Databend is an open-source Elastic and Workload-Aware modern cloud data warehouse.
Databend completely separates storage from compute, which allows you easily scale up or scale down based on your application's needs.
Databend leverages data-level parallelism(Vectorized Query Execution) and instruction-level parallelism(SIMD) technology, offering blazing performance data analytics.
Databend stores data with snapshots. It's easy to query, clone, and restore historical data in tables.
Support for Semi-Structured Data
Databend supports ingestion of semi-structured data in various formats like CSV, JSON, and Parquet, which are located in the cloud or your local file system; Databend also supports semi-structured data types: ARRAY, MAP, JSON, which is easy to import and operate on semi-structured.
Easy to Use
Databend has no indexes to build, no manual tuning required, no manual figuring out partitions or shard data, its all done for you as data is loaded into the table.
Prepare the image (once) from Docker Hub (this will download about 170 MB data):
docker pull datafuselabs/databend
To run Databend quickly:
docker run --net=host datafuselabs/databend
MySQL wire protocol on port
mysql -h127.0.0.1 -uroot -P3307
Let's run some benchmark queries.
Databend is an open source project, you can help with ideas, code, or documentation, we appreciate any efforts that help us to make the project better! Once the code been merged, your name will be stored in the system.contributors table forever.
To get started with contributing:
For general help in using Databend, please refer to the official documentation. For additional help, you can use one of these channels to ask a question:
Databend is licensed under Apache 2.0.