Google Cloud API

Package cloud is the root of the packages used to access Google Cloud Services. See https://pkg.go.dev/cloud.google.com/go for a full list of sub-modules. All clients in sub-packages are configurable via client options. These options are described here: https://pkg.go.dev/google.golang.org/api/option. Endpoint configuration is used to specify the URL to which requests are sent. It is used for services that support or require regional endpoints, as well as for other use cases such as testing against fake servers. For example, the Vertex AI service recommends that you configure the endpoint to the location with the features you want that is closest to your physical location or the location of your users. There is no global endpoint for Vertex AI. See Vertex AI - Locations for more details. The following example demonstrates configuring a Vertex AI client with a regional endpoint: All of the clients support authentication via Google Application Default Credentials, or by providing a JSON key file for a Service Account. See examples below. Google Application Default Credentials (ADC) is the recommended way to authorize and authenticate clients. For information on how to create and obtain Application Default Credentials, see https://cloud.google.com/docs/authentication/production. If you have your environment configured correctly you will not need to pass any extra information to the client libraries. Here is an example of a client using ADC to authenticate: You can use a file with credentials to authenticate and authorize, such as a JSON key file associated with a Google service account. Service Account keys can be created and downloaded from https://console.cloud.google.com/iam-admin/serviceaccounts. This example uses the Secret Manger client, but the same steps apply to the all other client libraries this package as well. Example: In some cases (for instance, you don't want to store secrets on disk), you can create credentials from in-memory JSON and use the WithCredentials option. This example uses the Secret Manager client, but the same steps apply to all other client libraries as well. Note that scopes can be found at https://developers.google.com/identity/protocols/oauth2/scopes, and are also provided in all auto-generated libraries: for example, cloud.google.com/go/secretmanager/apiv1 provides DefaultAuthScopes. Example: By default, non-streaming methods, like Create or Get, will have a default deadline applied to the context provided at call time, unless a context deadline is already set. Streaming methods have no default deadline and will run indefinitely. To set timeouts or arrange for cancellation, use context. Transient errors will be retried when correctness allows. Here is an example of setting a timeout for an RPC using context.WithTimeout: Here is an example of setting a timeout for an RPC using github.com/googleapis/gax-go/v2.WithTimeout: Here is an example of how to arrange for an RPC to be canceled, use context.WithCancel: Do not attempt to control the initial connection (dialing) of a service by setting a timeout on the context passed to NewClient. Dialing is non-blocking, so timeouts would be ineffective and would only interfere with credential refreshing, which uses the same context. Regardless of which transport is used, request headers can be set in the same way using [`callctx.SetHeaders`]setheaders. Here is a generic example: ## Google-reserved headers There are a some header keys that Google reserves for internal use that must not be ovewritten. The following header keys are broadly considered reserved and should not be conveyed by client library users unless instructed to do so: * `x-goog-api-client` * `x-goog-request-params` Be sure to check the individual package documentation for other service-specific reserved headers. For example, Storage supports a specific auditing header that is mentioned in that [module's documentation]storagedocs. ## Google Cloud system parameters Google Cloud services respect system parameterssystem parameters that can be used to augment request and/or response behavior. For the most part, they are not needed when using one of the enclosed client libraries. However, those that may be necessary are made available via the [`callctx`]callctx package. If not present there, consider opening an issue on that repo to request a new constant. Connection pooling differs in clients based on their transport. Cloud clients either rely on HTTP or gRPC transports to communicate with Google Cloud. Cloud clients that use HTTP rely on the underlying HTTP transport to cache connections for later re-use. These are cached to the http.MaxIdleConns and http.MaxIdleConnsPerHost settings in http.DefaultTransport by default. For gRPC clients, connection pooling is configurable. Users of Cloud Client Libraries may specify option.WithGRPCConnectionPool(n) as a client option to NewClient calls. This configures the underlying gRPC connections to be pooled and accessed in a round robin fashion. Minimal container images like Alpine lack CA certificates. This causes RPCs to appear to hang, because gRPC retries indefinitely. See https://github.com/googleapis/google-cloud-go/issues/928 for more information. For tips on how to write tests against code that calls into our libraries check out our Debugging Guide. For tips on how to write tests against code that calls into our libraries check out our Testing Guide. Most of the errors returned by the generated clients are wrapped in an github.com/googleapis/gax-go/v2/apierror.APIError and can be further unwrapped into a google.golang.org/grpc/status.Status or google.golang.org/api/googleapi.Error depending on the transport used to make the call (gRPC or REST). Converting your errors to these types can be a useful way to get more information about what went wrong while debugging. APIError gives access to specific details in the error. The transport-specific errors can still be unwrapped using the APIError. If the gRPC transport was used, the google.golang.org/grpc/status.Status can still be parsed using the google.golang.org/grpc/status.FromError function. Semver is used to communicate stability of the sub-modules of this package. Note, some stable sub-modules do contain packages, and sometimes features, that are considered unstable. If something is unstable it will be explicitly labeled as such. Example of package does in an unstable package: Clients that contain alpha and beta in their import path may change or go away without notice. Clients marked stable will maintain compatibility with future versions for as long as we can reasonably sustain. Incompatible changes might be made in some situations, including:

Package api is the root of the packages used to access Google Cloud Services. See https://godoc.org/google.golang.org/api for a full list of sub-packages. Within api there exist numerous clients which connect to Google APIs, and various utility packages. All clients in sub-packages are configurable via client options. These options are described here: https://godoc.org/google.golang.org/api/option. All the clients in sub-packages support authentication via Google Application Default Credentials (see https://cloud.google.com/docs/authentication/production), or by providing a JSON key file for a Service Account. See the authentication examples in https://godoc.org/google.golang.org/api/transport for more details. Due to the auto-generated nature of this collection of libraries, complete APIs or specific versions can appear or go away without notice. As a result, you should always locally vendor any API(s) that your code relies upon. Google APIs follow semver as specified by https://cloud.google.com/apis/design/versioning. The code generator and the code it produces - the libraries in the google.golang.org/api/... subpackages - are beta. Note that versioning and stability is strictly not communicated through Go modules. Go modules are used only for dependency management. Many parameters are specified using ints. However, underlying APIs might operate on a finer granularity, expecting int64, int32, uint64, or uint32, all of whom have different maximum values. Subsequently, specifying an int parameter in one of these clients may result in an error from the API because the value is too large. To see the exact type of int that the API expects, you can inspect the API's discovery doc. A global catalogue pointing to the discovery doc of APIs can be found at https://www.googleapis.com/discovery/v1/apis. This field can be found on all Request/Response structs in the generated clients. All of these types have the JSON `omitempty` field tag present on their fields. This means if a type is set to its default value it will not be marshalled. Sometimes you may actually want to send a default value, for instance sending an int of `0`. In this case you can override the `omitempty` feature by adding the field name to the `ForceSendFields` slice. See docs on any struct for more details. This may be used to include empty fields in Patch requests. This field can be found on all Request/Response structs in the generated clients. It can be be used to send JSON null values for the listed fields. By default, fields with empty values are omitted from API requests because of the presence of the `omitempty` field tag on all fields. However, any field with an empty value appearing in NullFields will be sent to the server as null. It is an error if a field in this list has a non-empty value. This may be used to include null fields in Patch requests. An error returned by a client's Do method may be cast to a *googleapi.Error or unwrapped to an *apierror.APIError. The https://pkg.go.dev/google.golang.org/api/googleapi#Error type is useful for getting the HTTP status code: The https://pkg.go.dev/github.com/googleapis/gax-go/v2/apierror#APIError type is useful for inspecting structured details of the underlying API response, such as the reason for the error and the error domain, which is typically the registered service name of the tool or product that generated the error: If an API call returns an Operation, that means it could take some time to complete the work initiated by the API call. Applications that are interested in the end result of the operation they initiated should wait until the Operation.Done field indicates it is finished. To do this, use the service's Operation client, and a loop, like so:

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 metadata provides access to Google Compute Engine (GCE) metadata and API service accounts. This package is a wrapper around the GCE metadata service, as documented at https://cloud.google.com/compute/docs/metadata/overview.

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 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 logging contains a Cloud Logging client suitable for writing logs. For reading logs, and working with sinks, metrics and monitored resources, see package cloud.google.com/go/logging/logadmin. This client uses Logging API v2. See https://cloud.google.com/logging/docs/api/v2/ for an introduction to the API. Use a Client to interact with the Cloud Logging API. For most use cases, you'll want to add log entries to a buffer to be periodically flushed (automatically and asynchronously) to the Cloud Logging service. You should call Client.Close before your program exits to flush any buffered log entries to the Cloud Logging service. For critical errors, you may want to send your log entries immediately. LogSync is slow and will block until the log entry has been sent, so it is not recommended for normal use. For cases when runtime environment supports out-of-process log ingestion, like logging agent, you can opt-in to write log entries to io.Writer instead of ingesting them to Cloud Logging service. Usually, you will use os.Stdout or os.Stderr as writers because Google Cloud logging agents are configured to capture logs from standard output. The entries will be Jsonified and wrote as one line strings following the structured logging format. See https://cloud.google.com/logging/docs/structured-logging#special-payload-fields for the format description. To instruct Logger to redirect log entries add RedirectAsJSON() LoggerOption`s. An entry payload can be a string, as in the examples above. It can also be any value that can be marshaled to a JSON object, like a map[string]interface{} or a struct: If you have a []byte of JSON, wrap it in json.RawMessage: If you have proto.Message and want to send it as a protobuf payload, marshal it to anypb.Any: You may want use a standard log.Logger in your program. An Entry may have one of a number of severity levels associated with it. You can view Cloud logs for projects at https://console.cloud.google.com/logs/viewer. Use the dropdown at the top left. When running from a Google Cloud Platform VM, select "GCE VM Instance". Otherwise, select "Google Project" and then the project ID. Logs for organizations, folders and billing accounts can be viewed on the command line with the "gcloud logging read" command. To group all the log entries written during a single HTTP request, create two Loggers, a "parent" and a "child," with different log IDs. Both should be in the same project, and have the same MonitoredResource type and labels. - A child entry's timestamp must be within the time interval covered by the parent request. (i.e., before the parent.Timestamp and after the parent.Timestamp - parent.HTTPRequest.Latency. This assumes the parent.Timestamp marks the end of the request.) - The trace field must be populated in all of the entries and match exactly. You should observe the child log entries grouped under the parent on the console. The parent entry will not inherit the severity of its children; you must update the parent severity yourself. You can automatically populate the Trace, SpanID, and TraceSampled fields of an Entry object by providing an http.Request object within the Entry's HTTPRequest field: When Entry with an http.Request is logged, its Trace, SpanID, and TraceSampled fields may be automatically populated as follows: Note that if Trace, SpanID, or TraceSampled are explicitly provided within an Entry object, then those values take precedence over values automatically extracted values.

Package firestore provides a client for reading and writing to a Cloud Firestore database. See https://cloud.google.com/firestore/docs for an introduction to Cloud Firestore and additional help on using the Firestore API. See https://godoc.org/cloud.google.com/go for authentication, timeouts, connection pooling and similar aspects of this package. Note: you can't use both Cloud Firestore and Cloud Datastore in the same project. To start working with this package, create a client with a project ID: In Firestore, documents are sets of key-value pairs, and collections are groups of documents. A Firestore database consists of a hierarchy of alternating collections and documents, referred to by slash-separated paths like "States/California/Cities/SanFrancisco". This client is built around references to collections and documents. CollectionRefs and DocumentRefs are lightweight values that refer to the corresponding database entities. Creating a ref does not involve any network traffic. Use DocumentRef.Get to read a document. The result is a DocumentSnapshot. Call its Data method to obtain the entire document contents as a map. You can also obtain a single field with DataAt, or extract the data into a struct with DataTo. With the type definition we can extract the document's data into a value of type State: Note that this client supports struct tags beginning with "firestore:" that work like the tags of the encoding/json package, letting you rename fields, ignore them, or omit their values when empty. To retrieve multiple documents from their references in a single call, use Client.GetAll. For writing individual documents, use the methods on DocumentReference. Create creates a new document. The first return value is a WriteResult, which contains the time at which the document was updated. Create fails if the document exists. Another method, Set, either replaces an existing document or creates a new one. To update some fields of an existing document, use Update. It takes a list of paths to update and their corresponding values. Use DocumentRef.Delete to delete a document. You can condition Deletes or Updates on when a document was last changed. Specify these preconditions as an option to a Delete or Update method. The check and the write happen atomically with a single RPC. Here we update a doc only if it hasn't changed since we read it. You could also do this with a transaction. To perform multiple writes at once, use a WriteBatch. Its methods chain for convenience. WriteBatch.Commit sends the collected writes to the server, where they happen atomically. You can use SQL to select documents from a collection. Begin with the collection, and build up a query using Select, Where and other methods of Query. Supported operators include '<', '<=', '>', '>=', '==', 'in', 'array-contains', and 'array-contains-any'. Call the Query's Documents method to get an iterator, and use it like the other Google Cloud Client iterators. To get all the documents in a collection, you can use the collection itself as a query. Firestore supports similarity search over embedding vectors. See Query.FindNearest for details. You can partition the documents of a Collection Group allowing for smaller subqueries. You can also Serialize/Deserialize queries making it possible to run/stream the queries elsewhere; another process or machine for instance. Use a transaction to execute reads and writes atomically. All reads must happen before any writes. Transaction creation, commit, rollback and retry are handled for you by the Client.RunTransaction method; just provide a function and use the read and write methods of the Transaction passed to it. This package supports the Cloud Firestore emulator, which is useful for testing and development. Environment variables are used to indicate that Firestore traffic should be directed to the emulator instead of the production Firestore service. To install and run the emulator and its environment variables, see the documentation at https://cloud.google.com/sdk/gcloud/reference/beta/emulators/firestore/. Once the emulator is running, set FIRESTORE_EMULATOR_HOST to the API endpoint.

