Columnar & Row-Based Storage

Traditional databases force you into a trade-off:

• Row-based storage is ideal for transactional workloads but slows down analytical queries.
• Columnar storage delivers excellent aggregation performance but struggles with frequent writes or schema changes.

CrateDB removes that limitation. Its hybrid model lets you ingest millions of records per second, while running complex, columnar-style analytics on the same data in real time. This makes CrateDB uniquely suited for IoT platforms, monitoring systems, AI pipelines, and real-time dashboards, where high-velocity data must remain instantly queryable.

CrateDB’s distributed storage layer automatically optimizes data layout for both write performance and query efficiency:

1. Incoming data is written row-wise for high-speed ingestion and low-latency commits.
2. Data blocks are stored in columnar format on disk, enabling highly compressed, vectorized scans for analytical queries.
3. The query planner automatically chooses the most efficient access path, combining columnar reads for aggregations and row access for lookups or point queries.

The result: real-time ingestion, fast analytics, and flexible querying, without maintaining separate systems for OLTP and OLAP workloads.

Whether you’re querying recent logs, aggregating across months of telemetry, or joining with text and vector data, CrateDB adapts automatically.

Operation type	Optimized storage	Result
Ingestion	Row-based (write path)	Millions of records per second
Aggregation	Columnar (read path)	Fast analytics and aggregations
Filtering & Search	Index-based (Lucene integration)	Instant access to recent and historical data
Hybrid queries	Row + Column combination	Real-time analytics on live data streams

CrateDB’s hybrid storage architecture provides distinct advantages:

• Fast ingestion: Row-based write paths enable continuous high-throughput data ingestion from IoT devices, logs, and streams.
• Efficient analytics: Columnar compression and vectorized reads deliver sub-second aggregations on billions of rows.
• Smaller storage footprint: Columnar encoding significantly reduces disk usage.
• Adaptive queries: The SQL engine automatically blends row and column access depending on query context.
• Unified system: No need to move data between OLTP and OLAP databases.

This unique design turns CrateDB into a real-time hybrid database, capable of handling both live operational workloads and analytical queries at scale.

CrateDB’s hybrid storage architecture works in harmony with its distributed query engine and automatic indexing:

• Distributed columnar execution ensures analytical queries scale linearly across nodes.
• Automatic indexing accelerates lookups, joins, and search across all data types.
• Dynamic schemas allow structure to evolve without reformatting storage.
• Shared-nothing design ensures balanced data distribution and resilience.

Every layer of CrateDB’s architecture is optimized to handle mixed workloads, without compromise.

Challenge	CrateDB solution
Slow analytics on live data	Hybrid columnar reads with row-based writes
Separate OLTP and OLAP systems	Unified storage and execution layer
Data duplication and ETL delays	Query data directly where it’s written
High storage costs	Compressed columnar format reduces footprint
Performance tuning complexity	Automatic optimization for each query type

• One engine for all workloads: handle ingestion, analytics, and search seamlessly.
• Real-time responsiveness: query fresh data instantly as it arrives.
• Lower cost and complexity: no need for pipelines or warehouse syncs.
• Optimized for scale: distributed architecture supports linear growth.
• Simplicity by design: a single SQL interface across all data models.

CrateDB bridges the gap between streaming ingestion and large-scale analytics, powering real-time intelligence at any scale.