Which database technologies are best suited for video index storage?

When storing video index data—such as timestamps, object detection results, transcripts, or scene metadata—the ideal database technologies balance efficient querying, scalability, and support for structured or semi-structured data. Three strong options are Elasticsearch, PostgreSQL with extensions, and Cassandra, each suited to different use cases based on query patterns and scalability needs.

Elasticsearch excels in scenarios requiring fast, complex text searches or aggregations. For example, if your video index includes speech-to-text transcripts or tagged objects (e.g., "find all clips containing ‘car"’), Elasticsearch’s inverted index and full-text search capabilities allow rapid retrieval. Its distributed architecture scales horizontally, making it suitable for large datasets. However, it’s less optimal for transactional updates or highly relational data. Netflix, for instance, uses Elasticsearch to power search across video metadata, demonstrating its effectiveness in media-heavy applications.

PostgreSQL (with extensions like TimescaleDB or pgvector) is ideal for structured metadata requiring ACID compliance or time-series analysis. TimescaleDB adds time-series optimizations, useful for frame-by-frame annotations or temporal queries (e.g., “show activity between 00:05:00 and 00:10:00”). PostgreSQL’s JSONB column type also supports semi-structured data, such as storing varying object detection results per frame. For example, a video analytics pipeline might use PostgreSQL to track detected objects while ensuring transactional consistency for concurrent writes. Its flexibility makes it a robust choice for mixed workloads.

Cassandra is optimal for write-heavy, globally distributed systems. If your application ingests video indexes from multiple sources at high velocity (e.g., security cameras), Cassandra’s decentralized architecture and tunable consistency handle massive scalability. However, its query flexibility is limited compared to Elasticsearch or PostgreSQL. Use cases include storing raw metadata with simple lookup patterns, like timestamp ranges. Disney+ Hotstar, for example, has used Cassandra for high-throughput data storage in streaming workflows, highlighting its scalability for large-scale media applications.

Choose Elasticsearch for search-centric needs, PostgreSQL for structured or time-series data with transactional guarantees, and Cassandra for scalable, write-intensive workloads. Combining these (e.g., PostgreSQL for metadata storage + Elasticsearch for search) can also address complex requirements.