Parallel Processing

Every node in a CrateDB cluster contributes to ingestion performance. Incoming data is automatically partitioned, replicated, and written across multiple shards, eliminating single-threaded bottlenecks.

Key benefits:

• True horizontal scaling for ingestion workloads
• Balanced resource utilization across nodes
• Continuous writes without blocking reads

Result: You can handle millions of incoming events per second while keeping data queryable in real time.

CrateDB indexes data automatically as it arrives, but unlike traditional systems, it performs indexing in parallel across nodes and shards. This ensures new records become searchable within seconds, not minutes or hours.

Why it matters:

• Fresh data instantly accessible for dashboards and alerts
• Low-latency ingestion without manual tuning
• Real-time consistency between ingestion and search layers

CrateDB’s distributed SQL engine executes queries in parallel across all nodes.
When you run an aggregation or search, each node processes its portion of data locally and returns results to be merged centrally, reducing response times from seconds to milliseconds.

Advantages:

• Millisecond query results on massive datasets
• Efficient CPU and memory utilization
• Linear scalability for both reads and writes

For IoT and edge deployments, CrateDB supports lightweight MQTT protocols, enabling millions of connected devices to stream telemetry data efficiently.
Combined with edge-friendly deployment options, this makes CrateDB ideal for industrial and sensor-based environments.

Use cases:

• Real-time factory and sensor monitoring
• Smart mobility and connected vehicles
• Edge-to-cloud data integration

CrateDB’s native HTTP endpoint provides a simple, flexible way to ingest data from custom applications, services, and microservices. This allows easy integration with any modern data producer, from web apps to serverless backends.

Highlights:

• JSON over HTTP for easy data pushes
• Works with any programming language
• Ideal for microservices architectures

Parallel processing in CrateDB also means parallel recovery. If a node fails, replicas on other nodes continue processing automatically, ensuring uninterrupted availability and no data loss.

Key features:

• Automatic rebalancing and recovery
• High availability with built-in replication
• No downtime during scaling or node replacement

Traditional databases	CrateDB’s distributed architecture
Sequential ingestion and indexing	Parallel ingestion and indexing across all nodes
Query performance degrades at scale	Linear scalability with distributed SQL
Manual tuning for performance	Self-balancing and auto-optimized cluster