Default request referring to version v1 of the Cloud Spanner API. This contains the host and root path used as a starting point for constructing service requests.

View and manage your data across Google Cloud Platform services

View and manage the contents of your Spanner databases

Administer your Spanner databases

The SQL string can contain parameter placeholders. A parameter placeholder consists of `'''` followed by the parameter name. Parameter names consist of any combination of letters, numbers, and underscores. Parameters can appear anywhere that a literal value is expected. The same parameter name can be used more than once, for example: `"WHERE id > 'msg_id AND id < 'msg_id + 100"` It is an error to execute an SQL statement with unbound parameters. Parameter values are specified using `params`, which is a JSON object whose keys are parameter names, and whose values are the corresponding parameter values.

See: executeSQLRequestParams smart constructor.

Creates a value of ExecuteSQLRequestParams with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• esqlrpAddtional

Properties of the object.

Cloud Labels are a flexible and lightweight mechanism for organizing cloud resources into groups that reflect a customer's organizational needs and deployment strategies. Cloud Labels can be used to filter collections of resources. They can be used to control how resource metrics are aggregated. And they can be used as arguments to policy management rules (e.g. route, firewall, load balancing, etc.). * Label keys must be between 1 and 63 characters long and must conform to the following regular expression: `[a-z]([-a-z0-9]*[a-z0-9])?`. * Label values must be between 0 and 63 characters long and must conform to the regular expression `([a-z]([-a-z0-9]*[a-z0-9])?)?`. * No more than 64 labels can be associated with a given resource. See https://goo.gl/xmQnxf for more information on and examples of labels. If you plan to use labels in your own code, please note that additional characters may be allowed in the future. And so you are advised to use an internal label representation, such as JSON, which doesn't rely upon specific characters being disallowed. For example, representing labels as the string: name + "_" + value would prove problematic if we were to allow "_" in a future release.

See: instanceLabels smart constructor.

Creates a value of InstanceLabels with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• ilAddtional

Metadata type for the operation returned by CreateDatabase.

See: createDatabaseMetadata smart constructor.

Creates a value of CreateDatabaseMetadata with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• cdmDatabase

The database being created.

