CrateDB

Distributed SQL database for real-time analytics at scale

CrateDB combines SQL simplicity with NoSQL scalability to ingest and analyze massive datasets in real-time across horizontally scalable, fault-tolerant clusters.

Overview

Real-Time Distributed SQL for Massive Data

CrateDB is a distributed SQL database engineered to store and analyze massive amounts of data in real-time. It bridges the gap between traditional SQL databases and NoSQL systems, offering familiar standard SQL querying alongside the horizontal scalability and flexibility typically found in document-oriented databases.

Built for Modern Infrastructure

Modest CrateDB clusters can ingest tens of thousands of records per second while supporting ad-hoc SQL queries through a blazing-fast distributed query execution engine that parallelizes workloads across the entire cluster. The database excels in containerized environments and scales horizontally using ephemeral virtual machines on Kubernetes, AWS, Azure, or hybrid cloud architectures with a shared-nothing design.

Flexible Data Models and Operations

CrateDB supports dynamic table schemas and queryable objects for document-oriented workflows, alongside native capabilities for time-series data, real-time full-text search, and geospatial operations. Auto-partitioning, auto-sharding, auto-replication, and self-healing features minimize operational overhead. Access data via the PostgreSQL wire protocol or HTTP API, and extend functionality with user-defined functions. Whether deployed on personal computers, multi-region clouds, or edge networks, CrateDB delivers high availability and fault tolerance without shared state.

Highlights

Standard SQL with PostgreSQL wire protocol and HTTP API support

Horizontal scalability with auto-sharding, auto-replication, and self-healing

Native time-series, full-text search, and geospatial capabilities

Dynamic schemas and queryable objects for document-oriented workflows

Pros

Ingest tens of thousands of records per second with modest clusters
Distributed query engine parallelizes workloads across entire cluster
Shared-nothing architecture ideal for containerized and ephemeral infrastructure
Combines SQL familiarity with NoSQL flexibility and scalability

Considerations

Requires understanding of distributed database concepts for optimal deployment
Auto-sharding and partitioning may introduce complexity for small datasets
Learning curve for teams unfamiliar with hybrid SQL/NoSQL paradigms
Operational overhead of managing distributed clusters without managed service

Managed products teams compare with

When teams consider CrateDB, these hosted platforms usually appear on the same shortlist.

Amazon Aurora

MySQL- and PostgreSQL-compatible cloud relational database service offering high performance and high availability

Amazon Redshift

Fully managed, petabyte-scale cloud data warehouse for analytics and reporting

Amazon Timestream

Serverless time-series database for IoT, metrics, and operational telemetry

Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.

Fit guide

Great for

Real-time analytics on high-velocity time-series or IoT data streams
Applications requiring both relational queries and document-oriented flexibility
Containerized deployments on Kubernetes, AWS, Azure, or hybrid clouds
Workloads needing full-text search, geospatial queries, and SQL in one system

Not ideal when

Small datasets that do not require distributed architecture
Workloads demanding strict ACID transactions across complex joins
Teams seeking fully managed database services without self-hosting options
Use cases requiring extensive stored procedure or trigger logic

How teams use it

IoT Sensor Data Analytics

Ingest thousands of sensor readings per second and run real-time SQL queries for anomaly detection and trend analysis across distributed clusters.

Log Aggregation and Search

Centralize application logs with full-text search capabilities, enabling rapid troubleshooting and compliance reporting using standard SQL.

Geospatial Fleet Management

Store and query vehicle location data with native geospatial functions to optimize routing and monitor fleet performance in real-time.

Multi-Region E-Commerce Analytics

Deploy across hybrid cloud regions to analyze customer behavior and inventory trends with low-latency queries and automatic rebalancing.

Tech snapshot

Java100%

Python1%

ANTLR1%

Shell1%

Frequently asked questions

Does CrateDB support standard SQL?

Yes, CrateDB supports standard SQL and is accessible via the PostgreSQL wire protocol or an HTTP API, allowing use of familiar SQL clients and tools.

How does CrateDB scale horizontally?

CrateDB uses a shared-nothing architecture with auto-sharding, auto-partitioning, and auto-replication, enabling seamless scaling across ephemeral virtual machines and container orchestration platforms like Kubernetes.

Can CrateDB handle time-series and geospatial data?

Yes, CrateDB has native support for time-series data, real-time full-text search, and geospatial data types with built-in search capabilities.

What deployment options are available?

CrateDB can be deployed via Docker, Kubernetes, on-premises infrastructure, or cloud platforms like AWS and Azure. A fully managed CrateDB Cloud service is also available.

How does CrateDB combine SQL and NoSQL features?

CrateDB offers dynamic table schemas and queryable objects for document-oriented workflows while maintaining relational SQL query capabilities, providing flexibility without sacrificing familiarity.

Project at a glance

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