DBMS > 4D vs. Elasticsearch vs. Microsoft SQL Server vs. RDF4J vs. Spark SQL

System Properties Comparison 4D vs. Elasticsearch vs. Microsoft SQL Server vs. RDF4J vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Microsofts flagship relational DBMS

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.4d.com

www.elastic.co/elasticsearch

www.microsoft.com/en-us/sql-server

rdf4j.org

spark.apache.org/sql

Technical documentation

developer.4d.com

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

learn.microsoft.com/en-US/sql/sql-server

rdf4j.org/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

4D, Inc

Elastic

Microsoft

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

Apache Software Foundation

Initial release

1984

2010

1989

2004

2014

Current release

v20, April 2023

8.6, January 2023

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Elastic License

commercial

restricted free version is available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Java

Scala

Server operating systems

OS X
Windows

All OS with a Java VM

Linux
Windows

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

RDF Schemas

yes

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

yes

SQL-like DML and DDL statements

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

Supported programming languages

4D proprietary IDE
PHP

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Memcached and Redis integration

yes

User concepts

Access control

Users and groups

fine grained access rights according to SQL-standard

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