GitHub API

Package model provides utilities to transform from the OpenTelemetry OTLP data model to the Datadog Agent data model. This module is used in the Datadog Agent and the OpenTelemetry Collector Datadog exporter. End-user behavior is stable, but there are no stability guarantees on its public Go API. Nonetheless, if editing try to avoid breaking API changes if possible and double check the API usage on all module dependents. The 'attributes' packages provide utilities for semantic conventions mapping, while the translator model translates telemetry signals (currently only metrics are translated).

Package acm provides the API client, operations, and parameter types for AWS Certificate Manager. You can use Certificate Manager (ACM) to manage SSL/TLS certificates for your Amazon Web Services-based websites and applications. For more information about using ACM, see the Certificate Manager User Guide.

Package docdb provides the API client, operations, and parameter types for Amazon DocumentDB with MongoDB compatibility. Amazon DocumentDB is a fast, reliable, and fully managed database service. Amazon DocumentDB makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud. With Amazon DocumentDB, you can run the same application code and use the same drivers and tools that you use with MongoDB.

Package semver provides the ability to work with Semantic Versions (http://semver.org) in Go. Specifically it provides the ability to: There are two functions that can parse semantic versions. The `StrictNewVersion` function only parses valid version 2 semantic versions as outlined in the specification. The `NewVersion` function attempts to coerce a version into a semantic version and parse it. For example, if there is a leading v or a version listed without all 3 parts (e.g. 1.2) it will attempt to coerce it into a valid semantic version (e.g., 1.2.0). In both cases a `Version` object is returned that can be sorted, compared, and used in constraints. When parsing a version an optional error can be returned if there is an issue parsing the version. For example, The version object has methods to get the parts of the version, compare it to other versions, convert the version back into a string, and get the original string. For more details please see the documentation at https://godoc.org/github.com/Masterminds/semver. A set of versions can be sorted using the `sort` package from the standard library. For example, There are two methods for comparing versions. One uses comparison methods on `Version` instances and the other is using Constraints. There are some important differences to notes between these two methods of comparison. There are differences between the two methods or checking versions because the comparison methods on `Version` follow the specification while comparison ranges are not part of the specification. Different packages and tools have taken it upon themselves to come up with range rules. This has resulted in differences. For example, npm/js and Cargo/Rust follow similar patterns which PHP has a different pattern for ^. The comparison features in this package follow the npm/js and Cargo/Rust lead because applications using it have followed similar patters with their versions. Checking a version against version constraints is one of the most featureful parts of the package. There are two elements to the comparisons. First, a comparison string is a list of comma or space separated AND comparisons. These are then separated by || (OR) comparisons. For example, `">= 1.2 < 3.0.0 || >= 4.2.3"` is looking for a comparison that's greater than or equal to 1.2 and less than 3.0.0 or is greater than or equal to 4.2.3. This can also be written as `">= 1.2, < 3.0.0 || >= 4.2.3"` The basic comparisons are: There are multiple methods to handle ranges and the first is hyphens ranges. These look like: The `x`, `X`, and `*` characters can be used as a wildcard character. This works for all comparison operators. When used on the `=` operator it falls back to the tilde operation. For example, Tilde Range Comparisons (Patch) The tilde (`~`) comparison operator is for patch level ranges when a minor version is specified and major level changes when the minor number is missing. For example, Caret Range Comparisons (Major) The caret (`^`) comparison operator is for major level changes once a stable (1.0.0) release has occurred. Prior to a 1.0.0 release the minor versions acts as the API stability level. This is useful when comparisons of API versions as a major change is API breaking. For example, In addition to testing a version against a constraint, a version can be validated against a constraint. When validation fails a slice of errors containing why a version didn't meet the constraint is returned. For example,

Package semver provides the ability to work with Semantic Versions (http://semver.org) in Go. Specifically it provides the ability to: To parse a semantic version use the `NewVersion` function. For example, If there is an error the version wasn't parseable. The version object has methods to get the parts of the version, compare it to other versions, convert the version back into a string, and get the original string. For more details please see the documentation at https://godoc.org/github.com/Masterminds/semver. A set of versions can be sorted using the `sort` package from the standard library. For example, Checking a version against version constraints is one of the most featureful parts of the package. There are two elements to the comparisons. First, a comparison string is a list of comma separated and comparisons. These are then separated by || separated or comparisons. For example, `">= 1.2, < 3.0.0 || >= 4.2.3"` is looking for a comparison that's greater than or equal to 1.2 and less than 3.0.0 or is greater than or equal to 4.2.3. The basic comparisons are: There are multiple methods to handle ranges and the first is hyphens ranges. These look like: The `x`, `X`, and `*` characters can be used as a wildcard character. This works for all comparison operators. When used on the `=` operator it falls back to the pack level comparison (see tilde below). For example, Tilde Range Comparisons (Patch) The tilde (`~`) comparison operator is for patch level ranges when a minor version is specified and major level changes when the minor number is missing. For example, Caret Range Comparisons (Major) The caret (`^`) comparison operator is for major level changes. This is useful when comparisons of API versions as a major change is API breaking. For example,