# Transactions Each session can have at most one active transaction at a time. After the active transaction is completed, the session can immediately be re-used for the next transaction. It is not necessary to create a new session for each transaction. # Transaction Modes Cloud Spanner supports three transaction modes: 1. Locking read-write. This type of transaction is the only way to write data into Cloud Spanner. These transactions rely on pessimistic locking and, if necessary, two-phase commit. Locking read-write transactions may abort, requiring the application to retry. 2. Snapshot read-only. This transaction type provides guaranteed consistency across several reads, but does not allow writes. Snapshot read-only transactions can be configured to read at timestamps in the past. Snapshot read-only transactions do not need to be committed. 3. Partitioned DML. This type of transaction is used to execute a single Partitioned DML statement. Partitioned DML partitions the key space and runs the DML statement over each partition in parallel using separate, internal transactions that commit independently. Partitioned DML transactions do not need to be committed. For transactions that only read, snapshot read-only transactions provide simpler semantics and are almost always faster. In particular, read-only transactions do not take locks, so they do not conflict with read-write transactions. As a consequence of not taking locks, they also do not abort, so retry loops are not needed. Transactions may only read/write data in a single database. They may, however, read/write data in different tables within that database. ## Locking Read-Write Transactions Locking transactions may be used to atomically read-modify-write data anywhere in a database. This type of transaction is externally consistent. Clients should attempt to minimize the amount of time a transaction is active. Faster transactions commit with higher probability and cause less contention. Cloud Spanner attempts to keep read locks active as long as the transaction continues to do reads, and the transaction has not been terminated by Commit or Rollback. Long periods of inactivity at the client may cause Cloud Spanner to release a transaction's locks and abort it. Conceptually, a read-write transaction consists of zero or more reads or SQL statements followed by Commit. At any time before Commit, the client can send a Rollback request to abort the transaction. ### Semantics Cloud Spanner can commit the transaction if all read locks it acquired are still valid at commit time, and it is able to acquire write locks for all writes. Cloud Spanner can abort the transaction for any reason. If a commit attempt returns `ABORTED`, Cloud Spanner guarantees that the transaction has not modified any user data in Cloud Spanner. Unless the transaction commits, Cloud Spanner makes no guarantees about how long the transaction's locks were held for. It is an error to use Cloud Spanner locks for any sort of mutual exclusion other than between Cloud Spanner transactions themselves. ### Retrying Aborted Transactions When a transaction aborts, the application can choose to retry the whole transaction again. To maximize the chances of successfully committing the retry, the client should execute the retry in the same session as the original attempt. The original session's lock priority increases with each consecutive abort, meaning that each attempt has a slightly better chance of success than the previous. Under some circumstances (e.g., many transactions attempting to modify the same row(s)), a transaction can abort many times in a short period before successfully committing. Thus, it is not a good idea to cap the number of retries a transaction can attempt; instead, it is better to limit the total amount of wall time spent retrying. ### Idle Transactions A transaction is considered idle if it has no outstanding reads or SQL queries and has not started a read or SQL query within the last 10 seconds. Idle transactions can be aborted by Cloud Spanner so that they don't hold on to locks indefinitely. In that case, the commit will fail with error `ABORTED`. If this behavior is undesirable, periodically executing a simple SQL query in the transaction (e.g., `SELECT 1`) prevents the transaction from becoming idle. ## Snapshot Read-Only Transactions Snapshot read-only transactions provides a simpler method than locking read-write transactions for doing several consistent reads. However, this type of transaction does not support writes. Snapshot transactions do not take locks. Instead, they work by choosing a Cloud Spanner timestamp, then executing all reads at that timestamp. Since they do not acquire locks, they do not block concurrent read-write transactions. Unlike locking read-write transactions, snapshot read-only transactions never abort. They can fail if the chosen read timestamp is garbage collected; however, the default garbage collection policy is generous enough that most applications do not need to worry about this in practice. Snapshot read-only transactions do not need to call Commit or Rollback (and in fact are not permitted to do so). To execute a snapshot transaction, the client specifies a timestamp bound, which tells Cloud Spanner how to choose a read timestamp. The types of timestamp bound are: - Strong (the default). - Bounded staleness. - Exact staleness. If the Cloud Spanner database to be read is geographically distributed, stale read-only transactions can execute more quickly than strong or read-write transaction, because they are able to execute far from the leader replica. Each type of timestamp bound is discussed in detail below. ### Strong Strong reads are guaranteed to see the effects of all transactions that have committed before the start of the read. Furthermore, all rows yielded by a single read are consistent with each other -- if any part of the read observes a transaction, all parts of