Classes
BatchCreateSessionsRequest
The request for [BatchCreateSessions][google.spanner.v1.Spanner.BatchCreateSessions].
BatchCreateSessionsResponse
The response for [BatchCreateSessions][google.spanner.v1.Spanner.BatchCreateSessions].
BeginTransactionRequest
The request for [BeginTransaction][google.spanner.v1.Spanner.BeginTransaction].
CommitRequest
The request for [Commit][google.spanner.v1.Spanner.Commit].
CommitResponse
The response for [Commit][google.spanner.v1.Spanner.Commit].
CommitResponse.Types
Container for nested types declared in the CommitResponse message type.
CommitResponse.Types.CommitStats
Additional statistics about a commit.
CreateSessionRequest
The request for [CreateSession][google.spanner.v1.Spanner.CreateSession].
DeleteSessionRequest
The request for [DeleteSession][google.spanner.v1.Spanner.DeleteSession].
ExecuteBatchDmlRequest
The request for [ExecuteBatchDml][google.spanner.v1.Spanner.ExecuteBatchDml].
ExecuteBatchDmlRequest.Types
Container for nested types declared in the ExecuteBatchDmlRequest message type.
ExecuteBatchDmlRequest.Types.Statement
A single DML statement.
ExecuteBatchDmlResponse
The response for [ExecuteBatchDml][google.spanner.v1.Spanner.ExecuteBatchDml]. Contains a list of [ResultSet][google.spanner.v1.ResultSet] messages, one for each DML statement that has successfully executed, in the same order as the statements in the request. If a statement fails, the status in the response body identifies the cause of the failure.
To check for DML statements that failed, use the following approach:
- Check the status in the response message. The [google.rpc.Code][google.rpc.Code] enum
value
OK
indicates that all statements were executed successfully. - If the status was not
OK
, check the number of result sets in the response. If the response containsN
[ResultSet][google.spanner.v1.ResultSet] messages, then statementN+1
in the request failed.
Example 1:
- Request: 5 DML statements, all executed successfully.
- Response: 5 [ResultSet][google.spanner.v1.ResultSet] messages, with the status
OK
.
Example 2:
- Request: 5 DML statements. The third statement has a syntax error.
- Response: 2 [ResultSet][google.spanner.v1.ResultSet] messages, and a syntax error (
INVALID_ARGUMENT
) status. The number of [ResultSet][google.spanner.v1.ResultSet] messages indicates that the third statement failed, and the fourth and fifth statements were not executed.
ExecuteSqlRequest
The request for [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql] and [ExecuteStreamingSql][google.spanner.v1.Spanner.ExecuteStreamingSql].
ExecuteSqlRequest.Types
Container for nested types declared in the ExecuteSqlRequest message type.
ExecuteSqlRequest.Types.QueryOptions
Query optimizer configuration.
GetSessionRequest
The request for [GetSession][google.spanner.v1.Spanner.GetSession].
KeyRange
KeyRange represents a range of rows in a table or index.
A range has a start key and an end key. These keys can be open or closed, indicating if the range includes rows with that key.
Keys are represented by lists, where the ith value in the list corresponds to the ith component of the table or index primary key. Individual values are encoded as described [here][google.spanner.v1.TypeCode].
For example, consider the following table definition:
CREATE TABLE UserEvents ( UserName STRING(MAX), EventDate STRING(10) ) PRIMARY KEY(UserName, EventDate);
The following keys name rows in this table:
["Bob", "2014-09-23"] ["Alfred", "2015-06-12"]
Since the UserEvents
table's PRIMARY KEY
clause names two
columns, each UserEvents
key has two elements; the first is the
UserName
, and the second is the EventDate
.
Key ranges with multiple components are interpreted
lexicographically by component using the table or index key's declared
sort order. For example, the following range returns all events for
user "Bob"
that occurred in the year 2015:
"start_closed": ["Bob", "2015-01-01"] "end_closed": ["Bob", "2015-12-31"]
Start and end keys can omit trailing key components. This affects the inclusion and exclusion of rows that exactly match the provided key components: if the key is closed, then rows that exactly match the provided components are included; if the key is open, then rows that exactly match are not included.
For example, the following range includes all events for "Bob"
that
occurred during and after the year 2000:
"start_closed": ["Bob", "2000-01-01"] "end_closed": ["Bob"]
The next example retrieves all events for "Bob"
:
"start_closed": ["Bob"] "end_closed": ["Bob"]
To retrieve events before the year 2000:
"start_closed": ["Bob"] "end_open": ["Bob", "2000-01-01"]
The following range includes all rows in the table:
"start_closed": [] "end_closed": []
This range returns all users whose UserName
begins with any
character from A to C:
"start_closed": ["A"] "end_open": ["D"]
This range returns all users whose UserName
begins with B:
"start_closed": ["B"] "end_open": ["C"]
Key ranges honor column sort order. For example, suppose a table is defined as follows:
CREATE TABLE DescendingSortedTable { Key INT64, ... ) PRIMARY KEY(Key DESC);
The following range retrieves all rows with key values between 1 and 100 inclusive:
"start_closed": ["100"] "end_closed": ["1"]
Note that 100 is passed as the start, and 1 is passed as the end,
because Key
is a descending column in the schema.
