OpenTSDB Distributed Time Series Database | Generated by AI
OpenTSDB is an open-source, distributed, and scalable time series database (TSDB) designed for storing, indexing, and querying large volumes of time-stamped numerical data, such as metrics from monitoring systems. It was originally developed by Benoit Sigoure at StumbleUpon (now part of eBay) to handle massive datasets without losing data granularity, and it’s built on top of Apache HBase for its backend storage, leveraging HBase’s distributed nature for horizontal scaling across clusters.
Key Features
- Scalability: Can manage billions of data points per day by distributing data across multiple nodes, with no single point of failure.
- Data Model: Stores data as key-value pairs where keys include a timestamp, metric name, and tags (e.g., for categorization like “cpu.usage” with tags for host and region). This enables flexible querying without predefined schemas.
- Components:
- Time Series Daemon (TSD): The core server that handles ingestion, querying, and HTTP API interactions.
- Command-line tools for data import/export and administration.
- Querying: Supports SQL-like queries via its API, with aggregation functions (e.g., sum, average) over time ranges. It integrates with visualization tools like Grafana.
- Use Cases: Primarily for monitoring and observability in DevOps, IoT, finance, and performance tracking, where high ingestion rates and long-term retention are critical.
It’s lightweight, requiring no custom storage engine, and remains actively maintained on GitHub. While powerful for big data environments, it may require HBase expertise for setup and can be resource-intensive for smaller deployments.