DBMS > Databend vs. Oracle Rdb vs. Splice Machine vs. STSdb

System Properties Comparison Databend vs. Oracle Rdb vs. Splice Machine vs. STSdb

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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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Key-value store

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

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

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

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

splicemachine.com

github.com/STSSoft/STSdb4

Technical documentation

docs.databend.com

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

splicemachine.com/how-it-works

Developer

Databend Labs

Oracle, originally developed by Digital Equipment Corporation (DEC)

Splice Machine

STS Soft SC

Initial release

2021

1984

2014

2011

Current release

1.0.59, April 2023

7.4.1.1, 2021

3.1, March 2021

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

GPLv2, commercial license available

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

Java

Server operating systems

hosted
Linux
macOS

HP Open VMS

Linux
OS X
Solaris
Windows

Windows

Data scheme

yes

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

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

.NET Client API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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.

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles

Access rights for users, groups and roles according to SQL-standard

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