Events API

Package controllerruntime provides tools to construct Kubernetes-style controllers that manipulate both Kubernetes CRDs and aggregated/built-in Kubernetes APIs. It defines easy helpers for the common use cases when building CRDs, built on top of customizable layers of abstraction. Common cases should be easy, and uncommon cases should be possible. In general, controller-runtime tries to guide users towards Kubernetes controller best-practices. The main entrypoint for controller-runtime is this root package, which contains all of the common types needed to get started building controllers: The examples in this package walk through a basic controller setup. The kubebuilder book (https://book.kubebuilder.io) has some more in-depth walkthroughs. controller-runtime favors structs with sane defaults over constructors, so it's fairly common to see structs being used directly in controller-runtime. A brief-ish walkthrough of the layout of this library can be found below. Each package contains more information about how to use it. Frequently asked questions about using controller-runtime and designing controllers can be found at https://github.com/kubernetes-sigs/controller-runtime/blob/main/FAQ.md. Every controller and webhook is ultimately run by a Manager (pkg/manager). A manager is responsible for running controllers and webhooks, and setting up common dependencies, like shared caches and clients, as well as managing leader election (pkg/leaderelection). Managers are generally configured to gracefully shut down controllers on pod termination by wiring up a signal handler (pkg/manager/signals). Controllers (pkg/controller) use events (pkg/event) to eventually trigger reconcile requests. They may be constructed manually, but are often constructed with a Builder (pkg/builder), which eases the wiring of event sources (pkg/source), like Kubernetes API object changes, to event handlers (pkg/handler), like "enqueue a reconcile request for the object owner". Predicates (pkg/predicate) can be used to filter which events actually trigger reconciles. There are pre-written utilities for the common cases, and interfaces and helpers for advanced cases. Controller logic is implemented in terms of Reconcilers (pkg/reconcile). A Reconciler implements a function which takes a reconcile Request containing the name and namespace of the object to reconcile, reconciles the object, and returns a Response or an error indicating whether to requeue for a second round of processing. Reconcilers use Clients (pkg/client) to access API objects. The default client provided by the manager reads from a local shared cache (pkg/cache) and writes directly to the API server, but clients can be constructed that only talk to the API server, without a cache. The Cache will auto-populate with watched objects, as well as when other structured objects are requested. The default split client does not promise to invalidate the cache during writes (nor does it promise sequential create/get coherence), and code should not assume a get immediately following a create/update will return the updated resource. Caches may also have indexes, which can be created via a FieldIndexer (pkg/client) obtained from the manager. Indexes can used to quickly and easily look up all objects with certain fields set. Reconcilers may retrieve event recorders (pkg/recorder) to emit events using the manager. Clients, Caches, and many other things in Kubernetes use Schemes (pkg/scheme) to associate Go types to Kubernetes API Kinds (Group-Version-Kinds, to be specific). Similarly, webhooks (pkg/webhook/admission) may be implemented directly, but are often constructed using a builder (pkg/webhook/admission/builder). They are run via a server (pkg/webhook) which is managed by a Manager. Logging (pkg/log) in controller-runtime is done via structured logs, using a log set of interfaces called logr (https://pkg.go.dev/github.com/go-logr/logr). While controller-runtime provides easy setup for using Zap (https://go.uber.org/zap, pkg/log/zap), you can provide any implementation of logr as the base logger for controller-runtime. Metrics (pkg/metrics) provided by controller-runtime are registered into a controller-runtime-specific Prometheus metrics registry. The manager can serve these by an HTTP endpoint, and additional metrics may be registered to this Registry as normal. You can easily build integration and unit tests for your controllers and webhooks using the test Environment (pkg/envtest). This will automatically stand up a copy of etcd and kube-apiserver, and provide the correct options to connect to the API server. It's designed to work well with the Ginkgo testing framework, but should work with any testing setup. This example creates a simple application Controller that is configured for ReplicaSets and Pods. * Create a new application for ReplicaSets that manages Pods owned by the ReplicaSet and calls into ReplicaSetReconciler. * Start the application. This example creates a simple application Controller that is configured for ExampleCRDWithConfigMapRef CRD. Any change in the configMap referenced in this Custom Resource will cause the re-reconcile of the parent ExampleCRDWithConfigMapRef due to the implementation of the .Watches method of "sigs.k8s.io/controller-runtime/pkg/builder".Builder. This example creates a simple application Controller that is configured for ReplicaSets and Pods. This application controller will be running leader election with the provided configuration in the manager options. If leader election configuration is not provided, controller runs leader election with default values. Default values taken from: https://github.com/kubernetes/component-base/blob/master/config/v1alpha1/defaults.go * defaultLeaseDuration = 15 * time.Second * defaultRenewDeadline = 10 * time.Second * defaultRetryPeriod = 2 * time.Second * Create a new application for ReplicaSets that manages Pods owned by the ReplicaSet and calls into ReplicaSetReconciler. * Start the application.

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

Package kms provides the API client, operations, and parameter types for AWS Key Management Service. Key Management Service (KMS) is an encryption and key management web service. This guide describes the KMS operations that you can call programmatically. For general information about KMS, see the Key Management Service Developer Guide. KMS has replaced the term customer master key (CMK) with KMS key and KMS key. The concept has not changed. To prevent breaking changes, KMS is keeping some variations of this term. Amazon Web Services provides SDKs that consist of libraries and sample code for various programming languages and platforms (Java, Ruby, .Net, macOS, Android, etc.). The SDKs provide a convenient way to create programmatic access to KMS and other Amazon Web Services services. For example, the SDKs take care of tasks such as signing requests (see below), managing errors, and retrying requests automatically. For more information about the Amazon Web Services SDKs, including how to download and install them, see Tools for Amazon Web Services. We recommend that you use the Amazon Web Services SDKs to make programmatic API calls to KMS. If you need to use FIPS 140-2 validated cryptographic modules when communicating with Amazon Web Services, use the FIPS endpoint in your preferred Amazon Web Services Region. For more information about the available FIPS endpoints, see Service endpointsin the Key Management Service topic of the Amazon Web Services General Reference. All KMS API calls must be signed and be transmitted using Transport Layer Security (TLS). KMS recommends you always use the latest supported TLS version. Clients must also support cipher suites with Perfect Forward Secrecy (PFS) such as Ephemeral Diffie-Hellman (DHE) or Elliptic Curve Ephemeral Diffie-Hellman (ECDHE). Most modern systems such as Java 7 and later support these modes. Requests must be signed using an access key ID and a secret access key. We strongly recommend that you do not use your Amazon Web Services account root access key ID and secret access key for everyday work. You can use the access key ID and secret access key for an IAM user or you can use the Security Token Service (STS) to generate temporary security credentials and use those to sign requests. All KMS requests must be signed with Signature Version 4. KMS supports CloudTrail, a service that logs Amazon Web Services API calls and related events for your Amazon Web Services account and delivers them to an Amazon S3 bucket that you specify. By using the information collected by CloudTrail, you can determine what requests were made to KMS, who made the request, when it was made, and so on. To learn more about CloudTrail, including how to turn it on and find your log files, see the CloudTrail User Guide. For more information about credentials and request signing, see the following: Amazon Web Services Security Credentials Temporary Security Credentials Signature Version 4 Signing Process Of the API operations discussed in this guide, the following will prove the most useful for most applications. You will likely perform operations other than these, such as creating keys and assigning policies, by using the console.

Package secretsmanager provides the API client, operations, and parameter types for AWS Secrets Manager. Amazon Web Services Secrets Manager provides a service to enable you to store, manage, and retrieve, secrets. This guide provides descriptions of the Secrets Manager API. For more information about using this service, see the Amazon Web Services Secrets Manager User Guide. This version of the Secrets Manager API Reference documents the Secrets Manager API version 2017-10-17. For a list of endpoints, see Amazon Web Services Secrets Manager endpoints. We welcome your feedback. Send your comments to awssecretsmanager-feedback@amazon.com, or post your feedback and questions in the Amazon Web Services Secrets Manager Discussion Forum. For more information about the Amazon Web Services Discussion Forums, see Forums Help. Amazon Web Services Secrets Manager supports Amazon Web Services 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 that's collected by Amazon Web Services CloudTrail, you can determine the requests successfully made to Secrets Manager, who made the request, when it was made, and so on. For more about Amazon Web Services Secrets Manager and support for Amazon Web Services CloudTrail, see Logging Amazon Web Services Secrets Manager Events with Amazon Web Services CloudTrailin the Amazon Web Services Secrets Manager User Guide. To learn more about CloudTrail, including enabling it and find your log files, see the Amazon Web Services CloudTrail User Guide.

Package cloudwatchlogs provides the API client, operations, and parameter types for Amazon CloudWatch Logs. You can use Amazon CloudWatch Logs to monitor, store, and access your log files from EC2 instances, CloudTrail, and other sources. You can then retrieve the associated log data from CloudWatch Logs using the CloudWatch console. Alternatively, you can use CloudWatch Logs commands in the Amazon Web Services CLI, CloudWatch Logs API, or CloudWatch Logs SDK. You can use CloudWatch Logs to: Monitor logs from EC2 instances in real time: You can use CloudWatch Logs to monitor applications and systems using log data. For example, CloudWatch Logs can track the number of errors that occur in your application logs. Then, it can send you a notification whenever the rate of errors exceeds a threshold that you specify. CloudWatch Logs uses your log data for monitoring so no code changes are required. For example, you can monitor application logs for specific literal terms (such as "NullReferenceException"). You can also count the number of occurrences of a literal term at a particular position in log data (such as "404" status codes in an Apache access log). When the term you are searching for is found, CloudWatch Logs reports the data to a CloudWatch metric that you specify. Monitor CloudTrail logged events: You can create alarms in CloudWatch and receive notifications of particular API activity as captured by CloudTrail. You can use the notification to perform troubleshooting. Archive log data: You can use CloudWatch Logs to store your log data in highly durable storage. You can change the log retention setting so that any log events earlier than this setting are automatically deleted. The CloudWatch Logs agent helps to quickly send both rotated and non-rotated log data off of a host and into the log service. You can then access the raw log data when you need it.

Package rod is a high-level driver directly based on DevTools Protocol. This example opens https://github.com/, searches for "git", and then gets the header element which gives the description for Git. Rod use https://golang.org/pkg/context to handle cancellations for IO blocking operations, most times it's timeout. Context will be recursively passed to all sub-methods. For example, methods like Page.Context(ctx) will return a clone of the page with the ctx, all the methods of the returned page will use the ctx if they have IO blocking operations. Page.Timeout or Page.WithCancel is just a shortcut for Page.Context. Of course, Browser or Element works the same way. Shows how we can further customize the browser with the launcher library. Usually you use launcher lib to set the browser's command line flags (switches). Doc for flags: https://peter.sh/experiments/chromium-command-line-switches Shows how to change the retry/polling options that is used to query elements. This is useful when you want to customize the element query retry logic. When rod doesn't have a feature that you need. You can easily call the cdp to achieve it. List of cdp API: https://github.com/go-rod/rod/tree/main/lib/proto Shows how to disable headless mode and debug. Rod provides a lot of debug options, you can set them with setter methods or use environment variables. Doc for environment variables: https://pkg.go.dev/github.com/go-rod/rod/lib/defaults We use "Must" prefixed functions to write example code. But in production you may want to use the no-prefix version of them. About why we use "Must" as the prefix, it's similar to https://golang.org/pkg/regexp/#MustCompile Shows how to share a remote object reference between two Eval. Shows how to listen for events. Shows how to intercept requests and modify both the request and the response. The entire process of hijacking one request: The --req-> and --res-> are the parts that can be modified. Show how to handle multiple results of an action. Such as when you login a page, the result can be success or wrong password. Example_search shows how to use Search to get element inside nested iframes or shadow DOMs. It works the same as https://developers.google.com/web/tools/chrome-devtools/dom#search Shows how to update the state of the current page. In this example we enable the network domain. Rod uses mouse cursor to simulate clicks, so if a button is moving because of animation, the click may not work as expected. We usually use WaitStable to make sure the target isn't changing anymore. When you want to wait for an ajax request to complete, this example will be useful.

