DBMS > GigaSpaces vs. Hazelcast vs. Realm vs. SAP HANA vs. TimesTen

System Properties Comparison GigaSpaces vs. Hazelcast vs. Realm vs. SAP HANA vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

A widely adopted in-memory data grid

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Key-value store

Document store

Relational DBMS

Secondary database models

Graph DBMS
Search engine

Document store

JSON support with IMDG 3.12

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.91
Rank	#197	Overall
	#33	Document stores
	#7	Object oriented DBMS

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	7.18
Rank	#52	Overall
	#8	Document stores

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

www.gigaspaces.com

hazelcast.com

realm.io

www.sap.com/products/hana.html

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.gigaspaces.com/latest/landing.html

hazelcast.org/imdg/docs

realm.io/docs

help.sap.com/hana

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Gigaspaces Technologies

Hazelcast

Realm, acquired by MongoDB in May 2019

SAP

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2000

2008

2014

2010

1998

Current release

15.5, September 2020

5.3.6, November 2023

2.0 SPS07 (April 4, 2023), April 2023

Release 22.1

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache Version 2; commercial licenses available

Open Source

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
macOS
Solaris
Windows

All OS with a Java VM

Android
Backend: server-less
iOS
Windows

Appliance or cloud-service

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

schema-free

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.

XML can be used for describing objects metadata

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like query language

yes

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

.Net
C++
Java
Python
Scala

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

.Net
Java

with Android only
Objective-C
React Native
Swift

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

runs within the applications so server-side scripts are unnecessary

SQLScript, R

PL/SQL

Triggers

yes, event driven architecture

yes

Events

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

Replicated Map

none

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

In-Memory realm

yes

User concepts

Access control

Role-based access control

yes

fine grained access rights according to SQL-standard

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