DBMS > Ignite vs. MonetDB vs. MySQL vs. OpenTenBase vs. Spark SQL

System Properties Comparison Ignite vs. MonetDB vs. MySQL vs. OpenTenBase vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

A relational database management system that stores data in columns

Widely used open source RDBMS

An enterprise-level distributed HTAP open source database based on PostgreSQL

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

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

ignite.apache.org

www.monetdb.org

www.mysql.com

github.com/OpenTenBase/OpenTenBase
www.opentenbase.org

spark.apache.org/sql

Technical documentation

apacheignite.readme.io/docs

www.monetdb.org/Documentation

dev.mysql.com/doc

docs.opentenbase.org
docs.opentenbase.org/en

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

Developer

Apache Software Foundation

MonetDB BV

Oracle

since 2010, originally MySQL AB, then Sun

OpenAtom Foundation, previously Tencent

Apache Software Foundation

Initial release

2015

2004

1995

2014

Current release

Apache Ignite 2.6

Dec2023 (11.49), December 2023

8.3.0, January 2024

2.5, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Mozilla Public License 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

BSD-3

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C++, Java, .Net

C and C++

Scala

Server operating systems

Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X
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

ANSI-99 for query and DML statements, subset of DDL

yes

SQL 2003 with some extensions

yes

with proprietary extensions

yes

SQL-like DML and DDL statements

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

C
Go
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes, in SQL, C, R

yes

proprietary syntax

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

Source-replica replication available in experimental status

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

In-memory capabilities

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

yes

User concepts

Access control

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

Access rights for users, groups and roles according to SQL-standard

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