Package translate is the v2 client for the Google Translation API. PLEASE NOTE: We recommend using the new v3 client for new projects: https://cloud.google.com/go/translate/apiv3. See https://cloud.google.com/translation for details.

Package stackdriver contains the OpenCensus exporters for Stackdriver Monitoring and Stackdriver Tracing. This exporter can be used to send metrics to Stackdriver Monitoring and traces to Stackdriver trace. The package uses Application Default Credentials to authenticate by default. See: https://developers.google.com/identity/protocols/application-default-credentials Alternatively, pass the authentication options in both the MonitoringClientOptions and the TraceClientOptions fields of Options. This exporter support exporting OpenCensus views to Stackdriver Monitoring. Each registered view becomes a metric in Stackdriver Monitoring, with the tags becoming labels. The aggregation function determines the metric kind: LastValue aggregations generate Gauge metrics and all other aggregations generate Cumulative metrics. In order to be able to push your stats to Stackdriver Monitoring, you must: These steps enable the API but don't require that your app is hosted on Google Cloud Platform. This exporter supports exporting Trace Spans to Stackdriver Trace. It also supports the Google "Cloud Trace" propagation format header.

Package vfs provides a pluggable, extensible, and opinionated set of file system functionality for Go across a number of file system types such as os, S3, and GCS. When building our platform, initially we wrote a library that was something to the effect of Not only was ugly but because the behaviors of each "file system" were different and we had to constantly alter the file locations and pass a bucket string (even if the fs didn't know what a bucket was). We found a handful of third-party libraries that were interesting but none of them had everything we needed/wanted. Of particular inspiration was https://github.com/spf13/afero in its composition of the super-powerful stdlib io.* interfaces. Unfortunately, it didn't support Google Cloud Storage and there was still a lot of passing around of strings and structs. Few, if any, of the vfs-like libraries provided interfaces to easily and confidently create new file system backends. What we needed/wanted was the following(and more): Pre 1.17: Post 1.17: Upgrading from v5 to v6 With v6.0.0, sftp.Options struct changed to accept an array of Key Exchange algorithms rather than a string. To update, change the syntax of the auth commands. becomes We provide vfssimple as basic way of initializing file system backends (see each implementation's docs about authentication). vfssimple pulls in every c2fo/vfs backend. If you need to reduce the backend requirements (and app memory footprint) or add a third party backend, you'll need to implement your own "factory". See backend doc for more info. You can then use those file systems to initialize locations which you'll be referencing frequently, or initialize files directly You can perform a number of actions without any consideration for the underlying system's api or implementation details. File's io.* interfaces may be used directly: * none so far Feel free to send a pull request if you want to add your backend to the list. # Ideas See https://github.com/C2FO/vfs/discussions Contributing Distributed under the MIT license. See `http://github.com/c2fo/vfs/License.md for more information. * absolute path - A path is said to be absolute if it provides the entire context need to find a file, including the file system root. An absolute path must begin with a slash and may include . and .. directories. * file path - A file path ends with a filename and therefore may not end with a slash. It may be relative or absolute. * location path - A location/dir path must end with a slash. It may be relative or absolute. * relative path - A relative path is a way to locate a dir or file relative to another directory. A relative path may not begin with a slash but may include . and .. directories. * URI - A Uniform Resource Identifier (URI) is a string of characters that unambiguously identifies a particular resource. To guarantee uniformity, all URIs follow a predefined set of syntax rules, but also maintain extensibility through a separately defined hierarchical naming scheme (e.g. http://).

Package metadata provides access to Google Compute Engine (GCE) metadata and API service accounts. This package is a wrapper around the GCE metadata service, as documented at https://developers.google.com/compute/docs/metadata.

Package gcpjwt has Google Cloud Platform (Cloud KMS, IAM API, & AppEngine App Identity API) jwt-go implementations. Should work across virtually all environments, on or off of Google's Cloud Platform. It is highly recommended that you override the default algorithm implementations that you want to leverage a GCP service for in dgrijalva/jwt-go. You otherwise will have to manually pick the verification method for your JWTs and they will place non-standard headers in the rendered JWT (with the exception of signJwt from the IAM API which overwrites the header with its own). You should only need to override the algorithm(s) you plan to use. It is also incorrect to override overlapping, algorithms such as `gcpjwt.SigningMethodKMSRS256.Override()` and `gcpjwt.SigningMethodIAMJWT.Override()` Example: As long as a you override a default algorithm implementation as shown above, using the dgrijalva/jwt-go is mostly unchanged. Token creation is more/less done the same way as in the dgrijalva/jwt-go package. The key that you need to provide is always going to be a context.Context, usuaully with a configuration object loaded in: Example: Finally, the steps to validate a token should be straight forward. This library provides you with helper jwt.Keyfunc implementations to do the heavy lifting around getting the public certificates for verification: Example:

Package vfs provides a pluggable, extensible, and opinionated set of filesystem functionality for Go across a number of filesystem types such as os, S3, and GCS. When building our platform, initially we wrote a library that was something to the effect of Not only was ugly but because the behaviors of each "filesystem" were different and we had to constantly alter the file locations and pass a bucket string (even if the fs didn't know what a bucket was). We found a handful of third-party libraries that were interesting but none of them had everything we needed/wanted. Of particular inspiration was https://github.com/spf13/afero in its composition of the super-powerful stdlib io.* interfaces. Unfortunately, it didn't support Google Cloud Storage and there was still a lot of passing around of strings and structs. Few, if any, of the vfs-like libraries provided interfaces to easily and confidently create new filesystem backends. What we needed/wanted was the following(and more): Go install: Glide installation: We provide vfssimple as basic way of initializing filesystem backends (see each implementations's docs about authentication). vfssimple pulls in every c2fo/vfs backend. If you need to reduce the backend requirements (and app memory footprint) or add a third party backend, you'll need to implement your own "factory". See backend doc for more info. You can then use those file systems to initialize locations which you'll be referencing frequently, or initialize files directly With a number of files and locations between s3 and the local file system you can perform a number of actions without any consideration for the system's api or implementation details. Third-party Backends Feel free to send a pull request if you want to add your backend to the list. Things to add: Brought to you by the Enterprise Pipeline team at C2FO: John Judd - john.judd@c2fo.com Jason Coble - [@jasonkcoble](https://twitter.com/jasonkcoble) - jason@c2fo.com Chris Roush – chris.roush@c2fo.com https://github.com/c2fo/ Contributing Distributed under the MIT license. See `http://github.com/c2fo/vfs/License.md for more information.

