The Hyper-Fast Database that Truly Scales
Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source distributed database running queries in milliseconds, whatever the complexity, volume and velocity of data.
Query all types of data with SQL
/* 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 |
+---------------+------------+--------------+-------------------+
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 |
+---------+------------------------------------+-------------------+----------------------+--------------+--------+-----------+
/* Based on the location of the International Space Station,
* this query returns the 10 closest capital cities from
* the last known position
*/
SELECT city as "City Name",
country as "Country",
DISTANCE(i.position, c.location)::LONG / 1000 AS "Distance [km]"
FROM demo.iss i
CROSS JOIN demo.world_cities c
WHERE capital = 'primary'
AND ts = (SELECT MAX(ts) FROM demo.iss)
ORDER BY 3 ASC
LIMIT 10;
+--------------+-----------------------------------+---------------+
| City Name | Country | Distance [km] |
+--------------+-----------------------------------+---------------+
| Papeete | French Polynesia | 3386 |
| Avarua | Cook Islands | 3708 |
| Wellington | New Zealand | 4565 |
| Alofi | Niue | 4628 |
| Nuku‘alofa | Tonga | 4887 |
| Pago Pago | American Samoa | 5063 |
| Santiago | Chile | 5112 |
| Apia | Samoa | 5182 |
| Stanley | Falkland Islands (Islas Malvinas) | 5266 |
| Suva | Fiji | 5611 |
+--------------+-----------------------------------+---------------+
SELECT
title AS title,
protagonist['first_name'] AS name,
date_format(
'%D %b %Y',
'GMT',
protagonist['details']['birthday']
) AS born,
quotation['words'] AS quote
FROM quotes limit 100;
+---------------+---------+--------------------+
| event_time | entries | avg_score |
+---------------+---------+--------------------+
| 1620220260000 | 4 | 1.5798743814229965 |
| 1620220200000 | 8 | 1.7750384211540222 |
| 1620220140000 | 10 | 1.6113891124725341 |
| 1620220080000 | 9 | 1.676726798216502 |
| 1620220020000 | 8 | 1.6908064410090446 |
| 1620219960000 | 8 | 1.690401442348957 |
| 1620219900000 | 7 | 1.7646006005150932 |
| 1620219840000 | 7 | 1.7795820917401994 |
| 1620219780000 | 10 | 1.5844267368316651 |
| 1620219720000 | 13 | 1.5637413492569556 |
+---------------+---------+--------------------+
Get a unified access to your different types of data
Multiple data models
Multiple project types
Adopt a fast database that scales with your business
Any type of data
Structured, semi-structured, unstructured, time-series, geospatial, BLOB
Response time in milliseconds
Even for complex ad-hoc queries
Native SQL
For query simplicity and quick onboarding
Aggregations on the fly
Even with complex joins, large datasets and historical data
Flexible data schema
Editable on the fly at runtime
PostgreSQL Wire Protocol
For 3rd party integrations
Full-text search
No need for any extra database
Vector search
No need for additional investment
Open source
No vendor lock-in / Power of the community
Simplify your database operations
High availability
Automatic failover, recovery and replication
Multiple deployment models
DBaaS or self-managed / Edge extension
Cost-efficient architecture
No need to combine and synchronize different databases / Low carbon footprint
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December 20, 2023
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