DBMS > 4D vs. MySQL vs. Spark SQL vs. XTDB

System Properties Comparison 4D vs. MySQL vs. Spark SQL vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Widely used open source RDBMS

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.4d.com

www.mysql.com

spark.apache.org/sql

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

developer.4d.com

dev.mysql.com/doc

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

www.xtdb.com/docs

Developer

4D, Inc

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Juxt Ltd.

Initial release

1984

1995

2014

2019

Current release

v20, April 2023

8.4.0, April 2024

3.5.0 ( 2.13), September 2023

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

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Implementation language

C and C++

Scala

Clojure

Server operating systems

OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

SQL

Support of SQL

yes

close to SQL 92

yes

with proprietary extensions

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

4D proprietary IDE
PHP

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Python
R
Scala

Clojure
Java

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

no user groups or roles

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