Fast SQL for time series

QuestDB is the fastest open source time series database

Query our demo dataset with 1.6 billion rows in milliseconds

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

Built for performance

SIMD-optimized analytics

Row- and column-based access

Vectorized query execution

Tiny memory footprint

C++ and zero-GC Java

A code editor with a chart that shows the result of the query

Optimized for time series

Relational model for time series

Data stored in chronological order

Time partitioned

Immediate consistency

Fast InfluxDB line protocol

A code editor containing a SQL statement

Implemented with SQL

Time series and relational joins

Postgres compatibility

Aggregations and downsampling

Unlimited sub-queries

Built-in SQL optimizer

Integration with the tools you love

Postgres

Grafana

Kafka

Python

Pandas

Telegraf

Tableau

Metabase

Why QuestDB?

Augmented SQL for time series

QuestDB enhances ANSI SQL with time series extensions to manipulate time stamped data

SELECT timestamp, tempC
FROM sensors
WHERE timestamp IN '2021-05-14;1M';

-- Search time
SELECT timestamp, tempC
FROM sensors
WHERE timestamp IN '2021-05-14;1M';

SELECT timestamp, avg(tempC)
FROM sensors
SAMPLE BY 5m;

-- Slice time
SELECT timestamp, avg(tempC)
FROM sensors
SAMPLE BY 5m;

SELECT timestamp, sensorName, tempC
FROM sensors
LATEST ON timestamp PARTITION BY sensorName;

-- Navigate time
SELECT timestamp, sensorName, tempC
FROM sensors
LATEST ON timestamp PARTITION BY sensorName;

SELECT sensors.timestamp ts, rain1H
FROM sensors
ASOF JOIN weather;

-- Merge time
SELECT sensors.timestamp ts, rain1H
FROM sensors
ASOF JOIN weather;

Search Time

Filter and search for specific timestamps with “WHERE”

Slice Time

Create time buckets and aggregate by intervals with “SAMPLE BY”

Navigate Time

Search time series from most recent values to oldest with “LATEST BY”

Merge Time

Join two tables based on timestamp where timestamps do not exactly match with “ASOF JOIN”

Why time series?

DevOps monitoring

Collect metrics and events from your infrastructure (CPU, memory, networks, etc.) and get real-time visibility into your entire stack.

Financial market data

Store market data to identify historical trends and correlations using statistical methods and generate trading signals.

Connected devices

Capture, store and respond to data from sensors at any resolution in industrial applications.

Application metrics

Empower your application users to track and visualize logs, API calls, and any application activity in real-time.

Machine learning with time-series data

Use QuestDB with popular Python frameworks and tools for leveraging anomaly detection algorithms, machine learning libraries, statistical analysis with Pandas, or Jupyter notebooks.

Integrated data

Pull together all your application, device, and infrastructure data for a complete, 360º view of all aspects of your business.

Interactive Console

Interactive console to import data (drag and drop) and start querying right away. Check our Web Console documentation to get started.

Artistic view of QuestDB's Web Console split in 3 components: the navigation tree, the SQL code editor and data displayed as a chart