Databend Community
Deploy fast,
query like a blast
Host Databend in your own environment to make it work even better for you.
Performance
Blazing-fast data analytics
Leverages data-level parallelism and instruction-level parallelism technologies for optimal performance.
No indexes to build, no manual tuning, and no need to figure out partitions or shard data.
Object Storage as first citizen
Supports various object storage platforms. Click here to see a full list of supported platforms
Allows instant elasticity, enabling users to scale up or down based on their application needs.
More than ANSI/SQL
Supports atomic operations such as SELECT, INSERT, DELETE, UPDATE, REPLACE, COPY, and MERGE.
Provides advanced features such as Time Travel and Multi Catalog (Apache Hive / Apache Iceberg).
Supports ingestion of semi-structured data in various formats like CSV, JSON, and Parquet.
Supports semi-structured data types such as ARRAY, MAP, and JSON.
Supports Git-like MVCC storage for easy querying, cloning, and restoration of historical data.
Architecture
How it works
Databend's architecture includes a meta-service layer, query layer, and storage layer.
Metadata
Security
Administration
Transaction
MetaService Layerelasticity
Planner
Processors
Optimizers
Cache
Query Layer
elasticity & serverless
Vectorized
Serverless
Pipeline
Streaming
Parquet
Native
Storage Layer
elasticity
Indexes
OpenDAL
Segments
Cache
Snapshots
Storage Provisionelasticity
