Design System

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 tusd provides ways to accept tus 1.0 calls using HTTP. tus is a protocol based on HTTP for resumable file uploads. Resumable means that an upload can be interrupted at any moment and can be resumed without re-uploading the previous data again. An interruption may happen willingly, if the user wants to pause, or by accident in case of an network issue or server outage (http://tus.io). tusd was designed in way which allows an flexible and customizable usage. We wanted to avoid binding this package to a specific storage system – particularly a proprietary third-party software. Therefore tusd is an abstract layer whose only job is to accept incoming HTTP requests, validate them according to the specification and finally passes them to the data store. The data store is another important component in tusd's architecture whose purpose is to do the actual file handling. It has to write the incoming upload to a persistent storage system and retrieve information about an upload's current state. Therefore it is the only part of the system which communicates directly with the underlying storage system, whether it be the local disk, a remote FTP server or cloud providers such as AWS S3. The only hard requirements for a data store can be found in the DataStore interface. It contains methods for creating uploads (NewUpload), writing to them (WriteChunk) and retrieving their status (GetInfo). However, there are many more features which are not mandatory but may still be used. These are contained in their own interfaces which all share the *DataStore suffix. For example, GetReaderDataStore which enables downloading uploads or TerminaterDataStore which allows uploads to be terminated. The store composer offers a way to combine the basic data store - the core - implementation and these additional extensions: The corresponding methods for adding an extension to the composer are prefixed with Use* followed by the name of the corresponding interface. However, most data store provide multiple extensions and adding all of them manually can be tedious and error-prone. Therefore, all data store distributed with tusd provide an UseIn() method which does this job automatically. For example, this is the S3 store in action (see S3Store.UseIn): Finally, once you are done with composing your data store, you can pass it inside the Config struct in order to create create a new tusd HTTP handler: This handler can then be mounted to a specific path, e.g. /files:

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 amqp is an AMQP 0.9.1 client with RabbitMQ extensions Understand the AMQP 0.9.1 messaging model by reviewing these links first. Much of the terminology in this library directly relates to AMQP concepts. Most other broker clients publish to queues, but in AMQP, clients publish Exchanges instead. AMQP is programmable, meaning that both the producers and consumers agree on the configuration of the broker, instead of requiring an operator or system configuration that declares the logical topology in the broker. The routing between producers and consumer queues is via Bindings. These bindings form the logical topology of the broker. In this library, a message sent from publisher is called a "Publishing" and a message received to a consumer is called a "Delivery". The fields of Publishings and Deliveries are close but not exact mappings to the underlying wire format to maintain stronger types. Many other libraries will combine message properties with message headers. In this library, the message well known properties are strongly typed fields on the Publishings and Deliveries, whereas the user defined headers are in the Headers field. The method naming closely matches the protocol's method name with positional parameters mapping to named protocol message fields. The motivation here is to present a comprehensive view over all possible interactions with the server. Generally, methods that map to protocol methods of the "basic" class will be elided in this interface, and "select" methods of various channel mode selectors will be elided for example Channel.Confirm and Channel.Tx. The library is intentionally designed to be synchronous, where responses for each protocol message are required to be received in an RPC manner. Some methods have a noWait parameter like Channel.QueueDeclare, and some methods are asynchronous like Channel.Publish. The error values should still be checked for these methods as they will indicate IO failures like when the underlying connection closes. Clients of this library may be interested in receiving some of the protocol messages other than Deliveries like basic.ack methods while a channel is in confirm mode. The Notify* methods with Connection and Channel receivers model the pattern of asynchronous events like closes due to exceptions, or messages that are sent out of band from an RPC call like basic.ack or basic.flow. Any asynchronous events, including Deliveries and Publishings must always have a receiver until the corresponding chans are closed. Without asynchronous receivers, the sychronous methods will block. It's important as a client to an AMQP topology to ensure the state of the broker matches your expectations. For both publish and consume use cases, make sure you declare the queues, exchanges and bindings you expect to exist prior to calling Channel.Publish or Channel.Consume. SSL/TLS - Secure connections When Dial encounters an amqps:// scheme, it will use the zero value of a tls.Config. This will only perform server certificate and host verification. Use DialTLS when you wish to provide a client certificate (recommended), include a private certificate authority's certificate in the cert chain for server validity, or run insecure by not verifying the server certificate dial your own connection. DialTLS will use the provided tls.Config when it encounters an amqps:// scheme and will dial a plain connection when it encounters an amqp:// scheme. SSL/TLS in RabbitMQ is documented here: http://www.rabbitmq.com/ssl.html This exports a Session object that wraps this library. It automatically reconnects when the connection fails, and blocks all pushes until the connection succeeds. It also confirms every outgoing message, so none are lost. It doesn't automatically ack each message, but leaves that to the parent process, since it is usage-dependent. Try running this in one terminal, and `rabbitmq-server` in another. Stop & restart RabbitMQ to see how the queue reacts.

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 amqp091 is an AMQP 0.9.1 client with RabbitMQ extensions Understand the AMQP 0.9.1 messaging model by reviewing these links first. Much of the terminology in this library directly relates to AMQP concepts. Most other broker clients publish to queues, but in AMQP, clients publish Exchanges instead. AMQP is programmable, meaning that both the producers and consumers agree on the configuration of the broker, instead of requiring an operator or system configuration that declares the logical topology in the broker. The routing between producers and consumer queues is via Bindings. These bindings form the logical topology of the broker. In this library, a message sent from publisher is called a "Publishing" and a message received to a consumer is called a "Delivery". The fields of Publishings and Deliveries are close but not exact mappings to the underlying wire format to maintain stronger types. Many other libraries will combine message properties with message headers. In this library, the message well known properties are strongly typed fields on the Publishings and Deliveries, whereas the user defined headers are in the Headers field. The method naming closely matches the protocol's method name with positional parameters mapping to named protocol message fields. The motivation here is to present a comprehensive view over all possible interactions with the server. Generally, methods that map to protocol methods of the "basic" class will be elided in this interface, and "select" methods of various channel mode selectors will be elided for example Channel.Confirm and Channel.Tx. The library is intentionally designed to be synchronous, where responses for each protocol message are required to be received in an RPC manner. Some methods have a noWait parameter like Channel.QueueDeclare, and some methods are asynchronous like Channel.Publish. The error values should still be checked for these methods as they will indicate IO failures like when the underlying connection closes. Clients of this library may be interested in receiving some of the protocol messages other than Deliveries like basic.ack methods while a channel is in confirm mode. The Notify* methods with Connection and Channel receivers model the pattern of asynchronous events like closes due to exceptions, or messages that are sent out of band from an RPC call like basic.ack or basic.flow. Any asynchronous events, including Deliveries and Publishings must always have a receiver until the corresponding chans are closed. Without asynchronous receivers, the synchronous methods will block. It's important as a client to an AMQP topology to ensure the state of the broker matches your expectations. For both publish and consume use cases, make sure you declare the queues, exchanges and bindings you expect to exist prior to calling Channel.PublishWithContext or Channel.Consume. When Dial encounters an amqps:// scheme, it will use the zero value of a tls.Config. This will only perform server certificate and host verification. Use DialTLS when you wish to provide a client certificate (recommended), include a private certificate authority's certificate in the cert chain for server validity, or run insecure by not verifying the server certificate. DialTLS will use the provided tls.Config when it encounters an amqps:// scheme and will dial a plain connection when it encounters an amqp:// scheme. SSL/TLS in RabbitMQ is documented here: http://www.rabbitmq.com/ssl.html In order to be notified when a connection or channel gets closed, both structures offer the possibility to register channels using Channel.NotifyClose and Connection.NotifyClose functions: No errors will be sent in case of a graceful connection close. In case of a non-graceful closure due to e.g. network issue, or forced connection closure from the Management UI, the error will be notified synchronously by the library. The library sends to notification channels just once. After sending a notification to all channels, the library closes all registered notification channels. After receiving a notification, the application should create and register a new channel. To avoid deadlocks in the library, it is necessary to consume from the channels. This could be done inside a different goroutine with a select listening on the two channels inside a for loop like: It is strongly recommended to use buffered channels to avoid deadlocks inside the library. Using Channel.NotifyPublish allows the caller of the library to be notified, through a go channel, when a message has been received and confirmed by the broker. It's advisable to wait for all Confirmations to arrive before calling Channel.Close or Connection.Close. It is also necessary to consume from this channel until it gets closed. The library sends synchronously to the registered channel. It is advisable to use a buffered channel, with capacity set to the maximum acceptable number of unconfirmed messages. It is important to consume from the confirmation channel at all times, in order to avoid deadlocks in the library. This exports a Client object that wraps this library. It automatically reconnects when the connection fails, and blocks all pushes until the connection succeeds. It also confirms every outgoing message, so none are lost. It doesn't automatically ack each message, but leaves that to the parent process, since it is usage-dependent. Try running this in one terminal, and rabbitmq-server in another. Stop & restart RabbitMQ to see how the queue reacts.

