DBMS > Apache Kylin vs. Google BigQuery vs. MySQL vs. Oracle vs. OushuDB

System Properties Comparison Apache Kylin vs. Google BigQuery vs. MySQL vs. Oracle vs. OushuDB

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Large scale data warehouse service with append-only tables

Widely used open source RDBMS

Widely used RDBMS

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Primary database model

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Website

kylin.apache.org

cloud.google.com/bigquery

www.mysql.com

www.oracle.com/database

www.oushu.com/product/oushuDB

Technical documentation

kylin.apache.org/docs

cloud.google.com/bigquery/docs

dev.mysql.com/doc

docs.oracle.com/en/database

www.oushu.com/documentation

Developer

Apache Software Foundation, originally contributed from eBay Inc

Google

Oracle

since 2010, originally MySQL AB, then Sun

Oracle

Oushu

Initial release

2015

2010

1995

1980

Current release

3.1.0, July 2020

8.4.0, April 2024

23c, September 2023

4.0.1, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

Server operating systems

Linux

hosted

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux

Data scheme

yes

Schemaless in JSON and XML columns

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 SQL for queries (using Apache Calcite)

yes

with proprietary extensions

yes

with proprietary extensions

Full-featured ANSI SQL support

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

C
C++

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax

PL/SQL

also stored procedures in Java possible

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding, horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

can be realized in PL/SQL

Hadoop integration

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

Since BigQuery is designed for querying data

ACID

not for MyISAM storage engine

ACID

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

Kerberos, SSL and role based access

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