DBMS > GridGain vs. JanusGraph vs. Microsoft Azure Synapse Analytics vs. MySQL vs. SAP SQL Anywhere

System Properties Comparison GridGain vs. JanusGraph vs. Microsoft Azure Synapse Analytics vs. MySQL vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Widely used open source RDBMS

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Key-value store
Relational DBMS

Graph DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

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

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

www.gridgain.com

janusgraph.org

azure.microsoft.com/services/synapse-analytics

www.mysql.com

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

www.gridgain.com/docs/index.html

docs.janusgraph.org

docs.microsoft.com/azure/synapse-analytics

dev.mysql.com/doc

help.sap.com/docs/SAP_SQL_Anywhere

Developer

GridGain Systems, Inc.

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

SAP

formerly Sybase

Initial release

2007

2017

2016

1995

1992

Current release

GridGain 8.5.1

0.6.3, February 2023

8.4.0, April 2024

17, July 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java, C++, .Net

Java

C++

C and C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux
OS X
Unix
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
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

SQL

Support of SQL

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

yes

with proprietary extensions

yes

APIs and other access methods

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Clojure
Java
Python

C#
Java
PHP

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

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

Server-side scripts

Stored procedures

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

yes

Transact SQL

yes

proprietary syntax

yes, in C/C++, Java, .Net or Perl

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding, horizontal partitioning

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

Multi-source replication
Source-replica replication

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

Security Hooks for custom implementations

User authentification and security via Rexster Graph Server

yes

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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