KeySet
KeySet
defines a collection of Cloud Spanner keys and/or key ranges. All
the keys are expected to be in the same table or index. The keys need
not be sorted in any particular way.
If the same key is specified multiple times in the set (for example if two ranges, two keys, or a key and a range overlap), Cloud Spanner behaves as if the key were only specified once.
ListSessionsRequest
The request for [ListSessions][google.spanner.v1.Spanner.ListSessions].
ListSessionsResponse
The response for [ListSessions][google.spanner.v1.Spanner.ListSessions].
Mutation
A modification to one or more Cloud Spanner rows. Mutations can be applied to a Cloud Spanner database by sending them in a [Commit][google.spanner.v1.Spanner.Commit] call.
Mutation.Types
Container for nested types declared in the Mutation message type.
Mutation.Types.Delete
Arguments to [delete][google.spanner.v1.Mutation.delete] operations.
Mutation.Types.Write
Arguments to [insert][google.spanner.v1.Mutation.insert], [update][google.spanner.v1.Mutation.update], [insert_or_update][google.spanner.v1.Mutation.insert_or_update], and [replace][google.spanner.v1.Mutation.replace] operations.
PartialResultSet
Partial results from a streaming read or SQL query. Streaming reads and SQL queries better tolerate large result sets, large rows, and large values, but are a little trickier to consume.
Partition
Information returned for each partition returned in a PartitionResponse.
PartitionOptions
Options for a PartitionQueryRequest and PartitionReadRequest.
PartitionQueryRequest
The request for [PartitionQuery][google.spanner.v1.Spanner.PartitionQuery]
PartitionReadRequest
The request for [PartitionRead][google.spanner.v1.Spanner.PartitionRead]
PartitionResponse
The response for [PartitionQuery][google.spanner.v1.Spanner.PartitionQuery] or [PartitionRead][google.spanner.v1.Spanner.PartitionRead]
PlanNode
Node information for nodes appearing in a [QueryPlan.plan_nodes][google.spanner.v1.QueryPlan.plan_nodes].
PlanNode.Types
Container for nested types declared in the PlanNode message type.
PlanNode.Types.ChildLink
Metadata associated with a parent-child relationship appearing in a [PlanNode][google.spanner.v1.PlanNode].
PlanNode.Types.ShortRepresentation
Condensed representation of a node and its subtree. Only present for
SCALAR
[PlanNode(s)][google.spanner.v1.PlanNode].
PooledSession
A session from a SessionPool, with an associated transaction if requested. Instances must be released back to the pool via ReleaseToPool(Boolean).
QueryPlan
Contains an ordered list of nodes appearing in the query plan.
ReadRequest
The request for [Read][google.spanner.v1.Spanner.Read] and [StreamingRead][google.spanner.v1.Spanner.StreamingRead].
ReliableStreamReader
Provides streaming access to a Spanner SQL query that automatically retries, handles chunking and recoverable errors.
RequestOptions
Common request options for various APIs.
RequestOptions.Types
Container for nested types declared in the RequestOptions message type.
ResultSet
Results from [Read][google.spanner.v1.Spanner.Read] or [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql].
ResultSetMetadata
Metadata about a [ResultSet][google.spanner.v1.ResultSet] or [PartialResultSet][google.spanner.v1.PartialResultSet].
ResultSetStats
Additional statistics about a [ResultSet][google.spanner.v1.ResultSet] or [PartialResultSet][google.spanner.v1.PartialResultSet].
RollbackRequest
The request for [Rollback][google.spanner.v1.Spanner.Rollback].
Session
A session in the Cloud Spanner API.
SessionName
Resource name for the Session
resource.
SessionPool
A pool of sessions associated with a SpannerClient. Sessions can be acquired by specifying the desired transaction options, if any. A session/transaction pair is then returned, which should be returned to the pool when it is no longer required. Sessions are kept alive automatically, and retired if they are expired by the server.
SessionPool.DatabaseStatistics
A snapshot of statistics for one database within a SessionPool.
SessionPool.Statistics
A snapshot of statistics for a SessionPool.
SessionPoolOptions
Options for session pools.
Spanner
Cloud Spanner API
The Cloud Spanner API can be used to manage sessions and execute transactions on data stored in Cloud Spanner databases.
Spanner.SpannerBase
Base class for server-side implementations of Spanner
Spanner.SpannerClient
Client for Spanner
SpannerClient
Spanner client wrapper, for convenient use.
SpannerClient.ExecuteStreamingSqlStream
Server streaming methods for ExecuteStreamingSql(ExecuteSqlRequest, CallSettings).
