DBMS > AllegroGraph vs. GigaSpaces vs. Hive vs. SQLite

System Properties Comparison AllegroGraph vs. GigaSpaces vs. Hive vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

High performance, persistent RDF store with additional support for Graph DBMS

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Widely used embeddable, in-process RDBMS

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

Spatial DBMS

Graph DBMS
Search engine

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

Score	1.16
Rank	#179	Overall
	#30	Document stores
	#17	Graph DBMS
	#7	RDF stores
	#7	Vector DBMS

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

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Score	116.01
Rank	#10	Overall
	#7	Relational DBMS

Website

Technical documentation

franz.com/agraph/support/documentation/current/agraph-introduction.html

docs.gigaspaces.com/latest/landing.html

cwiki.apache.org/confluence/display/Hive/Home

www.sqlite.org/docs.html

Developer

Franz Inc.

Gigaspaces Technologies

Apache Software Foundation

initially developed by Facebook

Dwayne Richard Hipp

Initial release

2004

2000

2012

2000

Current release

8.0, December 2023

15.5, September 2020

3.1.3, April 2022

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Limited community edition free

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache Version 2

Open Source

Public Domain

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

Java, C++, .Net

Java

Server operating systems

Linux
OS X
Windows

Linux
macOS
Solaris
Windows

All OS with a Java VM

server-less

Data scheme

yes

RDF schemas

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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.

bulk load of XML files possible

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

SQL-99 for query and DML statements

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP API
SPARQL

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

JDBC
ODBC
Thrift

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

.Net
C++
Java
Python
Scala

C++
Java
PHP
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

yes

user defined functions and integration of map-reduce

Triggers

yes

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

with Federation

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual Consistency

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

via file-system locks

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

Users with fine-grained authorization concept, user roles and pluggable authentication

Role-based access control

Access rights for users, groups and roles

More information provided by the system vendor

Specific characteristics

Knowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more

Competitive advantages

AllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more

News

Can Neuro-Symbolic AI Solve AI’s Weaknesses?
17 April 2024

100 Companies That Matter in KM – Franz Inc.
3 April 2024

Exploring AllegroGraph v8 – Unleashing the Power of Neuro-Symbolic AI (Recorded Webinar)
9 February 2024

What is Neuro-Symbolic AI?
23 January 2024

Allegro CL v11 – Now Available! – The Neuro-Symbolic AI Programming Platform
8 January 2024

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