Package vfs provides a pluggable, extensible, and opinionated set of file system functionality for Go across a number of file system types such as os, S3, and GCS. When building our platform, initially we wrote a library that was something to the effect of Not only was ugly but because the behaviors of each "file system" were different and we had to constantly alter the file locations and pass a bucket string (even if the fs didn't know what a bucket was). We found a handful of third-party libraries that were interesting but none of them had everything we needed/wanted. Of particular inspiration was https://github.com/spf13/afero in its composition of the super-powerful stdlib io.* interfaces. Unfortunately, it didn't support Google Cloud Storage and there was still a lot of passing around of strings and structs. Few, if any, of the vfs-like libraries provided interfaces to easily and confidently create new file system backends. What we needed/wanted was the following(and more): Go install: We provide vfssimple as basic way of initializing file system backends (see each implementations's docs about authentication). vfssimple pulls in every c2fo/vfs backend. If you need to reduce the backend requirements (and app memory footprint) or add a third party backend, you'll need to implement your own "factory". See backend doc for more info. You can then use those file systems to initialize locations which you'll be referencing frequently, or initialize files directly You can perform a number of actions without any consideration for the underlying system's api or implementation details. File's io.* interfaces may be used directly: * none so far Feel free to send a pull request if you want to add your backend to the list. Things to add: Brought to you by the Enterprise Pipeline team at C2FO: * John Judd - john.judd@c2fo.com * Jason Coble - [@jasonkcoble](https://twitter.com/jasonkcoble) - jason@c2fo.com * Chris Roush – chris.roush@c2fo.com * Moe Zeid - moe.zeid@c2fo.com https://github.com/c2fo/ Contributing Distributed under the MIT license. See `http://github.com/c2fo/vfs/License.md for more information. * absolute path - A path is said to be absolute if it provides the entire context need to find a file, including the file system root. An absolute path must begin with a slash and may include . and .. directories. * file path - A file path ends with a filename and therefore may not end with a slash. It may be relative or absolute. * location path - A location/dir path must end with a slash. It may be relative or absolute. * relative path - A relative path is a way to locate a dir or file relative to another directory. A relative path may not begin with a slash but may include . and .. directories. * URI - A Uniform Resource Identifier (URI) is a string of characters that unambiguously identifies a particular resource. To guarantee uniformity, all URIs follow a predefined set of syntax rules, but also maintain extensibility through a separately defined hierarchical naming scheme (e.g. http://).

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. Use NewClient or NewAdminClient to create a client that can be used to access the data or admin APIs respectively. Both require credentials that have permission to access the Cloud Bigtable API. If your program is run on Google App Engine or Google Compute Engine, using the Application Default Credentials (https://developers.google.com/accounts/docs/application-default-credentials) is the simplest option. Those credentials will be used by default when NewClient or NewAdminClient are called. To use alternate credentials, pass them to NewClient or NewAdminClient using option.WithTokenSource. For instance, you can use service account credentials by visiting https://cloud.google.com/console/project/MYPROJECT/apiui/credential, creating a new OAuth "Client ID", storing the JSON key somewhere accessible, and writing Here, `google` means the golang.org/x/oauth2/google package and `option` means the google.golang.org/api/option 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 logging contains a Stackdriver Logging client suitable for writing logs. For reading logs, and working with sinks, metrics and monitored resources, see package cloud.google.com/go/logging/logadmin. This client uses Logging API v2. See https://cloud.google.com/logging/docs/api/v2/ for an introduction to the API. Note: This package is in beta. Some backwards-incompatible changes may occur. Use a Client to interact with the Stackdriver Logging API. For most use cases, you'll want to add log entries to a buffer to be periodically flushed (automatically and asynchronously) to the Stackdriver Logging service. You should call Client.Close before your program exits to flush any buffered log entries to the Stackdriver Logging service. For critical errors, you may want to send your log entries immediately. LogSync is slow and will block until the log entry has been sent, so it is not recommended for normal use. An entry payload can be a string, as in the examples above. It can also be any value that can be marshaled to a JSON object, like a map[string]interface{} or a struct: If you have a []byte of JSON, wrap it in json.RawMessage: You may want use a standard log.Logger in your program. An Entry may have one of a number of severity levels associated with it. You can view Stackdriver logs for projects at https://console.cloud.google.com/logs/viewer. Use the dropdown at the top left. When running from a Google Cloud Platform VM, select "GCE VM Instance". Otherwise, select "Google Project" and then the project ID. Logs for organizations, folders and billing accounts can be viewed on the command line with the "gcloud logging read" command. To group all the log entries written during a single HTTP request, create two Loggers, a "parent" and a "child," with different log IDs. Both should be in the same project, and have the same MonitoredResouce type and labels. - Parent entries must have HTTPRequest.Request populated. (Strictly speaking, only the URL is necessary.) - A child entry's timestamp must be within the time interval covered by the parent request (i.e., older than parent.Timestamp, and newer than parent.Timestamp - parent.HTTPRequest.Latency, assuming the parent timestamp marks the end of the request. - The trace field must be populated in all of the entries and match exactly. You should observe the child log entries grouped under the parent on the console. The parent entry will not inherit the severity of its children; you must update the parent severity yourself.

