DBMS > Dolt vs. Hazelcast vs. MySQL vs. NCache vs. SWC-DB

System Properties Comparison Dolt vs. Hazelcast vs. MySQL vs. NCache vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

A MySQL compatible DBMS with Git-like versioning of data and schema

A widely adopted in-memory data grid

Widely used open source RDBMS

Open-Source and Enterprise in-memory Key-Value Store

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Key-value store

Wide column store

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

Document store
Search engine

Using distributed Lucene

Time Series DBMS

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

Score	0.93
Rank	#194	Overall
	#90	Relational DBMS

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	1029.49
Rank	#2	Overall
	#2	Relational DBMS

Score	0.91
Rank	#196	Overall
	#29	Key-value stores

Score	0.01
Rank	#378	Overall
	#13	Wide column stores

Website

github.com/dolthub/dolt
www.dolthub.com

hazelcast.com

www.mysql.com

www.alachisoft.com/ncache

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

Technical documentation

docs.dolthub.com

hazelcast.org/imdg/docs

dev.mysql.com/doc

www.alachisoft.com/resources/docs

Developer

DoltHub Inc

Hazelcast

Oracle

since 2010, originally MySQL AB, then Sun

Alachisoft

Alex Kashirin

Initial release

2018

2008

1995

2005

2020

Current release

5.3.6, November 2023

9.0.0, July 2024

5.3.3, April 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Enterprise Edition available

Open Source

GPL V3

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 and C++

C#, .NET, .NET Core, Java

C++

Server operating systems

Linux
macOS
Windows

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Linux
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

yes

SQL-like query language

yes

with proprietary extensions

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

SQL-like query language

APIs and other access methods

CLI Client
HTTP REST

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

IDistributedCache
JCache
LINQ
Proprietary native API

Proprietary protocol
Thrift

Supported programming languages

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

.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
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

C++

Server-side scripts

Stored procedures

yes

currently in alpha release

yes

Event Listeners, Executor Services

yes

proprietary syntax

support for stored procedures with SQL-Server CLR

Triggers

yes

Events

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

yes

Replicated Map

Multi-source replication
Source-replica replication

yes, with selectable consistency level

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
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

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

ACID

not for MyISAM storage engine

optimistic locking and pessimistic 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

Only one user is configurable, and must be specified in the config file at startup

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

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