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QuestDB is the open-source, low-latency time-series database on open formats. It ingests millions of events per second, computes on live data in milliseconds, and keeps years of history queryable. One SQL engine spans ingestion, stream processing, and tiered storage.

The engine is zero-GC Java with C++ and Rust on the hot paths. Data lives in memory-mapped, time-partitioned columns. Queries run across all cores with SIMD and JIT-compiled filters. There are no third-party dependencies on the data path.

Storage is tiered: a parallel write-ahead log, native columnar partitions, and Apache Parquet. In open source you convert partitions to Parquet with ALTER TABLE, or create a table in Parquet directly. QuestDB Enterprise converts cold partitions automatically and tiers them to object storage. Parquet, Apache Iceberg, and Apache Arrow keep the data open to any tool.

Measured on QuestDB 10.0 over QWP:

Use Docker to start quickly:

docker run -p 9000:9000 -p 8812:8812 questdb/questdb

Or macOS users on Apple Silicon can use Homebrew:

brew install questdb
brew services start questdb

questdb start
questdb stop

QuestDB bundles native libraries for Apple Silicon only. On an Intel Mac, use the Docker image above.

For the full walkthrough, start with the quick start guide.

QuestDB Web Console - click to launch demo

New in QuestDB 10.0: the QuestDB Wire Protocol (QWP), one binary columnar protocol for writes and for Arrow reads, plus native arrays and live views in beta. Release notes.

QWP is a binary columnar protocol over WebSocket. The same connection writes rows in and streams query results back out as columns. The Python client returns those columns as Apache Arrow by default, and Rust and C/C++ can enable it with a flag.

On the way in, the QWP vs ILP ingestion benchmark measures 19M rows/sec over the network, and that peak holds from one thousand to one million series. On the way out, Streaming 500 million rows into Arrow measures 220M rows/sec with eight readers, with the first batch after 32 ms and client memory flat however large the result. The QWP overview covers the protocol design.

One engine covers the full lifecycle of the data:

• Capture. Ingest millions of ordered and out-of-order events per second without pre-aggregation. Deduplication and out-of-order correction are built in.
• Compute. Transform, enrich, and aggregate events as they arrive. Materialized views aggregate by time slice, many rows in and one row out. Live views run window functions such as indicators, one row in and one row out, on an in-memory tier.
• Query. Run time-series SQL across live and historical data with predictable low latency.
• Retain. Keep the complete record online in native storage and Parquet, queried through the same SQL. Enterprise tiers cold partitions to object storage automatically.

The SQL is standard, extended where time series needs it: SAMPLE BY, LATEST ON, ASOF JOIN, WINDOW JOIN, HORIZON JOIN, materialized views, live views, and n-dimensional arrays.

Both queries run on the live demo:

-- 15-minute OHLCV bars for EURUSD, today
SELECT timestamp, symbol,
  first(price) AS open,
  max(price) AS high,
  min(price) AS low,
  last(price) AS close,
  sum(quantity) AS total_volume
FROM fx_trades
WHERE symbol = 'EURUSD'
  AND timestamp IN '$today'
SAMPLE BY 15m;

-- Match each trade to the most recent quote by timestamp
SELECT t.timestamp, t.symbol, t.price, q.bid_price, q.ask_price
FROM fx_trades t
ASOF JOIN core_price q ON (symbol)
WHERE t.timestamp IN '$today';

Where it runs:

• capital markets: tick data, order books, trades, pre-trade analysis and post-trade analytics, with the finance SQL cookbook, finance functions and order book analytics
• aerospace and robotics: flight-test telemetry, fleet data, mission replay
• energy and infrastructure: reactor, turbine, and grid telemetry at full resolution
• AI coding agents, through the agent skills and the MCP server

A time-series engine takes care of what a general database leaves to you: out-of-order data, deduplication and exactly-once semantics, continuous ingest under concurrent queries, bursty load, and schema changes while streaming.