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 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

Package eventbridge provides the API client, operations, and parameter types for Amazon EventBridge. Amazon EventBridge helps you to respond to state changes in your Amazon Web Services resources. When your resources change state, they automatically send events to an event stream. You can create rules that match selected events in the stream and route them to targets to take action. You can also use rules to take action on a predetermined schedule. For example, you can configure rules to: Automatically invoke an Lambda function to update DNS entries when an event notifies you that Amazon EC2 instance enters the running state. Direct specific API records from CloudTrail to an Amazon Kinesis data stream for detailed analysis of potential security or availability risks. Periodically invoke a built-in target to create a snapshot of an Amazon EBS volume. For more information about the features of Amazon EventBridge, see the Amazon EventBridge User Guide.

Package guardduty provides the API client, operations, and parameter types for Amazon GuardDuty. Amazon GuardDuty is a continuous security monitoring service that analyzes and processes the following foundational data sources - VPC flow logs, Amazon Web Services CloudTrail management event logs, CloudTrail S3 data event logs, EKS audit logs, DNS logs, Amazon EBS volume data, runtime activity belonging to container workloads, such as Amazon EKS, Amazon ECS (including Amazon Web Services Fargate), and Amazon EC2 instances. It uses threat intelligence feeds, such as lists of malicious IPs and domains, and machine learning to identify unexpected, potentially unauthorized, and malicious activity within your Amazon Web Services environment. This can include issues like escalations of privileges, uses of exposed credentials, or communication with malicious IPs, domains, or presence of malware on your Amazon EC2 instances and container workloads. For example, GuardDuty can detect compromised EC2 instances and container workloads serving malware, or mining bitcoin. GuardDuty also monitors Amazon Web Services account access behavior for signs of compromise, such as unauthorized infrastructure deployments like EC2 instances deployed in a Region that has never been used, or unusual API calls like a password policy change to reduce password strength. GuardDuty informs you about the status of your Amazon Web Services environment by producing security findings that you can view in the GuardDuty console or through Amazon EventBridge. For more information, see the Amazon GuardDuty User Guide.

Package mailgun provides methods for interacting with the Mailgun API. It automates the HTTP request/response cycle, encodings, and other details needed by the API. This SDK lets you do everything the API lets you, in a more Go-friendly way. For further information please see the Mailgun documentation at http://documentation.mailgun.com/ All functions and method have a corresponding test, so if you don't find an example for a function you'd like to know more about, please check for a corresponding test. Of course, contributions to the documentation are always welcome as well. Feel free to submit a pull request or open a Github issue if you cannot find an example to suit your needs. Most methods that begin with `List` return an iterator which simplfies paging through large result sets returned by the mailgun API. Most `List` methods allow you to specify a `Limit` parameter which as you'd expect, limits the number of items returned per page. Note that, at present, Mailgun imposes its own cap of 100 items per page, for all API endpoints. For example, the following iterates over all pages of events 100 items at a time Copyright (c) 2013-2019, Michael Banzon. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the names of Mailgun, Michael Banzon, nor the names of their 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 cloudwatchevents provides the API client, operations, and parameter types for Amazon CloudWatch Events. Amazon EventBridge helps you to respond to state changes in your Amazon Web Services resources. When your resources change state, they automatically send events to an event stream. You can create rules that match selected events in the stream and route them to targets to take action. You can also use rules to take action on a predetermined schedule. For example, you can configure rules to: Automatically invoke an Lambda function to update DNS entries when an event notifies you that Amazon EC2 instance enters the running state. Direct specific API records from CloudTrail to an Amazon Kinesis data stream for detailed analysis of potential security or availability risks. Periodically invoke a built-in target to create a snapshot of an Amazon EBS volume. For more information about the features of Amazon EventBridge, see the Amazon EventBridge User Guide.

Package pagerduty is a Go API client for both the PagerDuty v2 REST and Events API. Most methods should be implemented, and it's recommended to use the WithContext variant of each method and to specify a context with a timeout. To debug responses from the API, you can instruct the client to capture the last response from the API. Please see the documentation for the SetDebugFlag() and LastAPIResponse() methods for more details.

Package centrifuge is a real-time messaging library that abstracts several bidirectional transports (Websocket and its emulation over HTTP-Streaming, SSE/EventSource) and provides primitives to build scalable real-time applications with Go. It's also used as a core of Centrifugo server (https://github.com/centrifugal/centrifugo). Centrifuge library provides several features on top of plain Websocket implementation and comes with several client SDKs – see more details in the library README on GitHub – https://github.com/centrifugal/centrifuge. The API of this library is almost all goroutine-safe except cases where one-time operations like setting callback handlers performed, also your code inside event handlers should be synchronized since event handlers can be called concurrently. Library expects that code inside event handlers will not block. See more information about client connection lifetime and event handler order/concurrency in README on GitHub. Also check out examples in repo to see main library concepts in action.

Package rpcclient implements a websocket-enabled Decred JSON-RPC client. This client provides a robust and easy to use client for interfacing with a Decred RPC server that uses a mostly btcd/bitcoin core style Decred JSON-RPC API. This client has been tested with dcrd (https://github.com/decred/dcrd) and dcrwallet (https://github.com/decred/dcrwallet). In addition to the compatible standard HTTP POST JSON-RPC API, dcrd and dcrwallet provide a websocket interface that is more efficient than the standard HTTP POST method of accessing RPC. The section below discusses the differences between HTTP POST and websockets. By default, this client assumes the RPC server supports websockets and has TLS enabled. In practice, this currently means it assumes you are talking to dcrd or dcrwallet by default. However, configuration options are provided to fall back to HTTP POST and disable TLS to support talking with inferior bitcoin core style RPC servers. In HTTP POST-based JSON-RPC, every request creates a new HTTP connection, issues the call, waits for the response, and closes the connection. This adds quite a bit of overhead to every call and lacks flexibility for features such as notifications. In contrast, the websocket-based JSON-RPC interface provided by dcrd and dcrwallet only uses a single connection that remains open and allows asynchronous bi-directional communication. The websocket interface supports all of the same commands as HTTP POST, but they can be invoked without having to go through a connect/disconnect cycle for every call. In addition, the websocket interface provides other nice features such as the ability to register for asynchronous notifications of various events. The client provides both a synchronous (blocking) and asynchronous API. The synchronous (blocking) API is typically sufficient for most use cases. It works by issuing the RPC and blocking until the response is received. This allows straightforward code where you have the response as soon as the function returns. The asynchronous API works on the concept of futures. When you invoke the async version of a command, it will quickly return an instance of a type that promises to provide the result of the RPC at some future time. In the background, the RPC call is issued and the result is stored in the returned instance. Invoking the Receive method on the returned instance will either return the result immediately if it has already arrived, or block until it has. This is useful since it provides the caller with greater control over concurrency. The first important part of notifications is to realize that they will only work when connected via websockets. This should intuitively make sense because HTTP POST mode does not keep a connection open! All notifications provided by dcrd require registration to opt-in. For example, if you want to be notified when funds are received by a set of addresses, you register the addresses via the NotifyReceived (or NotifyReceivedAsync) function. Notifications are exposed by the client through the use of callback handlers which are setup via a NotificationHandlers instance that is specified by the caller when creating the client. It is important that these notification handlers complete quickly since they are intentionally in the main read loop and will block further reads until they complete. This provides the caller with the flexibility to decide what to do when notifications are coming in faster than they are being handled. In particular this means issuing a blocking RPC call from a callback handler will cause a deadlock as more server responses won't be read until the callback returns, but the callback would be waiting for a response. Thus, any additional RPCs must be issued an a completely decoupled manner. By default, when running in websockets mode, this client will automatically keep trying to reconnect to the RPC server should the connection be lost. There is a back-off in between each connection attempt until it reaches one try per minute. Once a connection is re-established, all previously registered notifications are automatically re-registered and any in-flight commands are re-issued. This means from the caller's perspective, the request simply takes longer to complete. The caller may invoke the Shutdown method on the client to force the client to cease reconnect attempts and return ErrClientShutdown for all outstanding commands. The automatic reconnection can be disabled by setting the DisableAutoReconnect flag to true in the connection config when creating the client. Minor RPC Server Differences and Chain/Wallet Separation Some of the commands are extensions specific to a particular RPC server. For example, the DebugLevel call is an extension only provided by dcrd (and dcrwallet passthrough). Therefore if you call one of these commands against an RPC server that doesn't provide them, you will get an unimplemented error from the server. An effort has been made to call out which commmands are extensions in their documentation. Also, it is important to realize that dcrd intentionally separates the wallet functionality into a separate process named dcrwallet. This means if you are connected to the dcrd RPC server directly, only the RPCs which are related to chain services will be available. Depending on your application, you might only need chain-related RPCs. In contrast, dcrwallet provides pass through treatment for chain-related RPCs, so it supports them in addition to wallet-related RPCs. There are 3 categories of errors that will be returned throughout this package: The first category of errors are typically one of ErrInvalidAuth, ErrInvalidEndpoint, ErrClientDisconnect, or ErrClientShutdown. NOTE: The ErrClientDisconnect will not be returned unless the DisableAutoReconnect flag is set since the client automatically handles reconnect by default as previously described. The second category of errors typically indicates a programmer error and as such the type can vary, but usually will be best handled by simply showing/logging it. The third category of errors, that is errors returned by the server, can be detected by type asserting the error in a *dcrjson.RPCError. For example, to detect if a command is unimplemented by the remote RPC server: The following full-blown client examples are in the examples directory:

Package mailgun provides methods for interacting with the Mailgun API. It automates the HTTP request/response cycle, encodings, and other details needed by the API. This SDK lets you do everything the API lets you, in a more Go-friendly way. For further information please see the Mailgun documentation at http://documentation.mailgun.com/ This document includes a number of examples which illustrates some aspects of the GUI which might be misleading or confusing. All examples included are derived from an acceptance test. Note that every SDK function has a corresponding acceptance test, so if you don't find an example for a function you'd like to know more about, please check the acceptance sub-package for a corresponding test. Of course, contributions to the documentation are always welcome as well. Feel free to submit a pull request or open a Github issue if you cannot find an example to suit your needs. Many SDK functions consume a pair of parameters called limit and skip. These help control how much data Mailgun sends over the wire. Limit, as you'd expect, gives a count of the number of records you want to receive. Note that, at present, Mailgun imposes its own cap of 100, for all API endpoints. Skip indicates where in the data set you want to start receiving from. Mailgun defaults to the very beginning of the dataset if not specified explicitly. If you don't particularly care how much data you receive, you may specify DefaultLimit. If you similarly don't care about where the data starts, you may specify DefaultSkip. Functions which accept a limit and skip setting, in general, will also return a total count of the items returned. Note that this total count is not the total in the bundle returned by the call. You can determine that easily enough with Go's len() function. The total that you receive actually refers to the complete set of data on the server. This total may well exceed the size returned from the API. If this happens, you may find yourself needing to iterate over the dataset of interest. For example: Copyright (c) 2013-2014, Michael Banzon. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the names of Mailgun, Michael Banzon, nor the names of their 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 XGB provides the X Go Binding, which is a low-level API to communicate with the core X protocol and many of the X extensions. It is *very* closely modeled on XCB, so that experience with XCB (or xpyb) is easily translatable to XGB. That is, it uses the same cookie/reply model and is thread safe. There are otherwise no major differences (in the API). Most uses of XGB typically fall under the realm of window manager and GUI kit development, but other applications (like pagers, panels, tilers, etc.) may also require XGB. Moreover, it is a near certainty that if you need to work with X, xgbutil will be of great use to you as well: https://github.com/BurntSushi/xgbutil This is an extremely terse example that demonstrates how to connect to X, create a window, listen to StructureNotify events and Key{Press,Release} events, map the window, and print out all events received. An example with accompanying documentation can be found in examples/create-window. This is another small example that shows how to query Xinerama for geometry information of each active head. Accompanying documentation for this example can be found in examples/xinerama. XGB can benefit greatly from parallelism due to its concurrent design. For evidence of this claim, please see the benchmarks in xproto/xproto_test.go. xproto/xproto_test.go contains a number of contrived tests that stress particular corners of XGB that I presume could be problem areas. Namely: requests with no replies, requests with replies, checked errors, unchecked errors, sequence number wrapping, cookie buffer flushing (i.e., forcing a round trip every N requests made that don't have a reply), getting/setting properties and creating a window and listening to StructureNotify events. Both XCB and xpyb use the same Python module (xcbgen) for a code generator. XGB (before this fork) used the same code generator as well, but in my attempt to add support for more extensions, I found the code generator extremely difficult to work with. Therefore, I re-wrote the code generator in Go. It can be found in its own sub-package, xgbgen, of xgb. My design of xgbgen includes a rough consideration that it could be used for other languages. I am reasonably confident that the core X protocol is in full working form. I've also tested the Xinerama and RandR extensions sparingly. Many of the other existing extensions have Go source generated (and are compilable) and are included in this package, but I am currently unsure of their status. They *should* work. XKB is the only extension that intentionally does not work, although I suspect that GLX also does not work (however, there is Go source code for GLX that compiles, unlike XKB). I don't currently have any intention of getting XKB working, due to its complexity and my current mental incapacity to test it.