Package docopt parses command-line arguments based on a help message. Given a conventional command-line help message, docopt processes the arguments. See https://github.com/docopt/docopt#help-message-format for a description of the help message format. This package exposes three different APIs, depending on the level of control required. The first, simplest way to parse your docopt usage is to just call: This will use os.Args[1:] as the argv slice, and use the default parser options. If you want to provide your own version string and args, then use: If the last parameter (version) is a non-empty string, it will be printed when --version is given in the argv slice. Finally, we can instantiate our own docopt.Parser which gives us control over how things like help messages are printed and whether to exit after displaying usage messages, etc. In particular, setting your own custom HelpHandler function makes unit testing your own docs with example command line invocations much more enjoyable. All three of these return a map of option names to the values parsed from argv, and an error or nil. You can get the values using the helpers, or just treat it as a regular map: Additionally, you can `Bind` these to a struct, assigning option values to the exported fields of that struct, all at once.

Package svg generates SVG as defined by the Scalable Vector Graphics 1.1 Specification (<http://www.w3.org/TR/SVG11/>). Output goes to the specified io.Writer. Shapes, lines, text Paths Image and Gradients Transforms Filter Effects Metadata elements Usage: (assuming GOPATH is set) You can use godoc to browse the documentation from the command line: a minimal program, to generate SVG to standard output. Drawing in a web server: (http://localhost:2003/circle) Many functions use x, y to specify an object's location, and w, h to specify the object's width and height. Where applicable, a final optional argument specifies the style to be applied to the object. The style strings follow the SVG standard; name:value pairs delimited by semicolons, or a series of name="value" pairs. For example: `"fill:none; opacity:0.3"` or `fill="none" opacity="0.3"` (see: <http://www.w3.org/TR/SVG11/styling.html>) The SVG type: Most operations are methods on this type, specifying the destination io.Writer. The Offcolor type: is used to specify the offset, color, and opacity of stop colors in linear and radial gradients The Filterspec type: is used to specify inputs and results for filter effects Package svg provides an API for generating Scalable Vector Graphics (SVG)

Package organizations provides the API client, operations, and parameter types for AWS Organizations. Organizations is a web service that enables you to consolidate your multiple Amazon Web Services accounts into an organization and centrally manage your accounts and their resources. This guide provides descriptions of the Organizations operations. For more information about using this service, see the Organizations User Guide. We welcome your feedback. Send your comments to feedback-awsorganizations@amazon.com or post your feedback and questions in the Organizations support forum. For more information about the Amazon Web Services support forums, see Forums Help. For the current release of Organizations, specify the us-east-1 region for all Amazon Web Services API and CLI calls made from the commercial Amazon Web Services Regions outside of China. If calling from one of the Amazon Web Services Regions in China, then specify cn-northwest-1 . You can do this in the CLI by using these parameters and commands: --endpoint-url https://organizations.us-east-1.amazonaws.com (from commercial or --endpoint-url https://organizations.cn-northwest-1.amazonaws.com.cn (from aws configure set default.region us-east-1 (from commercial Amazon Web Services or aws configure set default.region cn-northwest-1 (from Amazon Web Services --region us-east-1 (from commercial Amazon Web Services Regions outside of or --region cn-northwest-1 (from Amazon Web Services Regions in China) Organizations supports CloudTrail, a service that records Amazon Web Services API calls for your Amazon Web Services account and delivers log files to an Amazon S3 bucket. By using information collected by CloudTrail, you can determine which requests the Organizations service received, who made the request and when, and so on. For more about Organizations and its support for CloudTrail, see Logging Organizations API calls with CloudTrailin the Organizations User Guide. To learn more about CloudTrail, including how to turn it on and find your log files, see the CloudTrail User Guide.

Package httpcache provides a http.RoundTripper implementation that works as a mostly RFC-compliant cache for http responses. It is only suitable for use as a 'private' cache (i.e. for a web-browser or an API-client and not for a shared proxy).

Package graphql provides a GraphQL client implementation. For more information, see package github.com/shurcooL/githubv4, which is a specialized version targeting GitHub GraphQL API v4. That package is driving the feature development. For now, see README for more details.

