A Perl script that analyzes PostgreSQL database configuration and provides tuning recommendations.
postgresqltuner is a command-line Perl script that analyzes PostgreSQL database configurations and provides actionable tuning advice. It connects to a PostgreSQL instance, examines its settings and runtime metrics, and generates a report highlighting performance issues, security risks, and optimization opportunities. The tool helps administrators ensure their databases are properly configured for stability and efficiency.
PostgreSQL database administrators, DevOps engineers, and system administrators responsible for maintaining and optimizing PostgreSQL instances in production or development environments.
Developers choose postgresqltuner because it offers a quick, automated way to audit PostgreSQL configurations without manual inspection. Its straightforward output with prioritized warnings (URGENT, MEDIUM, LOW) makes it easy to identify critical issues, and its support for various deployment methods (including Docker and SSH) adds flexibility.
Simple script to analyse your PostgreSQL database configuration, and give tuning advice
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Provides prioritized recommendations (e.g., [URGENT] for fsync off) based on best practices, making it easy to address critical issues quickly.
Supports Linux, macOS, and FreeBSD via package managers (apt, dnf, pacman) and Docker, ensuring broad deployment options as detailed in the installation section.
Flags dangerous settings like fsync off and verifies password encryption, offering built-in security auditing to prevent data loss or breaches.
Uses SSH to gather system memory, I/O scheduler, and Huge Pages info, ensuring recommendations are tailored to the host environment for accurate tuning.
Requires installation of specific Perl modules (e.g., DBD::Pg) which can be cumbersome and error-prone, especially on systems without pre-configured packages.
For cloud databases like AWS RDS, it skips SSH checks and relies on manual memory input (--skip-ssh), reducing the accuracy and completeness of recommendations.
It's a one-time script without features for continuous monitoring or integration with alerting systems, limiting its use in dynamic, automated DevOps pipelines.