Distributed Database 

Traditional databases were never designed for today’s scale, meaning billions of events, streaming data, diverse formats, and real-time decision-making.

CrateDB’s distributed architecture breaks through these limits by spreading both data and query execution across multiple nodes. Each node works independently, processing its share of the workload in parallel.

• Linear scalability: Add nodes to handle more data or queries instantly.
• High throughput: Ingest millions of events per second with consistent performance.
• Distributed execution: Queries are automatically parallelized across all nodes for millisecond-level results.
• Resilient by design: Built-in replication and failover ensure continuous availability.

1. Data sharding: Tables are automatically partitioned into shards distributed across nodes.
2. Replication: Each shard is replicated to multiple nodes to prevent data loss.
3. Query distribution: The distributed SQL engine sends query tasks to the nodes holding relevant data.
4. Parallel processing: Each node processes its part locally and returns intermediate results.
5. Result aggregation: The handler node merges results and returns the final output in milliseconds.
6. Everything happens automatically: no manual sharding, no complex clustering setup, no downtime.

CrateDB’s distributed engine isn’t limited to relational data. It processes time series, text, JSON, geospatial, and vector data, all in real time.

Whether you’re monitoring sensors, searching documents, or serving AI models, CrateDB delivers the same speed, scale, and simplicity across every data type.

• Automatic failover: Continuous operation, even if nodes fail.
• Self-healing clusters: Nodes rejoin and resynchronize automatically.
• Rolling upgrades: Apply maintenance without downtime.
• Multi-zone deployments: Distribute nodes across regions for resilience.

These capabilities make CrateDB a natural fit for mission-critical analytics, IoT platforms, and AI-driven applications.