Package dragonboat is a multi-group Raft implementation. The NodeHost struct is the facade interface for all features provided by the dragonboat package. Each NodeHost instance usually runs on a separate host managing its CPU, storage and network resources. Each NodeHost can manage Raft nodes from many different Raft groups known as Raft clusters. Each Raft cluster is identified by its ClusterID and it usually consists of multiple nodes, each identified its NodeID value. Nodes from the same Raft cluster can be considered as replicas of the same data, they are suppose to be distributed on different NodeHost instances across the network, this brings fault tolerance to machine and network failures as application data stored in the Raft cluster will be available as long as the majority of its managing NodeHost instances (i.e. its underlying hosts) are available. User applications can leverage the power of the Raft protocol implemented in dragonboat by implementing the IStateMachine or IOnDiskStateMachine component, as defined in github.com/lni/dragonboat/v3/statemachine. Known as user state machines, each IStateMachine and IOnDiskStateMachine instance is in charge of updating, querying and snapshotting application data with minimum exposure to the complexity of the Raft protocol implementation. User applications can use NodeHost's APIs to update the state of their IStateMachine or IOnDiskStateMachine instances, this is called making proposals. Once accepted by the majority nodes of a Raft cluster, the proposal is considered as committed and it will be applied on all member nodes of the Raft cluster. Applications can also make linearizable reads to query the state of the IStateMachine or IOnDiskStateMachine instances. Dragonboat employs the ReadIndex protocol invented by Diego Ongaro for fast linearizable reads. Dragonboat guarantees the linearizability of your I/O when interacting with the IStateMachine or IOnDiskStateMachine instances. In plain English, writes (via making proposal) to your Raft cluster appears to be instantaneous, once a write is completed, all later reads (linearizable read using the ReadIndex protocol as implemented and provided in dragonboat) should return the value of that write or a later write. Once a value is returned by a linearizable read, all later reads should return the same value or the result of a later write. To strictly provide such guarantee, we need to implement the at-most-once semantic required by linearizability. For a client, when it retries the proposal that failed to complete before its deadline during the previous attempt, it has the risk to have the same proposal committed and applied twice into the user state machine. Dragonboat prevents this by implementing the client session concept described in Diego Ongaro's PhD thesis. Arbitrary number of Raft clusters can be launched across the network simultaneously to aggregate distributed processing and storage capacities. Users can also make membership change requests to add or remove nodes from any interested Raft cluster. NodeHost APIs for making the above mentioned requests can be loosely classified into two categories, synchronous and asynchronous APIs. Synchronous APIs will not return until the completion of the requested operation. Their method names all start with Sync*. The asynchronous counterparts of such synchronous APIs, on the other hand, usually return immediately. This allows users to concurrently initiate multiple such asynchronous operations to save the total amount of time required to complete all of them. Dragonboat is a feature complete Multi-Group Raft implementation - snapshotting, membership change, leadership transfer, non-voting members and disk based state machine are all provided. Dragonboat is also extensively optimized. The Raft protocol implementation is fully pipelined, meaning proposals can start before the completion of previous proposals. This is critical for system throughput in high latency environment. Dragonboat is also fully batched, internal operations are batched whenever possible to maximize the overall throughput.

Package ses provides the API client, operations, and parameter types for Amazon Simple Email Service. This document contains reference information for the Amazon Simple Email Service (Amazon SES) API, version 2010-12-01. This document is best used in conjunction with the Amazon SES Developer Guide. For a list of Amazon SES endpoints to use in service requests, see Regions and Amazon SES in the Amazon SES Developer Guide. This documentation contains reference information related to the following: Amazon SES API Actions Amazon SES API Data Types Common Parameters Common Errors

The freetype package provides a convenient API to draw text onto an image. Use the freetype/raster and freetype/truetype packages for lower level control over rasterization and TrueType parsing.

Package sling is a Go HTTP client library for creating and sending API requests. Slings store HTTP Request properties to simplify sending requests and decoding responses. Check the examples to learn how to compose a Sling into your API client. Use a Sling to set path, method, header, query, or body properties and create an http.Request. Use Path to set or extend the URL for created Requests. Extension means the path will be resolved relative to the existing URL. Use Get, Post, Put, Patch, Delete, or Head which are exactly the same as Path except they set the HTTP method too. Add or Set headers for requests created by a Sling. Define url parameter structs (https://godoc.org/github.com/google/go-querystring/query). Use QueryStruct to encode a struct as query parameters on requests. Define JSON tagged structs (https://golang.org/pkg/encoding/json/). Use BodyJSON to JSON encode a struct as the Body on requests. Requests will include an "application/json" Content-Type header. Define url tagged structs (https://godoc.org/github.com/google/go-querystring/query). Use BodyForm to form url encode a struct as the Body on requests. Requests will include an "application/x-www-form-urlencoded" Content-Type header. Use Body to set a plain io.Reader on requests created by a Sling. Set a content type header, if desired (e.g. Set("Content-Type", "text/plain")). Each Sling generates an http.Request (say with some path and query params) each time Request() is called, based on its state. When creating different slings, you may wish to extend an existing Sling to minimize duplication (e.g. a common client). Each Sling instance provides a New() method which creates an independent copy, so setting properties on the child won't mutate the parent Sling. Without the calls to base.New(), tweetShowSling and tweetPostSling would reference the base Sling and POST to "https://api.twitter.com/1.1/statuses/show.json/statuses/update.json", which is undesired. Recap: If you wish to extend a Sling, create a new child copy with New(). Define a JSON struct to decode a type from 2XX success responses. Use ReceiveSuccess(successV interface{}) to send a new Request and decode the response body into successV if it succeeds. Most APIs return failure responses with JSON error details. To decode these, define success and failure JSON structs. Use Receive(successV, failureV interface{}) to send a new Request that will automatically decode the response into the successV for 2XX responses or into failureV for non-2XX responses. Pass a nil successV or failureV argument to skip JSON decoding into that value.

