A Go-based daemon that collects PostgreSQL schema, statistics, and OS metrics for monitoring and graphing systems.
pganalyze collector is a monitoring agent that gathers PostgreSQL database metrics, schema information, and system performance data. It converts collected data into a structured protocol buffers format suitable for integration with observability platforms. The tool helps database administrators and DevOps teams monitor PostgreSQL health and performance without relying on proprietary solutions.
Database administrators, DevOps engineers, and SREs who manage PostgreSQL databases and need comprehensive monitoring data. It's particularly useful for teams running self-hosted PostgreSQL instances or using managed services like Amazon RDS.
Developers choose pganalyze collector because it provides a standardized way to extract PostgreSQL monitoring data with multiple deployment options. Its open-source nature, protocol buffers output, and support for various environments make it a flexible alternative to commercial monitoring agents.
pganalyze statistics collector for gathering PostgreSQL metrics and log data
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Gathers schema details, database statistics, and OS metrics like CPU and storage, providing a holistic view of PostgreSQL health as outlined in the README.
Supports installation via APT/YUM packages, Docker containers, Heroku apps, and Helm charts, offering versatility for different infrastructure setups.
Allows previewing collected data in JSON format without transmission, enabling safe configuration testing and data validation before sending.
Converts all data to protocol buffers, making it easily integrable with modern monitoring and graphing systems that use structured formats.
Requires an API key from pganalyze.com, tying it to their platform and limiting use as a purely open-source, standalone solution.
For RDS monitoring, necessitates IAM roles for CloudWatch access and Enhanced Monitoring enabled, adding significant configuration overhead.
Collects extensive OS and database metrics, which can be heavy on system resources, especially for smaller or performance-sensitive environments.
Critical setup steps, like creating a monitoring user, are referenced via external links, requiring users to navigate multiple sources for complete guidance.