Launch your first database in seconds.
As low as $24/month.
VPC peering and IP whitelisting.
SOC2, ISO27001, HIPAA compliance.
Hosted and managed by Timescale on your behalf.
Write and read millions of metrics per second. Store hundreds of billions of rows of data.
Deploy in any major cloud provider in 75+ regions.
Rely on the same PostgreSQL you know and love, with full SQL, rock-solid reliability, and the largest ecosystem of developer and management tools.
-- What’s the memory consumption for -- each of my containers right now? SELECT time_bucket('10 seconds', time) AS period, container_id, avg(free_mem), min(free_mem) FROM metrics WHERE time > NOW () - interval '10 minutes' GROUP BY period, container_id ORDER BY period DESC, container_id;
Write millions of data points per second. Store 100s of terabytes on a single node or petabytes across multiple nodes. Handle high-cardinality data easily.
Simplify your stack and store your relational data alongside time-series data. Ask more complex queries, build more powerful applications faster.
Let us run TimescaleDB for you, fully managed on AWS, Azure, or GCP in over 75 regions, with a top-rated support team to ensure your success.
Spend less with compression savings from best-in-class algorithms, including delta-delta and Gorilla, and a memory-efficient architecture.
For our data warehousing needs, TimescaleDB was the ultimate winner. We get the reliability of a relational database, plus rapid ingest, fast queries, and unlimited scale.
TimescaleDB provides the best possible experience when working with time-series data with the power and convenience of SQL. Built on PostgreSQL and proven for mission critical workloads in the most demanding environments, TimescaleDB provides a familiar operational experience and works with any tool in the rich and vibrant PostgreSQL ecosystem.
Because of these characteristics and built-in time-series analytical features, TimescaleDB increases developer productivity and optimizes resource efficiency, all while minimizing operational costs. And now with distributed operation across many servers, TimescaleDB scales to your ingest, query, and storage needs.
The ability to ingest massive amounts of data at scale is a critical component to working with time-series data. TimescaleDB achieves a much higher and more stable ingest rate for time-series data by using automated time-space partitioning. This means that all writes to recent time intervals are only to data chunks that remain in memory, and all indexing also stays local in memory. Yet this complexity is invisible to users, who see only a single hypertable for all their data. TimescaleDB allows users to write millions of data points per second with a single machine, or even tens of millions per second using its distributed hypertable transparently spread over many servers.
TimescaleDB provides a hypertable abstraction layer on top of your time-series data for more efficient storage. A hypertable is structured especially for chunks (data partitioned into many individual tables), while still looking, feeling, and acting just like a normal SQL table. Virtually all user interactions with TimescaleDB are with hypertables. Creating tables and indexes, altering tables, inserting data, selecting data, etc. can (and should) all be executed on the hypertable, and this simple abstraction persists even for distributed hypertables.. And with its best-in-class type-specific native compression, TimescaleDB’s storage is highly efficient, offering 90-95% storage savings. Users can store 100s of billions of rows and 10s of terabytes of data on a single machine, or scale to petabytes across many servers.
TimescaleDB includes a number of time-oriented features that aren't found in traditional relational databases including functions for time-oriented analytics. These analytics provide a way to map irregular raw data to fixed time-intervals. For example, users can employ functions to write complex windowing, gap-filling, interpolation, and last-observation-carried-forward (LOCF) queries.
And, because TimescaleDB is built atop a PostgreSQL foundation, you can store your business data in the same relational database as your time series data, giving you a full view of your business and customers with a simple JOIN.
TimescaleDB also provides certain data management capabilities that are not readily available in traditional relational databases. These include data retention, downsampling, native compression, data tiering, hypertable and schema management, data lifecycle management, and more. Users can create policies to continuously aggregate data, building automated rollups that can be queried for higher performance, and data retention policies can then downsample so only aggregates are retained after certain time periods. We also offer the opportunity to automate many of these capabilities for simplified operations.