JSON Database

Package adp and its sub-packages implement the backend services to interact with multiple blockchains or other types of similar networks. adp provides you with two microservices: The wallet and explorer services communicate via a message broker. The user can request the explorer to monitor blockchain addresses or accounts channeling requests to the message broker. The explorer service consumes requests and monitors addresses. When an address is involved in a transaction, the explorer will send an event to the message broker. Wallet services can then listen to the broker to notify their users about these events in real-time. The message broker is implemented as a product agnostic layer (package lib/msg) and is configured via a JSON config file at service startup. Both wallet and explorer have their own database used for persistence. Each microservice's database can be standalone or shared by the microservices. It's layered implementation (package lib/store) provides a database product agnostic interface. A blockchain layer (package lib/block) is implemented so new blockchain interfaces can be developed and added. The layer provides basic functionality to request account balance, send and get transactions, etc. Both the wallet and explorer services will connect to the blockchains or networks indicated in the JSON config file provided at startup. Depending on workload and resources, one or more instances of the microservices can be orchestrated in order to provide the required service level to the users. The microservices can also be monitored via a Prometheus API by setting the flag "-m" at startup. The wallet microservice (package wallet) can be started running cmd/wallet/main.go or using Dockerfile.wallet. The wallet exposes an HTTP RESTful API that can be used by multiple clients. The API provides basic functionality to get the available networks, request account balances, set accounts for monitoring and send transactions to the blockchains. It also provides a hierarchical deterministic wallet (HD wallet) which comes quite handy in a single-user configuration. Transaction events sent by the explorer service are logged and can be read by clients. Any client front-end can also get the events by consuming the appropriate queues of the message broker. The explorer microservice (package explorer) can be started running cmd/explorer/main.go or using Dockerfile.explorer. The explorer scans mined blocks of the configured networks and sends transaction events to the message broker when an account or address being monitored is involved. Wallet services can send requests for the explorer to start or stop monitoring addresses so that real time eventing can be provided to the clients or front-end.

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

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

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

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql. It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jackc/pgx/stdlib to use pgx as a database/sql compatible driver. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. Connection pool usage is explicit and configurable. In pgx, a connection can be created and managed directly, or a connection pool with a configurable maximum connections can be used. The connection pool offers an after connect hook that allows every connection to be automatically setup before being made available in the connection pool. It delegates methods such as QueryRow to an automatically checked out and released connection so you can avoid manually acquiring and releasing connections when you do not need that level of control. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgx/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. If pgx does cannot natively encode a type and that type is a renamed type (e.g. type MyTime time.Time) pgx will attempt to encode the underlying type. While this is usually desired behavior it can produce suprising behavior if one the underlying type and the renamed type each implement database/sql interfaces and the other implements pgx interfaces. It is recommended that this situation be avoided by implementing pgx interfaces on the renamed type. []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin or BeginEx. The BeginEx variant can create a transaction with a specified isolation level. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the WaitForNotification function. It takes a maximum time to wait for a notification. The pgx ConnConfig struct has a TLSConfig field. If this field is nil, then TLS will be disabled. If it is present, then it will be used to configure the TLS connection. This allows total configuration of the TLS connection. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/sirupsen/logrus, and the testing log are provided in the log directory.

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

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

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

go-geoserve provides an ip geolocation server that's made so that it can run on Heroku. It uses the MaxMind GeoIP2City Lite data form the MaxMind website. It checks for updates every minute and automatically downloads the most recent version of that database. At the moment, only IPv4 is supported. The server caches JSON results by ip address for low-latency lookups. When starting the server, the following environment variables control its behavior: To request JSON geolocation information for your IP: To request JSON geolocation information for a specific IP: Sample response:

