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 active connections, transactions, and blocks read/written.

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.

Source

https://github.com/HariSekhon/SQL-scripts/blob/master/postgres_backends_per_database.sql