Build

Package main is a simple wrapper of the real etcd entrypoint package (located at github.com/coreos/etcd/etcdmain) to ensure that etcd is still "go getable"; e.g. `go get github.com/coreos/etcd` works as expected and builds a binary in $GOBIN/etcd This package should NOT be extended or modified in any way; to modify the etcd binary, work in the `github.com/coreos/etcd/etcdmain` package.

Package main is a simple wrapper of the real etcd entrypoint package (located at github.com/coreos/etcd/etcdmain) to ensure that etcd is still "go getable"; e.g. `go get github.com/coreos/etcd` works as expected and builds a binary in $GOBIN/etcd This package should NOT be extended or modified in any way; to modify the etcd binary, work in the `github.com/coreos/etcd/etcdmain` package.

Package raft sends and receives messages in the Protocol Buffer format defined in the raftpb package. Raft is a protocol with which a cluster of nodes can maintain a replicated state machine. The state machine is kept in sync through the use of a replicated log. For more details on Raft, see "In Search of an Understandable Consensus Algorithm" (https://raft.github.io/raft.pdf) by Diego Ongaro and John Ousterhout. A simple example application, _raftexample_, is also available to help illustrate how to use this package in practice: https://github.com/etcd-io/etcd/tree/main/contrib/raftexample The primary object in raft is a Node. You either start a Node from scratch using raft.StartNode or start a Node from some initial state using raft.RestartNode. To start a node from scratch: To restart a node from previous state: Now that you are holding onto a Node you have a few responsibilities: First, you must read from the Node.Ready() channel and process the updates it contains. These steps may be performed in parallel, except as noted in step 2. 1. Write HardState, Entries, and Snapshot to persistent storage if they are not empty. Note that when writing an Entry with Index i, any previously-persisted entries with Index >= i must be discarded. 2. Send all Messages to the nodes named in the To field. It is important that no messages be sent until the latest HardState has been persisted to disk, and all Entries written by any previous Ready batch (Messages may be sent while entries from the same batch are being persisted). To reduce the I/O latency, an optimization can be applied to make leader write to disk in parallel with its followers (as explained at section 10.2.1 in Raft thesis). If any Message has type MsgSnap, call Node.ReportSnapshot() after it has been sent (these messages may be large). Note: Marshalling messages is not thread-safe; it is important that you make sure that no new entries are persisted while marshalling. The easiest way to achieve this is to serialize the messages directly inside your main raft loop. 3. Apply Snapshot (if any) and CommittedEntries to the state machine. If any committed Entry has Type EntryConfChange, call Node.ApplyConfChange() to apply it to the node. The configuration change may be cancelled at this point by setting the NodeID field to zero before calling ApplyConfChange (but ApplyConfChange must be called one way or the other, and the decision to cancel must be based solely on the state machine and not external information such as the observed health of the node). 4. Call Node.Advance() to signal readiness for the next batch of updates. This may be done at any time after step 1, although all updates must be processed in the order they were returned by Ready. Second, all persisted log entries must be made available via an implementation of the Storage interface. The provided MemoryStorage type can be used for this (if you repopulate its state upon a restart), or you can supply your own disk-backed implementation. Third, when you receive a message from another node, pass it to Node.Step: Finally, you need to call Node.Tick() at regular intervals (probably via a time.Ticker). Raft has two important timeouts: heartbeat and the election timeout. However, internally to the raft package time is represented by an abstract "tick". The total state machine handling loop will look something like this: To propose changes to the state machine from your node take your application data, serialize it into a byte slice and call: If the proposal is committed, data will appear in committed entries with type raftpb.EntryNormal. There is no guarantee that a proposed command will be committed; you may have to re-propose after a timeout. To add or remove a node in a cluster, build ConfChange struct 'cc' and call: After config change is committed, some committed entry with type raftpb.EntryConfChange will be returned. You must apply it to node through: Note: An ID represents a unique node in a cluster for all time. A given ID MUST be used only once even if the old node has been removed. This means that for example IP addresses make poor node IDs since they may be reused. Node IDs must be non-zero. This implementation is up to date with the final Raft thesis (https://github.com/ongardie/dissertation/blob/master/stanford.pdf), although our implementation of the membership change protocol differs somewhat from that described in chapter 4. The key invariant that membership changes happen one node at a time is preserved, but in our implementation the membership change takes effect when its entry is applied, not when it is added to the log (so the entry is committed under the old membership instead of the new). This is equivalent in terms of safety, since the old and new configurations are guaranteed to overlap. To ensure that we do not attempt to commit two membership changes at once by matching log positions (which would be unsafe since they should have different quorum requirements), we simply disallow any proposed membership change while any uncommitted change appears in the leader's log. This approach introduces a problem when you try to remove a member from a two-member cluster: If one of the members dies before the other one receives the commit of the confchange entry, then the member cannot be removed any more since the cluster cannot make progress. For this reason it is highly recommended to use three or more nodes in every cluster. Package raft sends and receives message in Protocol Buffer format (defined in raftpb package). Each state (follower, candidate, leader) implements its own 'step' method ('stepFollower', 'stepCandidate', 'stepLeader') when advancing with the given raftpb.Message. Each step is determined by its raftpb.MessageType. Note that every step is checked by one common method 'Step' that safety-checks the terms of node and incoming message to prevent stale log entries:

Package main is a simple wrapper of the real etcd entrypoint package (located at go.etcd.io/etcd/etcdmain) to ensure that etcd is still "go getable"; e.g. `go get go.etcd.io/etcd` works as expected and builds a binary in $GOBIN/etcd This package should NOT be extended or modified in any way; to modify the etcd binary, work in the `go.etcd.io/etcd/etcdmain` package.

Package mux implements a request router and dispatcher. The name mux stands for "HTTP request multiplexer". Like the standard http.ServeMux, mux.Router matches incoming requests against a list of registered routes and calls a handler for the route that matches the URL or other conditions. The main features are: Let's start registering a couple of URL paths and handlers: Here we register three routes mapping URL paths to handlers. This is equivalent to how http.HandleFunc() works: if an incoming request URL matches one of the paths, the corresponding handler is called passing (http.ResponseWriter, *http.Request) as parameters. Paths can have variables. They are defined using the format {name} or {name:pattern}. If a regular expression pattern is not defined, the matched variable will be anything until the next slash. For example: Groups can be used inside patterns, as long as they are non-capturing (?:re). For example: The names are used to create a map of route variables which can be retrieved calling mux.Vars(): Note that if any capturing groups are present, mux will panic() during parsing. To prevent this, convert any capturing groups to non-capturing, e.g. change "/{sort:(asc|desc)}" to "/{sort:(?:asc|desc)}". This is a change from prior versions which behaved unpredictably when capturing groups were present. And this is all you need to know about the basic usage. More advanced options are explained below. Routes can also be restricted to a domain or subdomain. Just define a host pattern to be matched. They can also have variables: There are several other matchers that can be added. To match path prefixes: ...or HTTP methods: ...or URL schemes: ...or header values: ...or query values: ...or to use a custom matcher function: ...and finally, it is possible to combine several matchers in a single route: Setting the same matching conditions again and again can be boring, so we have a way to group several routes that share the same requirements. We call it "subrouting". For example, let's say we have several URLs that should only match when the host is "www.example.com". Create a route for that host and get a "subrouter" from it: Then register routes in the subrouter: The three URL paths we registered above will only be tested if the domain is "www.example.com", because the subrouter is tested first. This is not only convenient, but also optimizes request matching. You can create subrouters combining any attribute matchers accepted by a route. Subrouters can be used to create domain or path "namespaces": you define subrouters in a central place and then parts of the app can register its paths relatively to a given subrouter. There's one more thing about subroutes. When a subrouter has a path prefix, the inner routes use it as base for their paths: Note that the path provided to PathPrefix() represents a "wildcard": calling PathPrefix("/static/").Handler(...) means that the handler will be passed any request that matches "/static/*". This makes it easy to serve static files with mux: Now let's see how to build registered URLs. Routes can be named. All routes that define a name can have their URLs built, or "reversed". We define a name calling Name() on a route. For example: To build a URL, get the route and call the URL() method, passing a sequence of key/value pairs for the route variables. For the previous route, we would do: ...and the result will be a url.URL with the following path: This also works for host and query value variables: All variables defined in the route are required, and their values must conform to the corresponding patterns. These requirements guarantee that a generated URL will always match a registered route -- the only exception is for explicitly defined "build-only" routes which never match. Regex support also exists for matching Headers within a route. For example, we could do: ...and the route will match both requests with a Content-Type of `application/json` as well as `application/text` There's also a way to build only the URL host or path for a route: use the methods URLHost() or URLPath() instead. For the previous route, we would do: And if you use subrouters, host and path defined separately can be built as well: Mux supports the addition of middlewares to a Router, which are executed in the order they are added if a match is found, including its subrouters. Middlewares are (typically) small pieces of code which take one request, do something with it, and pass it down to another middleware or the final handler. Some common use cases for middleware are request logging, header manipulation, or ResponseWriter hijacking. Typically, the returned handler is a closure which does something with the http.ResponseWriter and http.Request passed to it, and then calls the handler passed as parameter to the MiddlewareFunc (closures can access variables from the context where they are created). A very basic middleware which logs the URI of the request being handled could be written as: Middlewares can be added to a router using `Router.Use()`: A more complex authentication middleware, which maps session token to users, could be written as: Note: The handler chain will be stopped if your middleware doesn't call `next.ServeHTTP()` with the corresponding parameters. This can be used to abort a request if the middleware writer wants to.