A dynamic and extensible music library organizer Demlo is a music library organizer. It can encode, fix case, change folder hierarchy according to tags or file properties, tag from an online database, copy covers while ignoring duplicates or those below a quality threshold, and much more. It makes it possible to manage your libraries uniformly and dynamically. You can write your own rules to fit your needs best. Demlo aims at being as lightweight and portable as possible. Its major runtime dependency is the transcoder FFmpeg. The scripts are written in Lua for portability and speed while allowing virtually unlimited extensibility. Usage: For usage options, see: First Demlo creates a list of all input files. When a folder is specified, all files matching the extensions from the 'extensions' variable will be appended to the list. Identical files are appended only once. Next all files get analyzed: - The audio file details (tags, stream properties, format properties, etc.) are stored into the 'input' variable. The 'output' variable gets its default values from 'input', or from an index file if specified from command-line. If no index has been specified and if an attached cuesheet is found, all cuesheet details are appended accordingly. Cuesheet tags override stream tags, which override format tags. Finally, still without index, tags can be retrieved from Internet if the command-line option is set. - If a prescript has been specified, it gets executed. It makes it possible to adjust the input values and global variables before running the other scripts. - The scripts, if any, get executed in the lexicographic order of their basename. The 'output' variable is transformed accordingly. Scripts may contain rules such as defining a new file name, new tags, new encoding properties, etc. You can use conditions on input values to set the output properties, which makes it virtually possible to process a full music library in one single run. - If a postscript has been specified, it gets executed. It makes it possible to adjust the output of the script for the current run only. - Demlo makes some last-minute tweaking if need be: it adjusts the bitrate, the path, the encoding parameters, and so on. - A preview of changes is displayed. - When applying changes, the covers get copied if required and the audio file gets processed: tags are modified as specified, the file is re-encoded if required, and the output is written to the appropriate folder. When destination already exists, the 'exist' action is executed. The program's default behaviour can be changed from the user configuration file. (See the 'Files' section for a template.) Most command-line flags default value can be changed. The configuration file is loaded on startup, before parsing the command-line options. Review the default value of the CLI flags with 'demlo -h'. If you wish to use no configuration file, set the environment variable DEMLORC to ".". Scripts can contain any safe Lua code. Some functions like 'os.execute' are not available for security reasons. It is not possible to print to the standard output/error unless running in debug mode and using the 'debug' function. See the 'sandbox.go' file for a list of allowed functions and variables. Lua patterns are replaced by Go regexps. See https://github.com/google/re2/wiki/Syntax. Scripts have no requirements at all. However, to be useful, they should set values of the 'output' table detailed in the 'Variables' section. You can use the full power of the Lua to set the variables dynamically. For instance: 'input' and 'output' are both accessible from any script. All default functions and variables (excluding 'output') are reset on every script call to enforce consistency. Local variables are lost from one script call to another. Global variables are preserved. Use this feature to pass data like options or new functions. 'output' structure consistency is guaranteed at the start of every script. Demlo will only extract the fields with the right type as described in the 'Variables' section. Warning: Do not abuse of global variables, especially when processing non-fixed size data (e.g. tables). Data could grow big and slow down the program. By default, when the destination exists, Demlo will append a suffix to the output destination. This behaviour can be changed from the 'exist' action specified by the user. Demlo comes with a few default actions. The 'exist' action works just like scripts with the following differences: - Any change to 'output.path' will be skipped. - An additional variable is accessible from the action: 'existinfo' holds the file details of the existing files in the same fashion as 'input'. This allows for comparing the input file and the existing destination. The writing rules can be tweaked the following way: Word of caution: overwriting breaks Demlo's rule of not altering existing files. It can lead to undesired results if the overwritten file is also part of the (yet to be processed) input. The overwrite capability can be useful when syncing music libraries however. The user scripts should be generic. Therefore they may not properly handle some uncommon input values. Tweak the input with temporary overrides from command-line. The prescript and postscript defined on command-line will let you run arbitrary code that is run before and after all other scripts, respectively. Use global variables to transfer data and parameters along. If the prescript and postscript end up being too long, consider writing a demlo script. You can also define shell aliases or use wrapper scripts as convenience. The 'input' table describes the file: Bitrate is in bits per seconds (bps). That is, for 320 kbps you would specify The 'time' is the modification time of the file. It holds the sec seconds and nsec nanoseconds since January 1, 1970 UTC. The entry 'streams' and 'format' are as returned by It gives access to most metadata that FFmpeg can return. For instance, to get the duration of the track in seconds, query the variable 'input.format.duration'. Since there may be more than one stream (covers, other data), the first audio stream is assumed to be the music stream. For convenience, the index of the music stream is stored in 'audioindex'. The tags returned by FFmpeg are found in streams, format and in the cuesheet. To make tag queries easier, all tags are stored in the 'tags' table, with the following precedence: You can remove a tag by setting it to 'nil' or the empty string. This is equivalent, except that 'nil' saves some memory during the process. The 'output' table describes the transformation to apply to the file: The 'parameters' array holds the CLI parameters passed to FFmpeg. It can be anything supported by FFmpeg, although this variable is supposed to hold encoding information. See the 'Examples' section. The 'embeddedcovers', 'externalcovers' and 'onlinecover' variables are detailed in the 'Covers' section. The 'write' variable is covered in the 'Existing destination' section. The 'rmsrc' variable is a boolean: when true, Demlo removes the source file after processing. This can speed up the process when not re-encoding. This option is ignored for multi-track files. For convenience, the following shortcuts are provided: Demlo provides some non-standard Lua functions to ease scripting. Display a message on stderr if debug mode is on. Return lowercase string without non-alphanumeric characters nor leading zeros. Return the relation coefficient of the two input strings. The result is a float in 0.0...1.0, 0.0 means no relation at all, 1.0 means identical strings. A format is a container in FFmpeg's terminology. 