The mailgun package provides methods for interacting with the Mailgun API. It automates the HTTP request/response cycle, encodings, and other details needed by the API. This SDK lets you do everything the API lets you, in a more Go-friendly way. For further information please see the Mailgun documentation at http://documentation.mailgun.com/ This document includes a number of examples which illustrates some aspects of the GUI which might be misleading or confusing. All examples included are derived from an acceptance test. Note that every SDK function has a corresponding acceptance test, so if you don't find an example for a function you'd like to know more about, please check the acceptance sub-package for a corresponding test. Of course, contributions to the documentation are always welcome as well. Feel free to submit a pull request or open a Github issue if you cannot find an example to suit your needs. Many SDK functions consume a pair of parameters called limit and skip. These help control how much data Mailgun sends over the wire. Limit, as you'd expect, gives a count of the number of records you want to receive. Note that, at present, Mailgun imposes its own cap of 100, for all API endpoints. Skip indicates where in the data set you want to start receiving from. Mailgun defaults to the very beginning of the dataset if not specified explicitly. If you don't particularly care how much data you receive, you may specify DefaultLimit. If you similarly don't care about where the data starts, you may specify DefaultSkip. Functions which accept a limit and skip setting, in general, will also return a total count of the items returned. Note that this total count is not the total in the bundle returned by the call. You can determine that easily enough with Go's len() function. The total that you receive actually refers to the complete set of data on the server. This total may well exceed the size returned from the API. If this happens, you may find yourself needing to iterate over the dataset of interest. For example: Copyright (c) 2013-2014, Michael Banzon. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the names of Mailgun, Michael Banzon, nor the names of their 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 bugsnag captures errors in real-time and reports them to BugSnag (http://bugsnag.com). Using bugsnag-go is a three-step process. 1. As early as possible in your program configure the notifier with your APIKey. This sets up handling of panics that would otherwise crash your app. 2. Add bugsnag to places that already catch panics. For example you should add it to the HTTP server when you call ListenAndServer: If that's not possible, you can also wrap each HTTP handler manually: 3. To notify BugSnag of an error that is not a panic, pass it to bugsnag.Notify. This will also log the error message using the configured Logger. For detailed integration instructions see https://docs.bugsnag.com/platforms/go. The only required configuration is the BugSnag API key which can be obtained by clicking "Project Settings" on the top of your BugSnag dashboard after signing up. We also recommend you set the ReleaseStage, AppType, and AppVersion if these make sense for your deployment workflow. If you need to attach extra data to BugSnag events, you can do that using the rawData mechanism. Most of the functions that send errors to BugSnag allow you to pass in any number of interface{} values as rawData. The rawData can consist of the Severity, Context, User or MetaData types listed below, and there is also builtin support for *http.Requests. If you want to add custom tabs to your bugsnag dashboard you can pass any value in as rawData, and then process it into the event's metadata using a bugsnag.OnBeforeNotify() hook. If necessary you can pass Configuration in as rawData, or modify the Configuration object passed into OnBeforeNotify hooks. Configuration passed in this way only affects the current notification.

Package mailgun provides methods for interacting with the Mailgun API. It automates the HTTP request/response cycle, encodings, and other details needed by the API. This SDK lets you do everything the API lets you, in a more Go-friendly way. For further information please see the Mailgun documentation at http://documentation.mailgun.com/ All functions and method have a corresponding test, so if you don't find an example for a function you'd like to know more about, please check for a corresponding test. Of course, contributions to the documentation are always welcome as well. Feel free to submit a pull request or open a Github issue if you cannot find an example to suit your needs. Most methods that begin with `List` return an iterator which simplfies paging through large result sets returned by the mailgun API. Most `List` methods allow you to specify a `Limit` parameter which as you'd expect, limits the number of items returned per page. Note that, at present, Mailgun imposes its own cap of 100 items per page, for all API endpoints. For example, the following iterates over all pages of events 100 items at a time Copyright (c) 2013-2019, Michael Banzon. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the names of Mailgun, Michael Banzon, nor the names of their 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 monkit is a flexible code instrumenting and data collection library. I'm going to try and sell you as fast as I can on this library. Example usage We've got tools that capture distribution information (including quantiles) about int64, float64, and bool types. We have tools that capture data about events (we've got meters for deltas, rates, etc). We have rich tools for capturing information about tasks and functions, and literally anything that can generate a name and a number. Almost just as importantly, the amount of boilerplate and code you have to write to get these features is very minimal. Data that's hard to measure probably won't get measured. This data can be collected and sent to Graphite (http://graphite.wikidot.com/) or any other time-series database. Here's a selection of live stats from one of our storage nodes: This library generates call graphs of your live process for you. These call graphs aren't created through sampling. They're full pictures of all of the interesting functions you've annotated, along with quantile information about their successes, failures, how often they panic, return an error (if so instrumented), how many are currently running, etc. The data can be returned in dot format, in json, in text, and can be about just the functions that are currently executing, or all the functions the monitoring system has ever seen. Here's another example of one of our production nodes: https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/callgraph2.png This library generates trace graphs of your live process for you directly, without requiring standing up some tracing system such as Zipkin (though you can do that too). Inspired by Google's Dapper (http://research.google.com/pubs/pub36356.html) and Twitter's Zipkin (http://zipkin.io), we have process-internal trace graphs, triggerable by a number of different methods. You get this trace information for free whenever you use Go contexts (https://blog.golang.org/context) and function monitoring. The output formats are svg and json. Additionally, the library supports trace observation plugins, and we've written a plugin that sends this data to Zipkin (http://github.com/spacemonkeygo/monkit-zipkin). https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/trace.png Before our crazy Go rewrite of everything (https://www.spacemonkey.com/blog/posts/go-space-monkey) (and before we had even seen Google's Dapper paper), we were a Python shop, and all of our "interesting" functions were decorated with a helper that collected timing information and sent it to Graphite. When we transliterated to Go, we wanted to preserve that functionality, so the first version of our monitoring package was born. Over time it started to get janky, especially as we found Zipkin and started adding tracing functionality to it. We rewrote all of our Go code to use Google contexts, and then realized we could get call graph information. We decided a refactor and then an all-out rethinking of our monitoring package was best, and so now we have this library. Sometimes you really want callstack contextual information without having to pass arguments through everything on the call stack. In other languages, many people implement this with thread-local storage. Example: let's say you have written a big system that responds to user requests. All of your libraries log using your log library. During initial development everything is easy to debug, since there's low user load, but now you've scaled and there's OVER TEN USERS and it's kind of hard to tell what log lines were caused by what. Wouldn't it be nice to add request ids to all of the log lines kicked off by that request? Then you could grep for all log lines caused by a specific request id. Geez, it would suck to have to pass all contextual debugging information through all of your callsites. Google solved this problem by always passing a context.Context interface through from call to call. A Context is basically just a mapping of arbitrary keys to arbitrary values that users can add new values for. This way if you decide to add a request context, you can add it to your Context and then all callsites that decend from that place will have the new data in their contexts. It is admittedly very verbose to add contexts to every function call. Painfully so. I hope to write more about it in the future, but Google also wrote up their thoughts about it (https://blog.golang.org/context), which you can go read. For now, just swallow your disgust and let's keep moving. Let's make a super simple Varnish (https://www.varnish-cache.org/) clone. Open up gedit! (Okay just kidding, open whatever text editor you want.) For this motivating program, we won't even add the caching, though there's comments for where to add it if you'd like. For now, let's just make a barebones system that will proxy HTTP requests. We'll call it VLite, but maybe we should call it VReallyLite. Run and build this and open localhost:8080 in your browser. If you use the default proxy target, it should inform you that the world hasn't been destroyed yet. The first thing you'll want to do is add the small amount of boilerplate to make the instrumentation we're going to add to your process observable later. Import the basic monkit packages: and then register environmental statistics and kick off a goroutine in your main method to serve debug requests: Rebuild, and then check out localhost:9000/stats (or localhost:9000/stats/json, if you prefer) in your browser! Remember what I said about Google's contexts (https://blog.golang.org/context)? It might seem a bit overkill for such a small project, but it's time to add them. To help out here, I've created a library that constructs contexts for you for incoming HTTP requests. Nothing that's about to happen requires my webhelp library (https://godoc.org/github.com/jtolds/webhelp), but here is the code now refactored to receive and pass contexts through our two per-request calls. You can create a new context for a request however you want. One reason to use something like webhelp is that the cancelation feature of Contexts is hooked up to the HTTP request getting canceled. Let's start to get statistics about how many requests we receive! First, this package (main) will need to get a monitoring Scope. Add this global definition right after all your imports, much like you'd create a logger with many logging libraries: Now, make the error return value of HandleHTTP named (so, (err error)), and add this defer line as the very first instruction of HandleHTTP: Let's also add the same line (albeit modified for the lack of error) to Proxy, replacing &err with nil: You should now have something like: We'll unpack what's going on here, but for now: For this new funcs dataset, if you want a graph, you can download a dot graph at localhost:9000/funcs/dot and json information from localhost:9000/funcs/json. You should see something like: with a similar report for the Proxy method, or a graph like: https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/handlehttp.png This data reports the overall callgraph of execution for known traces, along with how many of each function are currently running, the most running concurrently (the highwater), how many were successful along with quantile timing information, how many errors there were (with quantile timing information if applicable), and how many panics there were. Since the Proxy method isn't capturing a returned err value, and since HandleHTTP always returns nil, this example won't ever have failures. If you're wondering about the success count being higher than you expected, keep in mind your browser probably requested a favicon.ico. Cool, eh? How it works is an interesting line of code - there's three function calls. If you look at the Go spec, all of the function calls will run at the time the function starts except for the very last one. The first function call, mon.Task(), creates or looks up a wrapper around a Func. You could get this yourself by requesting mon.Func() inside of the appropriate function or mon.FuncNamed(). Both mon.Task() and mon.Func() are inspecting runtime.Caller to determine the name of the function. Because this is a heavy operation, you can actually store the result of mon.Task() and reuse it somehow else if you prefer, so instead of you could instead use which is more performant every time after the first time. runtime.Caller only gets called once. Careful! Don't use the same myFuncMon in different functions unless you want to screw up your statistics! The second function call starts all the various stop watches and bookkeeping to keep track of the function. It also mutates the context pointer it's given to extend the context with information about what current span (in Zipkin parlance) is active. Notably, you *can* pass nil for the context if you really don't want a context. You just lose callgraph information. The last function call stops all the stop watches ad makes a note of any observed errors or panics (it repanics after observing them). Turns out, we don't even need to change our program anymore to get rich tracing information! Open your browser and go to localhost:9000/trace/svg?regex=HandleHTTP. It won't load, and in fact, it's waiting for you to open another tab and refresh localhost:8080 again. Once you retrigger the actual application behavior, the trace regex will capture a trace starting on the first function that matches the supplied regex, and return an svg. Go back to your first tab, and you should see a relatively uninteresting but super promising svg. Let's make the trace more interesting. Add a to your HandleHTTP method, rebuild, and restart. Load localhost:8080, then start a new request to your trace URL, then reload localhost:8080 again. Flip back to your trace, and you should see that the Proxy method only takes a portion of the time of HandleHTTP! https://cdn.rawgit.com/spacemonkeygo/monkit/master/images/trace.svg There's multiple ways to select a trace. You can select by regex using the preselect method (default), which first evaluates the regex on all known functions for sanity checking. Sometimes, however, the function you want to trace may not yet be known to monkit, in which case you'll want to turn preselection off. You may have a bad regex, or you may be in this case if you get the error "Bad Request: regex preselect matches 0 functions." Another way to select a trace is by providing a trace id, which we'll get to next! Make sure to check out what the addition of the time.Sleep call did to the other reports. It's easy to write plugins for monkit! Check out our first one that exports data to Zipkin (http://zipkin.io/)'s Scribe API: https://github.com/spacemonkeygo/monkit-zipkin We plan to have more (for HTrace, OpenTracing, etc, etc), soon!

