DBMS > Altibase vs. Apache Impala vs. GigaSpaces

System Properties Comparison Altibase vs. Apache Impala vs. GigaSpaces

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Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Analytic DBMS for Hadoop

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

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Secondary database models

Document store

Graph DBMS
Search engine

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Trend Chart

Score	1.00
Rank	#194	Overall
	#91	Relational DBMS

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	1.02
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Website

altibase.com

impala.apache.org

www.gigaspaces.com

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

impala.apache.org/impala-docs.html

docs.gigaspaces.com/latest/landing.html

Developer

Altibase

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Gigaspaces Technologies

Initial release

1999

2013

2000

Current release

v7.3, 2023, August 2023

4.1.0, June 2022

15.5, September 2020

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

Open Source

Apache Version 2

Open Source

Apache Version 2; Commercial licenses available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java, C++, .Net

Server operating systems

AIX
HP-UX
Linux

Linux

Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

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

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL-92

SQL-like DML and DDL statements

SQL-99 for query and DML statements

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

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

Supported programming languages

C
C++
Java

All languages supporting JDBC/ODBC

.Net
C++
Java
Python
Scala

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Foreign keys

Referential integrity

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Role-based access control

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