the read see the transaction. Strong reads are not repeatable: two consecutive strong read-only transactions might return inconsistent results if there are concurrent writes. If consistency across reads is required, the reads should be executed within a transaction or at an exact read timestamp. See TransactionOptions.ReadOnly.strong. ### Exact Staleness These timestamp bounds execute reads at a user-specified timestamp. Reads at a timestamp are guaranteed to see a consistent prefix of the global transaction history: they observe modifications done by all transactions with a commit timestamp <= the read timestamp, and observe none of the modifications done by transactions with a larger commit timestamp. They will block until all conflicting transactions that may be assigned commit timestamps <= the read timestamp have finished. The timestamp can either be expressed as an absolute Cloud Spanner commit timestamp or a staleness relative to the current time. These modes do not require a "negotiation phase" to pick a timestamp. As a result, they execute slightly faster than the equivalent boundedly stale concurrency modes. On the other hand, boundedly stale reads usually return fresher results. See TransactionOptions.ReadOnly.read_timestamp and TransactionOptions.ReadOnly.exact_staleness. ### Bounded Staleness Bounded staleness modes allow Cloud Spanner to pick the read timestamp, subject to a user-provided staleness bound. Cloud Spanner chooses the newest timestamp within the staleness bound that allows execution of the reads at the closest available replica without blocking. All rows yielded are consistent with each other -- if any part of the read observes a transaction, all parts of the read see the transaction. Boundedly stale reads are not repeatable: two stale reads, even if they use the same staleness bound, can execute at different timestamps and thus return inconsistent results. Boundedly stale reads execute in two phases: the first phase negotiates a timestamp among all replicas needed to serve the read. In the second phase, reads are executed at the negotiated timestamp. As a result of the two phase execution, bounded staleness reads are usually a little slower than comparable exact staleness reads. However, they are typically able to return fresher results, and are more likely to execute at the closest replica. Because the timestamp negotiation requires up-front knowledge of which rows will be read, it can only be used with single-use read-only transactions. See TransactionOptions.ReadOnly.max_staleness and TransactionOptions.ReadOnly.min_read_timestamp. ### Old Read Timestamps and Garbage Collection Cloud Spanner continuously garbage collects deleted and overwritten data in the background to reclaim storage space. This process is known as "version GC". By default, version GC reclaims versions after they are one hour old. Because of this, Cloud Spanner cannot perform reads at read timestamps more than one hour in the past. This restriction also applies to in-progress reads and/or SQL queries whose timestamp become too old while executing. Reads and SQL queries with too-old read timestamps fail with the error `FAILED_PRECONDITION`. ## Partitioned DML Transactions Partitioned DML transactions are used to execute DML statements with a different execution strategy that provides different, and often better, scalability properties for large, table-wide operations than DML in a ReadWrite transaction. Smaller scoped statements, such as an OLTP workload, should prefer using ReadWrite transactions. Partitioned DML partitions the keyspace and runs the DML statement on each partition in separate, internal transactions. These transactions commit automatically when complete, and run independently from one another. To reduce lock contention, this execution strategy only acquires read locks on rows that match the WHERE clause of the statement. Additionally, the smaller per-partition transactions hold locks for less time. That said, Partitioned DML is not a drop-in replacement for standard DML used in ReadWrite transactions. - The DML statement must be fully-partitionable. Specifically, the statement must be expressible as the union of many statements which each access only a single row of the table. - The statement is not applied atomically to all rows of the table. Rather, the statement is applied atomically to partitions of the table, in independent transactions. Secondary index rows are updated atomically with the base table rows. - Partitioned DML does not guarantee exactly-once execution semantics against a partition. The statement will be applied at least once to each partition. It is strongly recommended that the DML statement should be idempotent to avoid unexpected results. For instance, it is potentially dangerous to run a statement such as `UPDATE table SET column = column + 1` as it could be run multiple times against some rows. - The partitions are committed automatically - there is no support for Commit or Rollback. If the call returns an error, or if the client issuing the ExecuteSql call dies, it is possible that some rows had the statement executed on them successfully. It is also possible that statement was never executed against other rows. - Partitioned DML transactions may only contain the execution of a single DML statement via ExecuteSql or ExecuteStreamingSql. - If any error is encountered during the execution of the partitioned DML operation (for instance, a UNIQUE INDEX violation, division by zero, or a value that cannot be stored due to schema constraints), then the operation is stopped at that point and an error is returned. It is possible that at this point, some partitions have been committed (or even committed multiple times), and other partitions have not been run at all. Given the above, Partitioned DML is good fit for large, database-wide, operations that are idempotent, such as deleting old rows from a very large table.

