Traditional relational databases struggle to meet the demands of video surveillance systems due to their design limitations around scalability, data types, and real-time processing. Relational databases, like MySQL or PostgreSQL, are optimized for structured, transactional data with predictable query patterns. Video surveillance, however, generates vast amounts of unstructured or semi-structured data—such as high-resolution video streams, frame-by-frame metadata, and event logs—that don’t fit neatly into tables with fixed schemas. Additionally, the sheer volume of data produced by continuous video feeds (e.g., terabytes per day from multiple cameras) can overwhelm traditional databases, which are not built for horizontal scaling or efficient storage of large binary objects like video files.
One key issue is the inability to handle high write throughput efficiently. For example, a surveillance system with hundreds of cameras recording 24/7 might generate thousands of concurrent writes per second. Relational databases, which often rely on row-based storage and locking mechanisms, can bottleneck under such loads, leading to latency or data loss. Storing video data as BLOBs (Binary Large Objects) in a relational database also creates performance overhead, as retrieving or querying large files requires significant I/O resources. Furthermore, time-series data, such as timestamps for motion detection events, are better handled by databases optimized for temporal queries (e.g., TimescaleDB), which can partition and index data by time ranges more effectively than a generic relational system.
Another limitation is the lack of native support for complex queries specific to video analytics. For instance, searching for a person wearing a specific color in a video feed across a week’s worth of footage requires both metadata filtering (time, location) and content-based analysis, which relational databases aren’t equipped to handle without extensive custom extensions. Modern video systems often integrate machine learning models for object detection or anomaly detection, generating semi-structured metadata (e.g., JSON logs of detected objects). Relational databases can store this data, but querying it efficiently—like aggregating all instances of a license plate across millions of frames—becomes cumbersome. Solutions like NoSQL databases or hybrid systems (e.g., PostgreSQL with TimescaleDB or Apache Cassandra) are better suited for these scenarios, offering scalability and flexibility that traditional relational architectures lack.