PostgreSQL Backend Connections via pg_stat_database

Database Connections: A Look at PostgreSQL Backends

This article delves into a PostgreSQL code snippet that sheds light on the number of backend connections established for each database within a PostgreSQL instance. We'll dissect the code, explore its purpose, and glean valuable insights for database administrators and developers.

Sample Code from Command Line

1SELECT
2  datname,
3  numbackends
4FROM
5  pg_catalog.pg_stat_database
6ORDER BY
7  numbackends DESC,
8  datname ASC;

Notes: PostgreSQL number of backends connected to each database. Tested on PostgreSQL 8.4, 9.x, 10.x, 11.x, 12.x, 13.0.

Understanding the Code:

The provided code leverages PostgreSQL's built-in view pg_stat_database to unveil backend connection information. Let's break down the code line by line:

1. SELECT datname, numbackends: This line selects two specific columns from the pg_stat_database view.

   • datname: This column represents the database name within the PostgreSQL cluster.
   • numbackends: This column signifies the current number of backend processes connected to the corresponding database.

2. FROM pg_catalog.pg_stat_database: This clause specifies the data source, which is the pg_stat_database view. This view offers a wealth of statistics pertaining to each database in the cluster, including the number of connected backends, transactions, and blocks read or found in cache.

3. ORDER BY numbackends DESC, datname ASC: This clause dictates the sorting order of the query results.

   • ORDER BY: This keyword designates the columns used for sorting the results.
   • numbackends DESC: This sorts the results in descending order based on the numbackends value. Databases with the highest number of backends will appear first.
   • , datname ASC: This sorts the results within each numbackends group alphabetically by the datname (database name) in ascending order.

Key Points and Insights:

• Purpose: This code snippet serves the crucial function of revealing the number of backend connections associated with each database in a PostgreSQL cluster.

• Benefits:

  • Resource Monitoring: By identifying databases with a high number of backends, administrators can assess potential resource constraints and optimize resource allocation if necessary.

  • Connection Pooling: This query can be instrumental in evaluating the effectiveness of connection pooling mechanisms, which can help limit the number of backend processes required.

  • Workload Analysis: Analyzing backend connections across databases can provide insights into application usage patterns and workload distribution within the cluster.

• Compatibility: The code's comment, -- Tested on PostgreSQL 8.4, 9.x, 10.x, 11.x, 12.x, 13.0, assures its functionality across a wide range of PostgreSQL versions, starting from version 8.4.

In Conclusion:

This PostgreSQL code snippet empowers database administrators with a valuable tool to monitor backend connections across databases. By incorporating this query into your monitoring practices, you can gain a deeper understanding of database resource utilization and optimize your PostgreSQL environment for peak performance.

Additional Considerations:

While the numbackends metric provides a basic overview of database connections, it's essential to consider additional factors for a comprehensive analysis. These factors include:

• Query complexity: Complex queries can consume more resources even with a lower number of backends.
• Connection pooling configuration: The efficiency of connection pooling mechanisms can significantly impact backend connection usage.

By combining backend connection data with these additional considerations, you can gain a more holistic view of your database workload and make informed decisions for optimization.

References

• HariSekhon/SQL-scripts: postgres_backends_per_database.sql — original seed script
• HariSekhon/SQL-scripts LICENSE (MIT) — Copyright 2020 Hari Sekhon. The script in this post is used under the MIT License.