When choosing a database, one question may arise: should you go for a relational database or a time series database? These databases serve distinct purposes, and understanding their strengths and limitations is crucial.
Time series databases are optimized for efficiently storing and retrieving time-stamped data. They outperform at organizing data chronologically, making time-based querying intuitive and faster.
Relational databases rely on structured tables and schemas to store data of various types and relationships. While this versatility suits general-purpose data management, it may introduce complexities when handling time series data.
Time Series Data Handling
Time series databases have several features tailored for handling time-stamped data. These solutions often offer built-in time indexing, allowing rapid retrieval of data points within specific time ranges, and dedicated functions for time series analysis, such as rolling averages and seasonality detection algorithms.
Time series databases like CrateDB support massive data ingestion from diverse sources, and offer real-time data analysis with instant queries in milliseconds.
While versatile, relational databases may have some limitations when managing time series data. These databases lack the built-in time series capabilities, such as window functions and time-based indexes, and struggle to efficiently handle high-frequency, continuously ingested data.
It’s important to consider that time-based queries may require complex SQL queries and indexing to guarantee the best performance.
/* 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;
Time series databases generally offer smoother and faster performance compared to relational databases. Time series databases are optimized for time-based queries, offering efficient data retrieval and making them ideal for time series analysis.
On the other hand, relational databases can handle time-based queries but often require complex SQL queries and indexing to accomplish better performance. This can lead to bottlenecks.
This is probably one of the aspects businesses worry about the most, since it can bring future costs.
Time series databases are tailored for time series data management, which can reduce maintenance complexity. They offer features like data synchronization, and scaling capabilities, minimizing the need for manual intervention.
Relational Databases may require ongoing manual maintenance for time series data, including data retention, indexing, and performance optimization. This increased complexity may necessitate dedicated people to work on it.
CrateDB: The Time Series Database That Truly Scales
CrateDB is a distributed time series database leveraging SQL that effortlessly handles large volumes of time series data, ensuring zero impact on performance.
It allows you to query data in milliseconds, regardless of the complexity of ad hoc queries, diverse data types, or even high ingest rates.
CrateDB can also handle structured data traditionally used in a relational database, and multiple other types of data (JSON, geospatial, full-text, vector, BLOB), all in one database and with SQL.