protoc-gen-go-grpc is a plugin for the Google protocol buffer compiler to generate Go code. Install it by building this program and making it accessible within your PATH with the name: The 'go-grpc' suffix becomes part of the argument for the protocol compiler, such that it can be invoked as: This generates Go service definitions for the protocol buffer defined by file.proto. With that input, the output will be written to:

Package zap provides fast, structured, leveled logging. For applications that log in the hot path, reflection-based serialization and string formatting are prohibitively expensive - they're CPU-intensive and make many small allocations. Put differently, using json.Marshal and fmt.Fprintf to log tons of interface{} makes your application slow. Zap takes a different approach. It includes a reflection-free, zero-allocation JSON encoder, and the base Logger strives to avoid serialization overhead and allocations wherever possible. By building the high-level SugaredLogger on that foundation, zap lets users choose when they need to count every allocation and when they'd prefer a more familiar, loosely typed API. In contexts where performance is nice, but not critical, use the SugaredLogger. It's 4-10x faster than other structured logging packages and supports both structured and printf-style logging. Like log15 and go-kit, the SugaredLogger's structured logging APIs are loosely typed and accept a variadic number of key-value pairs. (For more advanced use cases, they also accept strongly typed fields - see the SugaredLogger.With documentation for details.) By default, loggers are unbuffered. However, since zap's low-level APIs allow buffering, calling Sync before letting your process exit is a good habit. In the rare contexts where every microsecond and every allocation matter, use the Logger. It's even faster than the SugaredLogger and allocates far less, but it only supports strongly-typed, structured logging. Choosing between the Logger and SugaredLogger doesn't need to be an application-wide decision: converting between the two is simple and inexpensive. The simplest way to build a Logger is to use zap's opinionated presets: NewExample, NewProduction, and NewDevelopment. These presets build a logger with a single function call: Presets are fine for small projects, but larger projects and organizations naturally require a bit more customization. For most users, zap's Config struct strikes the right balance between flexibility and convenience. See the package-level BasicConfiguration example for sample code. More unusual configurations (splitting output between files, sending logs to a message queue, etc.) are possible, but require direct use of go.uber.org/zap/zapcore. See the package-level AdvancedConfiguration example for sample code. The zap package itself is a relatively thin wrapper around the interfaces in go.uber.org/zap/zapcore. Extending zap to support a new encoding (e.g., BSON), a new log sink (e.g., Kafka), or something more exotic (perhaps an exception aggregation service, like Sentry or Rollbar) typically requires implementing the zapcore.Encoder, zapcore.WriteSyncer, or zapcore.Core interfaces. See the zapcore documentation for details. Similarly, package authors can use the high-performance Encoder and Core implementations in the zapcore package to build their own loggers. An FAQ covering everything from installation errors to design decisions is available at https://github.com/uber-go/zap/blob/master/FAQ.md.