Package base64Captcha supports digits, numbers,alphabet, arithmetic, audio and digit-alphabet captcha. base64Captcha is used for fast development of RESTful APIs, web apps and backend services in Go. give a string identifier to the package and it returns with a base64-encoding-png-string

bindata converts any file into managable Go source code. Useful for embedding binary data into a go program. The file data is optionally gzip compressed before being converted to a raw byte slice. The following paragraphs cover some of the customization options which can be specified in the Config struct, which must be passed into the Translate() call. When used with the `Debug` option, the generated code does not actually include the asset data. Instead, it generates function stubs which load the data from the original file on disk. The asset API remains identical between debug and release builds, so your code will not have to change. This is useful during development when you expect the assets to change often. The host application using these assets uses the same API in both cases and will not have to care where the actual data comes from. An example is a Go webserver with some embedded, static web content like HTML, JS and CSS files. While developing it, you do not want to rebuild the whole server and restart it every time you make a change to a bit of javascript. You just want to build and launch the server once. Then just press refresh in the browser to see those changes. Embedding the assets with the `debug` flag allows you to do just that. When you are finished developing and ready for deployment, just re-invoke `go-bindata` without the `-debug` flag. It will now embed the latest version of the assets. The `NoMemCopy` option will alter the way the output file is generated. It will employ a hack that allows us to read the file data directly from the compiled program's `.rodata` section. This ensures that when we call call our generated function, we omit unnecessary memcopies. The downside of this, is that it requires dependencies on the `reflect` and `unsafe` packages. These may be restricted on platforms like AppEngine and thus prevent you from using this mode. Another disadvantage is that the byte slice we create, is strictly read-only. For most use-cases this is not a problem, but if you ever try to alter the returned byte slice, a runtime panic is thrown. Use this mode only on target platforms where memory constraints are an issue. The default behaviour is to use the old code generation method. This prevents the two previously mentioned issues, but will employ at least one extra memcopy and thus increase memory requirements. For instance, consider the following two examples: This would be the default mode, using an extra memcopy but gives a safe implementation without dependencies on `reflect` and `unsafe`: Here is the same functionality, but uses the `.rodata` hack. The byte slice returned from this example can not be written to without generating a runtime error. The NoCompress option indicates that the supplied assets are *not* GZIP compressed before being turned into Go code. The data should still be accessed through a function call, so nothing changes in the API. This feature is useful if you do not care for compression, or the supplied resource is already compressed. Doing it again would not add any value and may even increase the size of the data. The default behaviour of the program is to use compression. The keys used in the `_bindata` map are the same as the input file name passed to `go-bindata`. This includes the path. In most cases, this is not desireable, as it puts potentially sensitive information in your code base. For this purpose, the tool supplies another command line flag `-prefix`. This accepts a portion of a path name, which should be stripped off from the map keys and function names. For example, running without the `-prefix` flag, we get: Running with the `-prefix` flag, we get: With the optional Tags field, you can specify any go build tags that must be fulfilled for the output file to be included in a build. This is useful when including binary data in multiple formats, where the desired format is specified at build time with the appropriate tags. The tags are appended to a `// +build` line in the beginning of the output file and must follow the build tags syntax specified by the go tool.

Package log implements a simple structured logging API designed with few assumptions. Designed for centralized logging solutions such as Kinesis which require encoding and decoding before fanning-out to handlers. You may use this package with inline handlers, much like Logrus, however a centralized solution is recommended so that apps do not need to be re-deployed to add or remove logging service providers. Errors are passed to WithError(), populating the "error" field. Multiple fields can be set, via chaining, or WithFields(). Structured logging is supported with fields, and is recommended over the formatted message variants. Trace can be used to simplify logging of start and completion events, for example an upload which may fail. Unstructured logging is supported, but not recommended since it is hard to query.

Package notify implements access to filesystem events. Notify is a high-level abstraction over filesystem watchers like inotify, kqueue, FSEvents, FEN or ReadDirectoryChangesW. Watcher implementations are split into two groups: ones that natively support recursive notifications (FSEvents and ReadDirectoryChangesW) and ones that do not (inotify, kqueue, FEN). For more details see watcher and recursiveWatcher interfaces in watcher.go source file. On top of filesystem watchers notify maintains a watchpoint tree, which provides a strategy for creating and closing filesystem watches and dispatching filesystem events to user channels. An event set is just an event list joint using bitwise OR operator into a single event value. Both the platform-independent (see Constants) and specific events can be used. Refer to the event_*.go source files for information about the available events. A filesystem watch or just a watch is platform-specific entity which represents a single path registered for notifications for specific event set. Setting a watch means using platform-specific API calls for creating / initializing said watch. For each watcher the API call is: To rewatch means to either shrink or expand an event set that was previously registered during watch operation for particular filesystem watch. A watchpoint is a list of user channel and event set pairs for particular path (watchpoint tree's node). A single watchpoint can contain multiple different user channels registered to listen for one or more events. A single user channel can be registered in one or more watchpoints, recursive and non-recursive ones as well.