'output.parameters' contains CLI flags passed to FFmpeg. They are meant to set the stream codec, the bitrate, etc. If 'output.parameters' is {'-c:a', 'copy'} and the format is identical, then taglib will be used instead of FFmpeg. Use this rule from a (post)script to disable encoding by setting the same format and the copy parameters. This speeds up the process. The official scripts are usually very smart at guessing the right values. They might make mistakes however. If you are unsure, you can (and you are advised to) preview the results before proceeding. The 'diff' preview is printed to stderr. A JSON preview of the changes is printed to stdout if stdout is redirected. The initial values of the 'output' table can be completed with tags fetched from the MusicBrainz database. Audio files are fingerprinted for the queries, so even with initially wrong file names and tags, the right values should still be retrieved. The front album cover can also be retrieved. Proxy parameters will be fetched automatically from the 'http_proxy' and 'https_proxy' environment variables. As this process requires network access it can be quite slow. Nevertheless, Demlo is specifically optimized for albums, so that network queries are used for only one track per album, when possible. Some tracks can be released on different albums: Demlo tries to guess it from the tags, but if the tags are wrong there is no way to know which one it is. There is a case where the selection can be controlled: let's assume we have tracks A, B and C from the same album Z. A and B were also released in album Y, whereas C was release in Z only. Tags for A will be checked online; let's assume it gets tagged to album Y. B will use A details, so album Y too. Then C does not match neither A's nor B's album, so another online query will be made and it will be tagged to album Z. This is slow and does not yield the expected result. Now let's call Tags for C will be queried online, and C will be tagged to Z. Then both A and B will match album Z so they will be tagged using C details, which is the desired result. Conclusion: when using online tagging, the first argument should be the lesser known track of the album. Demlo can set the output variables according to the values set in a text file before calling the script. The input values are ignored as well as online tagging, but it is still possible to access the input table from scripts. This 'index' file is formatted in JSON. It corresponds to what Demlo outputs when printing the JSON preview. This is valid JSON except for the missing beginning and the missing end. It makes it possible to concatenate and to append to existing index files. Demlo will automatically complete the missing parts so that it becomes valid JSON. The index file is useful when you want to edit tags manually: You can redirect the output to a file, edit the content manually with your favorite text editor, then run Demlo again with the index as argument. See the 'Examples' section. This feature can also be used to interface Demlo with other programs. Demlo can manage embedded covers as well as external covers. External covers are queried from files matching known extensions in the file's folder. Embedded covers are queried from static video streams in the file. Covers are accessed from The embedded covers are indexed numerically by order of appearance in the streams. The first cover will be at index 1 and so on. This is not necessarily the index of the stream. 'inputcover' is the following structure: 'format' is the picture format. FFmpeg makes a distinction between format and codec, but it is not useful for covers. The name of the format is specified by Demlo, not by FFmpeg. Hence the 'jpeg' name, instead of 'mjpeg' as FFmpeg puts it. 'width' and 'height' hold the size in pixels. 'checksum' can be used to identify files uniquely. For performance reasons, only a partial checksum is performed. This variable is typically used for skipping duplicates. Cover transformations are specified in 'outputcover' has the following structure: The format is specified by FFmpeg this time. See the comments on 'format' for 'inputcover'. 'parameters' is used in the same fashion as 'output.parameters'. User configuration: This must be a Lua file. See the 'demlorc' file provided with this package for an exhaustive list of options. Folder containing the official scripts: User script folder: Create this folder and add your own scripts inside. This folder takes precedence over the system folder, so scripts with the same name will be found in the user folder first. The following examples will not proceed unless the '-p' command-line option is true. Important: you _must_ use single quotes for the runtime Lua command to prevent expansion. Inside the Lua code, use double quotes for strings and escape single quotes. Show default options: Preview changes made by the default scripts: Use 'alternate' script if found in user or system script folder (user folder first): Add the Lua file to the list of scripts. This feature is convenient if you want to write scripts that are too complex to fit on the command-line, but not generic enough to fit the user or system script folders. Remove all script from the list, then add '30-case' and '60-path' scripts. Note that '30-case' will be run before '60-path'. Do not use any script but '60-path'. The file content is unchanged and the file is renamed to a dynamically computed destination. Demlo performs an instant rename if destination is on the same device. Otherwise it copies the file and removes the source. Use the default scripts (if set in configuration file), but do not re-encode: Set 'artist' to the value of 'composer', and 'title' to be preceded by the new value of 'artist', then apply the default script. Do not re-encode. Order in runtime script matters. Mind the double quotes. Set track number to first number in input file name: Use the default scripts but keep original value for the 'artist' tag: 1) Preview default scripts transformation and save it to an index. 2) Edit file to fix any potential mistake. 3) Run Demlo over the same files using the index information only. Same as above but generate output filename according to the custom '61-rename' script. The numeric prefix is important: it ensures that '61-rename' will be run after all the default tag related scripts and after '60-path'. Otherwise, if a change in tags would occur later on, it would not affect the renaming script. Retrieve tags from Internet: Same as above but for a whole album, and saving the result to an index: Only download the cover for the album corresponding to the track. Use 'rmsrc' to avoid duplicating the audio file. Change tags inplace with entries from MusicBrainz: Set tags to titlecase while casing AC-DC correctly: To easily switch between formats from command-line, create one script per format (see 50-encoding.lua), e.g. ogg.lua and flac.lua. Then Add support for non-default formats from CLI: Overwrite existing destination if input is newer: ffmpeg(1), ffprobe(1), http://www.lua.org/pil/contents.html