Package sdk is the official AWS SDK for the Go programming language. The AWS SDK for Go provides APIs and utilities that developers can use to build Go applications that use AWS services, such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3). The SDK removes the complexity of coding directly against a web service interface. It hides a lot of the lower-level plumbing, such as authentication, request retries, and error handling. The SDK also includes helpful utilities on top of the AWS APIs that add additional capabilities and functionality. For example, the Amazon S3 Download and Upload Manager will automatically split up large objects into multiple parts and transfer them concurrently. See the s3manager package documentation for more information. https://docs.aws.amazon.com/sdk-for-go/api/service/s3/s3manager/ Checkout the Getting Started Guide and API Reference Docs detailed the SDK's components and details on each AWS client the SDK supports. The Getting Started Guide provides examples and detailed description of how to get setup with the SDK. https://docs.aws.amazon.com/sdk-for-go/v1/developer-guide/welcome.html The API Reference Docs include a detailed breakdown of the SDK's components such as utilities and AWS clients. Use this as a reference of the Go types included with the SDK, such as AWS clients, API operations, and API parameters. https://docs.aws.amazon.com/sdk-for-go/api/ The SDK is composed of two main components, SDK core, and service clients. The SDK core packages are all available under the aws package at the root of the SDK. Each client for a supported AWS service is available within its own package under the service folder at the root of the SDK. aws - SDK core, provides common shared types such as Config, Logger, and utilities to make working with API parameters easier. awserr - Provides the error interface that the SDK will use for all errors that occur in the SDK's processing. This includes service API response errors as well. The Error type is made up of a code and message. Cast the SDK's returned error type to awserr.Error and call the Code method to compare returned error to specific error codes. See the package's documentation for additional values that can be extracted such as RequestId. credentials - Provides the types and built in credentials providers the SDK will use to retrieve AWS credentials to make API requests with. Nested under this folder are also additional credentials providers such as stscreds for assuming IAM roles, and ec2rolecreds for EC2 Instance roles. endpoints - Provides the AWS Regions and Endpoints metadata for the SDK. Use this to lookup AWS service endpoint information such as which services are in a region, and what regions a service is in. Constants are also provided for all region identifiers, e.g UsWest2RegionID for "us-west-2". session - Provides initial default configuration, and load configuration from external sources such as environment and shared credentials file. request - Provides the API request sending, and retry logic for the SDK. This package also includes utilities for defining your own request retryer, and configuring how the SDK processes the request. service - Clients for AWS services. All services supported by the SDK are available under this folder. The SDK includes the Go types and utilities you can use to make requests to AWS service APIs. Within the service folder at the root of the SDK you'll find a package for each AWS service the SDK supports. All service clients follows a common pattern of creation and usage. When creating a client for an AWS service you'll first need to have a Session value constructed. The Session provides shared configuration that can be shared between your service clients. When service clients are created you can pass in additional configuration via the aws.Config type to override configuration provided by in the Session to create service client instances with custom configuration. Once the service's client is created you can use it to make API requests the AWS service. These clients are safe to use concurrently. In the AWS SDK for Go, you can configure settings for service clients, such as the log level and maximum number of retries. Most settings are optional; however, for each service client, you must specify a region and your credentials. The SDK uses these values to send requests to the correct AWS region and sign requests with the correct credentials. You can specify these values as part of a session or as environment variables. See the SDK's configuration guide for more information. https://docs.aws.amazon.com/sdk-for-go/v1/developer-guide/configuring-sdk.html See the session package documentation for more information on how to use Session with the SDK. https://docs.aws.amazon.com/sdk-for-go/api/aws/session/ See the Config type in the aws package for more information on configuration options. https://docs.aws.amazon.com/sdk-for-go/api/aws/#Config When using the SDK you'll generally need your AWS credentials to authenticate with AWS services. The SDK supports multiple methods of supporting these credentials. By default the SDK will source credentials automatically from its default credential chain. See the session package for more information on this chain, and how to configure it. The common items in the credential chain are the following: Environment Credentials - Set of environment variables that are useful when sub processes are created for specific roles. Shared Credentials file (~/.aws/credentials) - This file stores your credentials based on a profile name and is useful for local development. EC2 Instance Role Credentials - Use EC2 Instance Role to assign credentials to application running on an EC2 instance. This removes the need to manage credential files in production. Credentials can be configured in code as well by setting the Config's Credentials value to a custom provider or using one of the providers included with the SDK to bypass the default credential chain and use a custom one. This is helpful when you want to instruct the SDK to only use a specific set of credentials or providers. This example creates a credential provider for assuming an IAM role, "myRoleARN" and configures the S3 service client to use that role for API requests. See the credentials package documentation for more information on credential providers included with the SDK, and how to customize the SDK's usage of credentials. https://docs.aws.amazon.com/sdk-for-go/api/aws/credentials The SDK has support for the shared configuration file (~/.aws/config). This support can be enabled by setting the environment variable, "AWS_SDK_LOAD_CONFIG=1", or enabling the feature in code when creating a Session via the Option's SharedConfigState parameter. In addition to the credentials you'll need to specify the region the SDK will use to make AWS API requests to. In the SDK you can specify the region either with an environment variable, or directly in code when a Session or service client is created. The last value specified in code wins if the region is specified multiple ways. To set the region via the environment variable set the "AWS_REGION" to the region you want to the SDK to use. Using this method to set the region will allow you to run your application in multiple regions without needing additional code in the application to select the region. The endpoints package includes constants for all regions the SDK knows. The values are all suffixed with RegionID. These values are helpful, because they reduce the need to type the region string manually. To set the region on a Session use the aws package's Config struct parameter Region to the AWS region you want the service clients created from the session to use. This is helpful when you want to create multiple service clients, and all of the clients make API requests to the same region. See the endpoints package for the AWS Regions and Endpoints metadata. https://docs.aws.amazon.com/sdk-for-go/api/aws/endpoints/ In addition to setting the region when creating a Session you can also set the region on a per service client bases. This overrides the region of a Session. This is helpful when you want to create service clients in specific regions different from the Session's region. See the Config type in the aws package for more information and additional options such as setting the Endpoint, and other service client configuration options. https://docs.aws.amazon.com/sdk-for-go/api/aws/#Config Once the client is created you can make an API request to the service. Each API method takes a input parameter, and returns the service response and an error. The SDK provides methods for making the API call in multiple ways. In this list we'll use the S3 ListObjects API as an example for the different ways of making API requests. ListObjects - Base API operation that will make the API request to the service. ListObjectsRequest - API methods suffixed with Request will construct the API request, but not send it. This is also helpful when you want to get a presigned URL for a request, and share the presigned URL instead of your application making the request directly. ListObjectsPages - Same as the base API operation, but uses a callback to automatically handle pagination of the API's response. ListObjectsWithContext - Same as base API operation, but adds support for the Context pattern. This is helpful for controlling the canceling of in flight requests. See the Go standard library context package for more information. This method also takes request package's Option functional options as the variadic argument for modifying how the request will be made, or extracting information from the raw HTTP response. ListObjectsPagesWithContext - same as ListObjectsPages, but adds support for the Context pattern. Similar to ListObjectsWithContext this method also takes the request package's Option function option types as the variadic argument. In addition to the API operations the SDK also includes several higher level methods that abstract checking for and waiting for an AWS resource to be in a desired state. In this list we'll use WaitUntilBucketExists to demonstrate the different forms of waiters. WaitUntilBucketExists. - Method to make API request to query an AWS service for a resource's state. Will return successfully when that state is accomplished. WaitUntilBucketExistsWithContext - Same as WaitUntilBucketExists, but adds support for the Context pattern. In addition these methods take request package's WaiterOptions to configure the waiter, and how underlying request will be made by the SDK. The API method will document which error codes the service might return for the operation. These errors will also be available as const strings prefixed with "ErrCode" in the service client's package. If there are no errors listed in the API's SDK documentation you'll need to consult the AWS service's API documentation for the errors that could be returned. Pagination helper methods are suffixed with "Pages", and provide the functionality needed to round trip API page requests. Pagination methods take a callback function that will be called for each page of the API's response. Waiter helper methods provide the functionality to wait for an AWS resource state. These methods abstract the logic needed to to check the state of an AWS resource, and wait until that resource is in a desired state. The waiter will block until the resource is in the state that is desired, an error occurs, or the waiter times out. If a resource times out the error code returned will be request.WaiterResourceNotReadyErrorCode. This example shows a complete working Go file which will upload a file to S3 and use the Context pattern to implement timeout logic that will cancel the request if it takes too long. This example highlights how to use sessions, create a service client, make a request, handle the error, and process the response.

Package main is a simple wrapper of the real etcd entrypoint package (located at github.com/coreos/etcd/etcdmain) to ensure that etcd is still "go getable"; e.g. `go get github.com/coreos/etcd` works as expected and builds a binary in $GOBIN/etcd This package should NOT be extended or modified in any way; to modify the etcd binary, work in the `github.com/coreos/etcd/etcdmain` package.

Package chi is a small, idiomatic and composable router for building HTTP services. chi requires Go 1.10 or newer. Example: See github.com/go-chi/chi/_examples/ for more in-depth examples. URL patterns allow for easy matching of path components in HTTP requests. The matching components can then be accessed using chi.URLParam(). All patterns must begin with a slash. A simple named placeholder {name} matches any sequence of characters up to the next / or the end of the URL. Trailing slashes on paths must be handled explicitly. A placeholder with a name followed by a colon allows a regular expression match, for example {number:\\d+}. The regular expression syntax is Go's normal regexp RE2 syntax, except that regular expressions including { or } are not supported, and / will never be matched. An anonymous regexp pattern is allowed, using an empty string before the colon in the placeholder, such as {:\\d+} The special placeholder of asterisk matches the rest of the requested URL. Any trailing characters in the pattern are ignored. This is the only placeholder which will match / characters. Examples:

Package chi is a small, idiomatic and composable router for building HTTP services. chi requires Go 1.14 or newer. Example: See github.com/go-chi/chi/_examples/ for more in-depth examples. URL patterns allow for easy matching of path components in HTTP requests. The matching components can then be accessed using chi.URLParam(). All patterns must begin with a slash. A simple named placeholder {name} matches any sequence of characters up to the next / or the end of the URL. Trailing slashes on paths must be handled explicitly. A placeholder with a name followed by a colon allows a regular expression match, for example {number:\\d+}. The regular expression syntax is Go's normal regexp RE2 syntax, except that regular expressions including { or } are not supported, and / will never be matched. An anonymous regexp pattern is allowed, using an empty string before the colon in the placeholder, such as {:\\d+} The special placeholder of asterisk matches the rest of the requested URL. Any trailing characters in the pattern are ignored. This is the only placeholder which will match / characters. Examples:

Package storage provides an easy way to work with Google Cloud Storage. Google Cloud Storage stores data in named objects, which are grouped into buckets. More information about Google Cloud Storage is available at https://cloud.google.com/storage/docs. See https://pkg.go.dev/cloud.google.com/go for authentication, timeouts, connection pooling and similar aspects of this package. To start working with this package, create a Client: The client will use your default application credentials. Clients should be reused instead of created as needed. The methods of Client are safe for concurrent use by multiple goroutines. You may configure the client by passing in options from the google.golang.org/api/option package. You may also use options defined in this package, such as WithJSONReads. If you only wish to access public data, you can create an unauthenticated client with To use an emulator with this library, you can set the STORAGE_EMULATOR_HOST environment variable to the address at which your emulator is running. This will send requests to that address instead of to Cloud Storage. You can then create and use a client as usual: Please note that there is no official emulator for Cloud Storage. A Google Cloud Storage bucket is a collection of objects. To work with a bucket, make a bucket handle: A handle is a reference to a bucket. You can have a handle even if the bucket doesn't exist yet. To create a bucket in Google Cloud Storage, call BucketHandle.Create: Note that although buckets are associated with projects, bucket names are global across all projects. Each bucket has associated metadata, represented in this package by BucketAttrs. The third argument to BucketHandle.Create allows you to set the initial BucketAttrs of a bucket. To retrieve a bucket's attributes, use BucketHandle.Attrs: An object holds arbitrary data as a sequence of bytes, like a file. You refer to objects using a handle, just as with buckets, but unlike buckets you don't explicitly create an object. Instead, the first time you write to an object it will be created. You can use the standard Go io.Reader and io.Writer interfaces to read and write object data: Objects also have attributes, which you can fetch with ObjectHandle.Attrs: Listing objects in a bucket is done with the BucketHandle.Objects method: Objects are listed lexicographically by name. To filter objects lexicographically, [Query.StartOffset] and/or [Query.EndOffset] can be used: If only a subset of object attributes is needed when listing, specifying this subset using Query.SetAttrSelection may speed up the listing process: Both objects and buckets have ACLs (Access Control Lists). An ACL is a list of ACLRules, each of which specifies the role of a user, group or project. ACLs are suitable for fine-grained control, but you may prefer using IAM to control access at the project level (see Cloud Storage IAM docs. To list the ACLs of a bucket or object, obtain an ACLHandle and call ACLHandle.List: You can also set and delete ACLs. Every object has a generation and a metageneration. The generation changes whenever the content changes, and the metageneration changes whenever the metadata changes. Conditions let you check these values before an operation; the operation only executes if the conditions match. You can use conditions to prevent race conditions in read-modify-write operations. For example, say you've read an object's metadata into objAttrs. Now you want to write to that object, but only if its contents haven't changed since you read it. Here is how to express that: You can obtain a URL that lets anyone read or write an object for a limited time. Signing a URL requires credentials authorized to sign a URL. To use the same authentication that was used when instantiating the Storage client, use BucketHandle.SignedURL. You can also sign a URL without creating a client. See the documentation of SignedURL for details. A type of signed request that allows uploads through HTML forms directly to Cloud Storage with temporary permission. Conditions can be applied to restrict how the HTML form is used and exercised by a user. For more information, please see the XML POST Object docs as well as the documentation of BucketHandle.GenerateSignedPostPolicyV4. If the GoogleAccessID and PrivateKey option fields are not provided, they will be automatically detected by BucketHandle.SignedURL and BucketHandle.GenerateSignedPostPolicyV4 if any of the following are true: Detecting GoogleAccessID may not be possible if you are authenticated using a token source or using option.WithHTTPClient. In this case, you can provide a service account email for GoogleAccessID and the client will attempt to sign the URL or Post Policy using that service account. To generate the signature, you must have: Errors returned by this client are often of the type googleapi.Error. These errors can be introspected for more information by using errors.As with the richer googleapi.Error type. For example: Methods in this package may retry calls that fail with transient errors. Retrying continues indefinitely unless the controlling context is canceled, the client is closed, or a non-transient error is received. To stop retries from continuing, use context timeouts or cancellation. The retry strategy in this library follows best practices for Cloud Storage. By default, operations are retried only if they are idempotent, and exponential backoff with jitter is employed. In addition, errors are only retried if they are defined as transient by the service. See the Cloud Storage retry docs for more information. Users can configure non-default retry behavior for a single library call (using BucketHandle.Retryer and ObjectHandle.Retryer) or for all calls made by a client (using Client.SetRetry). For example: You can add custom headers to any API call made by this package by using callctx.SetHeaders on the context which is passed to the method. For example, to add a custom audit logging header: This package includes support for the Cloud Storage gRPC API. The implementation uses gRPC rather than the Default JSON & XML APIs to make requests to Cloud Storage. The Go Storage gRPC client is generally available. The Notifications, Serivce Account HMAC and GetServiceAccount RPCs are not supported through the gRPC client. To create a client which will use gRPC, use the alternate constructor: Using the gRPC API inside GCP with a bucket in the same region can allow for Direct Connectivity (enabling requests to skip some proxy steps and reducing response latency). A warning is emmitted if gRPC is not used within GCP to warn that Direct Connectivity could not be initialized. Direct Connectivity is not required to access the gRPC API. Dependencies for the gRPC API may slightly increase the size of binaries for applications depending on this package. If you are not using gRPC, you can use the build tag `disable_grpc_modules` to opt out of these dependencies and reduce the binary size. The gRPC client emits metrics by default and will export the gRPC telemetry discussed in gRFC/66 and gRFC/78 to Google Cloud Monitoring. The metrics are accessible through Cloud Monitoring API and you incur no additional cost for publishing the metrics. Google Cloud Support can use this information to more quickly diagnose problems related to GCS and gRPC. Sending this data does not incur any billing charges, and requires minimal CPU (a single RPC every minute) or memory (a few KiB to batch the telemetry). To access the metrics you can view them through Cloud Monitoring metric explorer with the prefix `storage.googleapis.com/client`. Metrics are emitted every minute. You can disable metrics using the following example when creating a new gRPC client using WithDisabledClientMetrics. The metrics exporter uses Cloud Monitoring API which determines project ID and credentials doing the following: * Project ID is determined using OTel Resource Detector for the environment otherwise it falls back to the project provided by google.FindCredentials. * Credentials are determined using Application Default Credentials. The principal must have `roles/monitoring.metricWriter` role granted. If not a logged warning will be emitted. Subsequent are silenced to prevent noisy logs. Certain control plane and long-running operations for Cloud Storage (including Folder and Managed Folder operations) are supported via the autogenerated Storage Control client, which is available as a subpackage in this module. See package docs at cloud.google.com/go/storage/control/apiv2 or reference the Storage Control API docs.

Package trace provides an implementation of the tracing part of the OpenTelemetry API. To participate in distributed traces a Span needs to be created for the operation being performed as part of a traced workflow. In its simplest form: A Tracer is unique to the instrumentation and is used to create Spans. Instrumentation should be designed to accept a TracerProvider from which it can create its own unique Tracer. Alternatively, the registered global TracerProvider from the go.opentelemetry.io/otel package can be used as a default. This package does not conform to the standard Go versioning policy; all of its interfaces may have methods added to them without a package major version bump. This non-standard API evolution could surprise an uninformed implementation author. They could unknowingly build their implementation in a way that would result in a runtime panic for their users that update to the new API. The API is designed to help inform an instrumentation author about this non-standard API evolution. It requires them to choose a default behavior for unimplemented interface methods. There are three behavior choices they can make: All interfaces in this API embed a corresponding interface from go.opentelemetry.io/otel/trace/embedded. If an author wants the default behavior of their implementations to be a compilation failure, signaling to their users they need to update to the latest version of that implementation, they need to embed the corresponding interface from go.opentelemetry.io/otel/trace/embedded in their implementation. For example, If an author wants the default behavior of their implementations to panic, they can embed the API interface directly. This option is not recommended. It will lead to publishing packages that contain runtime panics when users update to newer versions of go.opentelemetry.io/otel/trace, which may be done with a transitive dependency. Finally, an author can embed another implementation in theirs. The embedded implementation will be used for methods not defined by the author. For example, an author who wants to default to silently dropping the call can use go.opentelemetry.io/otel/trace/noop: It is strongly recommended that authors only embed go.opentelemetry.io/otel/trace/noop if they choose this default behavior. That implementation is the only one OpenTelemetry authors can guarantee will fully implement all the API interfaces when a user updates their API.