Package health provides the API client, operations, and parameter types for AWS Health APIs and Notifications. The Health API provides access to the Health information that appears in the Health Dashboard. You can use the API operations to get information about events that might affect your Amazon Web Services and resources. You must have a Business, Enterprise On-Ramp, or Enterprise Support plan from Amazon Web Services Support to use the Health API. If you call the Health API from an Amazon Web Services account that doesn't have a Business, Enterprise On-Ramp, or Enterprise Support plan, you receive a SubscriptionRequiredException error. For API access, you need an access key ID and a secret access key. Use temporary credentials instead of long-term access keys when possible. Temporary credentials include an access key ID, a secret access key, and a security token that indicates when the credentials expire. For more information, see Best practices for managing Amazon Web Services access keysin the Amazon Web Services General Reference. You can use the Health endpoint health.us-east-1.amazonaws.com (HTTPS) to call the Health API operations. Health supports a multi-Region application architecture and has two regional endpoints in an active-passive configuration. You can use the high availability endpoint example to determine which Amazon Web Services Region is active, so that you can get the latest information from the API. For more information, see Accessing the Health APIin the Health User Guide. For authentication of requests, Health uses the Signature Version 4 Signing Process. If your Amazon Web Services account is part of Organizations, you can use the Health organizational view feature. This feature provides a centralized view of Health events across all accounts in your organization. You can aggregate Health events in real time to identify accounts in your organization that are affected by an operational event or get notified of security vulnerabilities. Use the organizational view API operations to enable this feature and return event information. For more information, see Aggregating Health eventsin the Health User Guide. When you use the Health API operations to return Health events, see the following recommendations: Use the eventScopeCodeparameter to specify whether to return Health events that are public or account-specific. Use pagination to view all events from the response. For example, if you call the DescribeEventsForOrganization operation to get all events in your organization, you might receive several page results. Specify the nextToken in the next request to return more results.

Package pipes provides the API client, operations, and parameter types for Amazon EventBridge Pipes. Amazon EventBridge Pipes connects event sources to targets. Pipes reduces the need for specialized knowledge and integration code when developing event driven architectures. This helps ensures consistency across your company’s applications. With Pipes, the target can be any available EventBridge target. To set up a pipe, you select the event source, add optional event filtering, define optional enrichment, and select the target for the event data.

Package rpcclient implements a websocket-enabled Decred JSON-RPC client. This client provides a robust and easy to use client for interfacing with a Decred RPC server that uses a mostly btcd/bitcoin core style Decred JSON-RPC API. This client has been tested with dcrd (https://github.com/decred/dcrd) and dcrwallet (https://github.com/decred/dcrwallet). In addition to the compatible standard HTTP POST JSON-RPC API, dcrd and dcrwallet provide a websocket interface that is more efficient than the standard HTTP POST method of accessing RPC. The section below discusses the differences between HTTP POST and websockets. By default, this client assumes the RPC server supports websockets and has TLS enabled. In practice, this currently means it assumes you are talking to dcrd or dcrwallet by default. However, configuration options are provided to fall back to HTTP POST and disable TLS to support talking with inferior bitcoin core style RPC servers. In HTTP POST-based JSON-RPC, every request creates a new HTTP connection, issues the call, waits for the response, and closes the connection. This adds quite a bit of overhead to every call and lacks flexibility for features such as notifications. In contrast, the websocket-based JSON-RPC interface provided by dcrd and dcrwallet only uses a single connection that remains open and allows asynchronous bi-directional communication. The websocket interface supports all of the same commands as HTTP POST, but they can be invoked without having to go through a connect/disconnect cycle for every call. In addition, the websocket interface provides other nice features such as the ability to register for asynchronous notifications of various events. The client provides both a synchronous (blocking) and asynchronous API. The synchronous (blocking) API is typically sufficient for most use cases. It works by issuing the RPC and blocking until the response is received. This allows straightforward code where you have the response as soon as the function returns. The asynchronous API works on the concept of futures. When you invoke the async version of a command, it will quickly return an instance of a type that promises to provide the result of the RPC at some future time. In the background, the RPC call is issued and the result is stored in the returned instance. Invoking the Receive method on the returned instance will either return the result immediately if it has already arrived, or block until it has. This is useful since it provides the caller with greater control over concurrency. The first important part of notifications is to realize that they will only work when connected via websockets. This should intuitively make sense because HTTP POST mode does not keep a connection open! All notifications provided by dcrd require registration to opt-in. For example, if you want to be notified when funds are received by a set of addresses, you register the addresses via the NotifyReceived (or NotifyReceivedAsync) function. Notifications are exposed by the client through the use of callback handlers which are setup via a NotificationHandlers instance that is specified by the caller when creating the client. It is important that these notification handlers complete quickly since they are intentionally in the main read loop and will block further reads until they complete. This provides the caller with the flexibility to decide what to do when notifications are coming in faster than they are being handled. In particular this means issuing a blocking RPC call from a callback handler will cause a deadlock as more server responses won't be read until the callback returns, but the callback would be waiting for a response. Thus, any additional RPCs must be issued an a completely decoupled manner. By default, when running in websockets mode, this client will automatically keep trying to reconnect to the RPC server should the connection be lost. There is a back-off in between each connection attempt until it reaches one try per minute. Once a connection is re-established, all previously registered notifications are automatically re-registered and any in-flight commands are re-issued. This means from the caller's perspective, the request simply takes longer to complete. The caller may invoke the Shutdown method on the client to force the client to cease reconnect attempts and return ErrClientShutdown for all outstanding commands. The automatic reconnection can be disabled by setting the DisableAutoReconnect flag to true in the connection config when creating the client. Minor RPC Server Differences and Chain/Wallet Separation Some of the commands are extensions specific to a particular RPC server. For example, the DebugLevel call is an extension only provided by dcrd (and dcrwallet passthrough). Therefore if you call one of these commands against an RPC server that doesn't provide them, you will get an unimplemented error from the server. An effort has been made to call out which commmands are extensions in their documentation. Also, it is important to realize that dcrd intentionally separates the wallet functionality into a separate process named dcrwallet. This means if you are connected to the dcrd RPC server directly, only the RPCs which are related to chain services will be available. Depending on your application, you might only need chain-related RPCs. In contrast, dcrwallet provides pass through treatment for chain-related RPCs, so it supports them in addition to wallet-related RPCs. There are 3 categories of errors that will be returned throughout this package: The first category of errors are typically one of ErrInvalidAuth, ErrInvalidEndpoint, ErrClientDisconnect, or ErrClientShutdown. NOTE: The ErrClientDisconnect will not be returned unless the DisableAutoReconnect flag is set since the client automatically handles reconnect by default as previously described. The second category of errors typically indicates a programmer error and as such the type can vary, but usually will be best handled by simply showing/logging it. The third category of errors, that is errors returned by the server, can be detected by type asserting the error in a *dcrjson.RPCError. For example, to detect if a command is unimplemented by the remote RPC server: The following full-blown client examples are in the examples directory:

Package rpcclient implements a websocket-enabled Decred JSON-RPC client. This client provides a robust and easy to use client for interfacing with a Decred RPC server that uses a mostly btcd/bitcoin core style Decred JSON-RPC API. This client has been tested with dcrd (https://github.com/decred/dcrd) and dcrwallet (https://github.com/decred/dcrwallet). In addition to the compatible standard HTTP POST JSON-RPC API, dcrd and dcrwallet provide a websocket interface that is more efficient than the standard HTTP POST method of accessing RPC. The section below discusses the differences between HTTP POST and websockets. By default, this client assumes the RPC server supports websockets and has TLS enabled. In practice, this currently means it assumes you are talking to dcrd or dcrwallet by default. However, configuration options are provided to fall back to HTTP POST and disable TLS to support talking with inferior bitcoin core style RPC servers. In HTTP POST-based JSON-RPC, every request creates a new HTTP connection, issues the call, waits for the response, and closes the connection. This adds quite a bit of overhead to every call and lacks flexibility for features such as notifications. In contrast, the websocket-based JSON-RPC interface provided by dcrd and dcrwallet only uses a single connection that remains open and allows asynchronous bi-directional communication. The websocket interface supports all of the same commands as HTTP POST, but they can be invoked without having to go through a connect/disconnect cycle for every call. In addition, the websocket interface provides other nice features such as the ability to register for asynchronous notifications of various events. The client provides both a synchronous (blocking) and asynchronous API. The synchronous (blocking) API is typically sufficient for most use cases. It works by issuing the RPC and blocking until the response is received. This allows straightforward code where you have the response as soon as the function returns. The asynchronous API works on the concept of futures. When you invoke the async version of a command, it will quickly return an instance of a type that promises to provide the result of the RPC at some future time. In the background, the RPC call is issued and the result is stored in the returned instance. Invoking the Receive method on the returned instance will either return the result immediately if it has already arrived, or block until it has. This is useful since it provides the caller with greater control over concurrency. The first important part of notifications is to realize that they will only work when connected via websockets. This should intuitively make sense because HTTP POST mode does not keep a connection open! All notifications provided by dcrd require registration to opt-in. For example, if you want to be notified when funds are received by a set of addresses, you register the addresses via the NotifyReceived (or NotifyReceivedAsync) function. Notifications are exposed by the client through the use of callback handlers which are setup via a NotificationHandlers instance that is specified by the caller when creating the client. It is important that these notification handlers complete quickly since they are intentionally in the main read loop and will block further reads until they complete. This provides the caller with the flexibility to decide what to do when notifications are coming in faster than they are being handled. In particular this means issuing a blocking RPC call from a callback handler will cause a deadlock as more server responses won't be read until the callback returns, but the callback would be waiting for a response. Thus, any additional RPCs must be issued an a completely decoupled manner. By default, when running in websockets mode, this client will automatically keep trying to reconnect to the RPC server should the connection be lost. There is a back-off in between each connection attempt until it reaches one try per minute. Once a connection is re-established, all previously registered notifications are automatically re-registered and any in-flight commands are re-issued. This means from the caller's perspective, the request simply takes longer to complete. The caller may invoke the Shutdown method on the client to force the client to cease reconnect attempts and return ErrClientShutdown for all outstanding commands. The automatic reconnection can be disabled by setting the DisableAutoReconnect flag to true in the connection config when creating the client. Minor RPC Server Differences and Chain/Wallet Separation Some of the commands are extensions specific to a particular RPC server. For example, the DebugLevel call is an extension only provided by dcrd (and dcrwallet passthrough). Therefore if you call one of these commands against an RPC server that doesn't provide them, you will get an unimplemented error from the server. An effort has been made to call out which commands are extensions in their documentation. Also, it is important to realize that dcrd intentionally separates the wallet functionality into a separate process named dcrwallet. This means if you are connected to the dcrd RPC server directly, only the RPCs which are related to chain services will be available. Depending on your application, you might only need chain-related RPCs. In contrast, dcrwallet provides pass through treatment for chain-related RPCs, so it supports them in addition to wallet-related RPCs. There are 3 categories of errors that will be returned throughout this package: The first category of errors are typically one of ErrInvalidAuth, ErrInvalidEndpoint, ErrClientDisconnect, or ErrClientShutdown. NOTE: The ErrClientDisconnect will not be returned unless the DisableAutoReconnect flag is set since the client automatically handles reconnect by default as previously described. The second category of errors typically indicates a programmer error and as such the type can vary, but usually will be best handled by simply showing/logging it. The third category of errors, that is errors returned by the server, can be detected by type asserting the error in a *dcrjson.RPCError. For example, to detect if a command is unimplemented by the remote RPC server: The following full-blown client examples are in the examples directory:

Package eventsource implements a client and server to allow streaming data one-way over a HTTP connection using the Server-Sent Events API http://dev.w3.org/html5/eventsource/ The client and server respect the Last-Event-ID header. If the Repository interface is implemented on the server, events can be replayed in case of a network disconnection.

