Fast SQL for time series

QuestDB is the fastest open source time series database

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Built for performance

SIMD-optimized analytics
Row and column based access
Vectorized query execution
Tiny memory footprint
C++ and zero-GC Java

See Benchmarks

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Optimized for time series

Relational model for time series
Data stored in chronological order
Time partitioned
Immediate consistency
Fast InfluxDB line protocol

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Implemented with SQL

Time series and relational joins
Postgres compatibility
Aggregations and downsampling
Geospatial-native queries
Built-in SQL optimizer

Browse Docs

Use with the tools you love

Postgres Grafana Kafka Python Pandas Telegraf MindsDB Cube Redpanda Plotly

Why choose QuestDB?

Query with SQL

Deploy via Docker or binaries

Interactive web console

Postgres and InfluxDB line protocols

Cloud-native or on-premises

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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 and observability

Collect CPU, memory and storage metrics from your infrastructure and get real-time visibility into your entire stack.

Financial market data

Store market tick data to identify historical trends, find correlations and analyze trades in real-time. Build aggregated views across multiple venues and efficiently compute live order books.

Network traffic analysis

Collect sFlow or other network traffic metadata to run analytics and detect anomalies in real-time.

Connected devices

Capture, store and respond to sensor data and telemetry at any resolution in industrial or machine-to-machine applications.

Application metrics

Empower application developers and UX teams to track and visualize user behavior data, API calls, data latency, and other application events 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.

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

Join our developer community

QuestDB is open source. Follow us on Twitter, star our GitHub repo, and join our developer community on Slack!

Go to GitHub >Join Slack >

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