Package wire contains directives for Wire code generation. For an overview of working with Wire, see the user guide at https://github.com/google/wire/blob/master/docs/guide.md The directives in this package are used as input to the Wire code generation tool. The entry point of Wire's analysis are injector functions: function templates denoted by only containing a call to Build. The arguments to Build describes a set of providers and the Wire code generation tool builds a directed acylic graph of the providers' output types. The generated code will fill in the function template by using the providers from the provider set to instantiate any needed types.

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

Package tea provides a framework for building rich terminal user interfaces based on the paradigms of The Elm Architecture. It's well-suited for simple and complex terminal applications, either inline, full-window, or a mix of both. It's been battle-tested in several large projects and is production-ready. A tutorial is available at https://github.com/charmbracelet/bubbletea/tree/master/tutorials Example programs can be found at https://github.com/charmbracelet/bubbletea/tree/master/examples

Package bigquery provides a client for the BigQuery service. The following assumes a basic familiarity with BigQuery concepts. See https://cloud.google.com/bigquery/docs. See https://godoc.org/cloud.google.com/go for authentication, timeouts, connection pooling and similar aspects of this package. To start working with this package, create a client with NewClient: To query existing tables, create a Client.Query and call its Query.Read method, which starts the query and waits for it to complete: Then iterate through the resulting rows. You can store a row using anything that implements the ValueLoader interface, or with a slice or map of Value. A slice is simplest: You can also use a struct whose exported fields match the query: You can also start the query running and get the results later. Create the query as above, but call Query.Run instead of Query.Read. This returns a Job, which represents an asynchronous operation. Get the job's ID, a printable string. You can save this string to retrieve the results at a later time, even in another process. To retrieve the job's results from the ID, first look up the Job with the Client.JobFromID method: Use the Job.Read method to obtain an iterator, and loop over the rows. Calling Query.Read is preferred for queries with a relatively small result set, as it will call BigQuery jobs.query API for a optimized query path. If the query doesn't meet that criteria, the method will just combine Query.Run and Job.Read. You can refer to datasets in the client's project with the Client.Dataset method, and in other projects with the Client.DatasetInProject method: These methods create references to datasets, not the datasets themselves. You can have a dataset reference even if the dataset doesn't exist yet. Use Dataset.Create to create a dataset from a reference: You can refer to tables with Dataset.Table. Like Dataset, Table is a reference to an object in BigQuery that may or may not exist. You can create, delete and update the metadata of tables with methods on Table. For instance, you could create a temporary table with: We'll see how to create a table with a schema in the next section. There are two ways to construct schemas with this package. You can build a schema by hand with the Schema struct, like so: Or you can infer the schema from a struct with the InferSchema method: Struct inference supports tags like those of the encoding/json package, so you can change names, ignore fields, or mark a field as nullable (non-required). Fields declared as one of the Null types (NullInt64, NullFloat64, NullString, NullBool, NullTimestamp, NullDate, NullTime, NullDateTime, NullGeography, and NullJSON) are automatically inferred as nullable, so the "nullable" tag is only needed for []byte, *big.Rat and pointer-to-struct fields. Having constructed a schema, you can create a table with it using the Table.Create method like so: You can copy one or more tables to another table. Begin by constructing a Copier describing the copy using the Table.CopierFrom. Then set any desired copy options, and finally call Copier.Run to get a Job: You can chain the call to Copier.Run if you don't want to set options: You can wait for your job to complete with the Job.Wait method: Job.Wait polls with exponential backoff. You can also poll yourself, if you wish: There are two ways to populate a table with this package: load the data from a Google Cloud Storage object, or upload rows directly from your program. For loading, first create a GCSReference with the NewGCSReference method, configuring it if desired. Then make a Loader from a table with the Table.LoaderFrom method with the reference, optionally configure it as well, and call its Loader.Run method. To upload, first define a type that implements the ValueSaver interface, which has a single method named Save. Then create an Inserter, and call its Inserter.Put method with a slice of values. You can also upload a struct that doesn't implement ValueSaver. Use the StructSaver type to specify the schema and insert ID by hand: Lastly, but not least, you can just supply the struct or struct pointer directly and the schema will be inferred: BigQuery allows for higher throughput when omitting insertion IDs. To enable this, specify the sentinel NoDedupeID value for the insertion ID when implementing a ValueSaver. If you've been following so far, extracting data from a BigQuery table into a Google Cloud Storage object will feel familiar. First create an Extractor, then optionally configure it, and lastly call its Extractor.Run method. Errors returned by this client are often of the type googleapi.Error. These errors can be introspected for more information by using errors.As with the richer googleapi.Error type. For example: In some cases, your client may received unstructured googleapi.Error error responses. In such cases, it is likely that you have exceeded BigQuery request limits, documented at: https://cloud.google.com/bigquery/quotas