api is a part of dataset Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. api_docs.go is a part of dataset Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Package dataset includes the operations needed for processing collections of JSON documents and their attachments. Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Package dataset includes the operations needed for processing collections of JSON documents and their attachments. Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. cli is part of dataset Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. This is part of the dataset package. Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Package dataset includes the operations needed for processing collections of JSON documents and their attachments. Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Package dataset includes the operations needed for processing collections of JSON documents and their attachments. Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * config is a part of dataset Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Package dataset includes the operations needed for processing collections of JSON documents and their attachments. Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Dataset Project =============== The Dataset Project provides tools for working with collections of JSON Object documents stored on the local file system or via a dataset web service. Two tools are provided, a command line interface (dataset) and a web service (datasetd). dataset command line tool ------------------------- _dataset_ is a command line tool for working with collections of JSON objects. Collections are stored on the file system in a pairtree directory structure or can be accessed via dataset's web service. For collections storing data in a pairtree JSON objects are stored in collections as plain UTF-8 text files. This means the objects can be accessed with common Unix text processing tools as well as most programming languages. The _dataset_ command line tool supports common data management operations such as initialization of collections; document creation, reading, updating and deleting; listing keys of JSON objects in the collection; and associating non-JSON documents (attachments) with specific JSON documents in the collection. ### enhanced features include - aggregate objects into data frames - generate sample sets of keys and objects datasetd, dataset as a web service ---------------------------------- _datasetd_ is a web service implementation of the _dataset_ command line program. It features a sub-set of capability found in the command line tool. This allows dataset collections to be integrated safely into web applications or used concurrently by multiple processes. It achieves this by storing the dataset collection in a SQL database using JSON columns. Design choices -------------- _dataset_ and _datasetd_ are intended to be simple tools for managing collections JSON object documents in a predictable structured way. _dataset_ is guided by the idea that you should be able to work with JSON documents as easily as you can any plain text document on the Unix command line. _dataset_ is intended to be simple to use with minimal setup (e.g. `dataset init mycollection.ds` creates a new collection called 'mycollection.ds'). _datatset_ stores JSON object documents in a pairtree _datasetd_ stores JSON object documents in a table named for the collection The choice of plain UTF-8 is intended to help future proof reading dataset collections. Care has been taken to keep _dataset_ simple enough and light weight enough that it will run on a machine as small as a Raspberry Pi Zero while being equally comfortable on a more resource rich server or desktop environment. _dataset_ can be re-implement in any programming language supporting file input and output, common string operations and along with JSON encoding and decoding functions. The current implementation is in the Go language. Features -------- _dataset_ supports - Initialize a new dataset collection - Listing _keys_ in a collection - Object level actions _datasetd_ supports - List collections available from the web service - List or update a collection's metadata - List a collection's keys - Object level actions Both _dataset_ and _datasetd_ maybe useful for general data science applications needing JSON object management or in implementing repository systems in research libraries and archives. Limitations of _dataset_ and _datasetd_ ------------------------------------------- _dataset_ has many limitations, some are listed below _datasetd_ is a simple web service intended to run on "localhost:8485". Authors and history ------------------- - R. S. Doiel - Tommy Morrell Package dataset includes the operations needed for processing collections of JSON documents and their attachments. Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * Package dataset includes the operations needed for processing collections of JSON documents and their attachments. Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ptstore is a part of the dataset Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Package dataset includes the operations needed for processing collections of JSON documents and their attachments. Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. sqlstore is a part of dataset Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Package dataset includes the operations needed for processing collections of JSON documents and their attachments. Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. table.go provides some utility functions to move string one and two dimensional slices into/out of one and two dimensional slices. texts is part of dataset Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Package dataset includes the operations needed for processing collections of JSON documents and their attachments. Authors R. S. Doiel, <rsdoiel@library.caltech.edu> and Tom Morrel, <tmorrell@library.caltech.edu> Copyright (c) 2022, Caltech All rights not granted herein are expressly reserved by Caltech. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Package decimal implements immutable decimal floating-point numbers. It is specifically designed for transactional financial systems and adheres to the principles set by ANSI X3.274-1996. Decimal is a struct with three fields: The numerical value of a decimal is calculated as follows: This approach allows the same numeric value to have multiple representations, for example, 1, 1.0, and 1.00, which represent the same value but have different scales and coefficients. The range of a decimal is determined by its scale. Here are the ranges for frequently used scales: Subnormal numbers are not supported to ensure peak performance. Consequently, decimals between -0.00000000000000000005 and 0.00000000000000000005 inclusive, are rounded to 0. Special values such as NaN, Infinity, or negative zeros are not supported. This ensures that arithmetic operations always produce either valid decimals or errors. Each arithmetic operation, except for Decimal.Sqrt and Decimal.SqrtExact, occurs in two steps: The operation is initially performed using uint64 arithmetic. If no overflow occurs, the exact result is immediately returned. If overflow occurs, the operation proceeds to step 2. The operation is repeated with increased precision using big.Int arithmetic. The result is then rounded to 19 digits. If no significant digits are lost during rounding, the inexact result is returned. If any significant digit is lost, an overflow error is returned. Step 1 improves performance by avoiding performance impact associated with big.Int arithmetic. It is expected that, in transactional financial systems, most arithmetic operations will compute an exact result during step 1. The following rules determine the significance of digits during step 2: Unlike many other decimal libraries, this package does not provide an explicit context. Instead, the context is implicit and can be approximately equated to the following settings: The equality of Etiny and Emin implies that this package does not support subnormal numbers. Implicit rounding is applied when a result exceeds 19 digits, rounding it to 19 digits using half-to-even rounding. This method ensures that rounding errors are evenly distributed between rounding up and down. For all arithmetic operations, except for Decimal.Pow and Decimal.PowExact, the result is the one that would be obtained by computing the exact mathematical result with infinite precision and then rounding it to 19 digits. Decimal.Pow and Decimal.PowExact may occasionally produce a result that is off by 1 unit in the last place. In addition to implicit rounding, the package provides several methods for explicit rounding: See the documentation for each method for more details. All methods are panic-free and pure. Errors are returned in the following cases: Division by Zero: Unlike the standard library, Decimal.Quo, Decimal.QuoRem, and Decimal.Inv do not panic when dividing by 0. Instead, they return an error. Invalid Operation: Decimal.Pow and Decimal.PowExact return an error if 0 is raised to a negative power. Decimal.Sqrt and Decimal.SqrtExact return an error if the square root of a negative decimal is requested. Overflow: Unlike standard integers, there is no "wrap around" for decimals at certain sizes. For out-of-range values, arithmetic operations return an error. Errors are not returned in the following cases: A. JSON The package integrates seamlessly with standard encoding/json through the implementation of encoding.TextMarshaller and encoding.TextUnmarshaler interfaces. Below is an example structure: This package marshals decimals as quoted strings, ensuring the preservation of the exact numerical value. Below is an example OpenAPI schema: B. XML The package integrates with standard encoding/xml via the implementation of encoding.TextMarshaller and encoding.TextUnmarshaler interfaces. Below is an example structure: "xs:decimal" type can represent decimals in XML schema. It is possible to impose restrictions on the length of the decimals using the following type: C. Protocol Buffers Protocol Buffers can represent decimals as numerical strings, preserving trailing zeros. To convert between numerical strings and decimals, use Parse and Decimal.String. Below is an example of a proto definition: Alternatively, decimals can be represented as two integers: one for the integer part and another for the fractional part. However, this format does not preserve trailing zeros and rounds decimals with more than nine digits in the fractional part. For conversion between this format and decimals, use NewFromInt64 and Decimal.Int64 with a scale argument of "9". Below is an example of a proto definition: D. SQL The package integrates with the standard database/sql via the implementation of sql.Scanner and driver.Valuer interfaces. To ensure accurate preservation of decimal scales, it is essential to choose appropriate column types: Below are the reasons for these preferences: PostgreSQL: Always use DECIMAL without precision or scale specifications, that is, avoid DECIMAL(p) or DECIMAL(p, s). DECIMAL accurately preserves the scale of decimals. SQLite: Prefer TEXT, since DECIMAL is just an alias for binary floating-point numbers. TEXT accurately preserves the scale of decimals. MySQL: Use DECIMAL(19, d), as DECIMAL is merely an alias for DECIMAL(10, 0). The downside of this format is that MySQL automatically rescales all decimals: it rounds values with more than d digits in the fractional part (using half away from zero) and pads with trailing zeros those with fewer than d digits in the fractional part. To prevent automatic rescaling, consider using VARCHAR(22), which accurately preserves the scale of decimals. This example demonstrates the advantage of decimals for financial calculations. It computes the sum 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1. In decimal arithmetic, the result is exactly 1.0. In float64 arithmetic, the result slightly deviates from 1.0 due to binary floating-point representation. This example calculates an approximate value of π using the Leibniz formula. The Leibniz formula is an infinite series that converges to π/4, and is given by the equation: 1 - 1/3 + 1/5 - 1/7 + 1/9 - 1/11 + ... = π/4. This example computes the series up to the 500,000th term using decimal arithmetic and returns the approximate value of π. This example implements a simple calculator that evaluates mathematical expressions written in postfix notation. The calculator can handle basic arithmetic operations such as addition, subtraction, multiplication, and division.

