Reference documentation and code samples for the Cloud Spanner V1 Client class 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. Snapshot read-only transactions provide guaranteed consistency across several reads, but do not allow writes. Snapshot read-only transactions can be configured to read at timestamps in the past, or configured to perform a strong read (where Spanner will select a timestamp such that the read is guaranteed to see the effects of all transactions that have committed before the start of the read). Snapshot read-only transactions do not need to be committed. Queries on change streams must be performed with the snapshot read-only transaction mode, specifying a strong read. Please see TransactionOptions.ReadOnly.strong for more details.
- 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 (for example, 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 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. If an idle transaction is aborted, the commit will fail with errorABORTED. If this behavior is undesirable, periodically executing a simple SQL query in the transaction (for example,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 transactions, 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.
Queries on change streams (see below for more details) must also specify
the strong read timestamp bound.
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 less than or
equal to 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. You can configure and extend theVERSION_RETENTION_PERIODof a database up to a period as long as one week, which allows Cloud Spanner to perform reads up to one week in the past. Querying change Streams: A Change Stream is a schema object that can be configured to watch data changes on the entire database, a set of tables, or a set of columns in a database. When a change stream is created, Spanner automatically defines a corresponding SQL Table-Valued Function (TVF) that can be used to query the change records in the associated change stream using the ExecuteStreamingSql API. The name of the TVF for a change stream is generated from the name of the change stream: READ_<change_stream_name>. All queries on change stream TVFs must be executed using the ExecuteStreamingSql API with a single-use read-only transaction with a strong read-only timestamp_bound. The change stream TVF allows users to specify the start_timestamp and end_timestamp for the time range of interest. All change records within the retention period is accessible using the strong read-only timestamp_bound. All other TransactionOptions are invalid for change stream queries. In addition, if TransactionOptions.read_only.return_read_timestamp is set to true, a special value of 2^63 - 2 will be returned in the Transaction message that describes the transaction, instead of a valid read timestamp. This special value should be discarded and not used for any subsequent queries. Please see https://cloud.google.com/spanner/docs/change-streams for more details on how to query the change stream TVFs. 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 + 1as 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.
Generated from protobuf message google.spanner.v1.TransactionOptions
Methods
__construct
Constructor.
| Parameters | |
|---|---|
| Name | Description |
data |
array
Optional. Data for populating the Message object. |
↳ read_write |
Google\Cloud\Spanner\V1\TransactionOptions\ReadWrite
Transaction may write. Authorization to begin a read-write transaction requires |
↳ partitioned_dml |
Google\Cloud\Spanner\V1\TransactionOptions\PartitionedDml
Partitioned DML transaction. Authorization to begin a Partitioned DML transaction requires |
↳ read_only |
Google\Cloud\Spanner\V1\TransactionOptions\PBReadOnly
Transaction will not write. Authorization to begin a read-only transaction requires |
getReadWrite
Transaction may write.
Authorization to begin a read-write transaction requires
spanner.databases.beginOrRollbackReadWriteTransaction permission
on the session resource.
Generated from protobuf field .google.spanner.v1.TransactionOptions.ReadWrite read_write = 1;
| Returns | |
|---|---|
| Type | Description |
Google\Cloud\Spanner\V1\TransactionOptions\ReadWrite|null | |
hasReadWrite
setReadWrite
Transaction may write.
Authorization to begin a read-write transaction requires
spanner.databases.beginOrRollbackReadWriteTransaction permission
on the session resource.
Generated from protobuf field .google.spanner.v1.TransactionOptions.ReadWrite read_write = 1;
| Parameter | |
|---|---|
| Name | Description |
var |
Google\Cloud\Spanner\V1\TransactionOptions\ReadWrite
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getPartitionedDml
Partitioned DML transaction.
Authorization to begin a Partitioned DML transaction requires
spanner.databases.beginPartitionedDmlTransaction permission
on the session resource.
Generated from protobuf field .google.spanner.v1.TransactionOptions.PartitionedDml partitioned_dml = 3;
| Returns | |
|---|---|
| Type | Description |
Google\Cloud\Spanner\V1\TransactionOptions\PartitionedDml|null | |
hasPartitionedDml
setPartitionedDml
Partitioned DML transaction.
Authorization to begin a Partitioned DML transaction requires
spanner.databases.beginPartitionedDmlTransaction permission
on the session resource.
Generated from protobuf field .google.spanner.v1.TransactionOptions.PartitionedDml partitioned_dml = 3;
| Parameter | |
|---|---|
| Name | Description |
var |
Google\Cloud\Spanner\V1\TransactionOptions\PartitionedDml
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getReadOnly
Transaction will not write.
Authorization to begin a read-only transaction requires
spanner.databases.beginReadOnlyTransaction permission
on the session resource.
Generated from protobuf field .google.spanner.v1.TransactionOptions.ReadOnly read_only = 2;
| Returns | |
|---|---|
| Type | Description |
Google\Cloud\Spanner\V1\TransactionOptions\PBReadOnly|null | |
hasReadOnly
setReadOnly
Transaction will not write.
Authorization to begin a read-only transaction requires
spanner.databases.beginReadOnlyTransaction permission
on the session resource.
Generated from protobuf field .google.spanner.v1.TransactionOptions.ReadOnly read_only = 2;
| Parameter | |
|---|---|
| Name | Description |
var |
Google\Cloud\Spanner\V1\TransactionOptions\PBReadOnly
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getMode
| Returns | |
|---|---|
| Type | Description |
string | |