RedisTimeSeries Quick Start Tutorial
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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.
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
RedisTimeSeries works well with RedisAI and 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.
Size your RedisTimeSeries deployment according to your specific requirements.
Anomaly detection
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.
Telemetry
Collect telemetry data from multiple remote devices on-premises, in any cloud, or on the edge for insights into IoT devices.
Application monitoring
Gain deep insights into infrastructure and application health with integrations into Prometheus, Grafana, and Telegraf.
RedisInsight supports RedisGraph and allows you to:
• Build and execute queries
• Navigate your graphs
• Browse, analyze, and export results
As benefits, you get faster turnarounds when building your RedisGears scripts.
RedisTimeSeries automatically executes downsampling and retention rules with double-delta compression to space-efficiently store large time-series datasets.
On the left are the raw data samples. The high point cardinality obscures the overall trend and requires more storage. On the right is a downsampled representation of the same data. RedisTimeSeries can automatically perform downsampling by aggregating many points over time, reducing both noise in historical data and the storage requirements.
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.
RedisTimeSeries automatically executes downsampling and retention rules with double-delta compression to space-efficiently store large time-series datasets.
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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