DBMS > 4D vs. AllegroGraph vs. Amazon Redshift vs. InfinityDB vs. Sphinx

System Properties Comparison 4D vs. AllegroGraph vs. Amazon Redshift vs. InfinityDB vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

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

Large scale data warehouse service for use with business intelligence tools

A Java embedded Key-Value Store which extends the Java Map interface

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Relational DBMS

Key-value store

Search engine

Secondary database models

Spatial DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	1.06
Rank	#187	Overall
	#30	Document stores
	#17	Graph DBMS
	#7	RDF stores
	#7	Vector DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.4d.com

allegrograph.com

aws.amazon.com/redshift

boilerbay.com

sphinxsearch.com

Technical documentation

developer.4d.com

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

docs.aws.amazon.com/redshift

boilerbay.com/infinitydb/manual

sphinxsearch.com/docs

Developer

4D, Inc

Franz Inc.

Amazon (based on PostgreSQL)

Boiler Bay Inc.

Sphinx Technologies Inc.

Initial release

1984

2004

2012

2002

2001

Current release

v20, April 2023

8.0, December 2023

4.0

3.5.1, February 2023

License

Commercial or Open Source

commercial

Limited community edition free

commercial

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

OS X
Windows

Linux
OS X
Windows

hosted

All OS with a Java VM

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

RDF schemas

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

bulk load of XML files possible

Secondary indexes

yes

restricted

manual creation possible, using inversions based on multi-value capability

yes

full-text index on all search fields

SQL

Support of SQL

yes

close to SQL 92

SPARQL is used as query language

yes

does not fully support an SQL-standard

SQL-like query language (SphinxQL)

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

RESTful HTTP API
SPARQL

JDBC
ODBC

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

Proprietary protocol

Supported programming languages

4D proprietary IDE
PHP

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

All languages supporting JDBC/ODBC

Java

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

user defined functions

in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

with Federation

Sharding

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

READ-COMMITTED or SERIALIZED

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

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 Sphinx index.

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 and groups

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Knowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
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Competitive advantages

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

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9 February 2024

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23 January 2024

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8 January 2024

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