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

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

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

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql. It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jackc/pgx/stdlib to use pgx as a database/sql compatible driver. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. Connection pool usage is explicit and configurable. In pgx, a connection can be created and managed directly, or a connection pool with a configurable maximum connections can be used. The connection pool offers an after connect hook that allows every connection to be automatically setup before being made available in the connection pool. It delegates methods such as QueryRow to an automatically checked out and released connection so you can avoid manually acquiring and releasing connections when you do not need that level of control. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgx/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. If pgx does cannot natively encode a type and that type is a renamed type (e.g. type MyTime time.Time) pgx will attempt to encode the underlying type. While this is usually desired behavior it can produce suprising behavior if one the underlying type and the renamed type each implement database/sql interfaces and the other implements pgx interfaces. It is recommended that this situation be avoided by implementing pgx interfaces on the renamed type. []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin or BeginEx. The BeginEx variant can create a transaction with a specified isolation level. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the WaitForNotification function. It takes a maximum time to wait for a notification. The pgx ConnConfig struct has a TLSConfig field. If this field is nil, then TLS will be disabled. If it is present, then it will be used to configure the TLS connection. This allows total configuration of the TLS connection. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/sirupsen/logrus, and the testing log are provided in the log directory.

Package scribble is a tiny JSON database

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

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

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql. It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jackc/pgx/stdlib to use pgx as a database/sql compatible driver. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. Connection pool usage is explicit and configurable. In pgx, a connection can be created and managed directly, or a connection pool with a configurable maximum connections can be used. The connection pool offers an after connect hook that allows every connection to be automatically setup before being made available in the connection pool. It delegates methods such as QueryRow to an automatically checked out and released connection so you can avoid manually acquiring and releasing connections when you do not need that level of control. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgx/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin or BeginEx. The BeginEx variant can create a transaction with a specified isolation level. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the WaitForNotification function. It takes a maximum time to wait for a notification. The pgx ConnConfig struct has a TLSConfig field. If this field is nil, then TLS will be disabled. If it is present, then it will be used to configure the TLS connection. This allows total configuration of the TLS connection. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/sirupsen/logrus, and the testing log are provided in the log directory.

