Rely on the same PostgreSQL you know and love, with full SQL, rock-solid reliability, and a massive ecosystem.
Achieve 10-100x faster queries than PostgreSQL, InfluxDB, and MongoDB. Native optimizations for time-series.
Write millions of data points per second. Store 100s of terabytes or petabytes. Don’t worry about cardinality.
Simplify your stack, ask more complex questions, and build more powerful applications.
Let us run TimescaleDB for you, fully managed on AWS, Azure, or GCP in 75+ regions. Access top-rated support.
Spend less with 94% compression rates from best-in-class algorithms and other performance improvements.
All monitoring data is time-series data. Efficiently finding and addressing infrastructure and application issues is a time-series problem. TimescaleDB helps you cost-effectively store and analyze data at scale to identify and predict performance anomalies and service outages, conduct post-mortems, and plan for future capacity needs. Whether you’re collecting home-grown monitoring data, Prometheus metrics, Kubernetes events, logs, or custom metrics, TimescaleDB enables you to measure what matters.
Product data is time-series data. Quickly understanding how your product is used over time, segmenting your customer base, and making product and business decisions is a time-series problem. Or use TimescaleDB to drive user-facing dashboards and analytics when your product is time-series data. TimescaleDB can store all of your application metrics at a fraction of the cost of an analytics service. Timescale gives you all the reliability and flexibility of PostgreSQL, meaning you can use full SQL to construct queries to better understand your products and delight your users.
Device and sensor data is time-series data. Tracking device performance with pinpoint geospatial and temporal accuracy is a time-series problem. TimescaleDB helps you cost-effectively store and analyze relentless streams of device telemetry and sensor readings at scale, in order to manage industrial equipment maintenance, fleet management, asset tracking, route planning, yield optimization, oil and gas production, and more.
Financial data is time-series data. Understanding market and tick data accurately and combining that data with other sources of information is a time-series problem and the foundation of modern financial analysis. TimescaleDB is PostgreSQL with superpowers, meaning you can easily track your time-series tick data, order books, and other market data in a proven database with rock-solid reliability, and correlate it with other relational trend data at your disposal using full SQL. From historical tick data to complex financial modeling, TimescaleDB can help you build insightful products.
-- What is the change of memory consumption for each -- of my k8s containers over the past 10 minutes? SELECT time_bucket('10 seconds', time) AS period, container_id, avg(free_mem) FROM metrics WHERE time > NOW () - interval '10 minutes' GROUP BY period, container_id ORDER BY period DESC, container_id;
| period | container_id | avg | |------------------------|--------------|---------| | 2020-07-01 12:01:00+00 | 16 | 72202 | | 2020-07-01 12:01:00+00 | 73 | 837725 | | 2020-07-01 12:01:00+00 | 96 | 412237 | | 2020-07-01 12:01:00+00 | 142 | 1173393 | | 2020-07-01 12:00:50+00 | 16 | 90104 | | 2020-07-01 12:00:50+00 | 73 | 784596 | | 2020-07-01 12:00:50+00 | 96 | 574134 | | 2020-07-01 12:00:50+00 | 142 | 960104 |
Use the PostgreSQL tools and utilities you know and love