DBMS > Hazelcast vs. MySQL vs. Solr vs. SWC-DB vs. Warp 10

System Properties Comparison Hazelcast vs. MySQL vs. Solr vs. SWC-DB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

Widely used open source RDBMS

A widely used distributed, scalable search engine based on Apache Lucene

A high performance, scalable Wide Column DBMS

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Key-value store

Relational DBMS

Key/Value like access via memcached API

Search engine

Wide column store

Time Series DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

Spatial DBMS

Time Series DBMS

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

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

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

hazelcast.com

www.mysql.com

solr.apache.org

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

www.warp10.io

Technical documentation

hazelcast.org/imdg/docs

dev.mysql.com/doc

solr.apache.org/resources.html

www.warp10.io/content/02_Getting_started

Developer

Hazelcast

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Alex Kashirin

SenX

Initial release

2008

1995

2006

2020

2015

Current release

5.3.6, November 2023

8.4.0, April 2024

9.6.1, May 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2

Open Source

GPL V3

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

Java

C++

Java

Server operating systems

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

Solr Parallel SQL Interface

SQL-like query language

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Java API
RESTful HTTP/JSON API

Proprietary protocol
Thrift

HTTP API
Jupyter
WebSocket

Supported programming languages

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

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C++

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

proprietary syntax

Java plugins

yes

WarpScript

Triggers

yes

Events

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Multi-source replication
Source-replica replication

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

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

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

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

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

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

Users with fine-grained authorization concept

no user groups or roles

yes

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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