DBMS > MySQL vs. Quasardb vs. Realm vs. Spark SQL vs. Trafodion

System Properties Comparison MySQL vs. Quasardb vs. Realm vs. Spark SQL vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Widely used open source RDBMS

Distributed, high-performance timeseries database

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Document store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

dev.mysql.com/doc

doc.quasar.ai/master

realm.io/docs

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

trafodion.apache.org/documentation.html

Developer

Oracle

since 2010, originally MySQL AB, then Sun

quasardb

Realm, acquired by MongoDB in May 2019

Apache Software Foundation

Apache Software Foundation, originally developed by HP

Initial release

1995

2009

2014

Current release

8.4.0, April 2024

3.14.1, January 2024

3.5.0 ( 2.13), September 2023

2.3.0, February 2019

License

Commercial or Open Source

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache 2.0

Open Source

Apache 2.0

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.

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Implementation language

C and C++

C++

Scala

C++, Java

Server operating systems

FreeBSD
Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

Android
Backend: server-less
iOS
Windows

Linux
OS X
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
Java

with Android only
Objective-C
React Native
Swift

Java
Python
R
Scala

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

proprietary syntax

runs within the applications so server-side scripts are unnecessary

Java Stored Procedures

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

consistent hashing

none

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

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

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

In-Memory realm

User concepts

Access control

Users with fine-grained authorization concept

no user groups or roles

Cryptographically strong user authentication and audit trail

yes

fine grained access rights according to SQL-standard

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