The Database
for Real-Time Analytics
and Hybrid Search
Any type of data (time-series, JSON, vector ...)
Distributed. Containerized. Native SQL.
[Rockset] is being discontinued.Migrate to CrateDB with a special offer ->
Real-time Analytics
Execute ad-hoc queries on billions of records in milliseconds. Columnar storage guarantees ultra-fast aggregations, enabling instant data-driven decisions. Begin with a simple query and delve into complex data relationships, revealing trends and patterns across diverse data types.
Hybrid Search
Effortless search across structured, semi-structured, geospatial, and vector data. Perform full-text, vector search or similarity searches and combine the results with other data types. The fully distributed SQL query engine, built on top of Apache Lucene, ensures unmatched performance and scalability.
Leading Companies Leveraging Real-Time Insights at Scale
Real-time Ingestion and Dynamic Indexing
Enjoy the power of instant indexing and adaptability, perfectly suited for handling complex and evolving data structures.
Real-time indexing automatically indexes all columns, including nested structures, as data is ingested, ensuring immediate query availability with no latency.
The flexible data schema dynamically adapts based on the data you ingest, offering seamless integration and instant readiness for analysis.
Real-time Querying and Search
Experience ultra-fast response times, even for complex ad-hoc queries, with results delivered in milliseconds. Perform on-the-fly aggregations, effortlessly handling complex joins, large datasets, and historical data.
Leverage the power of full-text and vector search without needing additional databases. Seamlessly integrate with AI/ML frameworks for advanced data analysis.
Enhanced Developer Productivity
Boost your developer productivity with native SQL for simple queries and quick onboarding. Analyze relational, JSON, time-series, geospatial, full-text, and vector data within a single system.
PostgreSQL compatibility ensures easy integration with third-party tools, enhancing compatibility and migration. Utilize the vector store to seamlessly integrate with AI/ML tools and LangChain, allowing you the freedom to choose your LLM and embedding algorithms.
The power and flexibility of the open-source licensing model liberates you from vendor lock-in, and provides support from the growing developer community.
/* Based on device data, this query returns the average
* of the battery level for every hour for each device_id
*/
WITH avg_metrics AS (
SELECT device_id,
DATE_BIN('1 hour'::INTERVAL, time, 0) AS period,
AVG(battery_level) AS avg_battery_level
FROM devices.readings
GROUP BY 1, 2
ORDER BY 1, 2
)
SELECT period,
t.device_id,
manufacturer,
avg_battery_level
FROM avg_metrics t, devices.info i
WHERE t.device_id = i.device_id
AND model = 'mustang'
LIMIT 10;
+---------------+------------+--------------+-------------------+
| period | device_id | manufacturer | avg_battery_level |
+---------------+------------+--------------+-------------------+
| 1480802400000 | demo000001 | iobeam | 49.25757575757576 |
| 1480806000000 | demo000001 | iobeam | 47.375 |
| 1480802400000 | demo000007 | iobeam | 25.53030303030303 |
| 1480806000000 | demo000007 | iobeam | 58.5 |
| 1480802400000 | demo000010 | iobeam | 34.90909090909091 |
| 1480806000000 | demo000010 | iobeam | 32.4 |
| 1480802400000 | demo000016 | iobeam | 36.06060606060606 |
| 1480806000000 | demo000016 | iobeam | 35.45 |
| 1480802400000 | demo000025 | iobeam | 12 |
| 1480806000000 | demo000025 | iobeam | 16.475 |
+---------------+------------+--------------+-------------------+
/* Return the name and truncated description for the 5 Chicago community
areas with populations over 50,000 people. */
SELECT name,
details['population'] AS population,
concat(left(details['description'], 25), '...') AS description
FROM community_areas
WHERE details['population'] > 50000
ORDER BY details['population'] DESC
LIMIT 5;
+-----------------+------------+------------------------------+
| name | population | description |
+-----------------+------------+------------------------------+
| NEAR NORTH SIDE | 105481 | The Near North Side is th... |
| LAKE VIEW | 103050 | Lakeview, also spelled La... |
| AUSTIN | 96557 | Austin is one of 77 commu... |
| WEST TOWN | 87781 | West Town, northwest of t... |
| BELMONT CRAGIN | 78116 | Belmont Cragin is one of ... |
+-----------------+------------+------------------------------+
SELECT text, _score
FROM word_embeddings
