This page describes best practices for using Data Manipulation Language (DML) and Partitioned DML.
Reducing the number of locked rows when executing a DML statement
You execute DML statements inside read-write transactions. Cloud Spanner acquires shared
read locks on all rows that it reads, including rows that might not match the filter condition of
WHERE clause. Cloud Spanner acquires exclusive locks on rows that it modifies.
The result is that Cloud Spanner might lock a large number of row ranges or an entire table, even
if the transaction only modifies a few of the rows it reads. To lock fewer rows, scope the
WHERE clause for
DELETE statements to small
primary key ranges by including the primary key columns in the
Modifying data using DML statements and mutations
Cloud Spanner buffers insertions, updates, and deletions performed using DML statements on the server-side, and the results are visible to subsequent SQL and DML statements within the same transaction. This behavior is different from the Mutation API, where Cloud Spanner buffers the mutations on the client-side and sends the mutations server-side as part of the commit operation. As a result, mutations in the commit request are not visible to SQL or DML statements within the same transaction.
You might want to combine DML statements and mutations in the same
transaction, because some operations are only supported in the Mutation API. An
If a transaction contains both DML statements and mutations in the commit
request, Cloud Spanner executes the DML statements before the mutations. To
avoid having to account for the order of execution in your client library code,
you should use either DML statements or the mutations in a single
transaction, but not both. If you use both, you should buffer writes only at the
very end of the transaction.
Calling the PENDING_COMMIT_TIMESTAMP function
You use the
function to write the commit timestamp in a DML statement. Cloud Spanner selects the commit
timestamp when the transaction commits.