Lock:tuple - Amazon Aurora
Supported engine versionsContextLikely causes of increased waitsActions
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Lock:tuple

The Lock:tuple event occurs when a backend process is waiting to acquire a lock on a tuple.

Supported engine versions

This wait event information is supported for all versions of Aurora PostgreSQL.

Context

The event Lock:tuple indicates that a backend is waiting to acquire a lock on a tuple while another backend holds a conflicting lock on the same tuple. The following table illustrates a scenario in which sessions generate the Lock:tuple event.

Time

Session 1

Session 2

Session 3

t1

Starts a transaction.

t2

Updates row 1.

t3

Updates row 1. The session acquires an exclusive lock on the tuple and then waits for session 1 to release the lock by committing or rolling back.

t4

Updates row 1. The session waits for session 2 to release the exclusive lock on the tuple.

Or you can simulate this wait event by using the benchmarking tool pgbench. Configure a high number of concurrent sessions to update the same row in a table with a custom SQL file.

To learn more about conflicting lock modes, see Explicit Locking in the PostgreSQL documentation. To learn more about pgbench, see pgbench in the PostgreSQL documentation.

Likely causes of increased waits

When this event appears more than normal, possibly indicating a performance problem, typical causes include the following:

  • A high number of concurrent sessions are trying to acquire a conflicting lock for the same tuple by running UPDATE or DELETE statements.

  • Highly concurrent sessions are running a SELECT statement using the FOR UPDATE or FOR NO KEY UPDATE lock modes.

  • Various factors drive application or connection pools to open more sessions to execute the same operations. As new sessions are trying to modify the same rows, DB load can spike, and Lock:tuple can appear.

For more information, see Row-Level Locks in the PostgreSQL documentation.

Actions

We recommend different actions depending on the causes of your wait event.

Investigate your application logic

Find out whether a blocker session has been in the idle in transaction state for long time. If so, consider ending the blocker session as a short-term solution. You can use the pg_terminate_backend function. For more information about this function, see Server Signaling Functions in the PostgreSQL documentation.

For a long-term solution, do the following:

  • Adjust the application logic.

  • Use the idle_in_transaction_session_timeout parameter. This parameter ends any session with an open transaction that has been idle for longer than the specified amount of time. For more information, see Client Connection Defaults in the PostgreSQL documentation.

  • Use autocommit as much as possible. For more information, see SET AUTOCOMMIT in the PostgreSQL documentation.

Find the blocker session

While the Lock:tuple wait event is occurring, identify the blocker and blocked session by finding out which locks depend on one another. For more information, see Lock dependency information in the PostgreSQL wiki. To analyze past Lock:tuple events, use the Aurora function aurora_stat_backend_waits.

The following example queries all sessions, filtering on tuple and ordering by wait_time.

--AURORA_STAT_BACKEND_WAITS
      SELECT a.pid, 
             a.usename, 
             a.app_name, 
             a.current_query,
             a.current_wait_type, 
             a.current_wait_event, 
             a.current_state, 
             wt.type_name AS wait_type, 
             we.event_name AS wait_event, 
             a.waits, 
             a.wait_time
        FROM (SELECT pid, 
                     usename, 
                     left(application_name,16) AS app_name,
                     coalesce(wait_event_type,'CPU') AS current_wait_type,
                     coalesce(wait_event,'CPU') AS current_wait_event, 
                     state AS current_state,
                     left(query,80) as current_query,
                     (aurora_stat_backend_waits(pid)).* 
                FROM pg_stat_activity 
               WHERE pid <> pg_backend_pid()
                 AND usename<>'rdsadmin') a
NATURAL JOIN aurora_stat_wait_type() wt 
NATURAL JOIN aurora_stat_wait_event() we
WHERE we.event_name = 'tuple'
    ORDER BY a.wait_time;

  pid  | usename | app_name |                 current_query                  | current_wait_type | current_wait_event | current_state | wait_type | wait_event | waits | wait_time
-------+---------+----------+------------------------------------------------+-------------------+--------------------+---------------+-----------+------------+-------+-----------
 32136 | sys     | psql     | /*session3*/ update tab set col=1 where col=1; | Lock              | tuple              | active        | Lock      | tuple      |     1 |   1000018
 11999 | sys     | psql     | /*session4*/ update tab set col=1 where col=1; | Lock              | tuple              | active        | Lock      | tuple      |     1 |   1000024

Reduce concurrency when it is high

The Lock:tuple event might occur constantly, especially in a busy workload time. In this situation, consider reducing the high concurrency for very busy rows. Often, just a few rows control a queue or the Boolean logic, which makes these rows very busy.

You can reduce concurrency by using different approaches based in the business requirement, application logic, and workload type. For example, you can do the following:

  • Redesign your table and data logic to reduce high concurrency.

  • Change the application logic to reduce high concurrency at the row level.

  • Leverage and redesign queries with row-level locks.

  • Use the NOWAIT clause with retry operations.

  • Consider using optimistic and hybrid-locking logic concurrency control.

  • Consider changing the database isolation level.

Troubleshoot bottlenecks

The Lock:tuple can occur with bottlenecks such as CPU starvation or maximum usage of Amazon EBS bandwidth. To reduce bottlenecks, consider the following approaches:

  • Scale up your instance class type.

  • Optimize resource-intensive queries.

  • Change the application logic.

  • Archive data that is rarely accessed.