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The Enterprise Database
for Time Series,
Documents, and Vectors

Distributed - Native SQL - Open Source - Ready for AI

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CrateDB is a leader in Time Series Databases on G2
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Leverage SQL to Query Time Series, Document, Vector Data, and More

 
        

/* 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
    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 |
+---------------+---------+--------------------+







        

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     |
+--------------+-----------------------------------+---------------+

Adopt an Easy-to-Use 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

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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 and vector search

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Simplify your Database Operations

High availability

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Multiple deployment models

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Cost-efficient architecture

No need to combine and synchronize different databases / Low carbon footprint

Embrace Multiple Data Use Cases

AI/ML

Integrate with popular AI/ML frameworks. Leverage full-text and vector search for meaningful insights.

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.

Real-time Analytics

Get immediate access to your data for informed decisions in real-time.

Log Analysis

Store all your logs into a single database and make instant queries with SQL.

Database Consolidation

Keep a single source of truth updated in near real-time with all types of  data in one place.

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