DBMS > GigaSpaces vs. Hazelcast vs. Manticore Search vs. SAP HANA

System Properties Comparison GigaSpaces vs. Hazelcast vs. Manticore Search vs. SAP HANA

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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

Multi-storage database for search, including full-text search.

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

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Key-value store

Search engine

Relational DBMS

Secondary database models

Graph DBMS
Search engine

Document store

JSON support with IMDG 3.12

Time Series DBMS

using the Manticore Columnar Library

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

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

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

www.gigaspaces.com

hazelcast.com

manticoresearch.com

www.sap.com/products/hana.html

Technical documentation

docs.gigaspaces.com/latest/landing.html

hazelcast.org/imdg/docs

manual.manticoresearch.com

help.sap.com/hana

Developer

Gigaspaces Technologies

Hazelcast

Manticore Software

SAP

Initial release

2000

2008

2017

2010

Current release

15.5, September 2020

5.3.6, November 2023

6.0, February 2023

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL version 2

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

C++

Server operating systems

Linux
macOS
Solaris
Windows

All OS with a Java VM

FreeBSD
Linux
macOS
Windows

Appliance or cloud-service

Data scheme

schema-free

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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

Can index from XML

Secondary indexes

yes

full-text index on all search fields

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

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

JDBC
ODBC

Supported programming languages

.Net
C++
Java
Python
Scala

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

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

user defined functions

SQLScript, R

Triggers

yes, event driven architecture

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

yes

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

Synchronous replication based on Galera library

yes

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

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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

Role-based access control

yes

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