DBMS > Databend

Databend System Properties

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Name

Description

An open-source, cloud-native data warehouse built in Rust on object storage (S3, Azure Blob, GCS), supporting large-scale analytics, full-text search, and vector search for AI workloads, with a SQL dialect highly compatible with Snowflake (90%+).

Primary database model

Relational DBMS

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Score	0.31
Rank	#280	Overall
	#126	Relational DBMS

Website

github.com/databendlabs/databend
www.databend.com

Technical documentation

docs.databend.com

Developer

Databend Labs

Initial release

2021

Current release

v1.2.881 (April 2026)

License

Commercial or Open Source

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Rust

Server operating systems

hosted
Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes — Databend provides several index types beyond base storage, including inverted (full-text) indexes, vector indexes, and bloom filter indexes for data skipping. (Databend also uses a cluster key to control physical data layout, which is a clustering mechanism rather than a secondary index.)

SQL

Support of SQL

yes

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Databend organizes data into snapshots / segments / blocks on object storage and uses a cluster key for data pruning, rather than traditional manual partitioning.

Replication methods

Methods for redundantly storing data on multiple nodes

Data durability and redundancy are handled by the underlying object storage (S3 / Azure / GCS), rather than by database-level replication.

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles

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