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The Enterprise Database for
Time Series, Documents, and Vectors

Distributed - Native SQL - Open Source - Ready for AI

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
    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

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 and vector search

No need for any extra database and easy integration with AI/ML frameworks

Massively scalable

From one to hundreds of nodes

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

Embrace Multiple Data Use Cases

Integrate with popular AI/ML frameworks. Leverage full-text and vector search for meaningful insights.
Ingest, enrich and query high volume of sensor data in real-time, where your data resides.

Reduce development efforts and optimize TCO for digital twin implementations.

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

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

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

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