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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.


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.

Size your Time Series deployment according to your specific requirements.

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.


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.

Time Series with RedisInsight

RedisInsight is an intuitive visual tool to explore and analyze your data in Redis.

RedisInsight supports 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.

Main Capabilities

Downsampling and retention

Time Series 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. Time Series can automatically perform downsampling by aggregating many points over time, reducing both noise in historical data and the storage requirements.

Aggregation, range queries, and special counter operations

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 apps.

Fast data ingest with infinite scale

Time Series automatically executes downsampling and retention rules with double-delta compression to space-efficiently store large time-series datasets.

You can read about Time Series performance
in this benchmark blog post

Visualization with Grafana, RedisInsight, and Telegraf

Time Series 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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