A fast distributed scalable time series database built on top of Cassandra.
KairosDB is a fast, distributed, and scalable time series database built on top of Cassandra. It is designed to efficiently store and query large volumes of time-stamped data, such as metrics from monitoring systems or IoT devices. The database provides high performance for both data ingestion and retrieval, making it suitable for real-time analytics and historical data analysis.
Developers and engineers building monitoring systems, IoT platforms, or analytics applications that require scalable and reliable time series data storage. It is particularly useful for teams needing to handle high-throughput metrics with low latency.
KairosDB offers a robust open-source alternative to proprietary time series databases, leveraging Cassandra's proven scalability and fault tolerance. Its distributed architecture ensures high availability and horizontal scaling, while active community support and flexible deployment options make it adaptable to various environments.
Fast scalable time series database
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Optimized for fast ingestion and querying, as highlighted in the key features, making it suitable for real-time metrics and IoT data with low latency.
Built on Cassandra, enabling easy scaling to handle growing data volumes and ensure high availability, which is critical for distributed applications.
Leverages Cassandra's distributed storage for durability and resilience against failures, providing reliable data persistence for mission-critical systems.
Supports Kubernetes deployment via Helm charts, as mentioned in the README, facilitating easy integration into modern containerized infrastructures.
Open-source with an active discussion group and contributions welcome, ensuring ongoing development and community-driven improvements.
Requires setting up and maintaining a Cassandra cluster, adding operational complexity compared to self-contained time series databases like InfluxDB.
Focuses solely on data storage and querying, lacking integrated visualization tools, so users must deploy separate solutions for dashboards and monitoring.
Documentation is split across external sites like GitHub pages and a wiki, which can make it harder to find comprehensive, up-to-date guides.
Demands expertise in both KairosDB and Cassandra for effective deployment and tuning, posing a barrier for teams new to distributed time series databases.