WHERE knn_match(embedding,[0.3, 0.6, 0.0, 0.9], 2)
ORDER BY _score DESC;
|------------------------|--------|
| text | _score |
|------------------------|--------|
|Discovering galaxies |0.917431|
|Discovering moon |0.909090|
|Exploring the cosmos |0.909090|
|Sending the mission |0.270270|
|------------------------|--------|
SELECT show_id, title, director, country, release_year, rating, _score
FROM "netflix_catalog"
WHERE MATCH(title_director_description_ft, 'title^2 Friday') USING best_fields
AND type='Movie'
ORDER BY _score DESC;
+---------+------------------------------------+-------------------+----------------------+--------------+--------+-----------+
| show_id | title | director | country | release_year | rating | _score |
+---------+------------------------------------+-------------------+----------------------+--------------+--------+-----------+
| s1674 | Black Friday | Anurag Kashyap | India | 2004 | TV-MA | 5.6455536 |
| s6805 | Friday the 13th | Marcus Nispel | United States | 2009 | R | 3.226806 |
| s1038 | Tuesdays & Fridays | Taranveer Singh | India | 2021 | TV-14 | 3.1089375 |
| s7494 | Monster High: Friday Night Frights | Dustin McKenzie | United States | 2013 | TV-Y7 | 3.0620003 |
| s3226 | Little Singham: Mahabali | Prakash Satam | NULL | 2019 | TV-Y7 | 3.002901 |
| s8233 | The Bye Bye Man | Stacy Title | United States, China | 2017 | PG-13 | 2.9638999 |
| s8225 | The Brawler | Ken Kushner | United States | 2019 | TV-MA | 2.8108454 |
+---------+------------------------------------+-------------------+----------------------+--------------+--------+-----------+
/* Using 311 data from the City of Chicago, this query returns 5 open
work orders for locations closest to the Willis Tower. */
SELECT srnumber,
srtype,
locationdetails['streetaddress'] AS address,
distance(
'POINT(-87.636256 41.8786492)'::GEO_POINT,
locationdetails['location']
) / 1000 AS distance_km
FROM three_eleven_calls
WHERE status != 'Completed'
ORDER BY distance_km ASC
LIMIT 5;
+---------------+-----------------------------------------------+--------------------+---------------------+
| srnumber | srtype | address | distance_km |
+---------------+-----------------------------------------------+--------------------+---------------------+
| SR24-00711535 | Cab Feedback | 200 S WACKER DR | 0.09800707616741176 |
| SR24-00694851 | No Building Permit and Construction Violation | 300 W ADAMS ST | 0.1346164665090538 |
| SR24-00651822 | Sign Repair Request - All Other Signs | 111 SW WACKER DR | 0.20355339153863516 |
| SR24-00608464 | Building Violation | 235 W VAN BUREN ST | 0.26374860571526554 |
| SR24-00608655 | Building Violation | 235 W VAN BUREN ST | 0.26374860571526554 |
+---------------+-----------------------------------------------+--------------------+---------------------+
Streamlined Operations
Experience a cost-efficient, robust, and scalable architecture that delivers high performance at any scale. Eliminate the hassle of combining and synchronizing different databases, reducing overhead, and minimizing your carbon footprint.
Ensure high availability with automatic failover, recovery, and replication, keeping your data safe and accessible. The resilient architecture detects failures and maintains cluster health, offering peace of mind even in distributed environments.
Choose from multiple deployment models: DBaaS, hybrid cloud, of self-managed, providing flexibility to meet your operational needs, even for Edge deployment with limited connectivity. Whether you're running on a single laptop or dozens of servers with terabytes of data, seamlessly scale from prototype to production.
Introduction to CrateDB
Key Concepts, Architecture, and Live Demo
Embrace Multiple Data Use Cases
AI/ML
Internet of Things
Ingest, enrich and query high volume of sensor data in real-time, where your data resides.
Digital twins
Reduce development efforts and optimize TCO for digital twin implementations.
Log Analysis
Database Consolidation
Keep a single source of truth updated in near real-time with all types of data in one place.
AI-powered chatbots
Store your vector embeddings and leverage the power of hybrid search to offer real-time interactions through chatbots.
Upcoming Events
Data silos and data latency remain major obstacles for many companies. All of the data streaming in from sensors, server logs, applications, online...
Join us in London for the CrateDB European City Tour, a series of local technical events focused on solving a complex data use case through several...