Package httpexpect helps with end-to-end HTTP and REST API testing. See example directory: There are two common ways to test API with httpexpect: The second approach works only if the server is a Go module and its handler can be imported in tests. Concrete behaviour is determined by Client implementation passed to Config struct. If you're using http.Client, set its Transport field (http.RoundTriper) to one of the following: Note that http handler can be usually obtained from http framework you're using. E.g., echo framework provides either http.Handler or fasthttp.RequestHandler. You can also provide your own implementation of RequestFactory (creates http.Request), or Client (gets http.Request and returns http.Response). If you're starting server from tests, it's very handy to use net/http/httptest. Whenever values are checked for equality in httpexpect, they are converted to "canonical form": This is equivalent to subsequently json.Marshal() and json.Unmarshal() the value and currently is implemented so. When some check fails, failure is reported. If non-fatal failures are used (see Reporter interface), execution is continued and instance that was checked is marked as failed. If specific instance is marked as failed, all subsequent checks are ignored for this instance and for any child instances retrieved after failure. Example:

Package v2 reexports a subset of the SDK v2 API.

Package route53domains provides the API client, operations, and parameter types for Amazon Route 53 Domains. Amazon Route 53 API actions let you register domain names and perform related operations.

Package resourcegroupstaggingapi provides the API client, operations, and parameter types for AWS Resource Groups Tagging API. Resource Groups Tagging API

Package azcosmos implements the client to interact with the Azure Cosmos DB SQL API. The azcosmos package is capable of: Types of Credentials The clients support different forms of authentication. The azcosmos library supports authorization via Azure Active Directory or an account key. Using Azure Active Directory To create a client, you can use any of the TokenCredential implementations provided by `azidentity`. Using account keys To create a client, you will need the account's endpoint URL and a key credential. Using connection string To create a client, you will need the account's connection string. The following are relevant concepts for the usage of the client: The following sections provide several code snippets covering some of the most common Table tasks, including: Create a database and obtain a `DatabaseClient` to perform operations on your newly created database. Create a container on an existing database and obtain a `ContainerClient` to perform operations on your newly created container. Creating, reading, and deleting items Querying items Querying items with parametrized queries Using Transactional batch

Package color is command line color library. Support rich color rendering output, universal API method, compatible with Windows system Source code and other details for the project are available at GitHub: More usage please see README and tests.

Package apigatewayv2 provides the API client, operations, and parameter types for AmazonApiGatewayV2. Amazon API Gateway V2

Package webview implements Go bindings to https://github.com/zserge/webview C library. Bindings closely repeat the C APIs and include both, a simplified single-function API to just open a full-screen webview window, and a more advanced and featureful set of APIs, including Go-to-JavaScript bindings. The library uses gtk-webkit, Cocoa/Webkit and MSHTML (IE8..11) as a browser engine and supports Linux, MacOS and Windows 7..10 respectively.

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

Package csrf (gorilla/csrf) provides Cross Site Request Forgery (CSRF) prevention middleware for Go web applications & services. It includes: * The `csrf.Protect` middleware/handler provides CSRF protection on routes attached to a router or a sub-router. * A `csrf.Token` function that provides the token to pass into your response, whether that be a HTML form or a JSON response body. * ... and a `csrf.TemplateField` helper that you can pass into your `html/template` templates to replace a `{{ .csrfField }}` template tag with a hidden input field. gorilla/csrf is easy to use: add the middleware to individual handlers with the below: ... and then collect the token with `csrf.Token(r)` before passing it to the template, JSON body or HTTP header (you pick!). gorilla/csrf inspects the form body (first) and HTTP headers (second) on subsequent POST/PUT/PATCH/DELETE/etc. requests for the token. Note that the authentication key passed to `csrf.Protect([]byte(key))` should be 32-bytes long and persist across application restarts. Generating a random key won't allow you to authenticate existing cookies and will break your CSRF validation. Here's the common use-case: HTML forms you want to provide CSRF protection for, in order to protect malicious POST requests being made: Note that the CSRF middleware will (by necessity) consume the request body if the token is passed via POST form values. If you need to consume this in your handler, insert your own middleware earlier in the chain to capture the request body. You can also send the CSRF token in the response header. This approach is useful if you're using a front-end JavaScript framework like Ember or Angular, or are providing a JSON API: If you're writing a client that's supposed to mimic browser behavior, make sure to send back the CSRF cookie (the default name is _gorilla_csrf, but this can be changed with the CookieName Option) along with either the X-CSRF-Token header or the gorilla.csrf.Token form field. In addition: getting CSRF protection right is important, so here's some background: * This library generates unique-per-request (masked) tokens as a mitigation against the BREACH attack (http://breachattack.com/). * The 'base' (unmasked) token is stored in the session, which means that multiple browser tabs won't cause a user problems as their per-request token is compared with the base token. * Operates on a "whitelist only" approach where safe (non-mutating) HTTP methods (GET, HEAD, OPTIONS, TRACE) are the *only* methods where token validation is not enforced. * The design is based on the battle-tested Django (https://docs.djangoproject.com/en/1.8/ref/csrf/) and Ruby on Rails (http://api.rubyonrails.org/classes/ActionController/RequestForgeryProtection.html) approaches. * Cookies are authenticated and based on the securecookie (https://github.com/gorilla/securecookie) library. They're also Secure (issued over HTTPS only) and are HttpOnly by default, because sane defaults are important. * Go's `crypto/rand` library is used to generate the 32 byte (256 bit) tokens and the one-time-pad used for masking them. This library does not seek to be adventurous.

