Getting-Started capnpc-go provides the compiler backend for capnp. capnpc-go requires two annotations for all files: package and import. package is needed to know what package to place at the head of the generated file and what identifier to use when referring to the type from another package. import should be the fully qualified import path and is used to generate import statement from other packages and to detect when two types are in the same package. For example: For adding documentation comments to the generated code, there's the doc annotation. This annotation adds the comment to a struct, enum or field so that godoc will pick it up. For example: In Cap'n Proto, the unit of communication is a message. A message consists of one or more segments -- contiguous blocks of memory. This allows large messages to be split up and loaded independently or lazily. Typically you will use one segment per message. Logically, a message is organized in a tree of objects, with the root always being a struct (as opposed to a list or primitive). Messages can be read from and written to a stream. The Message and Segment types are the main types that application code will use from this package. The Message type has methods for marshaling and unmarshaling its segments to the wire format. If the application needs to read or write from a stream, it should use the Encoder and Decoder types. The type for a generic reference to a Cap'n Proto object is Ptr. A Ptr can refer to a struct, a list, or an interface. Ptr, Struct, List, and Interface (the pointer types) have value semantics and refer to data in a single segment. All of the pointer types have a notion of "valid". An invalid pointer will return the default value from any accessor and panic when any setter is called. In previous versions of this package, the Pointer interface was used instead of the Ptr struct. This interface and functions that use it are now deprecated. See https://github.com/capnproto/go-capnproto2/wiki/New-Ptr-Type for details about this API change. Data accessors and setters (i.e. struct primitive fields and list elements) do not return errors, but pointer accessors and setters do. There are a few reasons that a read or write of a pointer can fail, but the most common are bad pointers or allocation failures. For accessors, an invalid object will be returned in case of an error. Since Go doesn't have generics, wrapper types provide type safety on lists. This package provides lists of basic types, and capnpc-go generates list wrappers for named types. However, if you need to use deeper nesting of lists (e.g. List(List(UInt8))), you will need to use a PointerList and wrap the elements. For the following schema: capnpc-go will generate: For each group a typedef is created with a different method set for just the groups fields: generates the following: That way the following may be used to access a field in a group: Note that group accessors just convert the type and so have no overhead. Named unions are treated as a group with an inner unnamed union. Unnamed unions generate an enum Type_Which and a corresponding Which() function: generates the following: Which() should be checked before using the getters, and the default case must always be handled. Setters for single values will set the union discriminator as well as set the value. For voids in unions, there is a void setter that just sets the discriminator. For example: generates the following: Similarly, for groups in unions, there is a group setter that just sets the discriminator. This must be called before the group getter can be used to set values. For example: and in usage: capnpc-go generates enum values as constants. For example in the capnp file: In the generated capnp.go file: In addition an enum.String() function is generated that will convert the constants to a string for debugging or logging purposes. By default, the enum name is used as the tag value, but the tags can be customized with a $Go.tag or $Go.notag annotation. For example: In the generated go file: capnpc-go generates type-safe Client wrappers for interfaces. For parameter lists and result lists, structs are generated as described above with the names Interface_method_Params and Interface_method_Results, unless a single struct type is used. For example, for this interface: capnpc-go generates the following Go code (along with the structs Calculator_evaluate_Params and Calculator_evaluate_Results): capnpc-go also generates code to implement the interface: Since a single capability may want to implement many interfaces, you can use multiple *_Methods functions to build a single slice to send to NewServer. An example of combining the client/server code to communicate with a locally implemented Calculator: A note about message ordering: by default, only one method per server will be invoked at a time; when implementing a server method which blocks or takes a long time, you calling the server.Go function to unblock future calls.

CBSD 3-Clause License Copyright (c) 2017-2022, Gerasimos (Makis) Maropoulos (kataras2006@hotmail.com) All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. /* Package golog provides an easy to use foundation for your logging operations. Source code and other details for the project are available at GitHub: 0.1.12 The only requirement is the Go Programming Language Example code: Golog has a default, package-level initialized instance for you, however you can choose to create and use a logger instance for a specific part of your application. Example Code: Golog sets colors to levels when its `Printer.Output` is actual a compatible terminal which can renders colors, otherwise it will downgrade itself to a white foreground. Golog has functions to print a formatted log too. Example Code: Golog takes a simple `io.Writer` as its underline Printer's Output. Example Code: You can even override the default line braker, "\n", by using the `golog#NewLine` function at startup. Example Code: Golog is a leveled logger, therefore you can set a level and print whenever the print level is valid with the set-ed one. Available built'n levels are: Below you'll learn a way to add a custom level or modify an existing level. The default colorful text(or raw text for unsupported outputs) for levels can be overridden by using the `golog#ErrorText, golog#WarnText, golog#InfoText and golog#DebugText` functions. Example Code: Golog gives you the power to add or modify existing levels is via Level Metadata. Example Code: The logger's level can be changed via passing one of the level constants to the `Level` field or by passing its string representation to the `SetLevel` function. Example Code: Transaction with your favorite, but deprecated logger is easy. Golog offers two basic interfaces, the `ExternalLogger` and the `StdLogger` that can be directly used as arguments to the `Install` function in order to adapt an external logger. Outline: Example Code: Example Code: But you should have a basic idea of the golog package by now, we just scratched the surface. If you enjoy what you just saw and want to learn more, please follow the below links: Examples:

