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API documentation for spanner_v1.types
package.
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]
.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
CommitResponse
The response for Commit][google.spanner.v1.Spanner.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]
.
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 valueOK
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 statusOK
.
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 ofResultSet][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]
.
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.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
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.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
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]
.
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]
.
RequestOptions
Common request options for various APIs.
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]
.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
RollbackRequest
The request for Rollback][google.spanner.v1.Spanner.Rollback]
.
Session
A session in the Cloud Spanner API.
StructType
StructType
defines the fields of a
STRUCT][google.spanner.v1.TypeCode.STRUCT]
type.
Transaction
A transaction. .. attribute:: id
id
may be used to identify the transaction in subsequent
Read][google.spanner.v1.Spanner.Read]
,
ExecuteSql][google.spanner.v1.Spanner.ExecuteSql]
,
Commit][google.spanner.v1.Spanner.Commit]
, or
Rollback][google.spanner.v1.Spanner.Rollback]
calls.
Single-use read-only transactions do not have IDs, because single-use transactions do not support multiple requests.
:type: bytes
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 (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. In that case, 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][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.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
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.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Type
Type
indicates the type of a Cloud Spanner value, as might be
stored in a table cell or returned from an SQL query.
TypeAnnotationCode
TypeAnnotationCode
is used as a part of
Type][google.spanner.v1.Type]
to disambiguate SQL types that should
be used for a given Cloud Spanner value. Disambiguation is needed
because the same Cloud Spanner type can be mapped to different SQL
types depending on SQL dialect. TypeAnnotationCode doesn't affect
the way value is serialized.
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
.