Package rpcclient implements a websocket-enabled Decred JSON-RPC client. This client provides a robust and easy to use client for interfacing with a Decred RPC server that uses a mostly btcd/bitcoin core style Decred JSON-RPC API. This client has been tested with dcrd (https://github.com/decred/dcrd) and dcrwallet (https://github.com/decred/dcrwallet). In addition to the compatible standard HTTP POST JSON-RPC API, dcrd and dcrwallet provide a websocket interface that is more efficient than the standard HTTP POST method of accessing RPC. The section below discusses the differences between HTTP POST and websockets. By default, this client assumes the RPC server supports websockets and has TLS enabled. In practice, this currently means it assumes you are talking to dcrd or dcrwallet by default. However, configuration options are provided to fall back to HTTP POST and disable TLS to support talking with inferior bitcoin core style RPC servers. In HTTP POST-based JSON-RPC, every request creates a new HTTP connection, issues the call, waits for the response, and closes the connection. This adds quite a bit of overhead to every call and lacks flexibility for features such as notifications. In contrast, the websocket-based JSON-RPC interface provided by dcrd and dcrwallet only uses a single connection that remains open and allows asynchronous bi-directional communication. The websocket interface supports all of the same commands as HTTP POST, but they can be invoked without having to go through a connect/disconnect cycle for every call. In addition, the websocket interface provides other nice features such as the ability to register for asynchronous notifications of various events. The client provides both a synchronous (blocking) and asynchronous API. The synchronous (blocking) API is typically sufficient for most use cases. It works by issuing the RPC and blocking until the response is received. This allows straightforward code where you have the response as soon as the function returns. The asynchronous API works on the concept of futures. When you invoke the async version of a command, it will quickly return an instance of a type that promises to provide the result of the RPC at some future time. In the background, the RPC call is issued and the result is stored in the returned instance. Invoking the Receive method on the returned instance will either return the result immediately if it has already arrived, or block until it has. This is useful since it provides the caller with greater control over concurrency. The first important part of notifications is to realize that they will only work when connected via websockets. This should intuitively make sense because HTTP POST mode does not keep a connection open! All notifications provided by dcrd require registration to opt-in. For example, if you want to be notified when funds are received by a set of addresses, you register the addresses via the NotifyReceived (or NotifyReceivedAsync) function. Notifications are exposed by the client through the use of callback handlers which are setup via a NotificationHandlers instance that is specified by the caller when creating the client. It is important that these notification handlers complete quickly since they are intentionally in the main read loop and will block further reads until they complete. This provides the caller with the flexibility to decide what to do when notifications are coming in faster than they are being handled. In particular this means issuing a blocking RPC call from a callback handler will cause a deadlock as more server responses won't be read until the callback returns, but the callback would be waiting for a response. Thus, any additional RPCs must be issued an a completely decoupled manner. By default, when running in websockets mode, this client will automatically keep trying to reconnect to the RPC server should the connection be lost. There is a back-off in between each connection attempt until it reaches one try per minute. Once a connection is re-established, all previously registered notifications are automatically re-registered and any in-flight commands are re-issued. This means from the caller's perspective, the request simply takes longer to complete. The caller may invoke the Shutdown method on the client to force the client to cease reconnect attempts and return ErrClientShutdown for all outstanding commands. The automatic reconnection can be disabled by setting the DisableAutoReconnect flag to true in the connection config when creating the client. Minor RPC Server Differences and Chain/Wallet Separation Some of the commands are extensions specific to a particular RPC server. For example, the DebugLevel call is an extension only provided by dcrd (and dcrwallet passthrough). Therefore if you call one of these commands against an RPC server that doesn't provide them, you will get an unimplemented error from the server. An effort has been made to call out which commmands are extensions in their documentation. Also, it is important to realize that dcrd intentionally separates the wallet functionality into a separate process named dcrwallet. This means if you are connected to the dcrd RPC server directly, only the RPCs which are related to chain services will be available. Depending on your application, you might only need chain-related RPCs. In contrast, dcrwallet provides pass through treatment for chain-related RPCs, so it supports them in addition to wallet-related RPCs. There are 3 categories of errors that will be returned throughout this package: The first category of errors are typically one of ErrInvalidAuth, ErrInvalidEndpoint, ErrClientDisconnect, or ErrClientShutdown. NOTE: The ErrClientDisconnect will not be returned unless the DisableAutoReconnect flag is set since the client automatically handles reconnect by default as previously described. The second category of errors typically indicates a programmer error and as such the type can vary, but usually will be best handled by simply showing/logging it. The third category of errors, that is errors returned by the server, can be detected by type asserting the error in a *dcrjson.RPCError. For example, to detect if a command is unimplemented by the remote RPC server: The following full-blown client examples are in the examples directory:

Package rpcclient implements a websocket-enabled Decred JSON-RPC client. This client provides a robust and easy to use client for interfacing with a Decred RPC server that uses a mostly btcd/bitcoin core style Decred JSON-RPC API. This client has been tested with dcrd (https://github.com/decred/dcrd) and dcrwallet (https://github.com/decred/dcrwallet). In addition to the compatible standard HTTP POST JSON-RPC API, dcrd and dcrwallet provide a websocket interface that is more efficient than the standard HTTP POST method of accessing RPC. The section below discusses the differences between HTTP POST and websockets. By default, this client assumes the RPC server supports websockets and has TLS enabled. In practice, this currently means it assumes you are talking to dcrd or dcrwallet by default. However, configuration options are provided to fall back to HTTP POST and disable TLS to support talking with inferior bitcoin core style RPC servers. In HTTP POST-based JSON-RPC, every request creates a new HTTP connection, issues the call, waits for the response, and closes the connection. This adds quite a bit of overhead to every call and lacks flexibility for features such as notifications. In contrast, the websocket-based JSON-RPC interface provided by dcrd and dcrwallet only uses a single connection that remains open and allows asynchronous bi-directional communication. The websocket interface supports all of the same commands as HTTP POST, but they can be invoked without having to go through a connect/disconnect cycle for every call. In addition, the websocket interface provides other nice features such as the ability to register for asynchronous notifications of various events. The client provides both a synchronous (blocking) and asynchronous API. The synchronous (blocking) API is typically sufficient for most use cases. It works by issuing the RPC and blocking until the response is received. This allows straightforward code where you have the response as soon as the function returns. The asynchronous API works on the concept of futures. When you invoke the async version of a command, it will quickly return an instance of a type that promises to provide the result of the RPC at some future time. In the background, the RPC call is issued and the result is stored in the returned instance. Invoking the Receive method on the returned instance will either return the result immediately if it has already arrived, or block until it has. This is useful since it provides the caller with greater control over concurrency. The first important part of notifications is to realize that they will only work when connected via websockets. This should intuitively make sense because HTTP POST mode does not keep a connection open! All notifications provided by dcrd require registration to opt-in. For example, if you want to be notified when funds are received by a set of addresses, you register the addresses via the NotifyReceived (or NotifyReceivedAsync) function. Notifications are exposed by the client through the use of callback handlers which are setup via a NotificationHandlers instance that is specified by the caller when creating the client. It is important that these notification handlers complete quickly since they are intentionally in the main read loop and will block further reads until they complete. This provides the caller with the flexibility to decide what to do when notifications are coming in faster than they are being handled. In particular this means issuing a blocking RPC call from a callback handler will cause a deadlock as more server responses won't be read until the callback returns, but the callback would be waiting for a response. Thus, any additional RPCs must be issued an a completely decoupled manner. By default, when running in websockets mode, this client will automatically keep trying to reconnect to the RPC server should the connection be lost. There is a back-off in between each connection attempt until it reaches one try per minute. Once a connection is re-established, all previously registered notifications are automatically re-registered and any in-flight commands are re-issued. This means from the caller's perspective, the request simply takes longer to complete. The caller may invoke the Shutdown method on the client to force the client to cease reconnect attempts and return ErrClientShutdown for all outstanding commands. The automatic reconnection can be disabled by setting the DisableAutoReconnect flag to true in the connection config when creating the client. This package only provides methods for dcrd RPCs. Using the websocket connection and request-response mapping provided by rpcclient with arbitrary methods or different servers is possible through the generic RawRequest and RawRequestAsync methods (each of which deal with json.RawMessage for parameters and return results). Previous versions of this package provided methods for dcrwallet's JSON-RPC server in addition to dcrd. These were removed in major version 6 of this module. Projects depending on these calls are advised to use the decred.org/dcrwallet/rpc/client/dcrwallet package which is able to wrap rpcclient.Client using the aforementioned RawRequest method: Using struct embedding, it is possible to create a single variable with the combined method sets of both rpcclient.Client and dcrwallet.Client: This technique is valuable as dcrwallet (syncing in RPC mode) will passthrough any unknown RPCs to the backing dcrd server, proxying requests and responses for the client. There are 3 categories of errors that will be returned throughout this package: The first category of errors are typically one of ErrInvalidAuth, ErrInvalidEndpoint, ErrClientDisconnect, or ErrClientShutdown. NOTE: The ErrClientDisconnect will not be returned unless the DisableAutoReconnect flag is set since the client automatically handles reconnect by default as previously described. The second category of errors typically indicates a programmer error and as such the type can vary, but usually will be best handled by simply showing/logging it. The third category of errors, that is errors returned by the server, can be detected by type asserting the error in a *dcrjson.RPCError. For example, to detect if a command is unimplemented by the remote RPC server: The following full-blown client examples are in the examples directory:

Package rpcclient implements a websocket-enabled Decred JSON-RPC client. This client provides a robust and easy to use client for interfacing with a Decred RPC server that uses a mostly btcd/bitcoin core style Decred JSON-RPC API. This client has been tested with dcrd (https://github.com/decred/dcrd) and dcrwallet (https://github.com/decred/dcrwallet). In addition to the compatible standard HTTP POST JSON-RPC API, dcrd and dcrwallet provide a websocket interface that is more efficient than the standard HTTP POST method of accessing RPC. The section below discusses the differences between HTTP POST and websockets. By default, this client assumes the RPC server supports websockets and has TLS enabled. In practice, this currently means it assumes you are talking to dcrd or dcrwallet by default. However, configuration options are provided to fall back to HTTP POST and disable TLS to support talking with inferior bitcoin core style RPC servers. In HTTP POST-based JSON-RPC, every request creates a new HTTP connection, issues the call, waits for the response, and closes the connection. This adds quite a bit of overhead to every call and lacks flexibility for features such as notifications. In contrast, the websocket-based JSON-RPC interface provided by dcrd and dcrwallet only uses a single connection that remains open and allows asynchronous bi-directional communication. The websocket interface supports all of the same commands as HTTP POST, but they can be invoked without having to go through a connect/disconnect cycle for every call. In addition, the websocket interface provides other nice features such as the ability to register for asynchronous notifications of various events. The client provides both a synchronous (blocking) and asynchronous API. The synchronous (blocking) API is typically sufficient for most use cases. It works by issuing the RPC and blocking until the response is received. This allows straightforward code where you have the response as soon as the function returns. The asynchronous API works on the concept of futures. When you invoke the async version of a command, it will quickly return an instance of a type that promises to provide the result of the RPC at some future time. In the background, the RPC call is issued and the result is stored in the returned instance. Invoking the Receive method on the returned instance will either return the result immediately if it has already arrived, or block until it has. This is useful since it provides the caller with greater control over concurrency. The first important part of notifications is to realize that they will only work when connected via websockets. This should intuitively make sense because HTTP POST mode does not keep a connection open! All notifications provided by dcrd require registration to opt-in. For example, if you want to be notified when funds are received by a set of addresses, you register the addresses via the NotifyReceived (or NotifyReceivedAsync) function. Notifications are exposed by the client through the use of callback handlers which are setup via a NotificationHandlers instance that is specified by the caller when creating the client. It is important that these notification handlers complete quickly since they are intentionally in the main read loop and will block further reads until they complete. This provides the caller with the flexibility to decide what to do when notifications are coming in faster than they are being handled. In particular this means issuing a blocking RPC call from a callback handler will cause a deadlock as more server responses won't be read until the callback returns, but the callback would be waiting for a response. Thus, any additional RPCs must be issued an a completely decoupled manner. By default, when running in websockets mode, this client will automatically keep trying to reconnect to the RPC server should the connection be lost. There is a back-off in between each connection attempt until it reaches one try per minute. Once a connection is re-established, all previously registered notifications are automatically re-registered and any in-flight commands are re-issued. This means from the caller's perspective, the request simply takes longer to complete. The caller may invoke the Shutdown method on the client to force the client to cease reconnect attempts and return ErrClientShutdown for all outstanding commands. The automatic reconnection can be disabled by setting the DisableAutoReconnect flag to true in the connection config when creating the client. This package only provides methods for dcrd RPCs. Using the websocket connection and request-response mapping provided by rpcclient with arbitrary methods or different servers is possible through the generic RawRequest and RawRequestAsync methods (each of which deal with json.RawMessage for parameters and return results). Previous versions of this package provided methods for dcrwallet's JSON-RPC server in addition to dcrd. These were removed in major version 6 of this module. Projects depending on these calls are advised to use the decred.org/dcrwallet/rpc/client/dcrwallet package which is able to wrap rpcclient.Client using the aforementioned RawRequest method: Using struct embedding, it is possible to create a single variable with the combined method sets of both rpcclient.Client and dcrwallet.Client: This technique is valuable as dcrwallet (syncing in RPC mode) will passthrough any unknown RPCs to the backing dcrd server, proxying requests and responses for the client. There are 3 categories of errors that will be returned throughout this package: The first category of errors are typically one of ErrInvalidAuth, ErrInvalidEndpoint, ErrClientDisconnect, or ErrClientShutdown. NOTE: The ErrClientDisconnect will not be returned unless the DisableAutoReconnect flag is set since the client automatically handles reconnect by default as previously described. The second category of errors typically indicates a programmer error and as such the type can vary, but usually will be best handled by simply showing/logging it. The third category of errors, that is errors returned by the server, can be detected by type asserting the error in a *dcrjson.RPCError. For example, to detect if a command is unimplemented by the remote RPC server: The following full-blown client examples are in the examples directory:

