DBMS > openGemini vs. Oracle Coherence vs. Splice Machine

System Properties Comparison openGemini vs. Oracle Coherence vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

Oracles in-memory data grid solution

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

Primary database model

Time Series DBMS

Key-value store

Relational DBMS

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

Score	0.01
Rank	#375	Overall
	#39	Time Series DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.opengemini.org
github.com/openGemini

www.oracle.com/java/coherence

splicemachine.com

Technical documentation

docs.opengemini.org/guide

docs.oracle.com/en/middleware/standalone/coherence

splicemachine.com/how-it-works

Developer

Huawei and openGemini community

Oracle

Splice Machine

Initial release

2022

2007

2014

Current release

1.1, July 2023

14.1, August 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

AGPL 3.0, 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

Server operating systems

Linux
Windows

All OS with a Java VM

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

Integer, Float, Boolean, String

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

SQL-like query language

yes

APIs and other access methods

HTTP REST

JCache
JPA
RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

.Net
C++
Java

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

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Live Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, with selectable consistency level

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

configurable

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

optionally

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

Administrators and common users accounts

authentification to access the cache via certificates or http basic authentication

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

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