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. Use NewClient or NewAdminClient to create a client that can be used to access the data or admin APIs respectively. Both require credentials that have permission to access the Cloud Bigtable API. If your program is run on Google App Engine or Google Compute Engine, using the Application Default Credentials (https://developers.google.com/accounts/docs/application-default-credentials) is the simplest option. Those credentials will be used by default when NewClient or NewAdminClient are called. To use alternate credentials, pass them to NewClient or NewAdminClient using option.WithTokenSource. For instance, you can use service account credentials by visiting https://cloud.google.com/console/project/MYPROJECT/apiui/credential, creating a new OAuth "Client ID", storing the JSON key somewhere accessible, and writing Here, `google` means the golang.org/x/oauth2/google package and `option` means the google.golang.org/api/option 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. Note: This package is in beta. Some backwards-incompatible changes may occur. 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. 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 abort 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: 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 firestore provides a client for reading and writing to a Cloud Firestore database. See https://cloud.google.com/firestore/docs for an introduction to Cloud Firestore and additional help on using the Firestore API. See https://godoc.org/cloud.google.com/go for authentication, timeouts, connection pooling and similar aspects of this package. Note: you can't use both Cloud Firestore and Cloud Datastore in the same project. To start working with this package, create a client with a project ID: In Firestore, documents are sets of key-value pairs, and collections are groups of documents. A Firestore database consists of a hierarchy of alternating collections and documents, referred to by slash-separated paths like "States/California/Cities/SanFrancisco". This client is built around references to collections and documents. CollectionRefs and DocumentRefs are lightweight values that refer to the corresponding database entities. Creating a ref does not involve any network traffic. Use DocumentRef.Get to read a document. The result is a DocumentSnapshot. Call its Data method to obtain the entire document contents as a map. You can also obtain a single field with DataAt, or extract the data into a struct with DataTo. With the type definition we can extract the document's data into a value of type State: Note that this client supports struct tags beginning with "firestore:" that work like the tags of the encoding/json package, letting you rename fields, ignore them, or omit their values when empty. To retrieve multiple documents from their references in a single call, use Client.GetAll. For writing individual documents, use the methods on DocumentReference. Create creates a new document. The first return value is a WriteResult, which contains the time at which the document was updated. Create fails if the document exists. Another method, Set, either replaces an existing document or creates a new one. To update some fields of an existing document, use Update. It takes a list of paths to update and their corresponding values. Use DocumentRef.Delete to delete a document. You can condition Deletes or Updates on when a document was last changed. Specify these preconditions as an option to a Delete or Update method. The check and the write happen atomically with a single RPC. Here we update a doc only if it hasn't changed since we read it. You could also do this with a transaction. To perform multiple writes at once, use a WriteBatch. Its methods chain for convenience. WriteBatch.Commit sends the collected writes to the server, where they happen atomically. You can use SQL to select documents from a collection. Begin with the collection, and build up a query using Select, Where and other methods of Query. Supported operators include `<`, `<=`, `>`, `>=`, `==`, and 'array-contains'. Call the Query's Documents method to get an iterator, and use it like the other Google Cloud Client iterators. To get all the documents in a collection, you can use the collection itself as a query. Use a transaction to execute reads and writes atomically. All reads must happen before any writes. Transaction creation, commit, rollback and retry are handled for you by the Client.RunTransaction method; just provide a function and use the read and write methods of the Transaction passed to it.

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. Note: This package is in beta. Some backwards-incompatible changes may occur. 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. 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 abort 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: 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 metadata provides access to Google Compute Engine (GCE) metadata and API service accounts. This package is a wrapper around the GCE metadata service, as documented at https://developers.google.com/compute/docs/metadata.

Package logging contains a Stackdriver Logging client suitable for writing logs. For reading logs, and working with sinks, metrics and monitored resources, see package cloud.google.com/go/logging/logadmin. This client uses Logging API v2. See https://cloud.google.com/logging/docs/api/v2/ for an introduction to the API. Note: This package is in beta. Some backwards-incompatible changes may occur. Use a Client to interact with the Stackdriver Logging API. For most use cases, you'll want to add log entries to a buffer to be periodically flushed (automatically and asynchronously) to the Stackdriver Logging service. You should call Client.Close before your program exits to flush any buffered log entries to the Stackdriver Logging service. For critical errors, you may want to send your log entries immediately. LogSync is slow and will block until the log entry has been sent, so it is not recommended for normal use. An entry payload can be a string, as in the examples above. It can also be any value that can be marshaled to a JSON object, like a map[string]interface{} or a struct: If you have a []byte of JSON, wrap it in json.RawMessage: You may want use a standard log.Logger in your program. An Entry may have one of a number of severity levels associated with it. You can view Stackdriver logs for projects at https://console.cloud.google.com/logs/viewer. Use the dropdown at the top left. When running from a Google Cloud Platform VM, select "GCE VM Instance". Otherwise, select "Google Project" and then the project ID. Logs for organizations, folders and billing accounts can be viewed on the command line with the "gcloud logging read" command. To group all the log entries written during a single HTTP request, create two Loggers, a "parent" and a "child," with different log IDs. Both should be in the same project, and have the same MonitoredResouce type and labels. - Parent entries must have HTTPRequest.Request populated. (Strictly speaking, only the URL is necessary.) - A child entry's timestamp must be within the time interval covered by the parent request (i.e., older than parent.Timestamp, and newer than parent.Timestamp - parent.HTTPRequest.Latency, assuming the parent timestamp marks the end of the request. - The trace field must be populated in all of the entries and match exactly. You should observe the child log entries grouped under the parent on the console. The parent entry will not inherit the severity of its children; you must update the parent severity yourself.

Package firestore provides a client for reading and writing to a Cloud Firestore database. See https://cloud.google.com/firestore/docs for an introduction to Cloud Firestore and additional help on using the Firestore API. See https://godoc.org/cloud.google.com/go for authentication, timeouts, connection pooling and similar aspects of this package. Note: you can't use both Cloud Firestore and Cloud Datastore in the same project. To start working with this package, create a client with a project ID: In Firestore, documents are sets of key-value pairs, and collections are groups of documents. A Firestore database consists of a hierarchy of alternating collections and documents, referred to by slash-separated paths like "States/California/Cities/SanFrancisco". This client is built around references to collections and documents. CollectionRefs and DocumentRefs are lightweight values that refer to the corresponding database entities. Creating a ref does not involve any network traffic. Use DocumentRef.Get to read a document. The result is a DocumentSnapshot. Call its Data method to obtain the entire document contents as a map. You can also obtain a single field with DataAt, or extract the data into a struct with DataTo. With the type definition we can extract the document's data into a value of type State: Note that this client supports struct tags beginning with "firestore:" that work like the tags of the encoding/json package, letting you rename fields, ignore them, or omit their values when empty. To retrieve multiple documents from their references in a single call, use Client.GetAll. For writing individual documents, use the methods on DocumentReference. Create creates a new document. The first return value is a WriteResult, which contains the time at which the document was updated. Create fails if the document exists. Another method, Set, either replaces an existing document or creates a new one. To update some fields of an existing document, use Update. It takes a list of paths to update and their corresponding values. Use DocumentRef.Delete to delete a document. You can condition Deletes or Updates on when a document was last changed. Specify these preconditions as an option to a Delete or Update method. The check and the write happen atomically with a single RPC. Here we update a doc only if it hasn't changed since we read it. You could also do this with a transaction. To perform multiple writes at once, use a WriteBatch. Its methods chain for convenience. WriteBatch.Commit sends the collected writes to the server, where they happen atomically. You can use SQL to select documents from a collection. Begin with the collection, and build up a query using Select, Where and other methods of Query. Supported operators include `<`, `<=`, `>`, `>=`, `==`, and 'array-contains'. Call the Query's Documents method to get an iterator, and use it like the other Google Cloud Client iterators. To get all the documents in a collection, you can use the collection itself as a query. Use a transaction to execute reads and writes atomically. All reads must happen before any writes. Transaction creation, commit, rollback and retry are handled for you by the Client.RunTransaction method; just provide a function and use the read and write methods of the Transaction passed to it.