Package neptune provides the API client, operations, and parameter types for Amazon Neptune. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security. This interface reference for Amazon Neptune contains documentation for a programming or command line interface you can use to manage Amazon Neptune. Note that Amazon Neptune is asynchronous, which means that some interfaces might require techniques such as polling or callback functions to determine when a command has been applied. In this reference, the parameter descriptions indicate whether a command is applied immediately, on the next instance reboot, or during the maintenance window. The reference structure is as follows, and we list following some related topics from the user guide.

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

Package firehose provides the API client, operations, and parameter types for Amazon Kinesis Firehose. Amazon Data Firehose was previously known as Amazon Kinesis Data Firehose. Amazon Data Firehose is a fully managed service that delivers real-time streaming data to destinations such as Amazon Simple Storage Service (Amazon S3), Amazon OpenSearch Service, Amazon Redshift, Splunk, and various other supported destinations.

Package mq provides the API client, operations, and parameter types for AmazonMQ. Amazon MQ is a managed message broker service for Apache ActiveMQ and RabbitMQ that makes it easy to set up and operate message brokers in the cloud. A message broker allows software applications and components to communicate using various programming languages, operating systems, and formal messaging protocols.

Package glue provides the API client, operations, and parameter types for AWS Glue. Defines the public endpoint for the Glue service.

Package stripe provides the binding for Stripe REST APIs.

Package selenium provides a client to drive web browser-based automation and testing. See the example below for how to get started with this API. This package can depend on several binaries being available, depending on which browsers will be used and how. To avoid needing to manage these dependencies, use a cloud-based browser testing environment, like Sauce Labs, BrowserStack or similar. Otherwise, use the methods provided by this API to specify the paths to the dependencies, which will have to be downloaded separately. This example shows how to navigate to a http://play.golang.org page, input a short program, run it, and inspect its output. If you want to actually run this example:

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

Package configservice provides the API client, operations, and parameter types for AWS Config. Config provides a way to keep track of the configurations of all the Amazon Web Services resources associated with your Amazon Web Services account. You can use Config to get the current and historical configurations of each Amazon Web Services resource and also to get information about the relationship between the resources. An Amazon Web Services resource can be an Amazon Compute Cloud (Amazon EC2) instance, an Elastic Block Store (EBS) volume, an elastic network Interface (ENI), or a security group. For a complete list of resources currently supported by Config, see Supported Amazon Web Services resources. You can access and manage Config through the Amazon Web Services Management Console, the Amazon Web Services Command Line Interface (Amazon Web Services CLI), the Config API, or the Amazon Web Services SDKs for Config. This reference guide contains documentation for the Config API and the Amazon Web Services CLI commands that you can use to manage Config. The Config API uses the Signature Version 4 protocol for signing requests. For more information about how to sign a request with this protocol, see Signature Version 4 Signing Process. For detailed information about Config features and their associated actions or commands, as well as how to work with Amazon Web Services Management Console, see What Is Configin the Config Developer Guide.

Package binance is a Golang SDK for binance APIs.

Package sfn provides the API client, operations, and parameter types for AWS Step Functions. Step Functions coordinates the components of distributed applications and microservices using visual workflows. You can use Step Functions to build applications from individual components, each of which performs a discrete function, or task, allowing you to scale and change applications quickly. Step Functions provides a console that helps visualize the components of your application as a series of steps. Step Functions automatically triggers and tracks each step, and retries steps when there are errors, so your application executes predictably and in the right order every time. Step Functions logs the state of each step, so you can quickly diagnose and debug any issues. Step Functions manages operations and underlying infrastructure to ensure your application is available at any scale. You can run tasks on Amazon Web Services, your own servers, or any system that has access to Amazon Web Services. You can access and use Step Functions using the console, the Amazon Web Services SDKs, or an HTTP API. For more information about Step Functions, see the Step Functions Developer Guide. If you use the Step Functions API actions using Amazon Web Services SDK integrations, make sure the API actions are in camel case and parameter names are in Pascal case. For example, you could use Step Functions API action startSyncExecution and specify its parameter as StateMachineArn .

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

Package httpexpect helps with end-to-end HTTP and REST API testing. See example directory: There are two common ways to test API with httpexpect: The second approach works only if the server is a Go module and its handler can be imported in tests. Concrete behaviour is determined by Client implementation passed to Config struct. If you're using http.Client, set its Transport field (http.RoundTriper) to one of the following: Note that http handler can be usually obtained from http framework you're using. E.g., echo framework provides either http.Handler or fasthttp.RequestHandler. You can also provide your own implementation of RequestFactory (creates http.Request), or Client (gets http.Request and returns http.Response). If you're starting server from tests, it's very handy to use net/http/httptest. Whenever values are checked for equality in httpexpect, they are converted to "canonical form": This is equivalent to subsequently json.Marshal() and json.Unmarshal() the value and currently is implemented so. When some check fails, failure is reported. If non-fatal failures are used (see Reporter interface), execution is continued and instance that was checked is marked as failed. If specific instance is marked as failed, all subsequent checks are ignored for this instance and for any child instances retrieved after failure. Example: If you want to be informed about every asserion made, successful or failed, you can use AssertionHandler interface. Default implementation of this interface ignores successful assertions and reports failed assertions using Formatter and Reporter objects. Custom AssertionHandler can handle all assertions (e.g. dump them in JSON format) and is free to use or not to use Formatter and Reporter in its sole discretion.

