DBMS > Ehcache vs. Splice Machine vs. StarRocks

System Properties Comparison Ehcache vs. Splice Machine vs. StarRocks

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Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

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

Primary database model

Key-value store

Relational DBMS

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

Score	4.26
Rank	#69	Overall
	#8	Key-value stores

Score	0.54
Rank	#246	Overall
	#114	Relational DBMS

Score	0.75
Rank	#218	Overall
	#101	Relational DBMS

Website

www.ehcache.org

splicemachine.com

www.starrocks.io

Technical documentation

www.ehcache.org/documentation

splicemachine.com/how-it-works

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

Developer

Terracotta Inc, owned by Software AG

Splice Machine

The Linux Foundation

since Feb 2023

Initial release

2009

2014

2020

Current release

3.10.0, March 2022

3.1, March 2021

3.3, June 2024

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

AGPL 3.0, commercial license available

Open Source

Apache Version 2.0

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

All OS with a Java VM

Linux
OS X
Solaris
Windows

Linux

Data scheme

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

APIs and other access methods

JCache

JDBC
Native Spark Datasource
ODBC

JDBC
MySQL protocol

Supported programming languages

Java

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

Java

Server-side scripts

Stored procedures

yes

Java

user defined functions

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Shared Nothhing Auto-Sharding, Columnar Partitioning

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

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

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

Role based access control and fine grained access rights

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