Package translate is a client for the Google Translation API. See https://cloud.google.com/translation for details.

Package translate is a client for the Google Translation API. See https://cloud.google.com/translation for details.

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. Note: This package is in beta. Some backwards-incompatible changes may occur. 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. 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 abort 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: 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 gcpjwt has Google Cloud Platform (Cloud KMS, IAM API, & AppEngine App Identity API) jwt-go implementations. Should work across virtually all environments, on or off of Google's Cloud Platform. It is highly recommended that you override the default algorithm implementations that you want to leverage a GCP service for in dgrijalva/jwt-go. You otherwise will have to manually pick the verification method for your JWTs and they will place non-standard headers in the rendered JWT (with the exception of signJwt from the IAM API which overwrites the header with its own). You should only need to override the algorithm(s) you plan to use. It is also incorrect to override overlapping, algorithms such as `gcpjwt.SigningMethodKMSRS256.Override()` and `gcpjwt.SigningMethodIAMJWT.Override()` Example: As long as a you override a default algorithm implementation as shown above, using the dgrijalva/jwt-go is mostly unchanged. Token creation is more/less done the same way as in the dgrijalva/jwt-go package. The key that you need to provide is always going to be a context.Context, usuaully with a configuration object loaded in: Example: Finally, the steps to validate a token should be straight forward. This library provides you with helper jwt.Keyfunc implementations to do the heavy lifting around getting the public certificates for verification: Example:

Package stackdriver contains the OpenCensus exporters for Stackdriver Monitoring and Stackdriver Tracing. This exporter can be used to send metrics to Stackdriver Monitoring and traces to Stackdriver trace. The package uses Application Default Credentials to authenticate by default. See: https://developers.google.com/identity/protocols/application-default-credentials Alternatively, pass the authentication options in both the MonitoringClientOptions and the TraceClientOptions fields of Options. This exporter support exporting OpenCensus views to Stackdriver Monitoring. Each registered view becomes a metric in Stackdriver Monitoring, with the tags becoming labels. The aggregation function determines the metric kind: LastValue aggregations generate Gauge metrics and all other aggregations generate Cumulative metrics. In order to be able to push your stats to Stackdriver Monitoring, you must: These steps enable the API but don't require that your app is hosted on Google Cloud Platform. This exporter supports exporting Trace Spans to Stackdriver Trace. It also supports the Google "Cloud Trace" propagation format header.

Package nds is a Go datastore API for Google Cloud Datastore that caches datastore calls in a cache in a strongly consistent manner. This often has the effect of making your app faster as cache access is often 10x faster than datastore access. It can also make your app cheaper to run as cache calls are typically cheaper. This package goes to great lengths to ensure that stale datastore values are never returned to clients, i.e. the caching layer is strongly consistent. It does this by using a similar strategy to Python's ndb. However, this package fixes a couple of subtle edge case bugs that are found in ndb. See http://goo.gl/3ByVlA for one such bug. There are currently no known consistency issues with the caching strategy employed by this package. Package nds' Client is used exactly the same way as the cloud.google.com/go/datastore.Client for implemented calls. Ensure that you change all your datastore client Get, Put, Delete, Mutate, and RunInTransaction function calls to use the nds client and Transaction type when converting your own code. The one caveat with transactions is when running queries, there is a helper function for adding the transaction to a datastore.Query. If you mix datastore and nds API calls then you are liable to get stale cache. You can implement your own nds.Cacher and use it in place of the cache backends provided by this package. The cache backends offered by Google such as AppEngine's Memcache and Cloud Memorystore (redis) are available via this package and can be used as references when adding your own.

Package vfs provides a pluggable, extensible, and opinionated set of filesystem functionality for Go across a number of filesystem types such as os, S3, and GCS. When building our platform, initially we wrote a library that was something to the effect of Not only was ugly but because the behaviors of each "filesystem" were different and we had to constantly alter the file locations and pass a bucket string (even if the fs didn't know what a bucket was). We found a handful of third-party libraries that were interesting but none of them had everything we needed/wanted. Of particular inspiration was https://github.com/spf13/afero in its composition of the super-powerful stdlib io.* interfaces. Unfortunately, it didn't support Google Cloud Storage and there was still a lot of passing around of strings and structs. Few, if any, of the vfs-like libraries provided interfaces to easily and confidently create new filesystem backends. What we needed/wanted was the following(and more): Go install: Glide installation: We provide vfssimple as basic way of initializing filesystem backends (see each implementations's docs about authentication). vfssimple pulls in every c2fo/vfs backend. If you need to reduce the backend requirements (and app memory footprint) or add a third party backend, you'll need to implement your own "factory". See backend doc for more info. You can then use those file systems to initialize locations which you'll be referencing frequently, or initialize files directly With a number of files and locations between s3 and the local file system you can perform a number of actions without any consideration for the system's api or implementation details. Third-party Backends Feel free to send a pull request if you want to add your backend to the list. Things to add: Brought to you by the Enterprise Pipeline team at C2FO: John Judd - john.judd@c2fo.com Jason Coble - [@jasonkcoble](https://twitter.com/jasonkcoble) - jason@c2fo.com Chris Roush – chris.roush@c2fo.com https://github.com/c2fo/ Contributing Distributed under the MIT license. See `http://github.com/c2fo/vfs/License.md for more information.