Package set provides both threadsafe and non-threadsafe implementations of a generic set data structure. In the threadsafe set, safety encompasses all operations on one set. Operations on multiple sets are consistent in that the elements of each set used was valid at exactly one point in time between the start and the end of the operation.

Package websocket implements the WebSocket protocol defined in RFC 6455. The Conn type represents a WebSocket connection. A server application calls the Upgrader.Upgrade method from an HTTP request handler to get a *Conn: net/http valyala/fasthttp Call the connection's WriteMessage and ReadMessage methods to send and receive messages as a slice of bytes. This snippet of code shows how to echo messages using these methods: In above snippet of code, p is a []byte and messageType is an int with value websocket.BinaryMessage or websocket.TextMessage. An application can also send and receive messages using the io.WriteCloser and io.Reader interfaces. To send a message, call the connection NextWriter method to get an io.WriteCloser, write the message to the writer and close the writer when done. To receive a message, call the connection NextReader method to get an io.Reader and read until io.EOF is returned. This snippet shows how to echo messages using the NextWriter and NextReader methods: The WebSocket protocol distinguishes between text and binary data messages. Text messages are interpreted as UTF-8 encoded text. The interpretation of binary messages is left to the application. This package uses the TextMessage and BinaryMessage integer constants to identify the two data message types. The ReadMessage and NextReader methods return the type of the received message. The messageType argument to the WriteMessage and NextWriter methods specifies the type of a sent message. It is the application's responsibility to ensure that text messages are valid UTF-8 encoded text. The WebSocket protocol defines three types of control messages: close, ping and pong. Call the connection WriteControl, WriteMessage or NextWriter methods to send a control message to the peer. Connections handle received close messages by calling the handler function set with the SetCloseHandler method and by returning a *CloseError from the NextReader, ReadMessage or the message Read method. The default close handler sends a close message to the peer. Connections handle received ping messages by calling the handler function set with the SetPingHandler method. The default ping handler sends a pong message to the peer. Connections handle received pong messages by calling the handler function set with the SetPongHandler method. The default pong handler does nothing. If an application sends ping messages, then the application should set a pong handler to receive the corresponding pong. The control message handler functions are called from the NextReader, ReadMessage and message reader Read methods. The default close and ping handlers can block these methods for a short time when the handler writes to the connection. The application must read the connection to process close, ping and pong messages sent from the peer. If the application is not otherwise interested in messages from the peer, then the application should start a goroutine to read and discard messages from the peer. A simple example is: Connections support one concurrent reader and one concurrent writer. Applications are responsible for ensuring that no more than one goroutine calls the write methods (NextWriter, SetWriteDeadline, WriteMessage, WriteJSON, EnableWriteCompression, SetCompressionLevel) concurrently and that no more than one goroutine calls the read methods (NextReader, SetReadDeadline, ReadMessage, ReadJSON, SetPongHandler, SetPingHandler) concurrently. The Close and WriteControl methods can be called concurrently with all other methods. Web browsers allow Javascript applications to open a WebSocket connection to any host. It's up to the server to enforce an origin policy using the Origin request header sent by the browser. The Upgrader calls the function specified in the CheckOrigin field to check the origin. If the CheckOrigin function returns false, then the Upgrade method fails the WebSocket handshake with HTTP status 403. If the CheckOrigin field is nil, then the Upgrader uses a safe default: fail the handshake if the Origin request header is present and the Origin host is not equal to the Host request header. The deprecated package-level Upgrade function does not perform origin checking. The application is responsible for checking the Origin header before calling the Upgrade function. Connections buffer network input and output to reduce the number of system calls when reading or writing messages. Write buffers are also used for constructing WebSocket frames. See RFC 6455, Section 5 for a discussion of message framing. A WebSocket frame header is written to the network each time a write buffer is flushed to the network. Decreasing the size of the write buffer can increase the amount of framing overhead on the connection. The buffer sizes in bytes are specified by the ReadBufferSize and WriteBufferSize fields in the Dialer and Upgrader. The Dialer uses a default size of 4096 when a buffer size field is set to zero. The Upgrader reuses buffers created by the HTTP server when a buffer size field is set to zero. The HTTP server buffers have a size of 4096 at the time of this writing. The buffer sizes do not limit the size of a message that can be read or written by a connection. Buffers are held for the lifetime of the connection by default. If the Dialer or Upgrader WriteBufferPool field is set, then a connection holds the write buffer only when writing a message. Applications should tune the buffer sizes to balance memory use and performance. Increasing the buffer size uses more memory, but can reduce the number of system calls to read or write the network. In the case of writing, increasing the buffer size can reduce the number of frame headers written to the network. Some guidelines for setting buffer parameters are: Limit the buffer sizes to the maximum expected message size. Buffers larger than the largest message do not provide any benefit. Depending on the distribution of message sizes, setting the buffer size to a value less than the maximum expected message size can greatly reduce memory use with a small impact on performance. Here's an example: If 99% of the messages are smaller than 256 bytes and the maximum message size is 512 bytes, then a buffer size of 256 bytes will result in 1.01 more system calls than a buffer size of 512 bytes. The memory savings is 50%. A write buffer pool is useful when the application has a modest number writes over a large number of connections. when buffers are pooled, a larger buffer size has a reduced impact on total memory use and has the benefit of reducing system calls and frame overhead. Per message compression extensions (RFC 7692) are experimentally supported by this package in a limited capacity. Setting the EnableCompression option to true in Dialer or Upgrader will attempt to negotiate per message deflate support. If compression was successfully negotiated with the connection's peer, any message received in compressed form will be automatically decompressed. All Read methods will return uncompressed bytes. Per message compression of messages written to a connection can be enabled or disabled by calling the corresponding Conn method: Currently this package does not support compression with "context takeover". This means that messages must be compressed and decompressed in isolation, without retaining sliding window or dictionary state across messages. For more details refer to RFC 7692. Use of compression is experimental and may result in decreased performance.

