Time series as a native data structure in Redis
RedisTimeSeries simplifies the use of Redis for time-series use cases like IoT, stock prices, and telemetry.
With RedisTimeSeries, you can ingest and query millions of samples and events at the speed of Redis. Advanced tooling such as downsampling and aggregation ensure 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.
Introducing RedisTimeSeries at Redisconf19
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.
RedisTimeSeries works well with RedisAI and RedisGears, enabling advanced use cases such as anomaly detection and predictive maintenance.
Rapidly integrate with tools like Grafana, Prometheus, StatsD, and Telegraf for monitoring, visualization, and data migration.
Ingest and process millions of time-stamped data points per second with minimal latency using minimum resources. With RedisTimeSeries, it’s possible to react to anomalies in real time.
Collect telemetry data from multiple remote devices on-premises, in any cloud, or on the edge for insights into IoT devices.
Gain deep insights into infrastructure and application health with integrations into Prometheus, Grafana, and Telegraf.
Size your RedisTimeSeries deployment according to your specific requirements.
RedisInsight is an intuitive visual tool to explore and analyze your data in Redis.
RedisInsight supports RedisTimeSeries modules and allows you to:
As a benefit, you get faster turnaround when building your application using Redis and RedisTimeSeries.
RedisTimeSeries automatically executes downsampling and retention rules with double-delta compression to space-efficiently store large time-series datasets.
Powered by labeling and search techniques, implement multiple range queries and aggregations across several time-series objects for real-time analysis. Use counter operations such as increment and decrement on the last value for telemetry applications.
Support millions of ingest operations/sec at sub-millisecond latency. RedisTimeSeries can achieve linear horizontal scalability thanks to Redis’ shared-nothing cluster architecture, allowing for fast operations regardless of the number of datapoints in a time series.
You can read about RedisTimeSeries performance in this benchmark blog post.
RedisTimeSeries is integrated with popular data collection, analytics, and monitoring libraries, including Telegraf for data ingest, Grafana for analytics, and monitoring dashboards with the Prometheus adaptor, and RedisInsight to inspect your data in Redis.
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