DBMS > GridGain vs. HugeGraph vs. SAP HANA

System Properties Comparison GridGain vs. HugeGraph vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Key-value store
Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

www.gridgain.com

github.com/hugegraph
hugegraph.apache.org

www.sap.com/products/hana.html

Technical documentation

www.gridgain.com/docs/index.html

hugegraph.apache.org/docs

help.sap.com/hana

Developer

GridGain Systems, Inc.

Baidu

SAP

Initial release

2007

2018

2010

Current release

GridGain 8.5.1

0.9

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

Java

Server operating systems

Linux
OS X
Solaris
Windows

Linux
macOS
Unix

Appliance or cloud-service

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

APIs and other access methods

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

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Groovy
Java
Python

Server-side scripts

Stored procedures

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

asynchronous Gremlin script jobs

SQLScript, R

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

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

yes

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

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

Security Hooks for custom implementations

Users, roles and permissions

yes

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