See: transactionOptions smart constructor.

Creates a value of TransactionOptions with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• toReadWrite
• toPartitionedDml
• toReadOnly

Transaction may write. Authorization to begin a read-write transaction requires `spanner.databases.beginOrRollbackReadWriteTransaction` permission on the `session` resource.

Partitioned DML transaction. Authorization to begin a Partitioned DML transaction requires `spanner.databases.beginPartitionedDmlTransaction` permission on the `session` resource.

Transaction will not write. Authorization to begin a read-only transaction requires `spanner.databases.beginReadOnlyTransaction` permission on the `session` resource.

The response for GetDatabaseDdl.

See: getDatabaseDdlResponse smart constructor.

Creates a value of GetDatabaseDdlResponse with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• gddrStatements

A list of formatted DDL statements defining the schema of the database specified in the request.

The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC. The error model is designed to be: - Simple to use and understand for most users - Flexible enough to meet unexpected needs # Overview The `Status` message contains three pieces of data: error code, error message, and error details. The error code should be an enum value of google.rpc.Code, but it may accept additional error codes if needed. The error message should be a developer-facing English message that helps developers *understand* and *resolve* the error. If a localized user-facing error message is needed, put the localized message in the error details or localize it in the client. The optional error details may contain arbitrary information about the error. There is a predefined set of error detail types in the package `google.rpc` that can be used for common error conditions. # Language mapping The `Status` message is the logical representation of the error model, but it is not necessarily the actual wire format. When the `Status` message is exposed in different client libraries and different wire protocols, it can be mapped differently. For example, it will likely be mapped to some exceptions in Java, but more likely mapped to some error codes in C. # Other uses The error model and the `Status` message can be used in a variety of environments, either with or without APIs, to provide a consistent developer experience across different environments. Example uses of this error model include: - Partial errors. If a service needs to return partial errors to the client, it may embed the `Status` in the normal response to indicate the partial errors. - Workflow errors. A typical workflow has multiple steps. Each step may have a `Status` message for error reporting. - Batch operations. If a client uses batch request and batch response, the `Status` message should be used directly inside batch response, one for each error sub-response. - Asynchronous operations. If an API call embeds asynchronous operation results in its response, the status of those operations should be represented directly using the `Status` message. - Logging. If some API errors are stored in logs, the message `Status` could be used directly after any stripping needed for security/privacy reasons.

See: status smart constructor.

Creates a value of Status with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• sDetails
• sCode
• sMessage

A list of messages that carry the error details. There is a common set of message types for APIs to use.

The status code, which should be an enum value of google.rpc.Code.

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

The request for CreateInstance.

See: createInstanceRequest smart constructor.

Creates a value of CreateInstanceRequest with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• cirInstanceId
• cirInstance

Required. The ID of the instance to create. Valid identifiers are of the form `a-z*[a-z0-9]` and must be between 6 and 30 characters in length.

Required. The instance to create. The name may be omitted, but if specified must be `/instances/`.

Message type to initiate a read-write transaction. Currently this transaction type has no options.

See: readWrite smart constructor.

Creates a value of ReadWrite with the minimum fields required to make a request.

The request for Rollback.

See: rollbackRequest smart constructor.

Creates a value of RollbackRequest with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• rrTransactionId

Required. The transaction to roll back.

The response for ListDatabases.

See: listDatabasesResponse smart constructor.

Creates a value of ListDatabasesResponse with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• ldrNextPageToken
• ldrDatabases

`next_page_token` can be sent in a subsequent ListDatabases call to fetch more of the matching databases.

Databases that matched the request.

Represents an expression text. Example: title: "User account presence" description: "Determines whether the request has a user account" expression: "size(request.user) > 0"

See: expr smart constructor.

Creates a value of Expr with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• eLocation
• eExpression
• eTitle
• eDescription

An optional string indicating the location of the expression for error reporting, e.g. a file name and a position in the file.

Textual representation of an expression in Common Expression Language syntax. The application context of the containing message determines which well-known feature set of CEL is supported.

An optional title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.

An optional description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.

The response message for Operations.ListOperations.

See: listOperationsResponse smart constructor.

Creates a value of ListOperationsResponse with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• lorNextPageToken
• lorOperations

The standard List next-page token.

A list of operations that matches the specified filter in the request.

Request message for `GetIamPolicy` method.

See: getIAMPolicyRequest smart constructor.

Creates a value of GetIAMPolicyRequest with the minimum fields required to make a request.

Metadata associated with a parent-child relationship appearing in a PlanNode.

See: childLink smart constructor.

Creates a value of ChildLink with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• clChildIndex
• clVariable
• clType

The node to which the link points.

Only present if the child node is SCALAR and corresponds to an output variable of the parent node. The field carries the name of the output variable. For example, a `TableScan` operator that reads rows from a table will have child links to the `SCALAR` nodes representing the output variables created for each column that is read by the operator. The corresponding `variable` fields will be set to the variable names assigned to the columns.

The type of the link. For example, in Hash Joins this could be used to distinguish between the build child and the probe child, or in the case of the child being an output variable, to represent the tag associated with the output variable.

The request for BeginTransaction.

See: beginTransactionRequest smart constructor.

Creates a value of BeginTransactionRequest with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• btrOptions

Required. Options for the new transaction.

Options for a PartitionQueryRequest and PartitionReadRequest.

See: partitionOptions smart constructor.

Creates a value of PartitionOptions with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

• poMaxPartitions
• poPartitionSizeBytes

• *Note:** This hint is currently ignored by PartitionQuery and PartitionRead requests. The desired maximum number of partitions to return. For example, this may be set to the number of workers available. The default for this option is currently 10,000. The maximum value is currently 200,000. This is only a hint. The actual number of partitions returned may be smaller or larger than this maximum count request.