Package cron implements a cron spec parser and job runner. To download the specific tagged release, run: Import it in your program as: It requires Go 1.11 or later due to usage of Go Modules. Callers may register Funcs to be invoked on a given schedule. Cron will run them in their own goroutines. A cron expression represents a set of times, using 5 space-separated fields. Month and Day-of-week field values are case insensitive. "SUN", "Sun", and "sun" are equally accepted. The specific interpretation of the format is based on the Cron Wikipedia page: https://en.wikipedia.org/wiki/Cron Alternative Cron expression formats support other fields like seconds. You can implement that by creating a custom Parser as follows. Since adding Seconds is the most common modification to the standard cron spec, cron provides a builtin function to do that, which is equivalent to the custom parser you saw earlier, except that its seconds field is REQUIRED: That emulates Quartz, the most popular alternative Cron schedule format: http://www.quartz-scheduler.org/documentation/quartz-2.x/tutorials/crontrigger.html Asterisk ( * ) The asterisk indicates that the cron expression will match for all values of the field; e.g., using an asterisk in the 5th field (month) would indicate every month. Slash ( / ) Slashes are used to describe increments of ranges. For example 3-59/15 in the 1st field (minutes) would indicate the 3rd minute of the hour and every 15 minutes thereafter. The form "*\/..." is equivalent to the form "first-last/...", that is, an increment over the largest possible range of the field. The form "N/..." is accepted as meaning "N-MAX/...", that is, starting at N, use the increment until the end of that specific range. It does not wrap around. Comma ( , ) Commas are used to separate items of a list. For example, using "MON,WED,FRI" in the 5th field (day of week) would mean Mondays, Wednesdays and Fridays. Hyphen ( - ) Hyphens are used to define ranges. For example, 9-17 would indicate every hour between 9am and 5pm inclusive. Question mark ( ? ) Question mark may be used instead of '*' for leaving either day-of-month or day-of-week blank. You may use one of several pre-defined schedules in place of a cron expression. You may also schedule a job to execute at fixed intervals, starting at the time it's added or cron is run. This is supported by formatting the cron spec like this: where "duration" is a string accepted by time.ParseDuration (http://golang.org/pkg/time/#ParseDuration). For example, "@every 1h30m10s" would indicate a schedule that activates after 1 hour, 30 minutes, 10 seconds, and then every interval after that. Note: The interval does not take the job runtime into account. For example, if a job takes 3 minutes to run, and it is scheduled to run every 5 minutes, it will have only 2 minutes of idle time between each run. By default, all interpretation and scheduling is done in the machine's local time zone (time.Local). You can specify a different time zone on construction: Individual cron schedules may also override the time zone they are to be interpreted in by providing an additional space-separated field at the beginning of the cron spec, of the form "CRON_TZ=Asia/Tokyo". For example: The prefix "TZ=(TIME ZONE)" is also supported for legacy compatibility. Be aware that jobs scheduled during daylight-savings leap-ahead transitions will not be run! A Cron runner may be configured with a chain of job wrappers to add cross-cutting functionality to all submitted jobs. For example, they may be used to achieve the following effects: Install wrappers for all jobs added to a cron using the `cron.WithChain` option: Install wrappers for individual jobs by explicitly wrapping them: Since the Cron service runs concurrently with the calling code, some amount of care must be taken to ensure proper synchronization. All cron methods are designed to be correctly synchronized as long as the caller ensures that invocations have a clear happens-before ordering between them. Cron defines a Logger interface that is a subset of the one defined in github.com/go-logr/logr. It has two logging levels (Info and Error), and parameters are key/value pairs. This makes it possible for cron logging to plug into structured logging systems. An adapter, [Verbose]PrintfLogger, is provided to wrap the standard library *log.Logger. For additional insight into Cron operations, verbose logging may be activated which will record job runs, scheduling decisions, and added or removed jobs. Activate it with a one-off logger as follows: Cron entries are stored in an array, sorted by their next activation time. Cron sleeps until the next job is due to be run. Upon waking:

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 spf implements SPF (Sender Policy Framework) lookup and validation. Sender Policy Framework (SPF) is a simple email-validation system designed to detect email spoofing by providing a mechanism to allow receiving mail exchangers to check that incoming mail from a domain comes from a host authorized by that domain's administrators [Wikipedia]. This is a Go implementation of it, which is used by the chasquid SMTP server (https://blitiri.com.ar/p/chasquid/). Supported mechanisms and modifiers: References:

Package mspec is a BDD context/specification testing package for Go(Lang) with a strong emphases on spec'ing your feature(s) and scenarios first, before any code is written using as little syntax noise as possible. This leaves you free to think of your project and features as a whole without the distraction of writing any code with the added benefit of having tests ready for your project. [](https://godoc.org/github.com/eduncan911/mspec) holds the source documentation (where else?) * Uses natural language (Given/When/Then) * Stubbing * Human-readable outputs * HTML output (coming soon) * Use custom Assertions * Configuration options * Uses Testify's rich assertions * Uses Go's built-in testing.T package Install it with one line of code: There are no external dependencies and it is built against Go's internal packages. The only dependency is that you have [GOPATH setup normaly](https://golang.org/doc/code.html). Create a new file to hold your specs. Using Dan North's original BDD definitions, you spec code using the Given/When/Then storyline similar to: But this is just a static example. Let's take a real example from one of my projects: You represent these thoughts in code like this: Note that `Given`, `when` and `it` all have optional variadic parameters. This allows you to spec things out with as little or as far as you want. That's it. Now run it: Print it out and stick it on your office door for everyone to see what you are working on. This is actually colored output in Terminal: It is not uncommon to go back and tweak your stories over time as you talk with your domain experts, modifying exactly the scenarios and specifications that should happen. `GoMSpec` is a testing package for the Go framework that extends Go's built-in testing package. It is modeled after the BDD Feature Specification story workflow such as: Currently it has an included `Expectation` struct that mimics basic assertion behaviors. Future plans may allow for custom assertion packages (like testify). Getting it Importing it Writing Specs Testing it Which outputs the following: Nice eh? There is nothing like using a testing package to test itself. There is some nice rich information available. ## Examples Be sure to check out more examples in the examples/ folder. Or just open the files and take a look. That's the most important part anyways. When evaluating several BDD frameworks, [Pranavraja's Zen](https://github.com/pranavraja/zen) package for Go came close - really close; but, it was lacking the more "story" overview I've been accustomed to over the years with [Machine.Specifications](https://github.com/machine/machine.specifications) in C# (.NET land). Do note that there is something to be said for simple testing in Go (and simple coding); therefore, if you are the type to keep it short and sweet and just code, then you may want to use Pranavraja's framework as it is just the context (Desc) and specs writing. I forked his code and submitted a few bug tweaks at first. But along the way, I started to have grand visions of my soul mate [Machine.Specifications](https://github.com/machine/machine.specifications) (which is called MSpec for short) for BDD testing. The ease of defining complete stories right down to the scenarios without having to implement them intrigued me in C#. It freed me from worrying about implementation details and just focus on the feature I was writing: What did it need to do? What context was I given to start with? What should it do? So while using Pranavraja's Zen framework, I kept asking myself: Could I bring those MSpec practices to Go, using a bare-bones framework? Ok, done. And since it was so heavily inspired by Aaron's MSpec project, I kept the name going here: `GoMSpec`. While keeping backwards compatibility with his existing Zen framework, I defined several goals for this package: * Had to stay simple with Give/When/Then definitions. No complex coding. * Keep the low syntax noise from the existing Zen package. * I had to be able to write features, scenarios and specs with no implementation details needed. That last goal above is key and I think is what speaks truly about what BDD is: focus on the story, feature and/or context you are designing - focus on the Behavior! I tended to design my C# code using Machine.Specifications in this BDD-style by writing entire stories and grand specs up front - designing the system I was building, or the feature I was extending. In C# land, it's not unheard of me hitting 50 to 100 specs across a single feature and a few different contexts in an hour or two, before writing any code. Which at that point, I had everything planned out pretty much the way it should behave. So with this framework, I came up with a simple method name, `NA()`, to keep the syntax noise down. Therefore, you are free to code specs with just a little syntax noise:

Package lunk provides a set of tools for structured logging in the style of Google's Dapper or Twitter's Zipkin. When we consider a complex event in a distributed system, we're actually considering a partially-ordered tree of events from various services, libraries, and modules. Consider a user-initiated web request. Their browser sends an HTTP request to an edge server, which extracts the credentials (e.g., OAuth token) and authenticates the request by communicating with an internal authentication service, which returns a signed set of internal credentials (e.g., signed user ID). The edge web server then proxies the request to a cluster of web servers, each running a PHP application. The PHP application loads some data from several databases, places the user in a number of treatment groups for running A/B experiments, writes some data to a Dynamo-style distributed database, and returns an HTML response. The edge server receives this response and proxies it to the user's browser. In this scenario we have a number of infrastructure-specific events: This scenario also involves a number of events which have little to do with the infrastructure, but are still critical information for the business the system supports: There are a number of different teams all trying to monitor and improve aspects of this system. Operational staff need to know if a particular host or service is experiencing a latency spike or drop in throughput. Development staff need to know if their application's response times have gone down as a result of a recent deploy. Customer support staff need to know if the system is operating nominally as a whole, and for customers in particular. Product designers and managers need to know the effect of an A/B test on user behavior. But the fact that these teams will be consuming the data in different ways for different purposes does mean that they are working on different systems. In order to instrument the various components of the system, we need a common data model. We adopt Dapper's notion of a tree to mean a partially-ordered tree of events from a distributed system. A tree in Lunk is identified by its root ID, which is the unique ID of its root event. All events in a common tree share a root ID. In our photo example, we would assign a unique root ID as soon as the edge server received the request. Events inside a tree are causally ordered: each event has a unique ID, and an optional parent ID. By passing the IDs across systems, we establish causal ordering between events. In our photo example, the two database queries from the app would share the same parent ID--the ID of the event corresponding to the app handling the request which caused those queries. Each event has a schema of properties, which allow us to record specific pieces of information about each event. For HTTP requests, we can record the method, the request URI, the elapsed time to handle the request, etc. Lunk is agnostic in terms of aggregation technologies, but two use cases seem clear: real-time process monitoring and offline causational analysis. For real-time process monitoring, events can be streamed to a aggregation service like Riemann (http://riemann.io) or Storm (http://storm.incubator.apache.org), which can calculate process statistics (e.g., the 95th percentile latency for the edge server responses) in real-time. This allows for adaptive monitoring of all services, with the option of including example root IDs in the alerts (e.g., 95th percentile latency is over 300ms, mostly as a result of requests like those in tree XXXXX). For offline causational analysis, events can be written in batches to batch processing systems like Hadoop or OLAP databases like Vertica. These aggregates can be queried to answer questions traditionally reserved for A/B testing systems. "Did users who were show the new navbar view more photos?" "Did the new image optimization algorithm we enabled for 1% of views run faster? Did it produce smaller images? Did it have any effect on user engagement?" "Did any services have increased exception rates after any recent deploys?" &tc &tc By capturing the root ID of a particular web request, we can assemble a partially-ordered tree of events which were involved in the handling of that request. All events with a common root ID are in a common tree, which allows for O(M) retrieval for a tree of M events. To send a request with a root ID and a parent ID, use the Event-ID HTTP header: The header value is simply the root ID and event ID, hex-encoded and separated with a slash. If the event has a parent ID, that may be included as an optional third parameter. A server that receives a request with this header can use this to properly parent its own events. Each event has a set of named properties, the keys and values of which are strings. This allows aggregation layers to take advantage of simplifying assumptions and either store events in normalized form (with event data separate from property data) or in denormalized form (essentially pre-materializing an outer join of the normalized relations). Durations are always recorded as fractional milliseconds. Lunk currently provides two formats for log entries: text and JSON. Text-based logs encode each entry as a single line of text, using key="value" formatting for all properties. Event property keys are scoped to avoid collisions. JSON logs encode each entry as a single JSON object.

Package netaddr provides utilities for manipulating IP addresses and CIDRs. It offers functionality to manage subnet ranges, determine whether IPs are included in CIDRs, split CIDRs into subranges, and perform various other operations related to IP addressing and network calculations. This library is a go port of the Python netaddr package, a lot of the credit for the existence of this package goes to the maintainers and contributors of python netaddr. This package is designed for network engineers, developers, and system administrators who need to handle IP address operations programmatically. It supports both IPv4 and IPv6 addresses and provides a range of tools to simplify working with IP addresses, CIDR blocks, and IP ranges. - Parse and manipulate individual IP addresses. - Create and manipulate CIDR blocks (IP networks). - Determine if an IP address belongs to a specific CIDR block. - Split a CIDR block into smaller subnets. - Calculate the range of IP addresses within a CIDR block. - Perform operations on IP ranges. To start using the package, import it into your Go application: Below are some examples to help you get started: ## Parsing an IP Address You can parse an IP address using the NewIPAddress function: ## Working with CIDR Blocks You can create a new CIDR block using the NewIPNetwork function: To check if an IP address belongs to this CIDR block: ## Splitting a CIDR Block into Subnets To split a CIDR block into smaller subnets: ## Working with IP Ranges You can create an IP range and perform operations on it: To check if an IP address is within this range: This package is licensed under the Apache 2.0 License. See the LICENSE file for details. Contributions are welcome! Please see the README file for guidelines on how to contribute. This package was inspired by the need for a robust and flexible IP manipulation library in Go.

