How Prometheus stores metrics data

Prometheus does not store its metrics data in a traditional relational database (like MySQL or PostgreSQL) or NoSQL database (like MongoDB or Cassandra). Instead, Prometheus uses its own custom-built time-series database designed specifically for handling time-series data efficiently. Here are the key points regarding how Prometheus stores data:

Storage Mechanism in Prometheus

1. Custom Time-Series Database:

   • Prometheus uses its internal time-series database optimized for handling metrics data.
   • This database is designed to efficiently store, query, and manage time-series data points (timestamped values) collected from various sources.

2. File-based Storage:

   • Metrics data in Prometheus is typically stored on disk in a compressed format.
   • Data is organized into blocks or chunks, where each chunk represents a time interval of collected metric samples.
   • These chunks are stored as files in the file system of the Prometheus server.

3. No External Database:

   • Prometheus does not require an external database (like MySQL or MongoDB) to store its metrics data.
   • All metrics data is managed and stored internally within Prometheus using its custom storage engine.

How Data is Accessed and Managed

1. Data Ingestion:

   • Prometheus collects metrics data via scraping HTTP endpoints exposed by applications, services, or infrastructure components.
   • Collected data is processed and stored locally within the Prometheus server.

2. Retention and Cleanup:

   • Administrators can configure retention policies within Prometheus to specify how long to retain metrics data.
   • Old data that exceeds the retention period is automatically deleted or aggregated based on configuration settings.

3. Querying and Visualization:

   • Users interact with Prometheus using its query language, PromQL, to retrieve and analyze metrics data.
   • Prometheus provides a web-based interface and API for querying data, creating dashboards, and visualizing metrics.

Advantages of Prometheus's Approach

• Performance: Designed for high-performance querying and ingestion of time-series data.
• Scalability: Scales well with increasing data volume and number of metrics.
• Simplicity: Eliminates the need for managing and scaling an external database for metrics storage.