Package vfs provides a pluggable, extensible, and opinionated set of filesystem functionality for Go across a number of filesystem types such as os, S3, and GCS. When building our platform, initially we wrote a library that was something to the effect of Not only was ugly but because the behaviors of each "filesystem" were different and we had to constantly alter the file locations and pass a bucket string (even if the fs didn't know what a bucket was). We found a handful of third-party libraries that were interesting but none of them had everything we needed/wanted. Of particular inspiration was https://github.com/spf13/afero in its composition of the super-powerful stdlib io.* interfaces. Unfortunately, it didn't support Google Cloud Storage and there was still a lot of passing around of strings and structs. Few, if any, of the vfs-like libraries provided interfaces to easily and confidently create new filesystem backends. What we needed/wanted was the following(and more): Go install: Glide installation: We provide vfssimple as basic way of initializing filesystem backends (see each implementations's docs about authentication). vfssimple pulls in every c2fo/vfs backend. If you need to reduce the backend requirements (and app memory footprint) or add a third party backend, you'll need to implement your own "factory". See backend doc for more info. You can then use those file systems to initialize locations which you'll be referencing frequently, or initialize files directly With a number of files and locations between s3 and the local file system you can perform a number of actions without any consideration for the system's api or implementation details. Third-party Backends Feel free to send a pull request if you want to add your backend to the list. Things to add: Brought to you by the Enterprise Pipeline team at C2FO: John Judd - john.judd@c2fo.com Jason Coble - [@jasonkcoble](https://twitter.com/jasonkcoble) - jason@c2fo.com Chris Roush – chris.roush@c2fo.com https://github.com/c2fo/ Contributing Distributed under the MIT license. See `http://github.com/c2fo/vfs/License.md for more information.

Package metadata provides access to Google Compute Engine (GCE) metadata and API service accounts. This package is a wrapper around the GCE metadata service, as documented at https://developers.google.com/compute/docs/metadata.

Package metadata provides access to Google Compute Engine (GCE) metadata and API service accounts. This package is a wrapper around the GCE metadata service, as documented at https://developers.google.com/compute/docs/metadata.

Package vertexai provides a client for interacting with Vertex AI APIs. See cloud.google.com/go/vertexai/genai for the currently supported functionality.

Package gcpjwt has Google Cloud Platform (Cloud KMS, IAM API, & AppEngine App Identity API) jwt-go implementations. Should work across virtually all environments, on or off of Google's Cloud Platform. It is highly recommended that you override the default algorithm implementations that you want to leverage a GCP service for in dgrijalva/jwt-go. You otherwise will have to manually pick the verification method for your JWTs and they will place non-standard headers in the rendered JWT (with the exception of signJwt from the IAM API which overwrites the header with its own). You should only need to override the algorithm(s) you plan to use. It is also incorrect to override overlapping, algorithms such as `gcpjwt.SigningMethodKMSRS256.Override()` and `gcpjwt.SigningMethodIAMJWT.Override()` Example: As long as a you override a default algorithm implementation as shown above, using the dgrijalva/jwt-go is mostly unchanged. Token creation is more/less done the same way as in the dgrijalva/jwt-go package. The key that you need to provide is always going to be a context.Context, usuaully with a configuration object loaded in: Example: Finally, the steps to validate a token should be straight forward. This library provides you with helper jwt.Keyfunc implementations to do the heavy lifting around getting the public certificates for verification: Example:

Package gcscache provides storage, backed by Google Cloud Storage, for certificates managed by the golang.org/x/crypto/acme/autocert package. This package is a work in progress and makes no API stability promises.

Package api is the root of the packages used to access Google Cloud Services. See https://godoc.org/google.golang.org/api for a full list of sub-packages. Within api there exist numerous clients which connect to Google APIs, and various utility packages. All clients in sub-packages are configurable via client options. These options are described here: https://godoc.org/google.golang.org/api/option. All the clients in sub-packages support authentication via Google Application Default Credentials (see https://cloud.google.com/docs/authentication/production), or by providing a JSON key file for a Service Account. See the authentication examples in https://godoc.org/google.golang.org/api/transport for more details. Due to the auto-generated nature of this collection of libraries, complete APIs or specific versions can appear or go away without notice. As a result, you should always locally vendor any API(s) that your code relies upon. Google APIs follow semver as specified by https://cloud.google.com/apis/design/versioning. The code generator and the code it produces - the libraries in the google.golang.org/api/... subpackages - are beta. Note that versioning and stability is strictly not communicated through Go modules. Go modules are used only for dependency management. Many parameters are specified using ints. However, underlying APIs might operate on a finer granularity, expecting int64, int32, uint64, or uint32, all of whom have different maximum values. Subsequently, specifying an int parameter in one of these clients may result in an error from the API because the value is too large. To see the exact type of int that the API expects, you can inspect the API's discovery doc. A global catalogue pointing to the discovery doc of APIs can be found at https://www.googleapis.com/discovery/v1/apis. This field can be found on all Request/Response structs in the generated clients. All of these types have the JSON `omitempty` field tag present on their fields. This means if a type is set to its default value it will not be marshalled. Sometimes you may actually want to send a default value, for instance sending an int of `0`. In this case you can override the `omitempty` feature by adding the field name to the `ForceSendFields` slice. See docs on any struct for more details. This may be used to include empty fields in Patch requests. This field can be found on all Request/Response structs in the generated clients. It can be be used to send JSON null values for the listed fields. By default, fields with empty values are omitted from API requests because of the presence of the `omitempty` field tag on all fields. However, any field with an empty value appearing in NullFields will be sent to the server as null. It is an error if a field in this list has a non-empty value. This may be used to include null fields in Patch requests. An error returned by a client's Do method may be cast to a *googleapi.Error or unwrapped to an *apierror.APIError. The https://pkg.go.dev/google.golang.org/api/googleapi#Error type is useful for getting the HTTP status code: The https://pkg.go.dev/github.com/googleapis/gax-go/v2/apierror#APIError type is useful for inspecting structured details of the underlying API response, such as the reason for the error and the error domain, which is typically the registered service name of the tool or product that generated the error: If an API call returns an Operation, that means it could take some time to complete the work initiated by the API call. Applications that are interested in the end result of the operation they initiated should wait until the Operation.Done field indicates it is finished. To do this, use the service's Operation client, and a loop, like so:

Package stackdriver contains the OpenCensus exporters for Stackdriver Monitoring and Stackdriver Tracing. This exporter can be used to send metrics to Stackdriver Monitoring and traces to Stackdriver trace. The package uses Application Default Credentials to authenticate by default. See: https://developers.google.com/identity/protocols/application-default-credentials Alternatively, pass the authentication options in both the MonitoringClientOptions and the TraceClientOptions fields of Options. This exporter support exporting OpenCensus views to Stackdriver Monitoring. Each registered view becomes a metric in Stackdriver Monitoring, with the tags becoming labels. The aggregation function determines the metric kind: LastValue aggregations generate Gauge metrics and all other aggregations generate Cumulative metrics. In order to be able to push your stats to Stackdriver Monitoring, you must: These steps enable the API but don't require that your app is hosted on Google Cloud Platform. This exporter supports exporting Trace Spans to Stackdriver Trace. It also supports the Google "Cloud Trace" propagation format header.