Package disgord provides Go bindings for the documented Discord API, and allows for a stateful Client using the Session interface, with the option of a configurable caching system or bypass the built-in caching logic all together. Create a Disgord client to get access to the REST API and gateway functionality. In the following example, we listen for new messages and respond with "hello". Session interface: https://pkg.go.dev/github.com/andersfylling/disgord?tab=doc#Session You don't have to use a callback function, channels are supported too! Never close a channel without removing the handler from Disgord, as it will cause a panic. You can control the lifetime of a handler or injected channel by in injecting a controller: disgord.HandlerCtrl. Since you are the owner of the channel, disgord will never close it for you. Disgord handles sharding for you automatically; when starting the bot, when discord demands you to scale up your shards (during runtime), etc. It also gives you control over the shard setup in case you want to run multiple instances of Disgord (in these cases you must handle scaling yourself as Disgord can not). Sharding is done behind the scenes, so you do not need to worry about any settings. Disgord will simply ask Discord for the recommended amount of shards for your bot on startup. However, to set specific amount of shards you can use the `disgord.ShardConfig` to specify a range of valid shard IDs (starts from 0). starting a bot with exactly 5 shards Running multiple instances each with 1 shard (note each instance must use unique shard ids) Handle scaling options yourself You can inject your own cache implementation. By default a read only LFU implementation is used, this should be sufficient for the average user. But you can overwrite certain methods as well! Say you dislike the implementation for MESSAGE_CREATE events, you can embed the default cache and define your own logic: > Note: if you inject your own cache, remember that the cache is also responsible for initiating the objects. > See disgord.CacheNop Whenever you call a REST method from the Session interface; the cache is always checked first. Upon a cache hit, no REST request is executed and you get the data from the cache in return. However, if this is problematic for you or there exist a bug which gives you bad/outdated data, you can bypass it by using Disgord flags. In addition to disgord.IgnoreCache, as shown above, you can pass in other flags such as: disgord.SortByID, disgord.OrderAscending, etc. You can find these flags in the flag.go file. `disgord_diagnosews` will store all the incoming and outgoing JSON data as files in the directory "diagnose-report/packets". The file format is as follows: unix_clientType_direction_shardID_operationCode_sequenceNumber[_eventName].json

Package dom provides GopherJS bindings for the JavaScript DOM APIs. This package is an in progress effort of providing idiomatic Go bindings for the DOM, wrapping the JavaScript DOM APIs. The API is neither complete nor frozen yet, but a great amount of the DOM is already useable. While the package tries to be idiomatic Go, it also tries to stick closely to the JavaScript APIs, so that one does not need to learn a new set of APIs if one is already familiar with it. One decision that hasn't been made yet is what parts exactly should be part of this package. It is, for example, possible that the canvas APIs will live in a separate package. On the other hand, types such as StorageEvent (the event that gets fired when the HTML5 storage area changes) will be part of this package, simply due to how the DOM is structured – even if the actual storage APIs might live in a separate package. This might require special care to avoid circular dependencies. The documentation for some of the identifiers is based on the MDN Web Docs by Mozilla Contributors (https://developer.mozilla.org/en-US/docs/Web/API), licensed under CC-BY-SA 2.5 (https://creativecommons.org/licenses/by-sa/2.5/). The usual entry point of using the dom package is by using the GetWindow() function which will return a Window, from which you can get things such as the current Document. The DOM has a big amount of different element and event types, but they all follow three interfaces. All functions that work on or return generic elements/events will return one of the three interfaces Element, HTMLElement or Event. In these interface values there will be concrete implementations, such as HTMLParagraphElement or FocusEvent. It's also not unusual that values of type Element also implement HTMLElement. In all cases, type assertions can be used. Example: Several functions in the JavaScript DOM return "live" collections of elements, that is collections that will be automatically updated when elements get removed or added to the DOM. Our bindings, however, return static slices of elements that, once created, will not automatically reflect updates to the DOM. This is primarily done so that slices can actually be used, as opposed to a form of iterator, but also because we think that magically changing data isn't Go's nature and that snapshots of state are a lot easier to reason about. This does not, however, mean that all objects are snapshots. Elements, events and generally objects that aren't slices or maps are simple wrappers around JavaScript objects, and as such attributes as well as method calls will always return the most current data. To reflect this behaviour, these bindings use pointers to make the semantics clear. Consider the following example: The above example will print `true`. Some objects in the JS API have two versions of attributes, one that returns a string and one that returns a DOMTokenList to ease manipulation of string-delimited lists. Some other objects only provide DOMTokenList, sometimes DOMSettableTokenList. To simplify these bindings, only the DOMTokenList variant will be made available, by the type TokenList. In cases where the string attribute was the only way to completely replace the value, our TokenList will provide Set([]string) and SetString(string) methods, which will be able to accomplish the same. Additionally, our TokenList will provide methods to convert it to strings and slices. This package has a relatively stable API. However, there will be backwards incompatible changes from time to time. This is because the package isn't complete yet, as well as because the DOM is a moving target, and APIs do change sometimes. While an attempt is made to reduce changing function signatures to a minimum, it can't always be guaranteed. Sometimes mistakes in the bindings are found that require changing arguments or return values. Interfaces defined in this package may also change on a semi-regular basis, as new methods are added to them. This happens because the bindings aren't complete and can never really be, as new features are added to the DOM.

Package cadence and its subdirectories contain the Cadence client side framework. The Cadence service is a task orchestrator for your application’s tasks. Applications using Cadence can execute a logical flow of tasks, especially long-running business logic, asynchronously or synchronously. They can also scale at runtime on distributed systems. A quick example illustrates its use case. Consider Uber Eats where Cadence manages the entire business flow from placing an order, accepting it, handling shopping cart processes (adding, updating, and calculating cart items), entering the order in a pipeline (for preparing food and coordinating delivery), to scheduling delivery as well as handling payments. Cadence consists of a programming framework (or client library) and a managed service (or backend). The framework enables developers to author and coordinate tasks in Go code. The root cadence package contains common data structures. The subpackages are: The Cadence hosted service brokers and persists events generated during workflow execution. Worker nodes owned and operated by customers execute the coordination and task logic. To facilitate the implementation of worker nodes Cadence provides a client-side library for the Go language. In Cadence, you can code the logical flow of events separately as a workflow and code business logic as activities. The workflow identifies the activities and sequences them, while an activity executes the logic. Dynamic workflow execution graphs - Determine the workflow execution graphs at runtime based on the data you are processing. Cadence does not pre-compute the execution graphs at compile time or at workflow start time. Therefore, you have the ability to write workflows that can dynamically adjust to the amount of data they are processing. If you need to trigger 10 instances of an activity to efficiently process all the data in one run, but only 3 for a subsequent run, you can do that. Child Workflows - Orchestrate the execution of a workflow from within another workflow. Cadence will return the results of the child workflow execution to the parent workflow upon completion of the child workflow. No polling is required in the parent workflow to monitor status of the child workflow, making the process efficient and fault tolerant. Durable Timers - Implement delayed execution of tasks in your workflows that are robust to worker failures. Cadence provides two easy to use APIs, **workflow.Sleep** and **workflow.Timer**, for implementing time based events in your workflows. Cadence ensures that the timer settings are persisted and the events are generated even if workers executing the workflow crash. Signals - Modify/influence the execution path of a running workflow by pushing additional data directly to the workflow using a signal. Via the Signal facility, Cadence provides a mechanism to consume external events directly in workflow code. Task routing - Efficiently process large amounts of data using a Cadence workflow, by caching the data locally on a worker and executing all activities meant to process that data on that same worker. Cadence enables you to choose the worker you want to execute a certain activity by scheduling that activity execution in the worker's specific task-list. Unique workflow ID enforcement - Use business entity IDs for your workflows and let Cadence ensure that only one workflow is running for a particular entity at a time. Cadence implements an atomic "uniqueness check" and ensures that no race conditions are possible that would result in multiple workflow executions for the same workflow ID. Therefore, you can implement your code to attempt to start a workflow without checking if the ID is already in use, even in the cases where only one active execution per workflow ID is desired. Perpetual/ContinueAsNew workflows - Run periodic tasks as a single perpetually running workflow. With the "ContinueAsNew" facility, Cadence allows you to leverage the "unique workflow ID enforcement" feature for periodic workflows. Cadence will complete the current execution and start the new execution atomically, ensuring you get to keep your workflow ID. By starting a new execution Cadence also ensures that workflow execution history does not grow indefinitely for perpetual workflows. At-most once activity execution - Execute non-idempotent activities as part of your workflows. Cadence will not automatically retry activities on failure. For every activity execution Cadence will return a success result, a failure result, or a timeout to the workflow code and let the workflow code determine how each one of those result types should be handled. Asynch Activity Completion - Incorporate human input or thrid-party service asynchronous callbacks into your workflows. Cadence allows a workflow to pause execution on an activity and wait for an external actor to resume it with a callback. During this pause the activity does not have any actively executing code, such as a polling loop, and is merely an entry in the Cadence datastore. Therefore, the workflow is unaffected by any worker failures happening over the duration of the pause. Activity Heartbeating - Detect unexpected failures/crashes and track progress in long running activities early. By configuring your activity to report progress periodically to the Cadence server, you can detect a crash that occurs 10 minutes into an hour-long activity execution much sooner, instead of waiting for the 60-minute execution timeout. The recorded progress before the crash gives you sufficient information to determine whether to restart the activity from the beginning or resume it from the point of failure. Timeouts for activities and workflow executions - Protect against stuck and unresponsive activities and workflows with appropriate timeout values. Cadence requires that timeout values are provided for every activity or workflow invocation. There is no upper bound on the timeout values, so you can set timeouts that span days, weeks, or even months. Visibility - Get a list of all your active and/or completed workflow. Explore the execution history of a particular workflow execution. Cadence provides a set of visibility APIs that allow you, the workflow owner, to monitor past and current workflow executions. Debuggability - Replay any workflow execution history locally under a debugger. The Cadence client library provides an API to allow you to capture a stack trace from any failed workflow execution history.

Package arikawa contains a set of modular packages that allows you to make a Discord bot or any type of session (OAuth unsupported). Package session is the most simple abstraction, which combines the API package and the Gateway websocket package together into one. This could be used for minimal bots that only use gateway events and such. Package state abstracts on top of session and provides a local cache of API calls and events. Bots that either don't need a command router or already has its own should use this package. Package bot abstracts on top of state and provides a command router based on Go code. This is similar to discord.py's API, only it's Go and there's no optional arguments (yet, although it could be worked around). Most bots are recommended to use this package, as it's the easiest way to make a bot. Package voice provides an abstraction on top of State and adds voice support. This allows bots to join voice channels and talk. The package uses an io.Writer approach rather than a channel, contrary to other Discord libraries.

Package bugsnag captures errors in real-time and reports them to BugSnag (http://bugsnag.com). Using bugsnag-go is a three-step process. 1. As early as possible in your program configure the notifier with your APIKey. This sets up handling of panics that would otherwise crash your app. 2. Add bugsnag to places that already catch panics. For example you should add it to the HTTP server when you call ListenAndServer: If that's not possible, you can also wrap each HTTP handler manually: 3. To notify BugSnag of an error that is not a panic, pass it to bugsnag.Notify. This will also log the error message using the configured Logger. For detailed integration instructions see https://docs.bugsnag.com/platforms/go. The only required configuration is the BugSnag API key which can be obtained by clicking "Project Settings" on the top of your BugSnag dashboard after signing up. We also recommend you set the ReleaseStage, AppType, and AppVersion if these make sense for your deployment workflow. If you need to attach extra data to BugSnag events, you can do that using the rawData mechanism. Most of the functions that send errors to BugSnag allow you to pass in any number of interface{} values as rawData. The rawData can consist of the Severity, Context, User or MetaData types listed below, and there is also builtin support for *http.Requests. If you want to add custom tabs to your bugsnag dashboard you can pass any value in as rawData, and then process it into the event's metadata using a bugsnag.OnBeforeNotify() hook. If necessary you can pass Configuration in as rawData, or modify the Configuration object passed into OnBeforeNotify hooks. Configuration passed in this way only affects the current notification.