The live, public demo runs the latest QuestDB release on sample datasets. Scan more than 2 billion rows in milliseconds:

• Trades: live crypto trades with 30M+ rows per month (OKX exchange)
• FX order book: live charts with orderbook FX pairs.
• Trips: 10 years of NYC taxi trips with 1.6 billion rows

We also have some public, real-time demo dashboards using our Grafana-native plugin:

• Real-time crypto trades: executed trades on OKX from more than 20 assets in real time
• FX order book: live depth/imbalance charts for major FX pairs

TSBS ingestion at 100,000 hosts with 32 workers on one AWS r8a.8xlarge (32 vCPU, 256 GB RAM). Competitors benchmarked August 2026, QuestDB 9.3.3 on the same instance.

The full comparisons, ingestion and queries:

• QuestDB vs InfluxDB
• QuestDB vs InfluxDB 3
• QuestDB vs ClickHouse
• QuestDB vs TimescaleDB
• QuestDB vs kdb+

As always, we encourage you to run your own benchmarks.

QuestDB works with Claude Code, Codex, Cursor, and any MCP client. There are two ways in.

Run SQL against the database from your agent. Install the QuestDB skill. It teaches the agent QuestDB's SQL dialect and how to inspect a schema. The agent then executes SQL through the REST API.

npx skills add questdb/skills

Prototype in the Web Console. For notebooks and SQL prototyping driven by the agent directly, connect it to the Web Console MCP server.

Setup for each agent is in the coding agents guide.

QuestDB clients for ingesting data and running queries over the QuestDB Wire Protocol:

• Python
• .NET
• C/C++
• Go
• Java
• NodeJS (QWP support coming soon)
• Rust

See the Connect overview for the full picture.

The QuestDB Wire Protocol (QWP) is the native protocol and the recommended way to talk to QuestDB. It is binary, streams in both directions over a single connection on port 9000, and covers ingestion and queries through one connect string. The first-party clients listed above speak it.

QuestDB also speaks compatibility protocols, so existing tooling works unchanged:

• PostgreSQL Wire Protocol on port 8812: run queries from any Postgres driver such as psycopg, JDBC or pgx, and from BI tools and ORMs. Inserts work too, but slower than QWP.
• InfluxDB Line Protocol on port 9000, for collectors and pipelines that already emit ILP
• REST API on port 9000, for HTTP scripting, cURL, and CSV import

For interactive use, the Web Console provides a SQL editor, charts, and CSV import on port 9000.

Ingest from:

• Kafka
• Redpanda
• Telegraf
• Apache Flink

Query from:

• Grafana
• Pandas
• Polars
• Apache Spark
• Superset
• PowerBI

The integrations overview lists the rest.

From streaming ingestion to visualization with Grafana, start with code scaffolds from our quickstart repository.

Find our capacity planning to fine-tune QuestDB for production workloads.

The QuestDB you already run, hardened for production. Enterprise adds:

• high availability: replication, read replicas, automatic failover
• multi-tier storage at petabyte scale: cold partitions tier automatically to Parquet on object storage and stay queryable in place
• security and governance: role-based access control, TLS, SSO, audit logs
• 24/7 support with a 99.9% uptime SLA

Run it on-prem, as a managed service, or inside your own AWS or Azure account with Bring Your Own Cloud. See the feature comparison and the Enterprise page.

• QuestDB documentation: begin the journey
• Product roadmap: check out our plan for upcoming releases
• Tutorials: learn what's possible with QuestDB, step by step

• Community Discourse forum: join technical discussions, ask questions, and meet other users!
• Public Slack: chat with the QuestDB team and community members
• GitHub issues: report bugs or issues with QuestDB
• Stack Overflow: look for common troubleshooting solutions

• Official Docker image
• Kubernetes Helm charts
• systemd
• AWS
• Azure
• Google Cloud Platform
• DigitalOcean
• Hetzner
• ZFS compression
• Bring Your Own Cloud (Enterprise)

Contributions welcome!

