TransactionOptions
import type { TransactionOptions } from "https://googleapis.deno.dev/v1/spanner:v1.ts";
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: 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. 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.
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. Note that the lock priority is preserved per session (not per
transaction). Lock priority is set by the first read or write in the first
attempt of a read-write transaction. If the application starts a new session
to retry the whole transaction, the transaction loses its original lock
priority. Moreover, the lock priority is only preserved if the transaction
fails with an ABORTED
error. 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 error ABORTED
. 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 the
VERSION_RETENTION_PERIOD
of 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_. 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 is 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.
§Properties
When exclude_txn_from_change_streams
is set to true
: * Modifications
from this transaction will not be recorded in change streams with DDL
option allow_txn_exclusion=true
that are tracking columns modified by
these transactions. * Modifications from this transaction will be recorded
in change streams with DDL option allow_txn_exclusion=false or not set
that are tracking columns modified by these transactions. When
exclude_txn_from_change_streams
is set to false
or not set,
Modifications from this transaction will be recorded in all change streams
that are tracking columns modified by these transactions.
exclude_txn_from_change_streams
may only be specified for read-write or
partitioned-dml transactions, otherwise the API will return an
INVALID_ARGUMENT
error.
Partitioned DML transaction. Authorization to begin a Partitioned DML
transaction requires spanner.databases.beginPartitionedDmlTransaction
permission on the session
resource.