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

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

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

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

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

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

Package ratchet is a library for performing data pipeline / ETL tasks in Go. The main construct in Ratchet is Pipeline. A Pipeline has a series of PipelineStages, which will each perform some type of data processing, and then send new data on to the next stage. Each PipelineStage consists of one or more DataProcessors, which are responsible for receiving, processing, and then sending data on to the next stage of processing. DataProcessors each run in their own goroutine, and therefore all data processing can be executing concurrently. Here is a conceptual drawing of a fairly simple Pipeline: In this example, we have a Pipeline consisting of 3 PipelineStages. The first stage has a DataProcessor that runs queries on a SQL database, the second is doing custom transformation work on that data, and the third stage branches into 2 DataProcessors, one writing the resulting data to a CSV file, and the other inserting into another SQL database. In the example above, Stage 1 and Stage 3 are using built-in DataProcessors (see the "processors" package/subdirectory). However, Stage 2 is using a custom implementation of DataProcessor. By using a combination of built-in processors, and supporting the writing of any Go code to process data, Ratchet makes it possible to write very custom and fast data pipeline systems. See the DataProcessor documentation to learn more. Since each DataProcessor is running in it's own goroutine, SQLReader can continue pulling and sending data while each subsequent stage is also processing data. Optimally-designed pipelines have processors that can each run in an isolated fashion, processing data without having to worry about what's coming next down the pipeline. All data payloads sent between DataProcessors are of type data.JSON ([]byte). This provides a good balance of consistency and flexibility. See the "data" package for details and helper functions for dealing with data.JSON. Another good read for handling JSON data in Go is http://blog.golang.org/json-and-go. Note that many of the concepts in Ratchet were taken from the Golang blog's post on pipelines (http://blog.golang.org/pipelines). While the details discussed in that blog post are largely abstracted away by Ratchet, it is still an interesting read and will help explain the general concepts being applied. There are two ways to construct and run a Pipeline. The first is a basic, non-branching Pipeline. For example: This is a 3-stage Pipeline that queries some SQL data in stage 1, does some custom data transformation in stage 2, and then writes the resulting data to a SQL table in stage 3. The code to create and run this basic Pipeline would look something like: The second way to construct a Pipeline is using a PipelineLayout. This method allows for more complex Pipeline configurations that support branching between stages that are running multiple DataProcessors. Here is a (fairly complex) example: This Pipeline consists of 4 stages where each DataProcessor is choosing which DataProcessors in the subsequent stage should receive the data it sends. The SQLReader in stage 2, for example, is sending data to only 2 processors in the next stage, while the Custom DataProcessor in stage 2 is sending it's data to 3. The code for constructing and running a Pipeline like this would look like: This example is only conceptual, the main points being to explain the flexibility you have when designing your Pipeline's layout and to demonstrate the syntax for constructing a new PipelineLayout.