Package nrlogrusplugin decorates logs for sending to the New Relic backend. Use this package if you already send your logs to New Relic and want to enable linking between your APM events and traces with your logs. Since Logrus is completely api-compatible with the stdlib logger, you can replace your `"log"` imports with `log "github.com/sirupsen/logrus"` and follow the steps below to enable the logging product for use with the stdlib Go logger. Using `logger.WithField` (https://godoc.org/github.com/sirupsen/logrus#Logger.WithField) and `logger.WithFields` (https://godoc.org/github.com/sirupsen/logrus#Logger.WithFields) is supported. However, if the field key collides with one of the keys used by the New Relic Formatter, the value will be overwritten. Reserved keys are those found in the `logcontext` package (https://godoc.org/github.com/newrelic/go-agent/v3/integrations/logcontext/#pkg-constants). Supported types for `logger.WithField` and `logger.WithFields` field values are numbers, booleans, strings, and errors. Func types are dropped and all other types are converted to strings. Requires v1.4.0 of the Logrus package or newer. For the best linking experience be sure to enable Distributed Tracing: To enable log decoration, set your log's formatter to the `nrlogrusplugin.ContextFormatter` or if you are using the logrus standard logger The logger will now look for a newrelic.Transaction inside its context and decorate logs accordingly. Therefore, the Transaction must be added to the context and passed to the logger. For example, this logging call must be transformed to include the context, such as: When properly configured, your log statements will be in JSON format with one message per line: If the `trace.id` key is missing, be sure that Distributed Tracing is enabled and that the Transaction context has been added to the logger using `WithContext` (https://godoc.org/github.com/sirupsen/logrus#Logger.WithContext).

Package temporal and its subdirectories contain the Temporal client side framework. The Temporal service is a task orchestrator for your application’s tasks. Applications using Temporal can execute a logical flow of tasks, especially long-running business logic, asynchronously or synchronously. They can also scale at runtime on distributed systems. A quick example illustrates its use case. Consider Uber Eats where Temporal manages the entire business flow from placing an order, accepting it, handling shopping cart processes (adding, updating, and calculating cart items), entering the order in a pipeline (for preparing food and coordinating delivery), to scheduling delivery as well as handling payments. Temporal consists of a programming framework (or client library) and a managed service (or backend). The framework enables developers to author and coordinate tasks in Go code. The root temporal package contains common data structures. The subpackages are: The Temporal hosted service brokers and persists events generated during workflow execution. Worker nodes owned and operated by customers execute the coordination and task logic. To facilitate the implementation of worker nodes Temporal provides a client-side library for the Go language. In Temporal, you can code the logical flow of events separately as a workflow and code business logic as activities. The workflow identifies the activities and sequences them, while an activity executes the logic. Dynamic workflow execution graphs - Determine the workflow execution graphs at runtime based on the data you are processing. Temporal does not pre-compute the execution graphs at compile time or at workflow start time. Therefore, you have the ability to write workflows that can dynamically adjust to the amount of data they are processing. If you need to trigger 10 instances of an activity to efficiently process all the data in one run, but only 3 for a subsequent run, you can do that. Child Workflows - Orchestrate the execution of a workflow from within another workflow. Temporal will return the results of the child workflow execution to the parent workflow upon completion of the child workflow. No polling is required in the parent workflow to monitor status of the child workflow, making the process efficient and fault tolerant. Durable Timers - Implement delayed execution of tasks in your workflows that are robust to worker failures. Temporal provides two easy to use APIs, **workflow.Sleep** and **workflow.Timer**, for implementing time based events in your workflows. Temporal ensures that the timer settings are persisted and the events are generated even if workers executing the workflow crash. Signals - Modify/influence the execution path of a running workflow by pushing additional data directly to the workflow using a signal. Via the Signal facility, Temporal provides a mechanism to consume external events directly in workflow code. Task routing - Efficiently process large amounts of data using a Temporal workflow, by caching the data locally on a worker and executing all activities meant to process that data on that same worker. Temporal enables you to choose the worker you want to execute a certain activity by scheduling that activity execution in the worker's specific task queue. Unique workflow ID enforcement - Use business entity IDs for your workflows and let Temporal ensure that only one workflow is running for a particular entity at a time. Temporal implements an atomic "uniqueness check" and ensures that no race conditions are possible that would result in multiple workflow executions for the same workflow ID. Therefore, you can implement your code to attempt to start a workflow without checking if the ID is already in use, even in the cases where only one active execution per workflow ID is desired. Perpetual/ContinueAsNew workflows - Run periodic tasks as a single perpetually running workflow. With the "ContinueAsNew" facility, Temporal allows you to leverage the "unique workflow ID enforcement" feature for periodic workflows. Temporal will complete the current execution and start the new execution atomically, ensuring you get to keep your workflow ID. By starting a new execution Temporal also ensures that workflow execution history does not grow indefinitely for perpetual workflows. At-most once activity execution - Execute non-idempotent activities as part of your workflows. Temporal will not automatically retry activities on failure. For every activity execution Temporal will return a success result, a failure result, or a timeout to the workflow code and let the workflow code determine how each one of those result types should be handled. Asynch Activity Completion - Incorporate human input or thrid-party service asynchronous callbacks into your workflows. Temporal allows a workflow to pause execution on an activity and wait for an external actor to resume it with a callback. During this pause the activity does not have any actively executing code, such as a polling loop, and is merely an entry in the Temporal datastore. Therefore, the workflow is unaffected by any worker failures happening over the duration of the pause. Activity Heartbeating - Detect unexpected failures/crashes and track progress in long running activities early. By configuring your activity to report progress periodically to the Temporal server, you can detect a crash that occurs 10 minutes into an hour-long activity execution much sooner, instead of waiting for the 60-minute execution timeout. The recorded progress before the crash gives you sufficient information to determine whether to restart the activity from the beginning or resume it from the point of failure. Timeouts for activities and workflow executions - Protect against stuck and unresponsive activities and workflows with appropriate timeout values. Temporal requires that timeout values are provided for every activity or workflow invocation. There is no upper bound on the timeout values, so you can set timeouts that span days, weeks, or even months. Visibility - Get a list of all your active and/or completed workflow. Explore the execution history of a particular workflow execution. Temporal provides a set of visibility APIs that allow you, the workflow owner, to monitor past and current workflow executions. Debuggability - Replay any workflow execution history locally under a debugger. The Temporal client library provides an API to allow you to capture a stack trace from any failed workflow execution history.

Package arikawa contains a set of modular packages that allows you to make a Discord bot or any type of session (OAuth unsupported). Package session is the most simple abstraction, which combines the API package and the Gateway websocket package together into one. This could be used for minimal bots that only use gateway events and such. Package state abstracts on top of session and provides a local cache of API calls and events. Bots that either don't need a command router or already has its own should use this package. Package bot abstracts on top of state and provides a command router based on Go code. This is similar to discord.py's API, only it's Go and there's no optional arguments (yet, although it could be worked around). Most bots are recommended to use this package, as it's the easiest way to make a bot. Package voice provides an abstraction on top of State and adds voice support. This allows bots to join voice channels and talk. The package uses an io.Writer approach rather than a channel, contrary to other Discord libraries.

Package pusher is the Golang library for interacting with the Pusher HTTP API. This package lets you trigger events to your client and query the state of your Pusher channels. When used with a server, you can validate Pusher webhooks and authenticate private- or presence-channels. In order to use this library, you need to have a free account on http://pusher.com. After registering, you will need the application credentials for your app. To create a new client, pass in your application credentials to a `pusher.Client` struct: To start triggering events on a channel, we call `pusherClient.Trigger`: Read on to see what more you can do with this library, such as authenticating private- and presence-channels, validating Pusher webhooks, and querying the HTTP API to get information about your channels. Author: Jamie Patel, Pusher

Package rpcclient implements a websocket-enabled Decred JSON-RPC client. This client provides a robust and easy to use client for interfacing with a Decred RPC server that uses a mostly btcd/bitcoin core style Decred JSON-RPC API. This client has been tested with dcrd (https://github.com/decred/dcrd) and dcrwallet (https://github.com/decred/dcrwallet). In addition to the compatible standard HTTP POST JSON-RPC API, dcrd and dcrwallet provide a websocket interface that is more efficient than the standard HTTP POST method of accessing RPC. The section below discusses the differences between HTTP POST and websockets. By default, this client assumes the RPC server supports websockets and has TLS enabled. In practice, this currently means it assumes you are talking to dcrd or dcrwallet by default. However, configuration options are provided to fall back to HTTP POST and disable TLS to support talking with inferior bitcoin core style RPC servers. In HTTP POST-based JSON-RPC, every request creates a new HTTP connection, issues the call, waits for the response, and closes the connection. This adds quite a bit of overhead to every call and lacks flexibility for features such as notifications. In contrast, the websocket-based JSON-RPC interface provided by dcrd and dcrwallet only uses a single connection that remains open and allows asynchronous bi-directional communication. The websocket interface supports all of the same commands as HTTP POST, but they can be invoked without having to go through a connect/disconnect cycle for every call. In addition, the websocket interface provides other nice features such as the ability to register for asynchronous notifications of various events. The client provides both a synchronous (blocking) and asynchronous API. The synchronous (blocking) API is typically sufficient for most use cases. It works by issuing the RPC and blocking until the response is received. This allows straightforward code where you have the response as soon as the function returns. The asynchronous API works on the concept of futures. When you invoke the async version of a command, it will quickly return an instance of a type that promises to provide the result of the RPC at some future time. In the background, the RPC call is issued and the result is stored in the returned instance. Invoking the Receive method on the returned instance will either return the result immediately if it has already arrived, or block until it has. This is useful since it provides the caller with greater control over concurrency. The first important part of notifications is to realize that they will only work when connected via websockets. This should intuitively make sense because HTTP POST mode does not keep a connection open! All notifications provided by dcrd require registration to opt-in. For example, if you want to be notified when funds are received by a set of addresses, you register the addresses via the NotifyReceived (or NotifyReceivedAsync) function. Notifications are exposed by the client through the use of callback handlers which are setup via a NotificationHandlers instance that is specified by the caller when creating the client. It is important that these notification handlers complete quickly since they are intentionally in the main read loop and will block further reads until they complete. This provides the caller with the flexibility to decide what to do when notifications are coming in faster than they are being handled. In particular this means issuing a blocking RPC call from a callback handler will cause a deadlock as more server responses won't be read until the callback returns, but the callback would be waiting for a response. Thus, any additional RPCs must be issued an a completely decoupled manner. By default, when running in websockets mode, this client will automatically keep trying to reconnect to the RPC server should the connection be lost. There is a back-off in between each connection attempt until it reaches one try per minute. Once a connection is re-established, all previously registered notifications are automatically re-registered and any in-flight commands are re-issued. This means from the caller's perspective, the request simply takes longer to complete. The caller may invoke the Shutdown method on the client to force the client to cease reconnect attempts and return ErrClientShutdown for all outstanding commands. The automatic reconnection can be disabled by setting the DisableAutoReconnect flag to true in the connection config when creating the client. This package only provides methods for dcrd RPCs. Using the websocket connection and request-response mapping provided by rpcclient with arbitrary methods or different servers is possible through the generic RawRequest and RawRequestAsync methods (each of which deal with json.RawMessage for parameters and return results). Previous versions of this package provided methods for dcrwallet's JSON-RPC server in addition to dcrd. These were removed in major version 6 of this module. Projects depending on these calls are advised to use the decred.org/dcrwallet/rpc/client/dcrwallet package which is able to wrap rpcclient.Client using the aforementioned RawRequest method: Using struct embedding, it is possible to create a single variable with the combined method sets of both rpcclient.Client and dcrwallet.Client: This technique is valuable as dcrwallet (syncing in RPC mode) will passthrough any unknown RPCs to the backing dcrd server, proxying requests and responses for the client. There are 3 categories of errors that will be returned throughout this package: The first category of errors are typically one of ErrInvalidAuth, ErrInvalidEndpoint, ErrClientDisconnect, or ErrClientShutdown. NOTE: The ErrClientDisconnect will not be returned unless the DisableAutoReconnect flag is set since the client automatically handles reconnect by default as previously described. The second category of errors typically indicates a programmer error and as such the type can vary, but usually will be best handled by simply showing/logging it. The third category of errors, that is errors returned by the server, can be detected by type asserting the error in a *dcrjson.RPCError. For example, to detect if a command is unimplemented by the remote RPC server: The following full-blown client examples are in the examples directory:

Package codestarnotifications provides the API client, operations, and parameter types for AWS CodeStar Notifications. This AWS CodeStar Notifications API Reference provides descriptions and usage examples of the operations and data types for the AWS CodeStar Notifications API. You can use the AWS CodeStar Notifications API to work with the following objects: Notification rules, by calling the following: CreateNotificationRule DeleteNotificationRule DescribeNotificationRule ListNotificationRules UpdateNotificationRule Subscribe Unsubscribe Targets, by calling the following: DeleteTarget ListTargets Events, by calling the following: ListEventTypes Tags, by calling the following: ListTagsForResource TagResource UntagResource For information about how to use AWS CodeStar Notifications, see the Amazon Web Services Developer Tools Console User Guide.

