- Defined in:
The request for BeginTransaction.
Instance Attribute Summary collapse
#options ⇒ Google::Apis::SpannerV1::TransactionOptions
Transactions Each session can have at most one active transaction at a time.
Instance Method Summary collapse
#initialize(**args) ⇒ BeginTransactionRequest
A new instance of BeginTransactionRequest.
#update!(**args) ⇒ Object
Update properties of this object.
Methods included from
Methods included from
Returns a new instance of BeginTransactionRequest.
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# File 'generated/google/apis/spanner_v1/classes.rb', line 411 def initialize(**args) update!(**args) end
Instance Attribute Details
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.
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
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
## 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
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
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
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 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
### 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
### 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
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
See TransactionOptions.ReadOnly.max_staleness and
### 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 + 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
Corresponds to the JSON property
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# File 'generated/google/apis/spanner_v1/classes.rb', line 409 def @options end
Instance Method Details
#update!(**args) ⇒ Object
Update properties of this object
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# File 'generated/google/apis/spanner_v1/classes.rb', line 416 def update!(**args) @options = args[:options] if args.key?(:options) end