Package gopacket provides packet decoding for the Go language. gopacket contains many sub-packages with additional functionality you may find useful, including: Also, if you're looking to dive right into code, see the examples subdirectory for numerous simple binaries built using gopacket libraries. Minimum go version required is 1.5 except for pcapgo/EthernetHandle, afpacket, and bsdbpf which need at least 1.7 due to x/sys/unix dependencies. gopacket takes in packet data as a []byte and decodes it into a packet with a non-zero number of "layers". Each layer corresponds to a protocol within the bytes. Once a packet has been decoded, the layers of the packet can be requested from the packet. Packets can be decoded from a number of starting points. Many of our base types implement Decoder, which allow us to decode packets for which we don't have full data. Most of the time, you won't just have a []byte of packet data lying around. Instead, you'll want to read packets in from somewhere (file, interface, etc) and process them. To do that, you'll want to build a PacketSource. First, you'll need to construct an object that implements the PacketDataSource interface. There are implementations of this interface bundled with gopacket in the gopacket/pcap and gopacket/pfring subpackages... see their documentation for more information on their usage. Once you have a PacketDataSource, you can pass it into NewPacketSource, along with a Decoder of your choice, to create a PacketSource. Once you have a PacketSource, you can read packets from it in multiple ways. See the docs for PacketSource for more details. The easiest method is the Packets function, which returns a channel, then asynchronously writes new packets into that channel, closing the channel if the packetSource hits an end-of-file. You can change the decoding options of the packetSource by setting fields in packetSource.DecodeOptions... see the following sections for more details. gopacket optionally decodes packet data lazily, meaning it only decodes a packet layer when it needs to handle a function call. Lazily-decoded packets are not concurrency-safe. Since layers have not all been decoded, each call to Layer() or Layers() has the potential to mutate the packet in order to decode the next layer. If a packet is used in multiple goroutines concurrently, don't use gopacket.Lazy. Then gopacket will decode the packet fully, and all future function calls won't mutate the object. By default, gopacket will copy the slice passed to NewPacket and store the copy within the packet, so future mutations to the bytes underlying the slice don't affect the packet and its layers. If you can guarantee that the underlying slice bytes won't be changed, you can use NoCopy to tell gopacket.NewPacket, and it'll use the passed-in slice itself. The fastest method of decoding is to use both Lazy and NoCopy, but note from the many caveats above that for some implementations either or both may be dangerous. During decoding, certain layers are stored in the packet as well-known layer types. For example, IPv4 and IPv6 are both considered NetworkLayer layers, while TCP and UDP are both TransportLayer layers. We support 4 layers, corresponding to the 4 layers of the TCP/IP layering scheme (roughly anagalous to layers 2, 3, 4, and 7 of the OSI model). To access these, you can use the packet.LinkLayer, packet.NetworkLayer, packet.TransportLayer, and packet.ApplicationLayer functions. Each of these functions returns a corresponding interface (gopacket.{Link,Network,Transport,Application}Layer). The first three provide methods for getting src/dst addresses for that particular layer, while the final layer provides a Payload function to get payload data. This is helpful, for example, to get payloads for all packets regardless of their underlying data type: A particularly useful layer is ErrorLayer, which is set whenever there's an error parsing part of the packet. Note that we don't return an error from NewPacket because we may have decoded a number of layers successfully before running into our erroneous layer. You may still be able to get your Ethernet and IPv4 layers correctly, even if your TCP layer is malformed. gopacket has two useful objects, Flow and Endpoint, for communicating in a protocol independent manner the fact that a packet is coming from A and going to B. The general layer types LinkLayer, NetworkLayer, and TransportLayer all provide methods for extracting their flow information, without worrying about the type of the underlying Layer. A Flow is a simple object made up of a set of two Endpoints, one source and one destination. It details the sender and receiver of the Layer of the Packet. An Endpoint is a hashable representation of a source or destination. For example, for LayerTypeIPv4, an Endpoint contains the IP address bytes for a v4 IP packet. A Flow can be broken into Endpoints, and Endpoints can be combined into Flows: Both Endpoint and Flow objects can be used as map keys, and the equality operator can compare them, so you can easily group together all packets based on endpoint criteria: For load-balancing purposes, both Flow and Endpoint have FastHash() functions, which provide quick, non-cryptographic hashes of their contents. Of particular importance is the fact that Flow FastHash() is symmetric: A->B will have the same hash as B->A. An example usage could be: This allows us to split up a packet stream while still making sure that each stream sees all packets for a flow (and its bidirectional opposite). If your network has some strange encapsulation, you can implement your own decoder. In this example, we handle Ethernet packets which are encapsulated in a 4-byte header. See the docs for Decoder and PacketBuilder for more details on how coding decoders works, or look at RegisterLayerType and RegisterEndpointType to see how to add layer/endpoint types to gopacket. TLDR: DecodingLayerParser takes about 10% of the time as NewPacket to decode packet data, but only for known packet stacks. Basic decoding using gopacket.NewPacket or PacketSource.Packets is somewhat slow due to its need to allocate a new packet and every respective layer. It's very versatile and can handle all known layer types, but sometimes you really only care about a specific set of layers regardless, so that versatility is wasted. DecodingLayerParser avoids memory allocation altogether by decoding packet layers directly into preallocated objects, which you can then reference to get the packet's information. A quick example: The important thing to note here is that the parser is modifying the passed in layers (eth, ip4, ip6, tcp) instead of allocating new ones, thus greatly speeding up the decoding process. It's even branching based on layer type... it'll handle an (eth, ip4, tcp) or (eth, ip6, tcp) stack. However, it won't handle any other type... since no other decoders were passed in, an (eth, ip4, udp) stack will stop decoding after ip4, and only pass back [LayerTypeEthernet, LayerTypeIPv4] through the 'decoded' slice (along with an error saying it can't decode a UDP packet). Unfortunately, not all layers can be used by DecodingLayerParser... only those implementing the DecodingLayer interface are usable. Also, it's possible to create DecodingLayers that are not themselves Layers... see layers.IPv6ExtensionSkipper for an example of this. By default, DecodingLayerParser uses native map to store and search for a layer to decode. Though being versatile, in some cases this solution may be not so optimal. For example, if you have only few layers faster operations may be provided by sparse array indexing or linear array scan. To accomodate these scenarios, DecodingLayerContainer interface is introduced along with its implementations: DecodingLayerSparse, DecodingLayerArray and DecodingLayerMap. You can specify a container implementation to DecodingLayerParser with SetDecodingLayerContainer method. Example: To skip one level of indirection (though sacrificing some capabilities) you may also use DecodingLayerContainer as a decoding tool as it is. In this case you have to handle unknown layer types and layer panics by yourself. Example: DecodingLayerSparse is the fastest but most effective when LayerType values that layers in use can decode are not large because otherwise that would lead to bigger memory footprint. DecodingLayerArray is very compact and primarily usable if the number of decoding layers is not big (up to ~10-15, but please do your own benchmarks). DecodingLayerMap is the most versatile one and used by DecodingLayerParser by default. Please refer to tests and benchmarks in layers subpackage to further examine usage examples and performance measurements. You may also choose to implement your own DecodingLayerContainer if you want to make use of your own internal packet decoding logic. As well as offering the ability to decode packet data, gopacket will allow you to create packets from scratch, as well. A number of gopacket layers implement the SerializableLayer interface; these layers can be serialized to a []byte in the following manner: SerializeTo PREPENDS the given layer onto the SerializeBuffer, and they treat the current buffer's Bytes() slice as the payload of the serializing layer. Therefore, you can serialize an entire packet by serializing a set of layers in reverse order (Payload, then TCP, then IP, then Ethernet, for example). The SerializeBuffer's SerializeLayers function is a helper that does exactly that. To generate a (empty and useless, because no fields are set) Ethernet(IPv4(TCP(Payload))) packet, for example, you can run: If you use gopacket, you'll almost definitely want to make sure gopacket/layers is imported, since when imported it sets all the LayerType variables and fills in a lot of interesting variables/maps (DecodersByLayerName, etc). Therefore, it's recommended that even if you don't use any layers functions directly, you still import with:

This is the source repository for OpenShift Origin - the best way to build, manage, and deploy applications in the cloud. The OpenShift 3.0 codebase is based around Docker images and containers and the Kubernetes container management system.

Package bigtable is an API to Google Cloud Bigtable. See https://cloud.google.com/bigtable/docs/ for general product documentation. See https://godoc.org/cloud.google.com/go for authentication, timeouts, connection pooling and similar aspects of this package. The principal way to read from a Bigtable is to use the ReadRows method on *Table. A RowRange specifies a contiguous portion of a table. A Filter may be provided through RowFilter to limit or transform the data that is returned. To read a single row, use the ReadRow helper method: This API exposes two distinct forms of writing to a Bigtable: a Mutation and a ReadModifyWrite. The former expresses idempotent operations. The latter expresses non-idempotent operations and returns the new values of updated cells. These operations are performed by creating a Mutation or ReadModifyWrite (with NewMutation or NewReadModifyWrite), building up one or more operations on that, and then using the Apply or ApplyReadModifyWrite methods on a Table. For instance, to set a couple of cells in a table: To increment an encoded value in one cell: If a read or write operation encounters a transient error it will be retried until a successful response, an unretryable error or the context deadline is reached. Non-idempotent writes (where the timestamp is set to ServerTime) will not be retried. In the case of ReadRows, retried calls will not re-scan rows that have already been processed.

