DBMS > Apache Derby vs. Apache Spark (SQL) vs. MySQL

System Properties Comparison Apache Derby vs. Apache Spark (SQL) vs. MySQL

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Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

Widely used open source RDBMS

Primary database model

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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

Score	3.04
Rank	#85	Overall
	#46	Relational DBMS

Score	21.62
Rank	#29	Overall
	#18	Relational DBMS

Score	964.98
Rank	#2	Overall
	#2	Relational DBMS

Website

db.apache.org/derby

spark.apache.org/sql

www.mysql.com

Technical documentation

db.apache.org/derby/manuals/index.html

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

dev.mysql.com/doc

Developer

Apache Software Foundation

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

1997

2014

1995

Current release

10.17.1.0, November 2023

3.5.0 ( 2.13), September 2023

9.0.0, July 2024

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

C and C++

Server operating systems

All OS with a Java VM

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

JDBC

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Java

Java
Python
R
Scala

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

Server-side scripts

Stored procedures

Java Stored Procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication
Source-replica replication

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

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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