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

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

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

shortinette is the core framework for managing and automating the process of grading coding bootcamps (Shorts). It provides a comprehensive set of tools for running and testing student submissions across various programming languages. The shortinette package is composed of several sub-packages, each responsible for a specific aspect of the grading pipeline: `logger`: Handles logging for the framework, including general informational messages, error reporting, and trace logging for feedback on individual submissions. This package ensures that all important events and errors are captured for debugging and auditing purposes. `requirements`: Validates the necessary environment variables and dependencies required by the framework. This includes checking for essential configuration values in a `.env` file and ensuring that all necessary tools (e.g., Docker images) are available before grading begins. `testutils`: Provides utility functions for compiling and running code submissions. This includes functions for compiling Rust code, running executables with various options (such as timeouts and real-time output), and manipulating files. The utility functions are designed to handle the intricacies of running untrusted student code safely and efficiently. `git`: Manages interactions with GitHub, including cloning repositories, managing collaborators, and uploading files. This package abstracts the GitHub API to simplify common tasks such as adding collaborators to repositories, creating branches, and pushing code or data to specific locations in a repository. `exercise`: Defines the structure and behavior of individual coding exercises. This includes specifying the files that students are allowed to submit, the expected output, and the functions to be tested. The `exercise` package provides the framework for setting up exercises, running tests, and reporting results. `module`: Organizes exercises into modules, allowing for the grouping of related exercises into a coherent curriculum. The `module` package handles the execution of all exercises within a module, aggregates results, and manages the overall grading process. `webhook`: Enables automatic grading triggered by GitHub webhooks. This allows for a fully automated workflow where student submissions are graded as soon as they are pushed to a specific branch in a GitHub repository. `short`: The central orchestrator of the grading process, integrating all sub-packages into a cohesive system. The `short` package handles the setup and teardown of grading environments, manages the execution of modules and exercises, and ensures that all results are properly recorded and reported.

Package mast provides an immutable, versioned, diffable map implementation of a Merkle Search Tree (MST). Masts can be huge (not limited to memory). Masts can be stored in anything, like a filesystem, KV store, or blob store. Masts are designed to be safely concurrently accessed by multiple threads/hosts. And like Merkle DAGs, Masts are designed to be easy to cache and synchronize. - Efficient storage of multiple versions of materialized views - Diffing of versions integrates CDC/streaming - Efficient copy-on-write alternative to Go builtin map Mast is an implementation of the structure described in the awesome paper, "Merkle Search Trees: Efficient State-Based CRDTs in Open Networks", by Alex Auvolat and François Taïani, 2019 (https://hal.inria.fr/hal-02303490/document). MSTs are similar to persistent B-Trees, except node splits are chosen deterministically based on key and tree height, rather than node size, so MSTs eliminate the dependence on insertion/deletion order that B-Trees have for equivalence, making MSTs an interesting choice for conflict-free replicated data types (CRDTs). (MSTs are also simpler to implement than B-Trees, needing less complicated rebalancing, and no rotations.) MSTs are like other Merkle structures in that two instances can easily be compared to confirm equality or find differences, since equal node hashes indicate equal contents. A Mast can be Clone()d for sharing between threads. Cloning creates a new version that can evolve independently, yet shares all the unmodified subtrees with its parent, and as such are relatively cheap to create. The immutable data types in Clojure, Haskell, ML and other functional languages really do make it easier to "reason about" systems; easier to test, provide a foundation to build more quickly on. https://github.com/bodil/im-rs, "Blazing fast immutable collection datatypes for Rust", by Bodil Stokke, is an exemplar: the diff algorithm and use of property testing, are instructive, and Chunks and PoolRefs fill gaps in understanding of Rust's ownership model for library writers coming from GC'd languages.

nxadm/tail provides a Go library that emulates the features of the BSD `tail` program. The library comes with full support for truncation/move detection as it is designed to work with log rotation tools. The library works on all operating systems supported by Go, including POSIX systems like Linux and *BSD, and MS Windows. Go 1.9 is the oldest compiler release supported.

Package gnark provides fast Zero Knowledge Proofs (ZKP) systems and a high level APIs to design ZKP circuits. gnark supports the following ZKP schemes: gnark supports the following curves: User documentation https://docs.gnark.consensys.net

Package cron implements a cron spec parser and job runner. To download the specific tagged release, run: Import it in your program as: It requires Go 1.11 or later due to usage of Go Modules. Callers may register Funcs to be invoked on a given schedule. Cron will run them in their own goroutines. A cron expression represents a set of times, using 5 space-separated fields. Month and Day-of-week field values are case insensitive. "SUN", "Sun", and "sun" are equally accepted. The specific interpretation of the format is based on the Cron Wikipedia page: https://en.wikipedia.org/wiki/Cron Alternative Cron expression formats support other fields like seconds. You can implement that by creating a custom Parser as follows. Since adding Seconds is the most common modification to the standard cron spec, cron provides a builtin function to do that, which is equivalent to the custom parser you saw earlier, except that its seconds field is REQUIRED: That emulates Quartz, the most popular alternative Cron schedule format: http://www.quartz-scheduler.org/documentation/quartz-2.x/tutorials/crontrigger.html Asterisk ( * ) The asterisk indicates that the cron expression will match for all values of the field; e.g., using an asterisk in the 5th field (month) would indicate every month. Slash ( / ) Slashes are used to describe increments of ranges. For example 3-59/15 in the 1st field (minutes) would indicate the 3rd minute of the hour and every 15 minutes thereafter. The form "*\/..." is equivalent to the form "first-last/...", that is, an increment over the largest possible range of the field. The form "N/..." is accepted as meaning "N-MAX/...", that is, starting at N, use the increment until the end of that specific range. It does not wrap around. Comma ( , ) Commas are used to separate items of a list. For example, using "MON,WED,FRI" in the 5th field (day of week) would mean Mondays, Wednesdays and Fridays. Hyphen ( - ) Hyphens are used to define ranges. For example, 9-17 would indicate every hour between 9am and 5pm inclusive. Question mark ( ? ) Question mark may be used instead of '*' for leaving either day-of-month or day-of-week blank. You may use one of several pre-defined schedules in place of a cron expression. You may also schedule a job to execute at fixed intervals, starting at the time it's added or cron is run. This is supported by formatting the cron spec like this: where "duration" is a string accepted by time.ParseDuration (http://golang.org/pkg/time/#ParseDuration). For example, "@every 1h30m10s" would indicate a schedule that activates after 1 hour, 30 minutes, 10 seconds, and then every interval after that. Note: The interval does not take the job runtime into account. For example, if a job takes 3 minutes to run, and it is scheduled to run every 5 minutes, it will have only 2 minutes of idle time between each run. By default, all interpretation and scheduling is done in the machine's local time zone (time.Local). You can specify a different time zone on construction: Individual cron schedules may also override the time zone they are to be interpreted in by providing an additional space-separated field at the beginning of the cron spec, of the form "CRON_TZ=Asia/Tokyo". For example: The prefix "TZ=(TIME ZONE)" is also supported for legacy compatibility. Be aware that jobs scheduled during daylight-savings leap-ahead transitions will not be run! A Cron runner may be configured with a chain of job wrappers to add cross-cutting functionality to all submitted jobs. For example, they may be used to achieve the following effects: Install wrappers for all jobs added to a cron using the `cron.WithChain` option: Install wrappers for individual jobs by explicitly wrapping them: Since the Cron service runs concurrently with the calling code, some amount of care must be taken to ensure proper synchronization. All cron methods are designed to be correctly synchronized as long as the caller ensures that invocations have a clear happens-before ordering between them. Cron defines a Logger interface that is a subset of the one defined in github.com/go-logr/logr. It has two logging levels (Info and Error), and parameters are key/value pairs. This makes it possible for cron logging to plug into structured logging systems. An adapter, [Verbose]PrintfLogger, is provided to wrap the standard library *log.Logger. For additional insight into Cron operations, verbose logging may be activated which will record job runs, scheduling decisions, and added or removed jobs. Activate it with a one-off logger as follows: Cron entries are stored in an array, sorted by their next activation time. Cron sleeps until the next job is due to be run. Upon waking:

Package hord provides a simple and extensible interface for interacting with various database systems in a uniform way. Hord is designed to be a database-agnostic library that provides a common interface for interacting with different database systems. It allows developers to write code that is decoupled from the underlying database technology, making it easier to switch between databases or support multiple databases in the same application. To use Hord, import it as follows: To create a database client, you need to import and use the appropriate driver package along with the `hord` package. For example, to use the Redis driver: Each driver provides its own `Dial` function to establish a connection to the database. Refer to the specific driver documentation for more details. Once you have a database client, you can use it to perform various database operations. The API is consistent across different drivers. Refer to the `hord.Database` interface documentation for a complete list of available methods. Hord provides common error types and constants for consistent error handling across drivers. Refer to the `hord` package documentation for more information on error handling. Contributions to Hord are welcome! If you want to add support for a new database driver or improve the existing codebase, please refer to the contribution guidelines in the project's repository.

