Time-series data in CrateDB — sub-second SQL analytics at any cardinality

Time series query examples

Sub-second results across billions of records.

        
/* Most time-series databases require you to pre-aggregate
 * hourly data at ingestion time. CrateDB computes it 
 * at query time across any dimension — no pre-processing pipeline needed. */
 
/* 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            |
+---------------+------------+--------------+-------------------+

        
/* Detecting missing readings in a continuous stream requires
 * generating a reference series and left-joining against it.
 * CrateDB handles this in standard SQL with generate_series. */

/* To identify gaps on the readings, the following queries generates a series
 * and by joining it with the original data, you can spot any gap */
with avg_battery AS (
  SELECT battery_level, time 
  FROM devices.readings
  WHERE device_id = 'demo000007' 
  AND time > 1480118400000 
  AND time < 1480301200000
  ORDER BY 2
),

all_hours AS (
  SELECT generate_series(1480118430000,1480301200000,'30 second'::interval) AS generated_hours
)

SELECT time, generated_hours, battery_level
FROM all_hours 
LEFT JOIN avg_battery ON generated_hours = time
ORDER BY 2
LIMIT 20;

        
+---------------+---------------+---------------+
|          time |         hours | battery_level |
+---------------+---------------+---------------+
| 1480118430000 | 1480118430000 |            67 |
| 1480118460000 | 1480118460000 |            66 |
| 1480118490000 | 1480118490000 |            66 |
| 1480118520000 | 1480118520000 |            66 |
| 1480118550000 | 1480118550000 |            66 |
| 1480118580000 | 1480118580000 |            66 |
| 1480118610000 | 1480118610000 |            65 |
| 1480118640000 | 1480118640000 |          NULL |
| 1480118670000 | 1480118670000 |            65 |
| 1480118700000 | 1480118700000 |            65 |
| 1480118730000 | 1480118730000 |            65 |
| 1480118760000 | 1480118760000 |            65 |
| 1480118790000 | 1480118790000 |            65 |
| 1480118820000 | 1480118820000 |            65 |
| 1480118850000 | 1480118850000 |            65 |
| 1480118880000 | 1480118880000 |            65 |
| 1480118910000 | 1480118910000 |            65 |
| 1480118940000 | 1480118940000 |            65 |
| 1480118970000 | 1480118970000 |          NULL |
| 1480119000000 | 1480119000000 |          NULL |
+---------------+---------------+---------------+

        
/* Counting state transitions — like battery charge cycles — 
 * across a time window is a common operational analytics query.
 * This runs in milliseconds on billions of rows. */

/* Based on device data, this query returns the number of battery charges 
 * per day for a given device_id */
WITH aux_charging AS (
    SELECT time, 
      DATE_BIN('P1D'::INTERVAL,time,0) AS day, 
      battery_status, 
      LAG(battery_status) OVER (PARTITION BY device_id ORDER BY time) AS prev_battery_status
    FROM devices.readings
    WHERE device_id = 'demo000001'
    ORDER BY time
  ),

count_start_charging AS (
    SELECT day, (case when battery_status <> prev_battery_status then 1 else 0 end) AS start_charging
    FROM aux_charging
    ORDER BY 1
  )

SELECT day, sum(start_charging) as charges_number
FROM count_start_charging
GROUP BY 1
ORDER BY 1;

        
+---------------+---------------+
| count_charges |           day |
+---------------+---------------+
|             2 | 1479168000000 |
|             4 | 1479254400000 |
|             2 | 1479340800000 |
|            10 | 1479427200000 |
|             7 | 1479600000000 |
|             8 | 1479686400000 |
|             6 | 1479772800000 |
|            11 | 1479859200000 |
|             5 | 1480032000000 |
|             7 | 1480118400000 |
|             6 | 1480204800000 |
|            10 | 1480291200000 |
|             3 | 1480464000000 |
|             3 | 1480550400000 |
|             7 | 1480636800000 |
|             2 | 1480723200000 |
+---------------+---------------+

        
/* Joining device readings with device metadata at query time, without pre-flattening. 
 * This is the high-cardinality pattern CrateDB is built for. */

/* Based on device data, this query returns the average of the battery temperature
 * for each OS version */
SELECT device_info['os_name'], avg(battery_temperature)
FROM "devices"."readings"
GROUP BY 1
LIMIT 100;

         
+---------+--------------------------+
| os_name | avg(battery_temperature) |
+---------+--------------------------+
| 4.4.4   |        90.85937893039049 |
| 5.1.0   |        90.86754559738132 |
| 6.0.1   |        90.84230101265824 |
| 5.0.0   |        90.8574802739726  |
+---------+--------------------------+

Demo: Querying heterogeneous time-series data with SQL

Demo: Querying heterogeneous time-series data with SQL

In this video, discover how to effortlessly create a table and seamlessly import weather data into CrateDB. Witness the power of CrateDB's time-series query capabilities in action with the weather dataset, showcasing the dynamic schema flexibility. Dive deeper into CrateDB's multi-modal features with demonstrations on handling JSON, geospatial data, and conducting full-text searches.

