DBMS > RisingWave vs. Splice Machine vs. UniData,UniVerse

System Properties Comparison RisingWave vs. Splice Machine vs. UniData,UniVerse

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Editorial information provided by DB-Engines

Name

Description

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

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

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

Relational DBMS

Multivalue DBMS

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

Score	0.61
Rank	#237	Overall
	#111	Relational DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	2.25
Rank	#112	Overall
	#2	Multivalue DBMS

Website

www.risingwave.com/database

splicemachine.com

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

Technical documentation

docs.risingwave.com/docs/current/intro

splicemachine.com/how-it-works

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

Developer

RisingWave Labs

Splice Machine

Rocket Software

Initial release

2022

2014

1985

Current release

1.2, September 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL 3.0, commercial license available

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

Java

Server operating systems

Docker
Linux
macOS

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

Standard SQL-types and JSON

yes

optional

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

JDBC
PostgreSQL wire protocol

JDBC
Native Spark Datasource
ODBC

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

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

.Net
Basic

U2 Basic
C
Java

Server-side scripts

Stored procedures

UDFs in Python or Java

yes

Java

yes

Triggers

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica 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

configurable

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

Users and Roles

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

Access rights according to SQL-standard and operating system based

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