Package multitenancy provides a Go framework for building multi-tenant applications, streamlining tenant management and model migrations. It abstracts multitenancy complexities through a unified, database-agnostic API compatible with GORM. The framework supports two primary multitenancy strategies: The following sections provide an overview of the package's features and usage instructions for implementing multitenancy in Go applications. The package supports multitenancy for both PostgreSQL and MySQL databases, offering three methods for establishing a new database connection with multitenancy support: OpenDB allows opening a database connection using a URL-like DSN string, providing a flexible and easy way to switch between drivers. This method abstracts the underlying driver mechanics, offering a straightforward connection process and a unified, database-agnostic API through the returned *DB instance, which embeds the gorm.DB instance. For constructing the DSN string, refer to the driver-specific documentation for the required parameters and formats. This approach is useful for applications that need to dynamically switch between different database drivers or configurations without changing the codebase. Postgres: MySQL: Open with a supported driver offers a unified, database-agnostic API for managing tenant-specific and shared data, embedding the gorm.DB instance. This method allows developers to initialize and configure the dialect themselves before opening the database connection, providing granular control over the database connection and configuration. It facilitates seamless switching between database drivers while maintaining access to GORM's full functionality. Postgres: MySQL: For direct access to the gorm.DB API and multitenancy features for specific tasks, this approach allows invoking driver-specific functions directly. It provides a lower level of abstraction, requiring manual management of database-specific operations. Prior to version 8, this was the only method available for using the package. Postgres: MySQL: All models must implement driver.TenantTabler, which extends GORM's Tabler interface. This extension allows models to define their table name and indicate whether they are shared across tenants. Public Models: These are models which are shared across tenants. driver.TenantTabler.TableName should return the table name prefixed with 'public.'. driver.TenantTabler.IsSharedModel should return true. Tenant-Specific Models: These models are specific to a single tenant and should not be shared across tenants. driver.TenantTabler.TableName should return the table name without any prefix. driver.TenantTabler.IsSharedModel should return false. Tenant Model: This package provides a TenantModel struct that can be embedded in any public model that requires tenant scoping, enriching it with essential fields for managing tenant-specific information. This structure incorporates fields for the tenant's domain URL and schema name, facilitating the linkage of tenant-specific models to their respective schemas. Before performing any migrations or operations on tenant-specific models, the models must be registered with the DB instance using DB.RegisterModels. Postgres Adapter: Use postgres.RegisterModels to register models. MySQL Adapter: Use mysql.RegisterModels to register models. To ensure data integrity and schema isolation across tenants,gorm.DB.AutoMigrate has been disabled. Instead, use the provided shared and tenant-specific migration methods. driver.ErrInvalidMigration is returned if the `AutoMigrate` method is called directly. To ensure tenant isolation and facilitate concurrent migrations, driver-specific locking mechanisms are used. These locks prevent concurrent migrations from interfering with each other, ensuring that only one migration process can run at a time for a given tenant. Consult the driver-specific documentation for more information. After registering models, shared models are migrated using DB.MigrateSharedModels. Postgres Adapter: Use postgres.MigrateSharedModels to migrate shared models. MySQL Adapter: Use mysql.MigrateSharedModels to migrate shared models. After registering models, tenant-specific models are migrated using DB.MigrateTenantModels. Postgres Adapter: Use postgres.MigrateTenantModels to migrate tenant-specific models. MySQL Adapter: Use mysql.MigrateTenantModels to migrate tenant-specific models. When a tenant is removed from the system, the tenant-specific schema and associated tables should be cleaned up using DB.OffboardTenant. Postgres Adapter: Use postgres.DropSchemaForTenant to offboard a tenant. MySQL Adapter: Use mysql.DropDatabaseForTenant to offboard a tenant. DB.UseTenant configures the database for operations specific to a tenant, abstracting database-specific operations for tenant context configuration. This method returns a reset function to revert the database context and an error if the operation fails. Postgres Adapter: Use postgres.SetSearchPath to set the search path for a tenant. MySQL Adapter: Use mysql.UseDatabase function to set the database for a tenant. For the most part, foreign key constraints work as expected, but there are some restrictions and considerations to keep in mind when working with multitenancy. The following guidelines outline the supported relationships between tables: Between Tables in the Public Schema: From Public Schema Tables to Tenant-Specific Tables: From Tenant-Specific Tables to Public Schema Tables: Between Tenant-Specific Tables: See the example application for a more comprehensive demonstration of the framework's capabilities. Always sanitize input to prevent SQL injection vulnerabilities. This framework does not perform any validation on the database name or schema name parameters. It is the responsibility of the caller to ensure that these parameters are sanitized. To facilitate this, the framework provides the following utilities: For a detailed technical overview of SQL design strategies adopted by the framework, see the STRATEGY.md file.

Package ed implements an event dispatcher that is meant to allow codebases to organize concerns around events. Instead of placing all concerns about a particular business logic event in one function, this package allows you to declare events by type and dispatach those events to other code so that other concerns may be maintained in separate areas of the codebase. This module is tagged as v0, thus complies with Go's definition and rules about v0 modules (https://go.dev/doc/modules/version-numbers#v0-number). In short, it means that the API of this module may change without incrementing the major version number. Each releasable version will simply increment the patch number. Given the surface area of this module is quite small, this should not be a huge issue if used in production code. ed uses Go's own type system to dispatch events to the correct event handlers. By simply registering an event handler that accepts an event of a particular type: That handler will be triggered when an event of the same type is dispatched from somewhere else in the program: Because it is Go's type system, event handlers can also be registered against interfaces instead of just concrete types. Thus, creating an event handler that will be triggered for all events is as simple as: Overall, this package's goal is to mostly be an aid in allowing application business logic to remain clear of ancillary conerns/activities that might otherwise pile up in certain code areas. For example, as a SaaS company grows, a lot of cross-cutting concerns can build up around a customer signing up for a service. In a monolithic codebase/service, the code that handles a new customer signup would become full of "do this check", "send this data to marketing systems", "screen this signup for abusive behavior" logic. This is where ed is meant to be: taking all of those concerns/side-effects that are ancillary to the core act of signing up a user, and placing them in there own code areas and allowing them to be communicated with via events. This package is not trying to be a job queue, or a messaging system. The dispatching of events is done synchronously and the events are designed to allow errors to be returned so that use-cases like synchronous validation are supported. That being said, a common use-case might be to use this package to allow the publishing of events/messages to a Kafka/SQS-like system, an event handler: Then in your main business logic for user signups:

