DBMS > Hazelcast vs. Kingbase vs. MySQL vs. SWC-DB vs. Vitess

System Properties Comparison Hazelcast vs. Kingbase vs. MySQL vs. SWC-DB vs. Vitess

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

Widely used open source RDBMS

A high performance, scalable Wide Column DBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Key-value store

Relational DBMS

Key/Value like access via memcached API

Wide column store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

Time Series DBMS

Document store
Spatial DBMS

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

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	0.50
Rank	#257	Overall
	#119	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

hazelcast.com

www.kingbase.com.cn

www.mysql.com

github.com/kashirin-alex/swc-db
www.swcdb.org

vitess.io

Technical documentation

hazelcast.org/imdg/docs

dev.mysql.com/doc

vitess.io/docs

Developer

Hazelcast

BeiJing KINGBASE Information technologies inc.

Oracle

since 2010, originally MySQL AB, then Sun

Alex Kashirin

The Linux Foundation, PlanetScale

Initial release

2008

1999

1995

2020

2013

Current release

5.3.6, November 2023

V8.0, August 2021

8.4.0, April 2024

0.5, April 2021

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL V3

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and Java

C and C++

C++

Server operating systems

All OS with a Java VM

Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Docker
Linux
macOS

Data scheme

schema-free

yes

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

Standard with numerous extensions

yes

with proprietary extensions

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

ADO.NET
JDBC
ODBC
Proprietary native API

Proprietary protocol
Thrift

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

C++

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

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

user defined functions

yes

proprietary syntax

yes

proprietary syntax

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

not for MyISAM storage engine

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 at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

table locks or row locks depending on storage engine

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

Users with fine-grained authorization concept

no user groups or roles

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