DBMS > Ehcache vs. EsgynDB vs. HugeGraph vs. Splice Machine

System Properties Comparison Ehcache vs. EsgynDB vs. HugeGraph vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A fast-speed and highly-scalable Graph DBMS

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

Primary database model

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

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.ehcache.org

www.esgyn.cn

github.com/hugegraph
hugegraph.apache.org

splicemachine.com

Technical documentation

www.ehcache.org/documentation

hugegraph.apache.org/docs

splicemachine.com/how-it-works

Developer

Terracotta Inc, owned by Software AG

Esgyn

Baidu

Splice Machine

Initial release

2009

2015

2018

2014

Current release

3.10.0, March 2022

0.9

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache Version 2.0

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

C++, Java

Java

Server operating systems

All OS with a Java VM

Linux

Linux
macOS
Unix

Linux
OS X
Solaris
Windows

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

APIs and other access methods

JCache

ADO.NET
JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Java

All languages supporting JDBC/ODBC/ADO.Net

Groovy
Java
Python

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

Server-side scripts

Stored procedures

Java Stored Procedures

asynchronous Gremlin script jobs

yes

Java

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Multi-source replication between multi datacenters

yes

depending on used storage backend, e.g. Cassandra and HBase

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

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)

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

fine grained access rights according to SQL-standard

Users, roles and permissions

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

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More resources

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