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

Gaby is an experimental new bot running in the Go issue tracker as @gabyhelp, to try to help automate various mundane things that a machine can do reasonably well, as well as to try to discover new things that a machine can do reasonably well. The name gaby is short for “Go AI Bot”, because one of the purposes of the experiment is to learn what LLMs can be used for effectively, including identifying what they should not be used for. Some of the gaby functionality will involve LLMs; other functionality will not. The guiding principle is to create something that helps maintainers and that maintainers like, which means to use LLMs when they make sense and help but not when they don't. In the long term, the intention is for this code base or a successor version to take over the current functionality of “gopherbot” and become @gopherbot, at which point the @gabyhelp account will be retired. At the moment we are not accepting new code contributions or PRs. We hope to move this code to somewhere more official soon, at which point we will accept contributions. The GitHub Discussion is a good place to leave feedback about @gabyhelp. The bot functionality is implemented in internal packages in subdirectories. This comment gives a brief tour of the structure. An explicit goal for the Gaby code base is that it run well in many different environments, ranging from a maintainer's home server or even Raspberry Pi all the way up to a hosted cloud. (At the moment, Gaby runs on a Linux server in my basement.) Due to this emphasis on portability, Gaby defines its own interfaces for all the functionality it needs from the surrounding environment and then also defines a variety of implementations of those interfaces. Another explicit goal for the Gaby code base is that it be very well tested. (See my [Go Testing talk] for more about why this is so important.) Abstracting the various external functionality into interfaces also helps make testing easier, and some packages also provide explicit testing support. The result of both these goals is that Gaby defines some basic functionality like time-ordered indexing for itself instead of relying on some specific other implementation. In the grand scheme of things, these are a small amount of code to maintain, and the benefits to both portability and testability are significant. Code interacting with services like GitHub and code running on cloud servers is typically difficult to test and therefore undertested. It is an explicit requirement this repo to test all the code, even (and especially) when testing is difficult. A useful command to have available when working in the code is rsc.io/uncover, which prints the package source lines not covered by a unit test. A useful invocation is: The first “go test” command checks that the test passes. The second repeats the test with coverage enabled. Running the test twice this way makes sure that any syntax or type errors reported by the compiler are reported without coverage, because coverage can mangle the error output. After both tests pass and second writes a coverage profile, running “uncover /tmp/c.out” prints the uncovered lines. In this output, there are three error paths that are untested. In general, error paths should be tested, so tests should be written to cover these lines of code. In limited cases, it may not be practical to test a certain section, such as when code is unreachable but left in case of future changes or mistaken assumptions. That part of the code can be labeled with a comment beginning “// Unreachable” or “// unreachable” (usually with explanatory text following), and then uncover will not report it. If a code section should be tested but the test is being deferred to later, that section can be labeled “// Untested” or “// untested” instead. The rsc.io/gaby/internal/testutil package provides a few other testing helpers. The overview of the code now proceeds from bottom up, starting with storage and working up to the actual bot. Gaby needs to manage a few secret keys used to access services. The rsc.io/gaby/internal/secret package defines the interface for obtaining those secrets. The only implementations at the moment are an in-memory map and a disk-based implementation that reads $HOME/.netrc. Future implementations may include other file formats as well as cloud-based secret storage services. Secret storage is intentionally separated from the main database storage, described below. The main database should hold public data, not secrets. Gaby defines the interface it expects from a large language model. The llm.Embedder interface abstracts an LLM that can take a collection of documents and return their vector embeddings, each of type llm.Vector. The only real implementation to date is rsc.io/gaby/internal/gemini. It would be good to add an offline implementation using Ollama as well. For tests that need an embedder but don't care about the quality of the embeddings, llm.QuoteEmbedder copies a prefix of the text into the vector (preserving vector unit length) in a deterministic way. This is good enough for testing functionality like vector search and simplifies tests by avoiding a dependence on a real LLM. At the moment, only the embedding interface is defined. In the future we expect to add more interfaces around text generation and tool use. As noted above, Gaby defines interfaces for all the functionality it needs from its external environment, to admit a wide variety of implementations for both execution and testing. The lowest level interface is storage, defined in rsc.io/gaby/internal/storage. Gaby requires a key-value store that supports ordered traversal of key ranges and atomic batch writes up to a modest size limit (at least a few megabytes). The basic interface is storage.DB. storage.MemDB returns an in-memory implementation useful for testing. Other implementations can be put through their paces using storage.TestDB. The only real storage.DB implementation is rsc.io/gaby/internal/pebble, which is a LevelDB-derived on-disk key-value store developed and used as part of CockroachDB. It is a production-quality local storage implementation and maintains the database as a directory of files. In the future we plan to add an implementation using Google Cloud Firestore, which provides a production-quality key-value lookup as a Cloud service without fixed baseline server costs. (Firestore is the successor to Google Cloud Datastore.) The storage.DB makes the simplifying assumption that storage never fails, or rather that if storage has failed then you'd rather crash your program than try to proceed through typically untested code paths. As such, methods like Get and Set do not return errors. They panic on failure, and clients of a DB can call the DB's Panic method to invoke the same kind of panic if they notice any corruption. It remains to be seen whether this decision is kept. In addition to the usual methods like Get, Set, and Delete, storage.DB defines Lock and Unlock methods that acquire and release named mutexes managed by the database layer. The purpose of these methods is to enable coordination when multiple instances of a Gaby program are running on a serverless cloud execution platform. So far Gaby has only run on an underground basement server (the opposite of cloud), so these have not been exercised much and the APIs may change. In addition to the regular database, package storage also defines storage.VectorDB, a vector database for use with LLM embeddings. The basic operations are Set, Get, and Search. storage.MemVectorDB returns an in-memory implementation that stores the actual vectors in a storage.DB for persistence but also keeps a copy in memory and searches by comparing against all the vectors. When backed by a storage.MemDB, this implementation is useful for testing, but when backed by a persistent database, the implementation suffices for small-scale production use (say, up to a million documents, which would require 3 GB of vectors). It is possible that the package ordering here is wrong and that VectorDB should be defined in the llm package, built on top of storage, and not the current “storage builds on llm”. Because Gaby makes minimal demands of its storage layer, any structure we want to impose must be implemented on top of it. Gaby uses the rsc.io/ordered encoding format to produce database keys that order in useful ways. For example, ordered.Encode("issue", 123) < ordered.Encode("issue", 1001), so that keys of this form can be used to scan through issues in numeric order. In contrast, using something like fmt.Sprintf("issue%d", n) would visit issue 1001 before issue 123 because "1001" < "123". Using this kind of encoding is common when using NoSQL key-value storage. See the rsc.io/ordered package for the details of the specific encoding. One of the implied jobs Gaby has is to collect all the relevant information about an open source project: its issues, its code changes, its documentation, and so on. Those sources are always changing, so derived operations like adding embeddings for documents need to be able to identify what is new and what has been processed already. To enable this, Gaby implements time-stamped—or just “timed”—storage, in which a collection of key-value pairs also has a “by time” index of ((timestamp, key), no-value) pairs to make it possible to scan only the key-value pairs modified after the previous scan. This kind of incremental scan only has to remember the last timestamp processed and then start an ordered key range scan just after that timestamp. This convention is implemented by rsc.io/gaby/internal/timed, along with a [timed.Watcher] that formalizes the incremental scan pattern. Various package take care of downloading state from issue trackers and the like, but then all that state needs to be unified into a common document format that can be indexed and searched. That document format is defined by rsc.io/gaby/internal/docs. A document consists of an ID (conventionally a URL), a document title, and document text. Documents are stored using timed storage, enabling incremental processing of newly added documents . The next stop for any new document is embedding it into a vector and storing that vector in a vector database. The rsc.io/gaby/internal/embeddocs package does this, and there is very little to it, given the abstractions of a document store with incremental scanning, an LLM embedder, and a vector database, all of which are provided by other packages. None of the packages mentioned so far involve network operations, but the next few do. It is important to test those but also equally important not to depend on external network services in the tests. Instead, the package rsc.io/gaby/internal/httprr provides an HTTP record/replay system specifically designed to help testing. It can be run once in a mode that does use external network servers and records the HTTP exchanges, but by default tests look up the expected responses in the previously recorded log, replaying those responses. The result is that code making HTTP request can be tested with real server traffic once and then re-tested with recordings of that traffic afterward. This avoids having to write entire fakes of services but also avoids needing the services to stay available in order for tests to pass. It also typically makes the tests much faster than using the real servers. Gaby uses GitHub in two main ways. First, it downloads an entire copy of the issue tracker state, with incremental updates, into timed storage. Second, it performs actions in the issue tracker, like editing issues or comments, applying labels, or posting new comments. These operations are provided by rsc.io/gaby/internal/github. Gaby downloads the issue tracker state using GitHub's REST API, which makes incremental updating very easy but does not provide access to a few newer features such as project boards and discussions, which are only available in the GraphQL API. Sync'ing using the GraphQL API is left for future work: there is enough data available from the REST API that for now we can focus on what to do with that data and not that a few newer GitHub features are missing. The github package provides two important aids for testing. For issue tracker state, it also allows loading issue data from a simple text-based issue description, avoiding any actual GitHub use at all and making it easier to modify the test data. For issue tracker actions, the github package defaults in tests to not actually making changes, instead diverting edits into an in-memory log. Tests can then check the log to see whether the right edits were requested. The rsc.io/gaby/internal/githubdocs package takes care of adding content from the downloaded GitHub state into the general document store. Currently the only GitHub-derived documents are one document per issue, consisting of the issue title and body. It may be worth experimenting with incorporating issue comments in some way, although they bring with them a significant amount of potential noise. Gaby will need to download and store Gerrit state into the database and then derive documents from it. That code has not yet been written, although rsc.io/gerrit/reviewdb provides a basic version that can be adapted. Gaby will also need to download and store project documentation into the database and derive documents from it corresponding to cutting the page at each heading. That code has been written but is not yet tested well enough to commit. It will be added later. The simplest job Gaby has is to go around fixing new comments, including issue descriptions (which look like comments but are a different kind of GitHub data). The rsc.io/gaby/internal/commentfix package implements this, watching GitHub state incrementally and applying a few kinds of rewrite rules to each new comment or issue body. The commentfix package allows automatically editing text, automatically editing URLs, and automatically hyperlinking text. The next job Gaby has is to respond to new issues with related issues and documents. The rsc.io/gaby/internal/related package implements this, watching GitHub state incrementally for new issues, filtering out ones that should be ignored, and then finding related issues and documents and posting a list. This package was originally intended to identify and automatically close duplicates, but the difference between a duplicate and a very similar or not-quite-fixed issue is too difficult a judgement to make for an LLM. Even so, the act of bringing forward related context that may have been forgotten or never known by the people reading the issue has turned out to be incredibly helpful. All of these pieces are put together in the main program, this package, rsc.io/gaby. The actual main package has no tests yet but is also incredibly straightforward. It does need tests, but we also need to identify ways that the hard-coded policies in the package can be lifted out into data that a natural language interface can manipulate. For example the current policy choices in package main amount to: These could be stored somewhere as data and manipulated and added to by the LLM in response to prompts from maintainers. And other features could be added and configured in a similar way. Exactly how to do this is an important thing to learn in future experimentation. As mentioned above, the two jobs Gaby does already are both fairly simple and straightforward. It seems like a general approach that should work well is well-written, well-tested deterministic traditional functionality such as the comment fixer and related-docs poster, configured by LLMs in response to specific directions or eventually higher-level goals specified by project maintainers. Other functionality that is worth exploring is rules for automatically labeling issues, rules for identifying issues or CLs that need to be pinged, rules for identifying CLs that need maintainer attention or that need submitting, and so on. Another stretch goal might be to identify when an issue needs more information and ask for that information. Of course, it would be very important not to ask for information that is already present or irrelevant, so getting that right would be a very high bar. There is no guarantee that today's LLMs work well enough to build a useful version of that. Another important area of future work will be running Gaby on top of cloud databases and then moving Gaby's own execution into the cloud. Getting it a server with a URL will enable GitHub callbacks instead of the current 2-minute polling loop, which will enable interactive conversations with Gaby. Overall, we believe that there are a few good ideas for ways that LLM-based bots can help make project maintainers' jobs easier and less monotonous, and they are waiting to be found. There are also many bad ideas, and they must be filtered out. Understanding the difference will take significant care, thought, and experimentation. We have work to do.