Package arikawa contains a set of modular packages that allows you to make a Discord bot or any type of session (OAuth unsupported). Package session is the most simple abstraction, which combines the API package and the Gateway websocket package together into one. This could be used for minimal bots that only use gateway events and such. Package state abstracts on top of session and provides a local cache of API calls and events. Bots that either don't need a command router or already has its own should use this package. Package bot abstracts on top of state and provides a command router based on Go code. This is similar to discord.py's API, only it's Go and there's no optional arguments (yet, although it could be worked around). Most bots are recommended to use this package, as it's the easiest way to make a bot. Package voice provides an abstraction on top of State and adds voice support. This allows bots to join voice channels and talk. The package uses an io.Writer approach rather than a channel, contrary to other Discord libraries.

Package securitylake provides the API client, operations, and parameter types for Amazon Security Lake. Amazon Security Lake is a fully managed security data lake service. You can use Security Lake to automatically centralize security data from cloud, on-premises, and custom sources into a data lake that's stored in your Amazon Web Services account. Amazon Web Services Organizations is an account management service that lets you consolidate multiple Amazon Web Services accounts into an organization that you create and centrally manage. With Organizations, you can create member accounts and invite existing accounts to join your organization. Security Lake helps you analyze security data for a more complete understanding of your security posture across the entire organization. It can also help you improve the protection of your workloads, applications, and data. The data lake is backed by Amazon Simple Storage Service (Amazon S3) buckets, and you retain ownership over your data. Amazon Security Lake integrates with CloudTrail, a service that provides a record of actions taken by a user, role, or an Amazon Web Services service. In Security Lake, CloudTrail captures API calls for Security Lake as events. The calls captured include calls from the Security Lake console and code calls to the Security Lake API operations. If you create a trail, you can enable continuous delivery of CloudTrail events to an Amazon S3 bucket, including events for Security Lake. If you don't configure a trail, you can still view the most recent events in the CloudTrail console in Event history. Using the information collected by CloudTrail you can determine the request that was made to Security Lake, the IP address from which the request was made, who made the request, when it was made, and additional details. To learn more about Security Lake information in CloudTrail, see the Amazon Security Lake User Guide. Security Lake automates the collection of security-related log and event data from integrated Amazon Web Services and third-party services. It also helps you manage the lifecycle of data with customizable retention and replication settings. Security Lake converts ingested data into Apache Parquet format and a standard open-source schema called the Open Cybersecurity Schema Framework (OCSF). Other Amazon Web Services and third-party services can subscribe to the data that's stored in Security Lake for incident response and security data analytics.

Package disgo is a collection of packages for interaction with the Discord Bot and OAuth2 API. Package discord is a collection of structs and types of the Discord API. Package bot connects the Gateway/Sharding, HTTPServer, Cache, Rest & Events packages into a single high level client interface. Package gateway is used to connect and interact with the Discord Gateway. Package sharding is used to connect and interact with the Discord Gateway. Package cache provides a generic cache interface for Discord entities. Package httpserver is used to interact with the Discord outgoing webhooks for interactions. Package events provide high level events around the Discord Events. Package rest is used to interact with the Discord REST API. Package webhook provides a high level client interface for interacting with Discord webhooks. Package oauth2 provides a high level client interface for interacting with Discord oauth2. Package voice provides a high level client interface for interacting with Discord voice.

Package event provides low-cost tracing, metrics and structured logging. These are often grouped under the term "observability". This package is highly experimental and in a state of flux; it is public only to allow for collaboration on the design and implementation, and may be changed or removed at any time. It uses a common event system to provide a way for libraries to produce observability information in a way that does not tie the libraries to a specific API or applications to a specific export format. It is designed for minimal overhead when no exporter is used so that it is safe to leave calls in libraries.

Package greengrass provides the API client, operations, and parameter types for AWS Greengrass. AWS IoT Greengrass seamlessly extends AWS onto physical devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. AWS IoT Greengrass ensures your devices can respond quickly to local events and operate with intermittent connectivity. AWS IoT Greengrass minimizes the cost of transmitting data to the cloud by allowing you to author AWS Lambda functions that execute locally.

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

Package unleash is a client library for connecting to an Unleash feature toggle server. See https://github.com/Unleash/unleash for more information. The API is very simple. The main functions of interest are Initialize and IsEnabled. Calling Initialize will create a default client and if a listener is supplied, it will start the sync loop. Internally the client consists of two components. The first is the repository which runs in a separate Go routine and polls the server to get the latest feature toggles. Once the feature toggles are fetched, they are stored by sending the data to an instance of the Storage interface which is responsible for storing the data both in memory and also persisting it somewhere. The second component is the metrics component which is responsible for tracking how often features were queried and whether or not they were enabled. The metrics components also runs in a separate Go routine and will occasionally upload the latest metrics to the Unleash server. The client struct creates a set of channels that it passes to both of the above components and it uses those for communicating asynchronously. It is important to ensure that these channels get regularly drained to avoid blocking those Go routines. There are two ways this can be done. The first and perhaps simplest way to "drive" the synchronization loop in the client is to provide a type that implements one or more of the listener interfaces. There are 3 interfaces and you can choose which ones you should implement: If you are only interesting in tracking errors and warnings and don't care about any of the other signals, then you only need to implement the ErrorListener and pass this instance to WithListener(). The DebugListener shows an example of implementing all of the listeners in a single type. If you would prefer to have control over draining the channels yourself, then you must not call WithListener(). Instead, you should read all of the channels continuously inside a select. The WithInstance example shows how to do this. Note that all channels must be drained, even if you are not interested in the result. The following examples show how to use the client in different scenarios. ExampleCustomStrategy demonstrates using a custom strategy. ExampleFallbackFunc demonstrates how to specify a fallback function. ExampleSimpleUsage demonstrates the simplest way to use the unleash client. ExampleWithInstance demonstrates how to create the client manually instead of using the default client. It also shows how to run the event loop manually.

Package getopt (v2) provides traditional getopt processing for implementing commands that use traditional command lines. The standard Go flag package cannot be used to write a program that parses flags the way ls or ssh does, for example. Version 2 of this package has a simplified API. See the github.com/pborman/options package for a simple structure based interface to this package. Getopt supports functionality found in both the standard BSD getopt as well as (one of the many versions of) the GNU getopt_long. Being a Go package, this package makes common usage easy, but still enables more controlled usage if needed. Typical usage: If you don't want the program to exit on error, use getopt.Getopt: Support is provided for both short (-f) and long (--flag) options. A single option may have both a short and a long name. Each option may be a flag or a value. A value takes an argument. Declaring no long names causes this package to process arguments like the traditional BSD getopt. Short flags may be combined into a single parameter. For example, "-a -b -c" may also be expressed "-abc". Long flags must stand on their own "--alpha --beta" Values require an argument. For short options the argument may either be immediately following the short name or as the next argument. Only one short value may be combined with short flags in a single argument; the short value must be after all short flags. For example, if f is a flag and v is a value, then: For the long value option val: Values with an optional value only set the value if the value is part of the same argument. In any event, the option count is increased and the option is marked as seen. There is no convenience function defined for making the value optional. The SetOptional method must be called on the actual Option. Parsing continues until the first non-option or "--" is encountered. The short name "-" can be used, but it either is specified as "-" or as part of a group of options, for example "-f-". If there are no long options specified then "--f" could also be used. If "-" is not declared as an option then the single "-" will also terminate the option processing but unlike "--", the "-" will be part of the remaining arguments. Normally the parsing is performed by calling the Parse function. If it is important to see the order of the options then the Getopt function should be used. The standard Parse function does the equivalent of: When calling Getopt it is the responsibility of the caller to print any errors. Normally the default option set, CommandLine, is used. Other option sets may be created with New. After parsing, the sets Args will contain the non-option arguments. If an error is encountered then Args will begin with argument that caused the error. It is valid to call a set's Parse a second time to amen flags or values. As an example: If called with set to { "prog", "-a", "cmd", "-b", "arg" } then both and and b would be set, cmd would be set to "cmd", and opts.Args() would return { "arg" }. Unless an option type explicitly prohibits it, an option may appear more than once in the arguments. The last value provided to the option is the value. An option marked as mandatory and not seen when parsing will cause an error to be reported such as: "program: --name is a mandatory option". An option is marked mandatory by using the Mandatory method: Mandatory options have (required) appended to their help message: Options can be marked as part of a mutually exclusive group. When two or more options in a mutually exclusive group are both seen while parsing then an error such as "program: options -a and -b are mutually exclusive" will be reported. Mutually exclusive groups are declared using the SetGroup method: A set can have multiple mutually exclusive groups. Mutually exclusive groups are identified with their group name in {}'s appeneded to their help message: The Flag and FlagLong functions support most standard Go types. For the list, see the description of FlagLong below for a list of supported types. There are also helper routines to allow single line flag declarations. These types are: Bool, Counter, Duration, Enum, Int16, Int32, Int64, Int, List, Signed, String, Uint16, Uint32, Uint64, Uint, and Unsigned. Each comes in a short and long flavor, e.g., Bool and BoolLong and include functions to set the flags on the standard command line or for a specific Set of flags. Except for the Counter, Enum, Signed and Unsigned types, all of these types can be declared using Flag and FlagLong by passing in a pointer to the appropriate type. A pointer to any type that implements the Value interface may be passed to Flag or FlagLong. All non-flag options are created with a "valuehelp" as the last parameter. Valuehelp should be 0, 1, or 2 strings. The first string, if provided, is the usage message for the option. If the second string, if provided, is the name to use for the value when displaying the usage. If not provided the term "value" is assumed. The usage message for the option created with is is

Package duit is a pure go, cross-platform, MIT-licensed, UI toolkit for developers. The examples/ directory has small code examples for working with duit and its UIs. Examples are the recommended starting point. Start with NewDUI to create a DUI: essentially a window and all the UI state. The user interface consists of a hierarchy of "UIs" like Box, Scroll, Button, Label, etc. They are called UIs, after the interface UI they all implement. The zero structs for UIs have sane default behaviour so you only have to fill in the fields you need. UIs are kept/wrapped in a Kid, to track their layout/draw state. Use NewKids() to build up the UIs for your application. You won't see much of the Kid-types/functions otherwise, unless you implement a new UI. You are in charge of the main event loop, receiving mouse/keyboard/window events from the dui.Inputs channel, and typically passing them on unchanged to dui.Input. All callbacks and functions on UIs are called from inside dui.Input. From there you can also safely change the the UIs, no locking required. After changing a UI you are responsible for calling MarkLayout or MarkDraw to tell duit the UI needs a new layout or draw. This may sound like more work, but this tradeoff keeps the API small and easy to use. If you need to change the UI from a goroutine outside of the main loop, e.g. for blocking calls, you can send a function that makes those modifications on the dui.Call channel, which will be run on the main channel through dui.Inputs. After handling an input, duit will layout or draw as necessary, no need to render explicitly. Embedding a UI into your own data structure is often an easy way to build up UI hiearchies. Scroll and Edit show a scrollbar. Use button 1 on the scrollbar to scroll up, button 3 to scroll down. If you click more near the top, you scroll less. More near the bottom, more. Button 2 scrolls to the absolute place, where you clicked. Button 4 and 5 are wheel up and wheel down, and also scroll less/more depending on position in the UI.