Package go_fsspec provides a unified interface for interacting with different cloud storage systems such as AWS S3, Google Cloud Storage, and Azure Blob Storage. go_fsspec offers a simple and powerful way to manage files and directories across multiple cloud providers without having to deal with provider-specific APIs or implementations. go_fsspec contains the following key functionalities: The core interface `CloudStorage` provides a set of common methods for file and directory operations, allowing users to interact with any supported cloud provider using the same interface. Supported operations include creating directories, listing files, downloading and uploading files, and deleting files and directories. Additional methods for downloading/uploading entire directories are also available for bulk operations. Each cloud provider (AWS S3, Google Cloud Storage, Azure Blob Storage) has its own implementation of the `CloudStorage` interface, allowing developers to switch providers with minimal code changes. Future versions of go_fsspec will include support for more cloud providers, advanced file versioning, and cross-cloud transfer capabilities. Contributions and feedback are welcome! Feel free to check out the repository and get involved.

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. Note: This package is in beta. Some backwards-incompatible changes may occur. 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. 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 abort 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: 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 firestore provides a client for reading and writing to a Cloud Firestore database. See https://cloud.google.com/firestore/docs for an introduction to Cloud Firestore and additional help on using the Firestore API. See https://godoc.org/cloud.google.com/go for authentication, timeouts, connection pooling and similar aspects of this package. Note: you can't use both Cloud Firestore and Cloud Datastore in the same project. To start working with this package, create a client with a project ID: In Firestore, documents are sets of key-value pairs, and collections are groups of documents. A Firestore database consists of a hierarchy of alternating collections and documents, referred to by slash-separated paths like "States/California/Cities/SanFrancisco". This client is built around references to collections and documents. CollectionRefs and DocumentRefs are lightweight values that refer to the corresponding database entities. Creating a ref does not involve any network traffic. Use DocumentRef.Get to read a document. The result is a DocumentSnapshot. Call its Data method to obtain the entire document contents as a map. You can also obtain a single field with DataAt, or extract the data into a struct with DataTo. With the type definition we can extract the document's data into a value of type State: Note that this client supports struct tags beginning with "firestore:" that work like the tags of the encoding/json package, letting you rename fields, ignore them, or omit their values when empty. To retrieve multiple documents from their references in a single call, use Client.GetAll. For writing individual documents, use the methods on DocumentReference. Create creates a new document. The first return value is a WriteResult, which contains the time at which the document was updated. Create fails if the document exists. Another method, Set, either replaces an existing document or creates a new one. To update some fields of an existing document, use Update. It takes a list of paths to update and their corresponding values. Use DocumentRef.Delete to delete a document. You can condition Deletes or Updates on when a document was last changed. Specify these preconditions as an option to a Delete or Update method. The check and the write happen atomically with a single RPC. Here we update a doc only if it hasn't changed since we read it. You could also do this with a transaction. To perform multiple writes at once, use a WriteBatch. Its methods chain for convenience. WriteBatch.Commit sends the collected writes to the server, where they happen atomically. You can use SQL to select documents from a collection. Begin with the collection, and build up a query using Select, Where and other methods of Query. Supported operators include `<`, `<=`, `>`, `>=`, `==`, and 'array-contains'. Call the Query's Documents method to get an iterator, and use it like the other Google Cloud Client iterators. To get all the documents in a collection, you can use the collection itself as a query. Use a transaction to execute reads and writes atomically. All reads must happen before any writes. Transaction creation, commit, rollback and retry are handled for you by the Client.RunTransaction method; just provide a function and use the read and write methods of the Transaction passed to it.

Package logging contains a Stackdriver Logging client suitable for writing logs. For reading logs, and working with sinks, metrics and monitored resources, see package cloud.google.com/go/logging/logadmin. This client uses Logging API v2. See https://cloud.google.com/logging/docs/api/v2/ for an introduction to the API. Note: This package is in beta. Some backwards-incompatible changes may occur. Use a Client to interact with the Stackdriver Logging API. For most use cases, you'll want to add log entries to a buffer to be periodically flushed (automatically and asynchronously) to the Stackdriver Logging service. You should call Client.Close before your program exits to flush any buffered log entries to the Stackdriver Logging service. For critical errors, you may want to send your log entries immediately. LogSync is slow and will block until the log entry has been sent, so it is not recommended for normal use. An entry payload can be a string, as in the examples above. It can also be any value that can be marshaled to a JSON object, like a map[string]interface{} or a struct: If you have a []byte of JSON, wrap it in json.RawMessage: You may want use a standard log.Logger in your program. An Entry may have one of a number of severity levels associated with it. You can view Stackdriver logs for projects at https://console.cloud.google.com/logs/viewer. Use the dropdown at the top left. When running from a Google Cloud Platform VM, select "GCE VM Instance". Otherwise, select "Google Project" and then the project ID. Logs for organizations, folders and billing accounts can be viewed on the command line with the "gcloud logging read" command. To group all the log entries written during a single HTTP request, create two Loggers, a "parent" and a "child," with different log IDs. Both should be in the same project, and have the same MonitoredResouce type and labels. - Parent entries must have HTTPRequest.Request populated. (Strictly speaking, only the URL is necessary.) - A child entry's timestamp must be within the time interval covered by the parent request (i.e., older than parent.Timestamp, and newer than parent.Timestamp - parent.HTTPRequest.Latency, assuming the parent timestamp marks the end of the request. - The trace field must be populated in all of the entries and match exactly. You should observe the child log entries grouped under the parent on the console. The parent entry will not inherit the severity of its children; you must update the parent severity yourself.

rcloadenv reads environment variables from the Google Cloud RuntimeConfig API. It outputs environment variables as separate lines (e.g. export VARIABLE_NAME=value) so that the output can be sourced to set the variables in a shell. If an environment variable is already set, it does not override it. The config name is set via the environment variable GOOGLE_RUNTIME_CONFIG_NAME If not set, the command exits without outputting. See https://cloud.google.com/deployment-manager/runtime-configurator/create-and-delete-runtimeconfig-resources for how to create a config and set variable values with the Cloud SDK.