Package enjinql provides an abstracted system for indexing and querying a collection of content. Sources is an interface for managing and querying a collection of Source instances. Source is an interface for managing the correlation of contextual information with the collection of content. There are currently three forms of what a Source is, conceptually speaking. The first is the "primary source" that the Enjin Query Language is tasked with indexing. This is not the actual data being stored in a table per-se, just the necessary bits for providing a reference back to the actual content (like most other indexing systems). For example, in the use of the fantastic bleve search system, the site developer must provide a mapping of fields to index and then for each url of content, pass the url and its specific fields to bleve to index. bleve analyses the individual fields of content and using its internal primary source (likely a btree or some other wizardry), links this listing of URLs to the content fields provided. Once the site indexing process is complete, the developer can easily produce a search page which end-users can use the bleve search syntax to find content present in the site. For the Enjin Query Language, more than just the URL is important. There is still the URL of course but there is a greater context present as well. There is the page's shasum identifier, the page's type of content, language and so on to form a contextual index for finding content. This isn't to replace bleve within Go-Enjin; end-user search features are notoriously difficult to get right and bleve excels at getting this right. As a developer of a Go-Enjin based system, writing features often requires querying the enjin for a contextual case that may not involve the URL at all and to be forced to query for a URL in order to query for a page's specific context becomes a rather tedious repetition of code that isn't easily abstracted. Cases like that are examples of what could be called the farming of technical debt. Of course developers don't like farming technical debt and because one of the main purposes of the Go-Enjin project is to cater to the needs of the developer rather than just the employers and end-users, Go-Enjin necessitates a system for managing and querying a context of information. The second form of Source is the "data source". These are sources of data that relate to the primary source but do not actually have any direct correlation with the primary source context. For example, if the developer needs to build up a list of distinct words found within any given page of content, it is beneficial to have an index of all distinct words in general and to then use another form of source which joins the data source with the primary source. The final form of Source is the "link source" and as noted in the data source description, these sources typically have only unique identifiers which link two data sources together. This is the basis for how the Enjin Query Language can construct SQL queries with multiple JOIN statements without the developer having to spell out the table relationships in their use of the Enjin Query Language. One of the Go-Enjin website projects is an experiment in exploring human thought through the strange indexing of a large set of quotes from various sources. This website is called Quoted.FYI and in specific, it allows visitors to select from a list of "first words" from all quotes and then select from another list of "next words", and so on, building up to a narrow selection of quotes sharing the same series of opening words. To implement this feature, systems like bleve just aren't designed for it. Let's take a little walkthrough of the Quoted.FYI website project. ## Quotes ## Statistics ## EnjinQL Setup Quoted.FYI of course uses the default Go-Enjin primary source which consists of a number of specific columns of information related to any given page. These are the commonly used context keys when implementing Go-Enjin features, and so they're all a part of the primary enjinql source. For each of the authors, topics and words information, Quoted.FYI needs additional indexing to support these various things. They're relatively the same but let's take a look at the words indexing in more detail. The above data source, describes the list of distinct words used to consolidate the space required by the underlying database. So, now there are two sources defined, the primary one and the list of unique words. These two sources, being entirely unrelated to each other would result in a query error if used together. For example, to look up a list of page shasums for the "en" language: The above query would work and return the list of all page shasums that are of the "en" language. The above query again would work, this time returning a list of all words starting with the letter "a". While not the most efficient due to the use of the "starts with" (^=) operator, it does perform well. Given just the primary and word data sources, the following query would not work (yet): The above query is supposed to return a list of page shasums where the page uses the word "thing" at least once. To make this work, the needs to be a link source connecting the relationship of pages to words within each page. The above link source joins the primary source with the word data source and includes an extra count of how many times that specific word is used within the specific quote. Now, the query to get the list of pages with the word "thing": This demonstrates the simplicity of the Enjin Query Language in that the EQL statements don't need to do an SQL magic directly, such as sorting out the table joins. This is all done by the developer simply defining the various sources and then populating them with the content available. The below code demonstrates how to create the primary and word data sources depicted above: With the above constructed, the developer can now proceed with updating the sources instance with the content available. That process needs to be expanded up of course for the complete site, but for just the primary, data and link sources defined so far, the enjinql instance is now ready to build the actual SQL from parsing EQL statements: Code generated by Participle. DO NOT EDIT.