It’s about time: time series in SurrealDB

The article delves into the handling and analysis of time series data using SurrealDB, emphasizing its philosophical and technical aspects. Time series data is categorized into events, which are discrete and irregular, and metrics, which are continuous and collected at regular intervals. SurrealDB utilizes ISO8601 timestamps for high precision and provides functionality for converting event data into metrics to gain insights and optimize storage. The article illustrates practical applications with examples like IoT sensor data, highlighting SurrealDB's ability to efficiently store and query data using complex record IDs. It also covers the creation of real-time metrics with pre-computed table views and live queries, as well as custom events for anomaly detection. SurrealDB's advantage lies in its multi-model database capabilities, offering a unified query language and reducing system complexity, although specialized time series databases might provide more advanced features like custom data retention.