PostgreSQL Vacuum Internals – What happens during PostgreSQL Vacuum Processing?
During PostgreSQL vacuum processing, the database system scans through the table or index being vacuumed, removing any dead or outdated rows. This process is necessary to reclaim space that is no longer being used by the database and to prevent the growth of the table or index to a point where it becomes unwieldy and affects performance.
There are two main types of vacuum:
- Full Vacuum: This process scans the entire table or index, removing all dead rows and compacting the table or index to minimize wasted space.
- Incremental Vacuum: This process scans only a portion of the table or index, removing dead rows and compacting the table or index. This process is less resource-intensive than a full vacuum and can be run more frequently.
During the vacuum process, PostgreSQL will also update the statistics used by the query planner. This helps the planner to make more informed decisions about how to execute queries, which can lead to improved query performance.
Additionally, when a vacuum process is running, it may temporarily lock the table or index it is working on. This can cause other queries that need to access that table or index to wait, which can cause a delay in query performance. To minimize this impact, it is best to schedule vacuuming during periods of low database activity.
How can PostgreSQL Vacuum impact performance?
PostgreSQL Vacuum can impact performance in a few ways:
- Resource Usage: Vacuum can use up a significant amount of resources, especially when it is running on a busy system. This can cause performance degradation for other processes running on the same system.
- Blocking Queries: Vacuum can block queries that need to access the table being vacuumed. This can cause delays and slow down query performance.
- Long-Running Transactions: Vacuum can take a long time to finish if there are long-running transactions that prevent it from cleaning up dead rows.
- Disk Space: Vacuum can cause disk space to fill up quickly if it is not able to reclaim space quickly enough. This can lead to performance issues and potential data loss.
- Indexes: Vacuum can cause index bloat which can slow down query performance.
- Time: Vacuum process can take a lot of time to complete, which can have an impact on performance, especially for large tables.
It is important to monitor the vacuum process and configure it properly to minimize the negative impact on performance.
Real-Time Monitoring of PostgreSQL Vacuum Process
This is a Python script that uses the psycopg2 library to connect to a PostgreSQL database and retrieve information about the current vacuum process by querying the pg_stat_progress_vacuum system view:
# Connect to the database
conn = psycopg2.connect(
# Execute the query
cur = conn.cursor()
cur.execute("SELECT * FROM pg_stat_progress_vacuum")
rows = cur.fetchall()
# Print the results
for row in rows:
# Wait 2 seconds before running the query again
# Close the connection
Note: You need to install the psycopg2 library to run this script. This script will print the current status of the vacuum process, such as the number of pages processed, the number of pages remaining, and the current table being vacuumed. This information can be used to monitor the progress of the vacuum process and identify any performance issues.
What is new with vacuum processing in PostgreSQL 15?
In PostgreSQL 15, there have been several improvements to vacuum processing, including:
- Parallel vacuum: In PostgreSQL 15, the vacuum process can now run in parallel, which can significantly speed up the process for large tables.
- Improved visibility of vacuum progress: PostgreSQL 15 includes new system views and functions that provide more detailed information about the progress of the vacuum process.
- Better handling of free space: PostgreSQL 15 includes a new free space map (FSM) that helps to reduce the number of pages that need to be scanned during vacuum processing.
- Faster index cleanup: PostgreSQL 15 includes a new index-only vacuum process that can be faster for certain types of indexes.
- Improved handling of parallelized vacuum: PostgreSQL 15 introduces a new parallelized vacuum process that allows more efficient parallel execution of vacuum, reducing contention and improving performance.
It’s worth noting that, in general, the vacuum process in PostgreSQL works by scanning through tables, marking dead rows as eligible for removal, and reclaiming the space occupied by those rows. The system view pg_stat_progress_vacuum and function pg_vacuum_progress() can be used to monitor the progress of the vacuum process.