We appreciate:

• source code
• documentation (see our documentation repository)
• bug reports
• feature requests or feedback.

To get started with contributing:

• Have a look through GitHub issues labelled "Good first issue"
• For Hacktoberfest, see the relevant labelled issues
• 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
• Stuck? Join our public Slack for assistance

✨ As a sign of our gratitude, we send QuestDB swag to our contributors!

A big thanks goes to the following wonderful people who have contributed to QuestDB emoji key:

clickingbuttons 💻 🤔 📓	ideoma 💻 📓 ⚠️	tonytamwk 💻 📓	sirinath 🤔	igor-suhorukov 💻 🤔	mick2004 💻 📦	rawkode 💻 🚇
solidnerd 💻 🚇	solanav 💻 📖	shantanoo-desai 📝 💡	alexprut 💻 🚧	lbowman 💻 ⚠️	chankeypathak 📝	upsidedownsmile 💻
Nagriar 💻	piotrrzysko 💻 ⚠️	mpsq 💻	siddheshlatkar 💻	Yitaek ✅ 💡	gabor-boros ✅ 💡	kovid-r ✅ 💡
TimBo93 🐛 📓	zikani03 💻	jaugsburger 💻 🚧	TheTanc 📆 🖋 🤔	davidgs 🐛 🖋	kaishin 💻 💡	bluestreak01 💻 🚧 ⚠️
patrickSpaceSurfer 💻 🚧 ⚠️	chenrui333 🚇	bsmth 📖 🖋	Ugbot 💬 📓 📢	lepolac 💻 🔧	tiagostutz 📓 🐛 📆	Lyncee59 🤔 💻
rrjanbiah 🐛	sarunas-stasaitis 🐛	RiccardoGiro 🐛	duggar 🐛	postol 🐛	petrjahoda 🐛	t00 🐛
snenkov 📓 🐛 🤔	marregui 💻 🤔 🎨	bratseth 💻 🤔 📓	welly87 🤔	fuzzthink 🤔 📓	nexthack 💻	g-metan 🐛
tim2skew 🐛 📓	ospqsp 🐛	SuperFluffy 🐛	nu11ptr 🐛	comunidadio 🐛	mugendi 🤔 🐛 📖	paulwoods222 🐛
mingodad 🤔 🐛 📖	houarizegai 📖	jjsaunier 🐛	zanek 🤔 📆	Geekaylee 📓 🤔	lg31415 🐛 📆	null-dev 🐛 📆
ultd 🤔 📆	ericsun2 🤔 🐛 📆	giovannibonetti 📓 🐛 📆	wavded 📓 🐛	puzpuzpuz 📖 💻 📓	rstreics 💻 🚇 📖	mariusgheorghies 💻 🚇 📖
pswu11 🖋 🤔 🎨	insmac 💻 🤔 🎨	eugenels 💻 🤔 🚧	bziobrowski 💻 📆	Zapfmeister 💻 📓	mkaruza 💻	DylanDKnight 📓 🐛
enolal826 💻	glasstiger 💻	argshook 💻 🤔 🎨 🐛	amunra 💻 📖 🐛	GothamsJoker 💻	kocko 💻	jerrinot 💻 🤔 🐛
rberrelleza 💻	Cobalt-27 💻	eschultz 💻	XinyiQiao 💻	terasum 📖	PlamenHristov 💻	tris0laris 📝 🤔
HeZean 💻 🐛	iridess 💻 📖	selmanfarukyilmaz 🐛	donet5 🤔 🐛	Zahlii 🐛	salsasepp 🐛	EmmettM 🐛 ⚠️
robd003 🤔	AllenEdison 🐛	CSharpDummy 🐛	shimondoodkin 🐛 🤔	huuhait 🐛 🤔	alexey-milovidov 🐛	suconghou 🐛
allegraharris 💻	oliver-daniel 💻	kerimsenturk5734 📖

This project adheres to the all-contributors specification. Contributions of any kind are welcome!