DBMS > Databend vs. Oracle Rdb vs. Transwarp StellarDB

System Properties Comparison Databend vs. Oracle Rdb vs. Transwarp StellarDB

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

A distributed graph DBMS built for enterprise-level graph applications

Primary database model

Relational DBMS

Graph DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.26
Rank	#291	Overall
	#132	Relational DBMS

Score	1.03
Rank	#182	Overall
	#82	Relational DBMS

Score	0.00
Rank	#388	Overall
	#40	Graph DBMS

Website

github.com/datafuselabs/databend
www.databend.com

www.oracle.com/database/technologies/related/rdb.html

www.transwarp.cn/en/product/stellardb

Technical documentation

docs.databend.com

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Databend Labs

Oracle, originally developed by Digital Equipment Corporation (DEC)

Transwarp

Initial release

2021

1984

Current release

1.0.59, April 2023

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Rust

Server operating systems

hosted
Linux
macOS

HP Open VMS

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

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

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

OpenCypher

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

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

yes

Transaction concepts

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

yes

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Users with fine-grained authorization concept, user roles

yes

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