Google Cloud API

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 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.

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 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 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 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.

Maestro is a SQL-centric tool for orchestrating BigQuery jobs. Maestro also supports data transfers from and to Google Cloud Storage (GCS) and relational databases (presently PostgresSQL and MySQL). Maestro is a "catalog" of SQL statements. Key feature of Maestro is the ability to infer dependencies by examining the SQL and without any additional configuration. Maestro can execute all tasks in correct order without a manually specified order (i.e. a "DAG"). Execution can be associated with a frequency (cadence) without requiring any cron or cron-like configuration. Maestro is an ever-running service (daemon). It uses PostgreSQL to store the SQL and all other configuration, state and history. The daemon takes great care to maintain all of its state in PostgreSQL so that it can be stopped or restarted without interrupting any in-progress jobs (in most cases). Maestro records all BigQuery job and other history and has a notion of users and groups which is useful for attributing costs and resource utilization to users and groups. Maestro has a basic web-based user interface implemented in React, though its API can also be used directly. Maestro can notify arbitrary applications of job completion via a simple HTTP request. Maestro also provides a Python client library for a more native Python experience. Maestro integrates with Google OAuth for authentication, Google Sheets for simple exports, Github (for SQL revision control) and Slack (for alerts and notifications). Maestro was designed with simplicity as one of its primary goals. It trades flexibility usually afforded by configurability in various languages for the transaprency and clarity achievable by leveraging the declarative nature of SQL. Maestro works best for environments where BigQuery is the primary store of all data for analyitcal purposes. E.g. the data may be periodically imported into BigQuery from various databases. Once imported, data may be subsequently summarized or transformed via a sequence of BigQuery jobs. The summarized data can then be exported to external databases/application for additional processing (e.g. SciPy) and possibly be imported back into BigQiery, and so on. Every step of this process can be orchestrated by Maestro without relying on any external scheduling facility such as cron. Below is the listing of all the key conepts with explanations. A table is the central object in Maestro. It always corresponds to a table in BigQuery. Maestro code and documentation use the verb "run" with respect to tables. To "run a table" means to perform whatever action is called for in its configuration and store the result in a BigQuery table. A table is (in most cases) defined by a BigQuery SQL statement. There are three kinds of tables in Maestro. A summary table is produced by executing a BigQuery SQL statement (a Query job). An import table is produced by executing SQL on an external database and importing the result into BigQuery. The SQL statement in this case it intentionally restricted to a primitive which supports only SELECT, FROM, WHERE and LIMIT. This is so as to discourage the users from running a complex and taxing query on the database server. The main reason for this SQL statement is to filter out or transform columns, any other processing is best done subsequently in BigQuery. This is a table whose data comes from GCS. The import is triggered via the Maestro API. Such tables are generally used when BigQuery data needs to be processed by an external tool, e.g. SciPy, etc. A job is a BigQuery job. BigQquery has three types of jobs: query, extract and load. All three types are used in Maestro. These details are internal but should be familiar to developers. A BigQquery query job is executed as part of running a table. A BigQuery extract job is executed as part of running a table, after the query job is complete. It results in one or more extract files in GCS. Maestro provides signed URLs to the GCS files so that external tools require no authentication to access the data. This is also facilitated via the Maestro pythonlib. A BigQuery load job is executed as part of running an import table. It is the last step of the import, after the external database table data has been