DBMS > Hazelcast vs. IBM Db2 vs. MySQL vs. Splice Machine

System Properties Comparison Hazelcast vs. IBM Db2 vs. MySQL vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

Common in IBM host environments, 2 different versions for host and Windows/Linux

Widely used open source RDBMS

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

Primary database model

Key-value store

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Document store
Spatial DBMS

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

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Score	127.49
Rank	#8	Overall
	#5	Relational DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Website

hazelcast.com

www.ibm.com/products/db2

www.mysql.com

splicemachine.com

Technical documentation

hazelcast.org/imdg/docs

www.ibm.com/docs/en/db2

dev.mysql.com/doc

splicemachine.com/how-it-works

Developer

Hazelcast

IBM

Oracle

since 2010, originally MySQL AB, then Sun

Splice Machine

Initial release

2008

1983

host version

1995

2014

Current release

5.3.6, November 2023

12.1, October 2016

8.3.0, January 2024

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

free version is available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

Java

C and C++

Java

Server operating systems

All OS with a Java VM

AIX
HP-UX
Linux
Solaris
Windows
z/OS

FreeBSD
Linux
OS X
Solaris
Windows

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.

yes

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

yes

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

proprietary syntax

yes

Java

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

with separate tools (MQ, InfoSphere)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes, multi-version concurrency control (MVCC)

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

Role-based access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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

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