A scalable time series database optimized for real-time metrics, events, and analytics with fast query response.
InfluxDB is an open-source time series database designed to collect, process, transform, and store event and time series data. It is optimized for use cases requiring real-time data ingestion and fast query response times to support monitoring, dashboards, and automation solutions. The database features a diskless architecture with object storage support, an embedded Python VM for custom processing, and compatibility with multiple query APIs.
Developers and engineers building real-time monitoring, observability, and analytics systems, such as those for IoT sensor data, server performance, application metrics, network traffic, financial trading, and user behavior analytics.
Developers choose InfluxDB for its fast query performance, with sub-10ms responses for last-value queries, and its flexible architecture that supports both cloud object storage and local disks. It offers broad API compatibility with previous InfluxDB versions and a SQL query engine, making it suitable for integrating into existing time series workflows.
Scalable datastore for metrics, events, and real-time analytics
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Delivers queries in under 10ms for last-value queries and 30ms for distinct metadata, as stated in the README, enabling real-time dashboards and monitoring.
Supports diskless setups with object storage or local disks, minimizing dependencies and allowing scalable data persistence, per the diskless architecture feature.
Maintains compatibility with InfluxDB 1.x and 2.x write APIs and InfluxQL, easing migration from older versions, as highlighted in the feature list.
Includes an embedded Python VM for plugins and triggers, enabling custom data processing without external tools, as mentioned in the README.
The README references separate branches and installation guides for v1, v2, and v3, which can confuse users and complicate setup and migration.
As a GA release in April 2025, it's relatively new with limited real-world testing, potentially leading to undiscovered bugs or gaps in community support.
Fast query times are specified for specific cases like last-value queries; more complex analytical queries might not achieve the same low latency, as implied by the focus on near real-time scenarios.