copied to GCS. A run is a complex process which happens periodically, according to a frequency. For example if a daily frequency is defined, then Maestro will construct a run once per day, selecting all tables (including import tables) assigned to this frequency, computing the dependency graph and creating jobs for each table. The jobs are then executed in correct order based on the position in the graph and the number of workers available. Maestro will also assign priority based on the number of child dependencies a table has, thus running the most "important" tables first. PostgreSQL 9.6 or later is required to run Maestro. Building a "production" binary, i.e. with all assets included in the binary itself requires Webpack. Webpack is not necessary for "development" mode which uses Babel for transpilation. Download and compile Maestro with "go get github.com/voxmedia/maestro". (Note that this will create a $GOPATH/bin/maestro binary, which is not very useful, you can delete it). From here cd $GOPATH/src/github.com/voxmedia/maestro and go build. You should now have a "maestro" binary in this directory. You can also create a "production" binary by running "make build". This will combine all the javascript code into a single file and pack it and all other assets into the maestro binary itself, so that to deploy you only need the binary and no other files. Create a PostgreSQL database named "maestro". If you name it something other than that, you will need to provide that name to Maestro via the -db-connect flag which defaults to "host=/var/run/postgresql dbname=maestro sslmode=disable", which should work on most Linux distros. On MacOS the Postgres socket is likely to be in "/private/tmp" and one way to address this is to run "ln -s /private/tmp /var/run/postgresql" Maestro connects to many services and needs credentials for all of them. These credentials are stored in the database, all encrypted using the same shared secret which must be specified on the command line via the -secret argument. The -secret argument is meant mostly for development, in production it is much more secure to use the -secretpath option pointing to the location of a file containing the secret. From the Google Cloud perspective, it is best to create a project entirely dedicated to Maestro, with BigQuery and GCS API's enabled, then create a Service Account (in IAM) dedicated to Maestro, as well as OAuth credentials. The service account will need BigQuery Editor, Job User and Storage Object Admin roles. Run Maestro like so: ./maestro -secret=whatever where "whatever" is the shared secret you invent and need to remember. You should now be able to visit the Maestro UI, by default it is at http://localhost:3000 When you click on the log-in link, since at this point Maestro has no OAuth configuration, you will be presented with a form asking for the relevant info, which you will need to provide. You should then be redirected to the Google OAuth login page. From here on the configuration is stored in the database in encrypted form. As the first user of this Maestro instance, you are automatically marked as "admin", which means you can perform any action. As an admin, you should see the "Admin" menu in the upper right. Click on it and select the "Credentials" option. You now need to populate the credentials. The BigQuery, default dataset and GCS bucket are required, while Git and Slack are optional, but highly recommended. Note that the BigQuery dataset and the GCS bucket are not created by Maestro, you need to create those manually. The GCS bucket is used for data exports, and it is generally a good idea to set the data in it to expire after several days or whatever works for you. If you need to import data from external databases, you can add those credentials under the Admin / Databases menu. You may want to create a frequency (also under Admin menu). A frequency is how periodic jobs are scheduled in Maestro. It is defined by a period and an offset. The period is passed to time.Truncate() function, and if the result is 0, this is when a run is triggered. The offset is an offset into the period. E.g. to define a frequency that start a run at 4am UTC, you need to specify a period of 86400 and an offset of 14400. Note that Maestro needs to be restarted after these configuration changes (this will be fixed later). At this point you should be able to create a summary table with some simple SQL, e.g. "SELECT 'hello' AS world", save it and run it. If it executes correctly, you should be able to see this new table in the BigQuery UI.

