Amazon Timestream
Serverless time-series database for IoT, metrics, and operational telemetry
TimescaleDB
PostgreSQL extension delivering high-performance real-time analytics on time-series data
TimescaleDB adds hypertables, columnar storage and compression to PostgreSQL, enabling fast queries, low storage costs, and analytics on time-series data.
Overview
Overview
TimescaleDB extends PostgreSQL with a purpose‑built engine for time‑series workloads. It targets developers, data engineers, and analysts who need to ingest large volumes of event data while preserving the familiar SQL interface and transactional guarantees of PostgreSQL.
Core Capabilities
The extension introduces hypertables that automatically partition data by time, a hybrid row‑columnar store that compresses historic rows by up to 90 %, and continuous aggregates that refresh incrementally in the background. These features together provide sub‑second query latency on massive datasets, reduce storage footprints, and simplify real‑time dashboarding without custom ETL pipelines.
Deployment Options
TimescaleDB can be run locally via Docker, installed on any supported PostgreSQL version (12‑17), or consumed as a managed service through Tiger Cloud. The Docker image offers a quick start for evaluation, while the managed offering adds automated backups, high availability, and scaling for production workloads.
Highlights
Hypertables with automatic time‑based partitioning
Hybrid row‑columnar storage delivering >90 % compression
Continuous aggregates for incremental materialized views
Full PostgreSQL compatibility with standard SQL
Pros
- High query performance on large time‑series datasets
- Significant storage savings through native compression
- Leverages existing PostgreSQL ecosystem and tooling
- Open‑source with active community contributions
Considerations
- Learning curve for hypertable design and policies
- Requires compatible PostgreSQL version (12‑17)
- Optimal compression may need tuning of chunk sizes
- Background jobs for continuous aggregates add operational overhead
Managed products teams compare with
When teams consider TimescaleDB, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- IoT sensor streams that generate millions of rows daily
- Financial tick data requiring fast rolling calculations
- Monitoring platforms that need real‑time metric dashboards
- Security log analysis with time‑bucketed anomaly detection
Not ideal when
- Simple CRUD applications without time‑series data
- Workloads that need a NoSQL document model
- Ultra‑low‑latency sub‑millisecond queries beyond PostgreSQL limits
- Small datasets where the added engine overhead outweighs benefits
How teams use it
IoT Device Telemetry Monitoring
Ingest millions of sensor readings per day, query recent trends instantly, and retain historic data at 90 % reduced storage.
Financial Market Tick Data Analysis
Store high‑frequency price updates, compute rolling averages via continuous aggregates, and run ad‑hoc queries without reprocessing the entire dataset.
Application Performance Metrics Dashboard
Collect per‑second metrics, bucket them into time intervals, and power real‑time dashboards with minimal query latency.
Log Event Correlation for Security
Combine event timestamps with contextual fields, compress logs, and run time‑bucketed queries to detect anomalies quickly.
Tech snapshot
C66%
PLpgSQL28%
Python3%
CMake2%
Shell1%
Ruby1%
Frequently asked questions
How does TimescaleDB store data differently from regular PostgreSQL?
It uses a hybrid row‑columnar engine: recent rows stay in a rowstore for fast writes, while older data is compressed in a columnstore, reducing size by up to 90 %.
Do I need to change my SQL queries to use TimescaleDB?
No. You create hypertables with standard CREATE TABLE syntax and then query them with ordinary SQL; TimescaleDB handles the optimization transparently.
What versions of PostgreSQL are supported?
TimescaleDB provides extensions for PostgreSQL 12 through 17; the Docker image in the README runs PostgreSQL 17.
Can I run TimescaleDB in a managed cloud service?
Yes. Tiger Cloud offers a fully managed PostgreSQL instance with TimescaleDB pre‑installed, handling backups, HA, and scaling.
How does continuous aggregation differ from a materialized view?
Continuous aggregates refresh incrementally in the background, recomputing only changed rows, whereas a standard materialized view must be rebuilt entirely on each refresh.
Project at a glance
Active- Stars
- 21,467
- Watchers
- 21,467
- Forks
- 1,034
Repo age8 years old
Last commit5 hours ago
Primary languageC
Last synced 3 hours ago