Package spanner provides a client for reading and writing to Cloud Spanner databases. See the packages under admin for clients that operate on databases and instances. See https://cloud.google.com/spanner/docs/getting-started/go/ for an introduction to Cloud Spanner and additional help on using this API. See https://godoc.org/cloud.google.com/go for authentication, timeouts, connection pooling and similar aspects of this package. To start working with this package, create a client that refers to the database of interest: Remember to close the client after use to free up the sessions in the session pool. To use an emulator with this library, you can set the SPANNER_EMULATOR_HOST environment variable to the address at which your emulator is running. This will send requests to that address instead of to Cloud Spanner. You can then create and use a client as usual: Two Client methods, Apply and Single, work well for simple reads and writes. As a quick introduction, here we write a new row to the database and read it back: All the methods used above are discussed in more detail below. Every Cloud Spanner row has a unique key, composed of one or more columns. Construct keys with a literal of type Key: The keys of a Cloud Spanner table are ordered. You can specify ranges of keys using the KeyRange type: By default, a KeyRange includes its start key but not its end key. Use the Kind field to specify other boundary conditions: A KeySet represents a set of keys. A single Key or KeyRange can act as a KeySet. Use the KeySets function to build the union of several KeySets: AllKeys returns a KeySet that refers to all the keys in a table: All Cloud Spanner reads and writes occur inside transactions. There are two types of transactions, read-only and read-write. Read-only transactions cannot change the database, do not acquire locks, and may access either the current database state or states in the past. Read-write transactions can read the database before writing to it, and always apply to the most recent database state. The simplest and fastest transaction is a ReadOnlyTransaction that supports a single read operation. Use Client.Single to create such a transaction. You can chain the call to Single with a call to a Read method. When you only want one row whose key you know, use ReadRow. Provide the table name, key, and the columns you want to read: Read multiple rows with the Read method. It takes a table name, KeySet, and list of columns: Read returns a RowIterator. You can call the Do method on the iterator and pass a callback: RowIterator also follows the standard pattern for the Google Cloud Client Libraries: Always call Stop when you finish using an iterator this way, whether or not you iterate to the end. (Failing to call Stop could lead you to exhaust the database's session quota.) To read rows with an index, use ReadUsingIndex. The most general form of reading uses SQL statements. Construct a Statement with NewStatement, setting any parameters using the Statement's Params map: You can also construct a Statement directly with a struct literal, providing your own map of parameters. Use the Query method to run the statement and obtain an iterator: Once you have a Row, via an iterator or a call to ReadRow, you can extract column values in several ways. Pass in a pointer to a Go variable of the appropriate type when you extract a value. You can extract by column position or name: You can extract all the columns at once: Or you can define a Go struct that corresponds to your columns, and extract into that: For Cloud Spanner columns that may contain NULL, use one of the NullXXX types, like NullString: To perform more than one read in a transaction, use ReadOnlyTransaction: You must call Close when you are done with the transaction. Cloud Spanner read-only transactions conceptually perform all their reads at a single moment in time, called the transaction's read timestamp. Once a read has started, you can call ReadOnlyTransaction's Timestamp method to obtain the read timestamp. By default, a transaction will pick the most recent time (a time where all previously committed transactions are visible) for its reads. This provides the freshest data, but may involve some delay. You can often get a quicker response if you are willing to tolerate "stale" data. You can control the read timestamp selected by a transaction by calling the WithTimestampBound method on the transaction before using it. For example, to perform a query on data that is at most one minute stale, use See the documentation of TimestampBound for more details. To write values to a Cloud Spanner database, construct a Mutation. The spanner package has functions for inserting, updating and deleting rows. Except for the Delete methods, which take a Key or KeyRange, each mutation-building function comes in three varieties. One takes lists of columns and values along with the table name: One takes a map from column names to values: And the third accepts a struct value, and determines the columns from the struct field names: To apply a list of mutations to the database, use Apply: If you need to read before writing in a single transaction, use a ReadWriteTransaction. ReadWriteTransactions may be aborted automatically by the backend and need to be retried. You pass in a function to ReadWriteTransaction, and the client will handle the retries automatically. Use the transaction's BufferWrite method to buffer mutations, which will all be executed at the end of the transaction: Cloud Spanner STRUCT (aka STRUCT) values (https://cloud.google.com/spanner/docs/data-types#struct-type) can be represented by a Go struct value. A proto StructType is built from the field types and field tag information of the Go struct. If a field in the struct type definition has a "spanner:<field_name>" tag, then the value of the "spanner" key in the tag is used as the name for that field in the built StructType, otherwise the field name in the struct definition is used. To specify a field with an empty field name in a Cloud Spanner STRUCT type, use the `spanner:""` tag annotation against the corresponding field in the Go struct's type definition. A STRUCT value can contain STRUCT-typed and Array-of-STRUCT typed fields and these can be specified using named struct-typed and []struct-typed fields inside a Go struct. However, embedded struct fields are not allowed. Unexported struct fields are ignored. NULL STRUCT values in Cloud Spanner are typed. A nil pointer to a Go struct value can be used to specify a NULL STRUCT value of the corresponding StructType. Nil and empty slices of a Go STRUCT type can be used to specify NULL and empty array values respectively of the corresponding StructType. A slice of pointers to a Go struct type can be used to specify an array of NULL-able STRUCT values. Spanner supports DML statements like INSERT, UPDATE and DELETE. Use ReadWriteTransaction.Update to run DML statements. It returns the number of rows affected. (You can call use ReadWriteTransaction.Query with a DML statement. The first call to Next on the resulting RowIterator will return iterator.Done, and the RowCount field of the iterator will hold the number of affected rows.) For large databases, it may be more efficient to partition the DML statement. Use client.PartitionedUpdate to run a DML statement in this way. Not all DML statements can be partitioned. This client has been instrumented to use OpenCensus tracing (http://opencensus.io). To enable tracing, see "Enabling Tracing for a Program" at https://godoc.org/go.opencensus.io/trace. OpenCensus tracing requires Go 1.8 or higher.

Package swagger (2.0) provides a powerful interface to your API Contains an implementation of Swagger 2.0. It knows how to serialize, deserialize and validate swagger specifications. Swagger is a simple yet powerful representation of your RESTful API. With the largest ecosystem of API tooling on the planet, thousands of developers are supporting Swagger in almost every modern programming language and deployment environment. With a Swagger-enabled API, you get interactive documentation, client SDK generation and discoverability. We created Swagger to help fulfill the promise of APIs. Swagger helps companies like Apigee, Getty Images, Intuit, LivingSocial, McKesson, Microsoft, Morningstar, and PayPal build the best possible services with RESTful APIs.Now in version 2.0, Swagger is more enabling than ever. And it's 100% open source software. More detailed documentation is available at https://goswagger.io. Install: The implementation also provides a number of command line tools to help working with swagger. Currently there is a spec validator tool: To generate a server for a swagger spec document: To generate a client for a swagger spec document: To generate a swagger spec document for a go application: There are several other sub commands available for the generate command You're free to add files to the directories the generated code lands in, but the files generated by the generator itself will be regenerated on following generation runs so any changes to those files will be lost. However extra files you create won't be lost so they are safe to use for customizing the application to your needs. To generate a server for a swagger spec document:

Package gofpdf implements a PDF document generator with high level support for text, drawing and images. - UTF-8 support - Choice of measurement unit, page format and margins - Page header and footer management - Automatic page breaks, line breaks, and text justification - Inclusion of JPEG, PNG, GIF, TIFF and basic path-only SVG images - Colors, gradients and alpha channel transparency - Outline bookmarks - Internal and external links - TrueType, Type1 and encoding support - Page compression - Lines, Bézier curves, arcs, and ellipses - Rotation, scaling, skewing, translation, and mirroring - Clipping - Document protection - Layers - Templates - Barcodes - Charting facility - Import PDFs as templates gofpdf has no dependencies other than the Go standard library. All tests pass on Linux, Mac and Windows platforms. gofpdf supports UTF-8 TrueType fonts and “right-to-left” languages. Note that Chinese, Japanese, and Korean characters may not be included in many general purpose fonts. For these languages, a specialized font (for example, NotoSansSC for simplified Chinese) can be used. Also, support is provided to automatically translate UTF-8 runes to code page encodings for languages that have fewer than 256 glyphs. This repository will not be maintained, at least for some unknown duration. But it is hoped that gofpdf has a bright future in the open source world. Due to Go’s promise of compatibility, gofpdf should continue to function without modification for a longer time than would be the case with many other languages. Forks should be based on the last viable commit. Tools such as active-forks can be used to select a fork that looks promising for your needs. If a particular fork looks like it has taken the lead in attracting followers, this README will be updated to point people in that direction. The efforts of all contributors to this project have been deeply appreciated. Best wishes to all of you. To install the package on your system, run Later, to receive updates, run The following Go code generates a simple PDF file. See the functions in the fpdf_test.go file (shown as examples in this documentation) for more advanced PDF examples. If an error occurs in an Fpdf method, an internal error field is set. After this occurs, Fpdf method calls typically return without performing any operations and the error state is retained. This error management scheme facilitates PDF generation since individual method calls do not need to be examined for failure; it is generally sufficient to wait until after Output() is called. For the same reason, if an error occurs in the calling application during PDF generation, it may be desirable for the application to transfer the error to the Fpdf instance by calling the SetError() method or the SetErrorf() method. At any time during the life cycle of the Fpdf instance, the error state can be determined with a call to Ok() or Err(). The error itself can be retrieved with a call to Error(). This package is a relatively straightforward translation from the original FPDF library written in PHP (despite the caveat in the introduction to Effective Go). The API names have been retained even though the Go idiom would suggest otherwise (for example, pdf.GetX() is used rather than simply pdf.X()). The similarity of the two libraries makes the original FPDF website a good source of information. It includes a forum and FAQ. However, some internal changes have been made. Page content is built up using buffers (of type bytes.Buffer) rather than repeated string concatenation. Errors are handled as explained above rather than panicking. Output is generated through an interface of type io.Writer or io.WriteCloser. A number of the original PHP methods behave differently based on the type of the arguments that are passed to them; in these cases additional methods have been exported to provide similar functionality. Font definition files are produced in JSON rather than PHP. A side effect of running go test ./... is the production of a number of example PDFs. These can be found in the gofpdf/pdf directory after the tests complete. Please note that these examples run in the context of a test. In order run an example as a standalone application, you’ll need to examine fpdf_test.go for some helper routines, for example exampleFilename() and summary(). Example PDFs can be compared with reference copies in order to verify that they have been generated as expected. This comparison will be performed if a PDF with the same name as the example PDF is placed in the gofpdf/pdf/reference directory and if the third argument to ComparePDFFiles() in internal/example/example.go is true. (By default it is false.) The routine that summarizes an example will look for this file and, if found, will call ComparePDFFiles() to check the example PDF for equality with its reference PDF. If differences exist between the two files they will be printed to standard output and the test will fail. If the reference file is missing, the comparison is considered to succeed. In order to successfully compare two PDFs, the placement of internal resources must be consistent and the internal creation timestamps must be the same. To do this, the methods SetCatalogSort() and SetCreationDate() need to be called for both files. This is done automatically for all examples. Nothing special is required to use the standard PDF fonts (courier, helvetica, times, zapfdingbats) in your documents other than calling SetFont(). You should use AddUTF8Font() or AddUTF8FontFromBytes() to add a TrueType UTF-8 encoded font. Use RTL() and LTR() methods switch between “right-to-left” and “left-to-right” mode. In order to use a different non-UTF-8 TrueType or Type1 font, you will need to generate a font definition file and, if the font will be embedded into PDFs, a compressed version of the font file. This is done by calling the MakeFont function or using the included makefont command line utility. To create the utility, cd into the makefont subdirectory and run “go build”. This will produce a standalone executable named makefont. Select the appropriate encoding file from the font subdirectory and run the command as in the following example. In your PDF generation code, call AddFont() to load the font and, as with the standard fonts, SetFont() to begin using it. Most examples, including the package example, demonstrate this method. Good sources of free, open-source fonts include Google Fonts and DejaVu Fonts. The draw2d package is a two dimensional vector graphics library that can generate output in different forms. It uses gofpdf for its document production mode. gofpdf is a global community effort and you are invited to make it even better. If you have implemented a new feature or corrected a problem, please consider contributing your change to the project. A contribution that does not directly pertain to the core functionality of gofpdf should be placed in its own directory directly beneath the contrib directory. Here are guidelines for making submissions. Your change should - be compatible with the MIT License - be properly documented - be formatted with go fmt - include an example in fpdf_test.go if appropriate - conform to the standards of golint and go vet, that is, golint . and go vet . should not generate any warnings - not diminish test coverage Pull requests are the preferred means of accepting your changes. gofpdf is released under the MIT License. It is copyrighted by Kurt Jung and the contributors acknowledged below. This package’s code and documentation are closely derived from the FPDF library created by Olivier Plathey, and a number of font and image resources are copied directly from it. Bruno Michel has provided valuable assistance with the code. Drawing support is adapted from the FPDF geometric figures script by David Hernández Sanz. Transparency support is adapted from the FPDF transparency script by Martin Hall-May. Support for gradients and clipping is adapted from FPDF scripts by Andreas Würmser. Support for outline bookmarks is adapted from Olivier Plathey by Manuel Cornes. Layer support is adapted from Olivier Plathey. Support for transformations is adapted from the FPDF transformation script by Moritz Wagner and Andreas Würmser. PDF protection is adapted from the work of Klemen Vodopivec for the FPDF product. Lawrence Kesteloot provided code to allow an image’s extent to be determined prior to placement. Support for vertical alignment within a cell was provided by Stefan Schroeder. Ivan Daniluk generalized the font and image loading code to use the Reader interface while maintaining backward compatibility. Anthony Starks provided code for the Polygon function. Robert Lillack provided the Beziergon function and corrected some naming issues with the internal curve function. Claudio Felber provided implementations for dashed line drawing and generalized font loading. Stani Michiels provided support for multi-segment path drawing with smooth line joins, line join styles, enhanced fill modes, and has helped greatly with package presentation and tests. Templating is adapted by Marcus Downing from the FPDF_Tpl library created by Jan Slabon and Setasign. Jelmer Snoeck contributed packages that generate a variety of barcodes and help with registering images on the web. Jelmer Snoek and Guillermo Pascual augmented the basic HTML functionality with aligned text. Kent Quirk implemented backwards-compatible support for reading DPI from images that support it, and for setting DPI manually and then having it properly taken into account when calculating image size. Paulo Coutinho provided support for static embedded fonts. Dan Meyers added support for embedded JavaScript. David Fish added a generic alias-replacement function to enable, among other things, table of contents functionality. Andy Bakun identified and corrected a problem in which the internal catalogs were not sorted stably. Paul Montag added encoding and decoding functionality for templates, including images that are embedded in templates; this allows templates to be stored independently of gofpdf. Paul also added support for page boxes used in printing PDF documents. Wojciech Matusiak added supported for word spacing. Artem Korotkiy added support of UTF-8 fonts. Dave Barnes added support for imported objects and templates. Brigham Thompson added support for rounded rectangles. Joe Westcott added underline functionality and optimized image storage. Benoit KUGLER contributed support for rectangles with corners of unequal radius, modification times, and for file attachments and annotations. - Remove all legacy code page font support; use UTF-8 exclusively - Improve test coverage as reported by the coverage tool. Example demonstrates the generation of a simple PDF document. Note that since only core fonts are used (in this case Arial, a synonym for Helvetica), an empty string can be specified for the font directory in the call to New(). Note also that the example.Filename() and example.Summary() functions belong to a separate, internal package and are not part of the gofpdf library. If an error occurs at some point during the construction of the document, subsequent method calls exit immediately and the error is finally retrieved with the output call where it can be handled by the application.

Ginkgo is a testing framework for Go designed to help you write expressive tests. https://github.com/onsi/ginkgo MIT-Licensed The godoc documentation outlines Ginkgo's API. Since Ginkgo is a Domain-Specific Language it is important to build a mental model for Ginkgo - the narrative documentation at https://onsi.github.io/ginkgo/ is designed to help you do that. You should start there - even a brief skim will be helpful. At minimum you should skim through the https://onsi.github.io/ginkgo/#getting-started chapter. Ginkgo's is best paired with the Gomega matcher library: https://github.com/onsi/gomega You can run Ginkgo specs with go test - however we recommend using the ginkgo cli. It enables functionality that go test does not (especially running suites in parallel). You can learn more at https://onsi.github.io/ginkgo/#ginkgo-cli-overview or by running 'ginkgo help'.