Package cloud contains Google Cloud Platform APIs related types and common functions.

Package gcppubsubdemo is a tool for teaching and learning Google Cloud Platform's pub/sub API. This package is not meant to be used as an API but it is documented like one for instructional purposes. Quick usage examples for the package are shown below. For more information, see the documentation for the individual items. To use the API for publishing, call the GetPublisher function to obtain a Publisher function to call. Call the Publisher function giving the data to publish as the only parameter. To shut down the Publisher, call the Publisher function with nil as the parameter. Failing to do so will leave pub/sub code and network operations initialized. Subscribe to a topic by callng the Subscribe function, giving it a callback to execute when data is received. The callback should return true when it no longer wants to retrieve messages and shut down the subscription. Basic logging is implemented using the standard log package. Logging is disabled by default. When enabled with a call to LogLevel(), log output goes to os.Stderr but can be redirected by using LogOutput().

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 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.

Package gogoo encapsulates google cloud api for more specific operation logic

Package function is a Google Cloud Function receiving webhook events from DNSimple (https://dnsimple.com/webhooks). It reacts to `dnssec.rotation_start` and `dnssec.rotation_complete` events and passes the new DS record on to DK Hostmaster via their DS Update protocol (https://github.com/DK-Hostmaster/dsu-service-specification). The cloud function needs to be configured through environment variables. The `TOKEN` environment variable is the access token that should be added as URL query parameter to the trigger URL (e.g. `?token=abcdefeghijklmnopqrstuvxyz0123456789`). The `DNSIMPLE_TOKEN` environment variable is a DNSimple API token that is used to retrieve DS records from DNsimple. For the domains in your DNSimple account that you would like this cloud function to update in DK Hostmaster you need to add three environment variables. They should all be prefix with the Domain ID from DNSimple (e.g. 123456). `123456_DOMAIN`: the (apex) domain name in DK Hostmaster. `123456_USERID`: the DK Hostmaster handle you use to login to their self service. `123456_PASSWORD`: the DK Hostmaster password you use to login to their self service.

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.

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 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 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 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 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 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 weasel provides means for serving content from a Google Cloud Storage (GCS) bucket, suitable for hosting on Google App Engine. See README.md for the design details. This package is a work in progress and makes no API stability promises.

Package log implements structured logging for Google App Engine, Cloud Run and Cloud Functions. The API is compatible with the standard library "log" module. All the severities conform to the Google Cloud Logging API v2 as described in https://cloud.google.com/logging/docs/reference/v2/rest/v2/LogEntry#logseverity. These severity levels are: DEBUG, INFO, NOTICE, WARNING, ERROR, CRITICAL, ALERT, EMERGENCY. The ERROR, CRITICAL, ALERT, EMERGENCY logs are written to the standard error stream, while the remaining logs are written to the standard output. Example shows typical severity recommendations.

Package gcf_notify Google cloud function is triggered by message published to pubsub topic `deltaanalyticstopic` by gcf_analytics cloud function. Uses google cloud secret manager grabbing sendgrid api key to send email message decoded from base64 encoding per JSON message schema received from pubsub. Only sends from verified sender. Sends to env email address for now, in future version will be able to send to email address specified in request Depends on gcf_analytics cloud function deployed and having run successfully. See System Diagram for more details.

cryptography provides functions implementing commonly needed cryptographic operations - symmetric encryption, digital signatures, hashes, time based one time passwords. It presents a compact and ready-to-go cryptographic toolbox for developers. The package is intended to step on trustworthy cryptographic implementations from the Go standard library and the Google Cloud KMS API offering easy to use and intendedly safe cryptographic utilities for a broad set of development cases. Note of caution: please do not try to change the internal workings of the functions unless you do know what you are doing. If there is a security concern or a recommendation it would be warmly welcomed and promtly addressed.

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 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 cloudiot provides access to the Cloud IoT API. For product documentation, see: https://cloud.google.com/iot Usage example: In this example, Google Application Default Credentials are used for authentication. For information on how to create and obtain Application Default Credentials, see https://developers.google.com/identity/protocols/application-default-credentials. By default, all available scopes (see "Constants") are used to authenticate. To restrict scopes, use option.WithScopes: To use an API key for authentication (note: some APIs do not support API keys), use option.WithAPIKey: To use an OAuth token (e.g., a user token obtained via a three-legged OAuth flow), use option.WithTokenSource: See https://godoc.org/google.golang.org/api/option/ for details on options.

Package cloudiot provides access to the Cloud IoT API. For product documentation, see: https://cloud.google.com/iot Usage example: In this example, Google Application Default Credentials are used for authentication. For information on how to create and obtain Application Default Credentials, see https://developers.google.com/identity/protocols/application-default-credentials. By default, all available scopes (see "Constants") are used to authenticate. To restrict scopes, use option.WithScopes: To use an API key for authentication (note: some APIs do not support API keys), use option.WithAPIKey: To use an OAuth token (e.g., a user token obtained via a three-legged OAuth flow), use option.WithTokenSource: See https://godoc.org/google.golang.org/api/option/ for details on options.

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 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. All of the errors returned by a client's `Do` method may be cast to a `*googleapis.Error`. This can be useful for getting more information on an error while debugging.