High-performance storage for time-series data

CrateDB’s columnar storage format accelerates analytics on time-stamped data at multi-year scale. It handles wide tables with hundreds of columns and delivers fast scans, aggregations, and filtering across large datasets — high-frequency sensor data, event streams, telemetry and operational metrics, application logs, and long-term analytical workloads.

No pre-aggregation, no retention compromise

CrateDB is designed for continuously growing cardinality without requiring pre-aggregation, rigid schemas, or query redesign. Traditional time-series databases rely on downsampling or rigid retention policies.

CrateDB removes these limitations through time-based partitioning and tiered storage — store granular data at full resolution, retain historical data cost-effectively, keep recent partitions on SSDs for fast queries, move older partitions to cheaper storage without sacrificing access, and query years of data with predictable performance.

Time-series functions built into SQL

CrateDB includes advanced SQL functions for time-series analysis without complex pipelines or custom tooling: DATE_BIN for consistent resampling, LEAD / LAG with IGNORE NULLS for filling gaps and trend detection, WINDOW functions for moving averages and rolling metrics, JOIN support to enrich metrics with metadata, and aggregation functions optimized for large continuous datasets.

Learn more about CrateDB

Supply chain & Logistics

TGW Logistics Group is one of the leading international suppliers of material handling solutions. As systems integrator, TGW plans, produces and implements complex logistics centres, from mechatronic products and robots to control systems and software.

Using CrateDB, TGW accelerates data aggregation and access from warehouse systems worldwide, resulting in increased database performance. The system can handle over 100,000 messages every few seconds.

"CrateDB is a highly scalable database for time series and event data with a very fast query engine using standard SQL".

Alexander Mann
Owner Connected Warehouse Architecture
TGW Logistics Group

Read more

TGW-Warehouse-2

Construction

Thomas Concrete Group is the world- leader organization in the construction industry with 150+ concrete plants and 2,100+ employees. They use CrateDB both for tracking of their delivery trucks and tracking the curing of the concrete in real-time.

"Thanks to CrateDB's great indexing, dedicated data types, and subsequent great performance, we could execute an event and data-driven architecture, with the performance and scalability necessary for storing time-series data over time. The SQL query syntax capability of CrateDB also played a part in achieving this great outcome, as it made it easy for the team to write good performing queries using existing know-how. CrateDB is now an integral part of our big data streaming architecture and it is delivering as promised."

Kristoffer Axelsson
Principal Solution Architect
Thomas Concrete Group

Read more

Thomas

Guide for time series data projects

Whitepaper" Guide for Time Series Data Projects

Guide for time series data projects

This comprehensive guide covers the different key aspects of time series data projects. It is divided in 3 distinct white papers. Part 1: data modeling, storage, lifecycle; Part 2: ingestion, indexing, analysis, and optimization; Part 3: visualization, and advanced analysis through machine learning.

Time-series data is a sequence of data points organized chronologically, illustrating how variables change over time. This format is characterized by patterns such as trends, seasonal variations, and irregularities. Effective time-series data management can significantly enhance query performance and data analysis. CrateDB supports time-series data with built-in functionalities like window functions and time-based indexes, making it easier to analyze and query the data efficiently.

Examples of time series data include temperature readings, stock prices, population growth, monthly subscriptions, quarterly sales, and interest rates. CrateDB supports time series data and offers a comprehensive solution by allowing the integration of multiple data types such as JSON, vector, full-text, geospatial, BLOB, and relational data, minimizing the need for additional technologies and complex maintenance.

Efficient storage of time series data is essential, considering factors like data volume, speed, query requirements, and scalability. The primary storage solutions include time-series databases, NoSQL databases, relational databases, and data warehouses, each offering unique strengths and limitations. CrateDB is designed to handle complex time series data with the simplicity of SQL, allowing for high ingest rates and integration of various data types, making it an ideal choice for storing and querying extensive time series data efficiently. Read more about time series data storage >

When choosing a time-series database, consider factors such as performance, scalability, query language capabilities, data model flexibility, security, maintainability, and reliability. Examples of time-series databases include CrateDB, InfluxDB, KX, and Timescale. CrateDB stands out as a distributed database that can scale horizontally across multiple nodes, making it especially suited for handling large volumes of time-series data from diverse sources in real-time. Learn how to choose the best database for time series data >

Time-series data analysis allows us to analyze and understand how variables change over time. It is widely used across various industries, such as manufacturing, transportation and logistics, and the energy sector. Regardless of the industry, time-series data is crucial for enabling data-driven decision-making. With CrateDB, you can easily enrich your time series data with other data types, handle high ingest rates, and store years of history. Read how TGW Logistics Group uses CrateDB as their modern time series database >