DBMS > Realm vs. Splice Machine vs. StarRocks vs. STSdb

System Properties Comparison Realm vs. Splice Machine vs. StarRocks vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Document store

Relational DBMS

Key-value store

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

Score	7.18
Rank	#52	Overall
	#8	Document stores

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.95
Rank	#193	Overall
	#89	Relational DBMS

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Website

realm.io

splicemachine.com

www.starrocks.io

github.com/STSSoft/STSdb4

Technical documentation

realm.io/docs

splicemachine.com/how-it-works

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Realm, acquired by MongoDB in May 2019

Splice Machine

The Linux Foundation

since Feb 2023

STS Soft SC

Initial release

2014

2020

2011

Current release

3.1, March 2021

3.3, June 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

AGPL 3.0, commercial license available

Open Source

Apache Version 2.0

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

Java

C++, Java

Server operating systems

Android
Backend: server-less
iOS
Windows

Linux
OS X
Solaris
Windows

Linux

Windows

Data scheme

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

JDBC
Native Spark Datasource
ODBC

JDBC
MySQL protocol

.NET Client API

Supported programming languages

.Net
Java

with Android only
Objective-C
React Native
Swift

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

Java

C#
Java

Server-side scripts

Stored procedures

runs within the applications so server-side scripts are unnecessary

yes

Java

user defined functions

Triggers

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

horizontal partitioning (by range and hash)

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

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

In-Memory realm

yes

User concepts

Access control

yes

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

Role based access control and fine grained access rights

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More resources

DB-Engines blog posts

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