Code generated by "make version". DO NOT EDIT.

Package githubv4 is a client library for accessing GitHub GraphQL API v4 (https://docs.github.com/en/graphql). If you're looking for a client library for GitHub REST API v3, the recommended package is github (also known as go-github). For now, see README for more details.

Package elasticbeanstalk provides the API client, operations, and parameter types for AWS Elastic Beanstalk. AWS Elastic Beanstalk makes it easy for you to create, deploy, and manage scalable, fault-tolerant applications running on the Amazon Web Services cloud. For more information about this product, go to the AWS Elastic Beanstalk details page. The location of the latest AWS Elastic Beanstalk WSDL is https://elasticbeanstalk.s3.amazonaws.com/doc/2010-12-01/AWSElasticBeanstalk.wsdl. To install the Software Development Kits (SDKs), Integrated Development Environment (IDE) Toolkits, and command line tools that enable you to access the API, go to Tools for Amazon Web Services. For a list of region-specific endpoints that AWS Elastic Beanstalk supports, go to Regions and Endpointsin the Amazon Web Services Glossary.

bindata converts any file into manageable Go source code. Useful for embedding binary data into a go program. The file data is optionally gzip compressed before being converted to a raw byte slice. The following paragraphs cover some of the customization options which can be specified in the Config struct, which must be passed into the Translate() call. When used with the `Debug` option, the generated code does not actually include the asset data. Instead, it generates function stubs which load the data from the original file on disk. The asset API remains identical between debug and release builds, so your code will not have to change. This is useful during development when you expect the assets to change often. The host application using these assets uses the same API in both cases and will not have to care where the actual data comes from. An example is a Go webserver with some embedded, static web content like HTML, JS and CSS files. While developing it, you do not want to rebuild the whole server and restart it every time you make a change to a bit of javascript. You just want to build and launch the server once. Then just press refresh in the browser to see those changes. Embedding the assets with the `debug` flag allows you to do just that. When you are finished developing and ready for deployment, just re-invoke `go-bindata` without the `-debug` flag. It will now embed the latest version of the assets. The `NoMemCopy` option will alter the way the output file is generated. It will employ a hack that allows us to read the file data directly from the compiled program's `.rodata` section. This ensures that when we call call our generated function, we omit unnecessary memcopies. The downside of this, is that it requires dependencies on the `reflect` and `unsafe` packages. These may be restricted on platforms like AppEngine and thus prevent you from using this mode. Another disadvantage is that the byte slice we create, is strictly read-only. For most use-cases this is not a problem, but if you ever try to alter the returned byte slice, a runtime panic is thrown. Use this mode only on target platforms where memory constraints are an issue. The default behaviour is to use the old code generation method. This prevents the two previously mentioned issues, but will employ at least one extra memcopy and thus increase memory requirements. For instance, consider the following two examples: This would be the default mode, using an extra memcopy but gives a safe implementation without dependencies on `reflect` and `unsafe`: Here is the same functionality, but uses the `.rodata` hack. The byte slice returned from this example can not be written to without generating a runtime error. The NoCompress option indicates that the supplied assets are *not* GZIP compressed before being turned into Go code. The data should still be accessed through a function call, so nothing changes in the API. This feature is useful if you do not care for compression, or the supplied resource is already compressed. Doing it again would not add any value and may even increase the size of the data. The default behaviour of the program is to use compression. The keys used in the `_bindata` map are the same as the input file name passed to `go-bindata`. This includes the path. In most cases, this is not desirable, as it puts potentially sensitive information in your code base. For this purpose, the tool supplies another command line flag `-prefix`. This accepts a portion of a path name, which should be stripped off from the map keys and function names. For example, running without the `-prefix` flag, we get: Running with the `-prefix` flag, we get: With the optional Tags field, you can specify any go build tags that must be fulfilled for the output file to be included in a build. This is useful when including binary data in multiple formats, where the desired format is specified at build time with the appropriate tags. The tags are appended to a `// +build` line in the beginning of the output file and must follow the build tags syntax specified by the go tool.

