DBMS > dBASE vs. GeoMesa vs. RDF4J vs. Solr vs. Sphinx

System Properties Comparison dBASE vs. GeoMesa vs. RDF4J vs. Solr vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

dBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

A widely used distributed, scalable search engine based on Apache Lucene

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

Primary database model

Secondary database models

Spatial DBMS

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

Score	9.70
Rank	#44	Overall
	#28	Relational DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

Technical documentation

www.dbase.com/support/knowledgebase

www.geomesa.org/documentation/stable/user/index.html

rdf4j.org/documentation

solr.apache.org/resources.html

sphinxsearch.com/docs

Developer

Asthon Tate

CCRi and others

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Apache Software Foundation

Sphinx Technologies Inc.

Initial release

1979

2014

2004

2006

2001

Current release

dBASE 2019, 2019

5.0.0, May 2024

9.6.1, May 2024

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache Version 2

Open Source

GPL version 2, commercial licence 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

Scala

Java

C++

Server operating systems

DOS

dBase Classic
Windows

dBase Pro

Linux
OS X
Unix
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

RDF Schemas

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

Secondary indexes

yes

All search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

Solr Parallel SQL Interface

SQL-like query language (SphinxQL)

APIs and other access methods

none

The IDE can access other DBMS or ODBC-sources.

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Java API
RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

dBase proprietary IDE

Java
PHP
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

The IDE can access stored procedures in other database systems.

yes

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

depending on storage layer

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

not for dBase internal data, but IDE does support transactions when accessing external DBMS

ACID

Isolation support depends on the API used

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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.

depending on storage layer

yes

User concepts

Access control

Access rights for users and roles

yes

depending on the DBMS used for storage

yes

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