SpannerClient.StreamingReadStream
Server streaming methods for StreamingRead(ReadRequest, CallSettings).
SpannerClientBuilder
Builder class for SpannerClient to provide simple configuration of credentials, endpoint etc.
SpannerClientImpl
Spanner client wrapper implementation, for convenient use.
SpannerSettings
Settings for SpannerClient instances.
StreamClosedEventArgs
Event argument type for StreamClosed.
StructType
StructType
defines the fields of a [STRUCT][google.spanner.v1.TypeCode.STRUCT] type.
StructType.Types
Container for nested types declared in the StructType message type.
StructType.Types.Field
Message representing a single field of a struct.
Transaction
A transaction.
TransactionOptions
Transactions
Each session can have at most one active transaction at a time (note that standalone reads and queries use a transaction internally and do count towards the one transaction limit). 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:
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.
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.
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][google.spanner.v1.Spanner.Commit] or [Rollback][google.spanner.v1.Spanner.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][google.spanner.v1.Spanner.Commit]. At any time before [Commit][google.spanner.v1.Spanner.Commit], the client can send a [Rollback][google.spanner.v1.Spanner.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][google.spanner.v1.Spanner.Commit] or [Rollback][google.spanner.v1.Spanner.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][google.spanner.v1.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][google.spanner.v1.TransactionOptions.ReadOnly.read_timestamp] and [TransactionOptions.ReadOnly.exact_staleness][google.spanner.v1.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][google.spanner.v1.TransactionOptions.ReadOnly.max_staleness] and [TransactionOptions.ReadOnly.min_read_timestamp][google.spanner.v1.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.
TransactionOptions.Types
Container for nested types declared in the TransactionOptions message type.
TransactionOptions.Types.PartitionedDml
Message type to initiate a Partitioned DML transaction.
TransactionOptions.Types.ReadOnly
Message type to initiate a read-only transaction.
TransactionOptions.Types.ReadWrite
Message type to initiate a read-write transaction. Currently this transaction type has no options.
TransactionSelector
This message is used to select the transaction in which a [Read][google.spanner.v1.Spanner.Read] or [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql] call runs.
See [TransactionOptions][google.spanner.v1.TransactionOptions] for more information about transactions.
Type
Type
indicates the type of a Cloud Spanner value, as might be stored in a
table cell or returned from an SQL query.
TypeCodeExtensions
Extension methods and factory methods for type codes.
Structs
SpannerNumeric
Representation of the Spanner NUMERIC type, which has 38 digits of precision, and a fixed scale of 9 decimal places to the right of the decimal point
Enums
CommitRequest.TransactionOneofCase
Enum of possible cases for the "transaction" oneof.
ExecuteSqlRequest.Types.QueryMode
Mode in which the statement must be processed.
KeyRange.EndKeyTypeOneofCase
Enum of possible cases for the "end_key_type" oneof.
KeyRange.StartKeyTypeOneofCase
Enum of possible cases for the "start_key_type" oneof.
LossOfPrecisionHandling
Handling for a conversion that would lose precision.
Mutation.OperationOneofCase
Enum of possible cases for the "operation" oneof.
PlanNode.Types.Kind
The kind of [PlanNode][google.spanner.v1.PlanNode]. Distinguishes between the two different kinds of nodes that can appear in a query plan.
RequestOptions.Types.Priority
The relative priority for requests. Note that priority is not applicable for [BeginTransaction][google.spanner.v1.Spanner.BeginTransaction].
The priority acts as a hint to the Cloud Spanner scheduler and does not guarantee priority or order of execution. For example:
- Some parts of a write operation always execute at
PRIORITY_HIGH
, regardless of the specified priority. This may cause you to see an increase in high priority workload even when executing a low priority request. This can also potentially cause a priority inversion where a lower priority request will be fulfilled ahead of a higher priority request. - If a transaction contains multiple operations with different priorities, Cloud Spanner does not guarantee to process the higher priority operations first. There may be other constraints to satisfy, such as order of operations.
ResourcesExhaustedBehavior
Specifies the behavior when MaximumActiveSessions is reached.
ResultSetStats.RowCountOneofCase
Enum of possible cases for the "row_count" oneof.
SessionName.ResourceNameType
The possible contents of SessionName.
TransactionOptions.ModeOneofCase
Enum of possible cases for the "mode" oneof.
TransactionOptions.Types.ReadOnly.TimestampBoundOneofCase
Enum of possible cases for the "timestamp_bound" oneof.
TransactionSelector.SelectorOneofCase
Enum of possible cases for the "selector" oneof.
TypeCode
TypeCode
is used as part of [Type][google.spanner.v1.Type] to
indicate the type of a Cloud Spanner value.
Each legal value of a type can be encoded to or decoded from a JSON
value, using the encodings described below. All Cloud Spanner values can
be null
, regardless of type; null
s are always encoded as a JSON
null
.