DBMS > Datomic vs. GridGain vs. HugeGraph vs. MySQL

System Properties Comparison Datomic vs. GridGain vs. HugeGraph vs. MySQL

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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

GridGain is an in-memory computing platform, built on Apache Ignite

A fast-speed and highly-scalable Graph DBMS

Widely used open source RDBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Graph DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

www.datomic.com

www.gridgain.com

github.com/hugegraph
hugegraph.apache.org

www.mysql.com

Technical documentation

docs.datomic.com

www.gridgain.com/docs/index.html

hugegraph.apache.org/docs

dev.mysql.com/doc

Developer

Cognitect

GridGain Systems, Inc.

Baidu

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2012

2007

2018

1995

Current release

1.0.7075, December 2023

GridGain 8.5.1

0.9

8.4.0, April 2024

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache Version 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

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Implementation language

Java, Clojure

Java, C++, .Net

Java

C and C++

Server operating systems

All OS with a Java VM

Linux
OS X
Solaris
Windows

Linux
macOS
Unix

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

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

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

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

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

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

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Clojure
Java

C#
C++
Java
PHP
Python
Ruby
Scala

Groovy
Java
Python

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

Transaction Functions

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

asynchronous Gremlin script jobs

yes

proprietary syntax

Triggers

By using transaction functions

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes (replicated cache)

yes

depending on used storage backend, e.g. Cassandra and HBase

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Users, roles and permissions

Users with fine-grained authorization concept

no user groups or roles

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