DBMS > Splice Machine vs. SWC-DB vs. Transwarp KunDB

System Properties Comparison Splice Machine vs. SWC-DB vs. Transwarp KunDB

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Editorial information provided by DB-Engines

Name

Description

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

A high performance, scalable Wide Column DBMS

OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.01
Rank	#378	Overall
	#13	Wide column stores

Score	0.04
Rank	#362	Overall
	#154	Relational DBMS

Website

splicemachine.com

github.com/kashirin-alex/swc-db
www.swcdb.org

www.transwarp.cn/en/product/kundb

Technical documentation

splicemachine.com/how-it-works

Developer

Splice Machine

Alex Kashirin

Transwarp

Initial release

2014

2020

Current release

3.1, March 2021

0.5, April 2021

License

Commercial or Open Source

Open Source

AGPL 3.0, commercial license available

Open Source

GPL V3

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

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

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

JDBC
Native Spark Datasource
ODBC

Proprietary protocol
Thrift

Supported programming languages

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

C++

Server-side scripts

Stored procedures

yes

Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

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

ACID

Concurrency

Support for concurrent manipulation of data

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

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

yes

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More resources

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