DBMS > Datomic vs. Ignite vs. mSQL vs. RocksDB

System Properties Comparison Datomic vs. Ignite vs. mSQL vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

mSQL (Mini SQL) is a simple and lightweight RDBMS

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Key-value store

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

www.datomic.com

ignite.apache.org

hughestech.com.au/products/msql

rocksdb.org

Technical documentation

docs.datomic.com

apacheignite.readme.io/docs

github.com/facebook/rocksdb/wiki

Developer

Cognitect

Apache Software Foundation

Hughes Technologies

Facebook, Inc.

Initial release

2012

2015

1994

2013

Current release

1.0.6735, June 2023

Apache Ignite 2.6

4.4, October 2021

8.11.4, April 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache 2.0

commercial

free licenses can be provided

Open Source

BSD

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, Clojure

C++, Java, .Net

C++

Server operating systems

All OS with a Java VM

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux

Data scheme

yes

schema-free

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

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

C++ API
Java API

Supported programming languages

Clojure
Java

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Delphi
Java
Perl
PHP
Tcl

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

yes (compute grid and cache interceptors can be used instead)

Triggers

By using transaction functions

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes (replicated cache)

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

Security Hooks for custom implementations

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