DBMS > EXASOL vs. Hive vs. MySQL vs. Oracle Rdb vs. Splice Machine

System Properties Comparison EXASOL vs. Hive vs. MySQL vs. Oracle Rdb vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Widely used open source RDBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

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	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

www.exasol.com

hive.apache.org

www.mysql.com

www.oracle.com/database/technologies/related/rdb.html

splicemachine.com

Technical documentation

www.exasol.com/resources

cwiki.apache.org/confluence/display/Hive/Home

dev.mysql.com/doc

www.oracle.com/database/technologies/related/rdb-doc.html

splicemachine.com/how-it-works

Developer

Exasol

Apache Software Foundation

initially developed by Facebook

Oracle

since 2010, originally MySQL AB, then Sun

Oracle, originally developed by Digital Equipment Corporation (DEC)

Splice Machine

Initial release

2000

2012

1995

1984

2014

Current release

3.1.3, April 2022

8.4.0, April 2024

7.4.1.1, 2021

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

Java

Server operating systems

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

HP Open VMS

Linux
OS X
Solaris
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

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

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Java
Lua
Python
R

C++
Java
PHP
Python

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

query execution via MapReduce

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

yes, multi-version concurrency control (MVCC)

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

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

Access rights for users, groups and roles

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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