Time series as a native data structure
Time Series simplifies the use of Redis for time-series use cases like IoT, stock prices, and telemetry. With Time Series, you can ingest and query millions of samples and events fast.
Advanced tooling, such as downsampling and aggregation, ensures a small memory footprint without impacting performance. Use a variety of queries for visualization and monitoring with built-in connectors to popular tools like Grafana, Prometheus, and Telegraf.
Benefits
Easy and efficient
The easiest and most efficient way to store time-series data in Redis. Retention rules, downsampling, and even multi-key queries are possible using just a few simple commands.
Tight coupling with other modules
Time Series works well with RedisGears, enabling advanced use cases such as anomaly detection and predictive maintenance.
Tight integrations with popular tools
Rapidly integrate with tools like Grafana, Prometheus, StatsD, and Telegraf for monitoring, visualization, and data migration.
Use cases
Anomaly detection
Ingest and process millions of time-stamped data points per second with minimal latency using minimum resources. With Time Series, it’s possible to react to anomalies in real time.
Telemetry
Collect telemetry data from multiple remote devices on-premises, in any cloud, or on the edge for insights into IoT devices.
App monitoring
Gain deep insights into infrastructure and app health with integrations into Prometheus, Grafana, and Telegraf.