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

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

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

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

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

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

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

Package scribble is a tiny JSON database

Package pgx is a PostgreSQL database driver. pgx provides lower level access to PostgreSQL than the standard database/sql It remains as similar to the database/sql interface as possible while providing better speed and access to PostgreSQL specific features. Import github.com/jack/pgx/stdlib to use pgx as a database/sql compatible driver. pgx implements Query and Scan in the familiar database/sql style. pgx also implements QueryRow in the same style as database/sql. Use Exec to execute a query that does not return a result set. Connection pool usage is explicit and configurable. In pgx, a connection can be created and managed directly, or a connection pool with a configurable maximum connections can be used. The connection pool offers an after connect hook that allows every connection to be automatically setup before being made available in the connection pool. It delegates methods such as QueryRow to an automatically checked out and released connection so you can avoid manually acquiring and releasing connections when you do not need that level of control. pgx maps between all common base types directly between Go and PostgreSQL. In particular: pgx can map nulls in two ways. The first is package pgtype provides types that have a data field and a status field. They work in a similar fashion to database/sql. The second is to use a pointer to a pointer. pgx maps between int16, int32, int64, float32, float64, and string Go slices and the equivalent PostgreSQL array type. Go slices of native types do not support nulls, so if a PostgreSQL array that contains a null is read into a native Go slice an error will occur. The pgtype package includes many more array types for PostgreSQL types that do not directly map to native Go types. pgx includes built-in support to marshal and unmarshal between Go types and the PostgreSQL JSON and JSONB. pgx encodes from net.IPNet to and from inet and cidr PostgreSQL types. In addition, as a convenience pgx will encode from a net.IP; it will assume a /32 netmask for IPv4 and a /128 for IPv6. pgx includes support for the common data types like integers, floats, strings, dates, and times that have direct mappings between Go and SQL. In addition, pgx uses the github.com/jackc/pgx/pgtype library to support more types. See documention for that library for instructions on how to implement custom types. See example_custom_type_test.go for an example of a custom type for the PostgreSQL point type. pgx also includes support for custom types implementing the database/sql.Scanner and database/sql/driver.Valuer interfaces. []byte passed as arguments to Query, QueryRow, and Exec are passed unmodified to PostgreSQL. Transactions are started by calling Begin or BeginEx. The BeginEx variant can create a transaction with a specified isolation level. Use CopyFrom to efficiently insert multiple rows at a time using the PostgreSQL copy protocol. CopyFrom accepts a CopyFromSource interface. If the data is already in a [][]interface{} use CopyFromRows to wrap it in a CopyFromSource interface. Or implement CopyFromSource to avoid buffering the entire data set in memory. CopyFrom can be faster than an insert with as few as 5 rows. pgx can listen to the PostgreSQL notification system with the WaitForNotification function. It takes a maximum time to wait for a notification. The pgx ConnConfig struct has a TLSConfig field. If this field is nil, then TLS will be disabled. If it is present, then it will be used to configure the TLS connection. This allows total configuration of the TLS connection. pgx defines a simple logger interface. Connections optionally accept a logger that satisfies this interface. Set LogLevel to control logging verbosity. Adapters for github.com/inconshreveable/log15, github.com/Sirupsen/logrus, and the testing log are provided in the log directory.

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

Package gopatch allows structures to be patched in a multitude of configurable ways. Patching is accomplished via Patchers, and a default is initialized for immediate use, found by calling `gopatch.Default()`. All initialized patchers can be used multiple times, and are thread safe. Use the default patcher... Or configure your own! Currently, gopatch cannot patch maps, and cannot replace maps not of the same key AND value types. Additionally, gopatch cannot patch or replace slices/arrays. Maps not of the same key and value types as well as all slices are currently skipped without error. However, it's easy to hook your own patch/replace logic by adding a custom Updater function to `gopatch.Updaters`. Note that these functions are run first to last, so you'll need to inject your function like so: `gopatch.Updaters = append(myUpdater, gopatch.Updaters...)`. Suggestions on how to remove these limitations are welcome. Please add an issue or make a pull request! Struct field names on Go often differ from field names of counterpart object representations such as JSON and BSON. This package refers to these representations as "Field Name Sources". In the above examples, the User struct's EmailAddress has a "struct" field name source (resulting in the field name "EmailAddress"), and a "json" field name source (resulting in the field name "email_address"). Thus, if you want to patch a struct from a map built from JSON bytes, you need to add json tags AND configure your Patcher to use the struct's "json" field name sources. In the second example above, you can see the Patcher has been configured with the "json" field name source and thus can use a JSON-derived map to patch. Patch operations return results or an error when they fail. These results can be used for purposes ranging from logging suspicious activity to persisting the changes to a database row or document. The results consist of an array of fields which were successfully patched, an array of fields which were found in the patch yet not permitted, and a map of the successfully updated fields and their values. Take the above example of patching a nefarious user's account. If `UnpermittedErrors` were false, the patch would succeed and result would not be nil; however, the Unpermitted array would contain "IsBanned", because the patching of that field wasn't permitted. Meanwhile, the "Fields" array would contain "Username" because it was permitted, and Map would contain the same data as `nefariousPatchRequest`, but without "is_banned". Patching behavior can be enforced while defining the structure by using the "gopatch" tag, which overrides configuration. This way, restrictions on how your database model can be patched can be limited while designing the model itself, rather than while designing the endpoint that patches it, reducing the chance of unexpected behavior. When the gopatch tag "patch" is used, the PatchResult's Map field will contain the struct field's values flattened with dot-notation keys created using the absolute path to the struct patched. For example, if the above User struct's `Profile.Motto` field is patched, the result's Map field would contain the following data: `"profile.motto": "..."`. This facilitates the patch-embedded-fields behavior of embedded objects in database servers such as MongoDB. When the gopatch tag "replace" is used, the PatchResult's Map field will contain the struct field's values inside the struct field's patch key, exactly as presented to the Patcher. For example, if the above User struct's `BanData.Length` field is patched, the result's Map field would contain the following data: `"ban_data": map[string]interface{}{ "length": 30 }`. This facilitates the patch-whole-object behavior of embedded objects in database servers such as MongoDB.