Package gcs provides an API for building and using a Golomb-coded set filter. A Golomb-Coded Set (GCS) is a space-efficient probabilistic data structure that is used to test set membership with a tunable false positive rate while simultaneously preventing false negatives. In other words, items that are in the set will always match, but items that are not in the set will also sometimes match with the chosen false positive rate. This package currently implements two different versions for backwards compatibility. Version 1 is deprecated and therefore should no longer be used. Version 2 is the GCS variation that follows the specification details in DCP0005: https://github.com/decred/dcps/blob/master/dcp-0005/dcp-0005.mediawiki#golomb-coded-sets. Version 2 sets do not permit empty items (data of zero length) to be added and are parameterized by the following: * A parameter `B` that defines the remainder code bit size * A parameter `M` that defines the false positive rate as `1/M` * A key for the SipHash-2-4 function * The items to include in the set Errors returned by this package are of type gcs.Error. This allows the caller to programmatically determine the specific error by examining the ErrorKind field of the type asserted gcs.Error while still providing rich error messages with contextual information. See ErrorKind in the package documentation for a full list. GCS is used as a mechanism for storing, transmitting, and committing to per-block filters. Consensus-validating full nodes commit to a single filter for every block and serve the filter to SPV clients that match against the filter locally to determine if the block is potentially relevant. The required parameters for Decred are defined by the blockcf2 package. For more details, see the Block Filters section of DCP0005: https://github.com/decred/dcps/blob/master/dcp-0005/dcp-0005.mediawiki#block-filters

Package validate provides methods to validate a swagger specification, as well as tools to validate data against their schema. This package follows Swagger 2.0. specification (aka OpenAPI 2.0). Reference can be found here: https://github.com/OAI/OpenAPI-Specification/blob/master/versions/2.0.md. Validates a spec document (from JSON or YAML) against the JSON schema for swagger, then checks a number of extra rules that can't be expressed in JSON schema. Entry points: Reported as errors: Reported as warnings: The schema validation toolkit validates data against JSON-schema-draft 04 schema. It is tested against the full json-schema-testing-suite (https://github.com/json-schema-org/JSON-Schema-Test-Suite), except for the optional part (bignum, ECMA regexp, ...). It supports the complete JSON-schema vocabulary, including keywords not supported by Swagger (e.g. additionalItems, ...) Entry points: With the current version of this package, the following aspects of swagger are not yet supported: