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QuestDB is an open-source time-series database for high throughput ingestion and fast SQL queries with operational simplicity. It supports schema-agnostic ingestion using the InfluxDB line protocol, PostgreSQL wire protocol, and a REST API for bulk imports and exports.
QuestDB is well suited for financial market data, application metrics, sensor data, real-time analytics, dashboards, and infrastructure monitoring.
QuestDB implements ANSI SQL with native time-series SQL extensions. These SQL extensions make it simple to correlate data from multiple sources using relational and time-series joins. We achieve high performance by adopting a column-oriented storage model, parallelized vector execution, SIMD instructions, and low-latency techniques. The entire codebase is built from the ground up in Java and C++, with no dependencies and zero garbage collection.
We provide a live demo provisioned with the latest QuestDB release and sample datasets:
Query | Execution time |
---|---|
SELECT sum(double) FROM trips |
0.15 secs |
SELECT sum(double), avg(double) FROM trips |
0.5 secs |
SELECT avg(double) FROM trips WHERE time in '2019' |
0.02 secs |
SELECT time, avg(double) FROM trips WHERE time in '2019-01-01' SAMPLE BY 1h |
0.01 secs |
SELECT * FROM trades LATEST ON time PARTITION BY symbol |
0.00025 secs |
Our demo is running on c5.metal
instance and using 24 cores out of 96.
To run QuestDB, Docker can be used to get started quickly:
docker run -p 9000:9000 -p 9009:9009 -p 8812:8812 questdb/questdb
macOS users can use Homebrew:
brew install questdb
brew services start questdb
questdb start // To start questdb
questdb stop // To stop questdb
The QuestDB downloads page provides direct downloads for binaries and has details for other installation and deployment methods.
You can interact with QuestDB using the following interfaces:
9000
9009
9000
8812
Below are our official questdb clients for popular programming languages:
This article compares QuestDB to other open source time series databases spanning functionality, maturity and performance.
Here are high-cardinality
Time Series Benchmark Suite
results using the cpu-only
use case with 6 workers on an AMD Ryzen 3970X:
We are always happy to have contributions to the project whether it is source code, documentation, bug reports, feature requests or feedback. To get started with contributing:
✨ As a sign of our gratitude, we also send QuestDB swag to our contributors. Claim your swag here.
A big thanks goes to the following wonderful people who have contributed to QuestDB: (emoji key):
This project adheres to the all-contributors specification. Contributions of any kind are welcome!