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

Copyright 2017 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. inserter loads gzipped json files into the database. File names must be of the pattern "ap-mca-dump-%d.json.gz", where %d is the unix timestamp. TODO: take timestamp from data? Example run: Copyright 2017 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. mkgraphs renders graphs from database. Example run:

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

Package freegeoip provides an API for searching the geolocation of IP addresses. It uses a database that can be either a local file or a remote resource from a URL. Local databases are monitored by fsnotify and reloaded when the file is either updated or overwritten. Remote databases are automatically downloaded and updated in background so you can focus on using the API and not managing the database. Also, the freegeoip package provides http handlers that any Go http server (net/http) can use. These handlers can process IP geolocation lookup requests and return data in multiple formats like CSV, XML, JSON and JSONP. It has also an API for supporting custom formats.

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

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

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

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

Package scribble is a tiny JSON database

Package ratchet is a library for performing data pipeline / ETL tasks in Go. The main construct in Ratchet is Pipeline. A Pipeline has a series of PipelineStages, which will each perform some type of data processing, and then send new data on to the next stage. Each PipelineStage consists of one or more DataProcessors, which are responsible for receiving, processing, and then sending data on to the next stage of processing. DataProcessors each run in their own goroutine, and therefore all data processing can be executing concurrently. Here is a conceptual drawing of a fairly simple Pipeline: In this example, we have a Pipeline consisting of 3 PipelineStages. The first stage has a DataProcessor that runs queries on a SQL database, the second is doing custom transformation work on that data, and the third stage branches into 2 DataProcessors, one writing the resulting data to a CSV file, and the other inserting into another SQL database. In the example above, Stage 1 and Stage 3 are using built-in DataProcessors (see the "processors" package/subdirectory). However, Stage 2 is using a custom implementation of DataProcessor. By using a combination of built-in processors, and supporting the writing of any Go code to process data, Ratchet makes it possible to write very custom and fast data pipeline systems. See the DataProcessor documentation to learn more. Since each DataProcessor is running in it's own goroutine, SQLReader can continue pulling and sending data while each subsequent stage is also processing data. Optimally-designed pipelines have processors that can each run in an isolated fashion, processing data without having to worry about what's coming next down the pipeline. All data payloads sent between DataProcessors are of type data.JSON ([]byte). This provides a good balance of consistency and flexibility. See the "data" package for details and helper functions for dealing with data.JSON. Another good read for handling JSON data in Go is http://blog.golang.org/json-and-go. Note that many of the concepts in Ratchet were taken from the Golang blog's post on pipelines (http://blog.golang.org/pipelines). While the details discussed in that blog post are largely abstracted away by Ratchet, it is still an interesting read and will help explain the general concepts being applied. There are two ways to construct and run a Pipeline. The first is a basic, non-branching Pipeline. For example: This is a 3-stage Pipeline that queries some SQL data in stage 1, does some custom data transformation in stage 2, and then writes the resulting data to a SQL table in stage 3. The code to create and run this basic Pipeline would look something like: The second way to construct a Pipeline is using a PipelineLayout. This method allows for more complex Pipeline configurations that support branching between stages that are running multiple DataProcessors. Here is a (fairly complex) example: This Pipeline consists of 4 stages where each DataProcessor is choosing which DataProcessors in the subsequent stage should receive the data it sends. The SQLReader in stage 2, for example, is sending data to only 2 processors in the next stage, while the Custom DataProcessor in stage 2 is sending it's data to 3. The code for constructing and running a Pipeline like this would look like: This example is only conceptual, the main points being to explain the flexibility you have when designing your Pipeline's layout and to demonstrate the syntax for constructing a new PipelineLayout.

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

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