TimescaleDB
The Cloud Advantage
Worry-free operations
Hosted and managed by Timescale on your behalf.
Enterprise-ready
VPC peering and IP whitelisting.
SOC2, ISO27001, HIPAA compliance.
Grows with you
Write and read millions of metrics per second. Store hundreds of billions of rows of data.
Get started quickly
Launch your first database in seconds.
As low as $49/month.
Global flexibility
Deploy in any major cloud provider in 75+ regions.
Supercharged PostgreSQL
Rely on the same PostgreSQL you know and love, with full SQL, rock‑solid reliability, and the largest ecosystem of developer and management tools.
- Full SQL, powerful analytics, no restrictions
- Leverage your favorite PostgreSQL extensions
- Entire toolset works: ORMs, connectors, JDBC, applications
- Connect Prometheus for all your observability metrics
- Visualize data using the dashboards you love
-- 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;
Accelerated performance
Achieve 10-100x faster queries than PostgreSQL, InfluxDB, and MongoDB. Leverage query parallelization, continuous aggregates, and other performance optimizations.
- Achieve 10X faster inserts and ingest 1.5M+ more metrics per second per server for high-cardinality workloads
- Optimized time-series queries and advanced time-series analytics
- Real-time insights over automated continuous aggregations
- Fast scans over compressed columnar storage
- Query faster over longer time horizons with downsampling
Massive scale
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.
- Store 100s of billions of rows and 10s of TBs of data per server
- Record billions of distinct time‑series, collect everything you need
- Best‑of‑breed datatype‑specific compression for 16x storage capacity
- Create distributed hypertables across many TimescaleDB nodes
- Parallelize scans and aggregation queries across many nodes
Relational and time-series, together
Simplify your stack and store your relational data alongside time‑series data. Ask more complex queries, build more powerful applications faster.
- Centralize storage of time‑series, application, and sensor data
- Correlate metrics with business and system‑of‑record data
- Unlimited metadata, don’t worry about cardinality of labels
- Perform JOINs to understand relations with time‑series
- Ensure clean, correct data with foreign keys and constraints
Worry-free operations
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.
- Spin up a pre‑configured instance in 30 seconds, pay‑as‑you‑go
- Effortless upgrades, fully managed without downtime
- Automated, continuous backups with point‑in‑time recovery
- Choose highly‑available replicated pairs for business continuity
- Integrated metrics, logs, security and user controls at your fingertips
Lower costs
Spend less with compression savings from best‑in‑class algorithms, including delta-delta and Gorilla, and a memory‑efficient architecture.
- Reduce storage costs with 94-97% lossless compression rates
- Downsampling keeps aggregated metrics without wasting disk space
- Optimize storage consumption with data retention policies
- Transparent pricing, always know what you’ll pay
- Dynamically scale compute and storage based on changing needs
TimescaleDB was the clear answer for AppDynamics' unified monitoring platform, providing an impressive 12x cost/performance improvement over our previous database architecture.
How TimescaleDB works
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.
Features
storage for every query, use case, and workflow.
Cloud
(gapfilling, LOCF, interpolation)
Microsoft Azure
Google Cloud Platform
Software(Community Edition)
(gapfilling, LOCF, interpolation)
Features
storage for every query, use case, and workflow.
(gapfilling, LOCF, interpolation)
Explore Pricing Configurations
