How long checkpoint queues influence PostgreSQL recovery?

Checkpoint queues can have a significant impact on the recovery process in PostgreSQL. Checkpoints are a mechanism that PostgreSQL uses to write dirty data from memory to disk, in order to ensure that the data is durable and can be recovered in the event of a system failure. Checkpoint queues are used to keep track of the checkpoints that have been created but not yet completed.

During recovery, PostgreSQL needs to replay all the transactions that occurred after the last completed checkpoint. If there are a large number of transactions in the checkpoint queue, it can take a long time to replay all of them, which can prolong the recovery process. This is because the transactions in the checkpoint queue need to be replayed before the recovery can be considered complete.

In addition, checkpoint queues can also impact the performance of the system during normal operation. If the checkpoint queue is too large, it can cause the system to become unresponsive, as PostgreSQL will need to spend more time writing data to disk.

To mitigate the impact of checkpoint queues on recovery, it’s important to ensure that checkpoints are being created frequently enough to prevent the queue from growing too large. This can be achieved by tuning the checkpoint-related configuration parameters in PostgreSQL, such as checkpoint_completion_target, checkpoint_timeout, and max_wal_size. These parameters control how often checkpoints are created and how much data is written to disk during each checkpoint.

It’s also important to monitor the checkpoint queue size and adjust the configuration parameters as necessary to keep the queue under control. This can be done using tools like pg_stat_activity and pg_stat_bgwriter, which provide information about the checkpoint queue and other performance metrics in PostgreSQL.

Monitoring checkpoint queue size in PostgreSQL using the pg_stat_activity and pg_stat_bgwriter system views

Here’s an SQL script that can be used to monitor the checkpoint queue size in PostgreSQL using the pg_stat_activity and pg_stat_bgwriter system views:

This script retrieves various checkpoint-related statistics from the pg_stat_activity and pg_stat_bgwriter system views. Here’s what each column in the output represents:

  • timestamp: The current system time.
  • last_checkpoint_time: The time of the last completed checkpoint write.
  • last_checkpoint_sync_time: The time of the last completed checkpoint sync.
  • checkpoint_completion_target: The checkpoint_completion_target setting, which controls how much of the WAL buffer should be written to disk during a checkpoint.
  • max_wal_size: The max_wal_size setting, which sets the maximum size of the WAL files.
  • checkpoint_timeout: The checkpoint_timeout setting, which controls how long PostgreSQL should wait between checkpoints.
  • checkpoint_active_count: The number of active checkpoints.
  • checkpoint_idle_count: The number of idle checkpoints.
  • idle_count: The number of idle sessions.
  • total_checkpoints: The total number of completed checkpoints since the last restart.
  • checkpoints_pending: The number of checkpoints that have been requested but have not yet been completed.
  • checkpoints_since_restart: The number of checkpoints that have been completed since the last restart.
  • avg_checkpoint_write_time: The average time to write a checkpoint.
  • avg_checkpoint_sync_time: The average time to sync a checkpoint to disk.

By monitoring the checkpoint queue size using this script, you can identify when the checkpoint queue is getting too large and take action to prevent it from impacting the performance of your PostgreSQL database.

About Shiv Iyer 455 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.