Package api is the root of the packages used to access Google Cloud Services. See https://godoc.org/github.com/danielchristian-tokped/google-api-go-client 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/github.com/danielchristian-tokped/google-api-go-client/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/github.com/danielchristian-tokped/google-api-go-client/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 github.com/danielchristian-tokped/google-api-go-client/... 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. All of the errors returned by a client's `Do` method may be cast to a `*googleapis.Error`. This can be useful for getting more information on an error while debugging.

Package cloud is the root of the packages used to access Google Cloud Services. See https://godoc.org/cloud.google.com/go for a full list of sub-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 this package for details. 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 contexts. See the examples for details. Transient errors will be retried when correctness allows. To opt out of default deadlines, set the temporary environment variable GOOGLE_API_GO_EXPERIMENTAL_DISABLE_DEFAULT_DEADLINE to "true" prior to client creation. This affects all Google Cloud Go client libraries. This opt-out mechanism will be removed in a future release. File an issue at https://github.com/googleapis/google-cloud-go if the default deadlines cannot work for you. 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. 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 (bigquery, compute, storage, and translate) rely on the underlying HTTP transport to cache connections for later re-use. These are cached to the default http.MaxIdleConns and http.MaxIdleConnsPerHost settings in http.DefaultTransport. For gRPC clients (all others in this repo), 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 addressed in a round robin fashion. Minimal docker 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. To see gRPC logs, set the environment variable GRPC_GO_LOG_SEVERITY_LEVEL. See https://godoc.org/google.golang.org/grpc/grpclog for more information. For HTTP logging, set the GODEBUG environment variable to "http2debug=1" or "http2debug=2". Most of the errors returned by the generated clients can be converted into a `grpc.Status`. Converting your errors to this type can be a useful to get more information about what went wrong while debugging. Clients in this repository are considered alpha or beta unless otherwise marked as stable in the README.md. Semver is not used to communicate stability of clients. Alpha and beta clients 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: - Security bugs may prompt backwards-incompatible changes. - Situations in which components are no longer feasible to maintain without making breaking changes, including removal. - Parts of the client surface may be outright unstable and subject to change. These parts of the surface will be labeled with the note, "It is EXPERIMENTAL and subject to change or removal without notice." Google Application Default Credentials 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. To arrange for an RPC to be canceled, use context.WithCancel. 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 Datastore client, but the same steps apply to the other client libraries underneath this package. 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. The google package in this example is at golang.org/x/oauth2/google. This example uses the Secret Manager client, but the same steps apply to the other client libraries underneath this package. 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. To set a timeout for an RPC, use context.WithTimeout.

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 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 cloud is the root of the packages used to access Google Cloud Services. See https://godoc.org/cloud.google.com/go for a full list of sub-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 this package for details. By default, all requests in sub-packages will run indefinitely, retrying on transient errors when correctness allows. To set timeouts or arrange for cancellation, use contexts. See the examples for details. 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. 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 (bigquery, compute, storage, and translate) rely on the underlying HTTP transport to cache connections for later re-use. These are cached to the default http.MaxIdleConns and http.MaxIdleConnsPerHost settings in http.DefaultTransport. For gRPC clients (all others in this repo), 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 addressed in a round robin fashion. Minimal docker images like Alpine lack CA certificates. This causes RPCs to appear to hang, because gRPC retries indefinitely. See https://github.com/GoogleCloudPlatform/google-cloud-go/issues/928 for more information. To see gRPC logs, set the environment variable GRPC_GO_LOG_SEVERITY_LEVEL. See https://godoc.org/google.golang.org/grpc/grpclog for more information. For HTTP logging, set the GODEBUG environment variable to "http2debug=1" or "http2debug=2". Google Application Default Credentials is the recommended way to authorize and authenticate clients. For information on how to create and obtain Application Default Credentials, see https://developers.google.com/identity/protocols/application-default-credentials. To arrange for an RPC to be canceled, use context.WithCancel. 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.developers.google.com/permissions/serviceaccounts. This example uses the Datastore client, but the same steps apply to the other client libraries underneath this package. 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. The google package in this example is at golang.org/x/oauth2/google. This example uses the PubSub client, but the same steps apply to the other client libraries underneath this package. To set a timeout for an RPC, use context.WithTimeout.

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 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: