DBMS > Dgraph vs. Hazelcast vs. Interbase vs. MarkLogic vs. MySQL

System Properties Comparison Dgraph vs. Hazelcast vs. Interbase vs. MarkLogic vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

A widely adopted in-memory data grid

Light-weight proven RDBMS

originally from Borland

Operational and transactional Enterprise NoSQL database

Widely used open source RDBMS

Primary database model

Graph DBMS

Key-value store

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

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

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

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

Score	4.08
Rank	#75	Overall
	#41	Relational DBMS

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

dgraph.io

hazelcast.com

www.embarcadero.com/products/interbase

www.marklogic.com

www.mysql.com

Technical documentation

dgraph.io/docs

hazelcast.org/imdg/docs

docs.embarcadero.com/products/interbase

docs.marklogic.com

dev.mysql.com/doc

Developer

Dgraph Labs, Inc.

Hazelcast

Embarcadero

MarkLogic Corp.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2016

2008

1984

2001

1995

Current release

5.3.6, November 2023

InterBase 2020, December 2019

11.0, December 2022

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses available

commercial

restricted free version is available

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

C++

C and C++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Android
iOS
Linux
OS X
Windows

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

Schema can be enforced

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

export as XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL92

yes

with proprietary extensions

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

yes

Interbase procedure and trigger language

yes

via XQuery or JavaScript

yes

proprietary syntax

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes

Replicated Map

Interbase Change Views

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

can act as a resource manager in an XA/JTA transaction

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

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

yes, with Range Indexes

yes

User concepts

Access control

Planned for future releases

Role-based access control

fine grained access rights according to SQL-standard

Role-based access control at the document and subdocument levels

Users with fine-grained authorization concept

no user groups or roles

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