bindata converts any file into managable Go source code. Useful for embedding binary data into a go program. The file data is optionally gzip compressed before being converted to a raw byte slice. The following paragraphs cover some of the customization options which can be specified in the Config struct, which must be passed into the Translate() call. When used with the `Debug` option, the generated code does not actually include the asset data. Instead, it generates function stubs which load the data from the original file on disk. The asset API remains identical between debug and release builds, so your code will not have to change. This is useful during development when you expect the assets to change often. The host application using these assets uses the same API in both cases and will not have to care where the actual data comes from. An example is a Go webserver with some embedded, static web content like HTML, JS and CSS files. While developing it, you do not want to rebuild the whole server and restart it every time you make a change to a bit of javascript. You just want to build and launch the server once. Then just press refresh in the browser to see those changes. Embedding the assets with the `debug` flag allows you to do just that. When you are finished developing and ready for deployment, just re-invoke `go-bindata` without the `-debug` flag. It will now embed the latest version of the assets. The `NoMemCopy` option will alter the way the output file is generated. It will employ a hack that allows us to read the file data directly from the compiled program's `.rodata` section. This ensures that when we call call our generated function, we omit unnecessary memcopies. The downside of this, is that it requires dependencies on the `reflect` and `unsafe` packages. These may be restricted on platforms like AppEngine and thus prevent you from using this mode. Another disadvantage is that the byte slice we create, is strictly read-only. For most use-cases this is not a problem, but if you ever try to alter the returned byte slice, a runtime panic is thrown. Use this mode only on target platforms where memory constraints are an issue. The default behaviour is to use the old code generation method. This prevents the two previously mentioned issues, but will employ at least one extra memcopy and thus increase memory requirements. For instance, consider the following two examples: This would be the default mode, using an extra memcopy but gives a safe implementation without dependencies on `reflect` and `unsafe`: Here is the same functionality, but uses the `.rodata` hack. The byte slice returned from this example can not be written to without generating a runtime error. The NoCompress option indicates that the supplied assets are *not* GZIP compressed before being turned into Go code. The data should still be accessed through a function call, so nothing changes in the API. This feature is useful if you do not care for compression, or the supplied resource is already compressed. Doing it again would not add any value and may even increase the size of the data. The default behaviour of the program is to use compression. The keys used in the `_bindata` map are the same as the input file name passed to `go-bindata`. This includes the path. In most cases, this is not desireable, as it puts potentially sensitive information in your code base. For this purpose, the tool supplies another command line flag `-prefix`. This accepts a portion of a path name, which should be stripped off from the map keys and function names. For example, running without the `-prefix` flag, we get: Running with the `-prefix` flag, we get: With the optional Tags field, you can specify any go build tags that must be fulfilled for the output file to be included in a build. This is useful when including binary data in multiple formats, where the desired format is specified at build time with the appropriate tags. The tags are appended to a `// +build` line in the beginning of the output file and must follow the build tags syntax specified by the go tool.

Package codedeploy provides the API client, operations, and parameter types for AWS CodeDeploy. CodeDeploy is a deployment service that automates application deployments to Amazon EC2 instances, on-premises instances running in your own facility, serverless Lambda functions, or applications in an Amazon ECS service. You can deploy a nearly unlimited variety of application content, such as an updated Lambda function, updated applications in an Amazon ECS service, code, web and configuration files, executables, packages, scripts, multimedia files, and so on. CodeDeploy can deploy application content stored in Amazon S3 buckets, GitHub repositories, or Bitbucket repositories. You do not need to make changes to your existing code before you can use CodeDeploy. CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during application deployment, and handles the complexity of updating your applications, without many of the risks associated with error-prone manual deployments. Use the information in this guide to help you work with the following CodeDeploy components: Application: A name that uniquely identifies the application you want to deploy. CodeDeploy uses this name, which functions as a container, to ensure the correct combination of revision, deployment configuration, and deployment group are referenced during a deployment. Deployment group: A set of individual instances, CodeDeploy Lambda deployment configuration settings, or an Amazon ECS service and network details. A Lambda deployment group specifies how to route traffic to a new version of a Lambda function. An Amazon ECS deployment group specifies the service created in Amazon ECS to deploy, a load balancer, and a listener to reroute production traffic to an updated containerized application. An Amazon EC2/On-premises deployment group contains individually tagged instances, Amazon EC2 instances in Amazon EC2 Auto Scaling groups, or both. All deployment groups can specify optional trigger, alarm, and rollback settings. Deployment configuration: A set of deployment rules and deployment success and failure conditions used by CodeDeploy during a deployment. Deployment: The process and the components used when updating a Lambda function, a containerized application in an Amazon ECS service, or of installing content on one or more instances. Application revisions: For an Lambda deployment, this is an AppSpec file that specifies the Lambda function to be updated and one or more functions to validate deployment lifecycle events. For an Amazon ECS deployment, this is an AppSpec file that specifies the Amazon ECS task definition, container, and port where production traffic is rerouted. For an EC2/On-premises deployment, this is an archive file that contains source content—source code, webpages, executable files, and deployment scripts—along with an AppSpec file. Revisions are stored in Amazon S3 buckets or GitHub repositories. For Amazon S3, a revision is uniquely identified by its Amazon S3 object key and its ETag, version, or both. For GitHub, a revision is uniquely identified by its commit ID. This guide also contains information to help you get details about the instances in your deployments, to make on-premises instances available for CodeDeploy deployments, to get details about a Lambda function deployment, and to get details about Amazon ECS service deployments. CodeDeploy User Guide CodeDeploy API Reference Guide CLI Reference for CodeDeploy CodeDeploy Developer Forum