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QuestDB
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 semantics. These SQL semantics 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.
Try QuestDB
We provide a live demo provisioned with the latest QuestDB release and sample datasets:
- 10 years of NYC taxi trips with 1.6 billion rows
- live trading data from a cryptocurrency exchange
- geolocations of 250k unique ships over time
Get started
Install QuestDB
To run QuestDB, Docker can be used to get started quickly:
docker run -p 9000:9000 -p 9009:9009 -p 8812:8812 questdb/questdbmacOS users can use Homebrew:
brew install questdb
brew services start questdb
questdb start // To start questdb
questdb stop // To stop questdbThe QuestDB downloads page provides direct downloads for binaries and has details for other installation and deployment methods.
Connect to QuestDB
You can interact with QuestDB using the following interfaces:
- Web Console for interactive
SQL editor on port
9000 - InfluxDB line protocol for
high-throughput ingestion on port
9009 - REST API on port
9000 - PostgreSQL wire protocol on
port
8812
Insert data
Below are our official InfluxDB line protocol clients for popular programming languages:
How QuestDB compares to other open source TSDBs
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:
The following table shows query execution time of a billion rows run on a
c5.metal instance using 16 of the 96 threads available:
| Query | Runtime |
|---|---|
SELECT sum(double) FROM 1bn |
0.061 secs |
SELECT tag, sum(double) FROM 1bn |
0.179 secs |
SELECT tag, sum(double) FROM 1bn WHERE timestamp in '2019' |
0.05 secs |
Resources
📚 Read the docs
- QuestDB documentation: understand how to run and configure QuestDB.
- Tutorials: learn what's possible with QuestDB step by step.
- Product roadmap: check out our plan for upcoming releases.
❓ Get support
- Community Slack: join technical discussions, ask questions, and meet other users!
- GitHub issues: report bugs or issues with QuestDB.
- Stack Overflow: look for common troubleshooting solutions.
🚢 Deploy QuestDB
Contribute
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:
- Have a look through GitHub issues labeled "Good first issue".
- Read the contribution guide.
- For details on building QuestDB, see the build instructions.
- Create a fork of QuestDB and submit a pull request with your proposed changes.
✨ 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!
![allcontributors[bot] avatar](https://avatars.githubusercontent.com/in/23186?v=4 "allcontributors[bot]")
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
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