DBMS > Apache Pinot vs. Google BigQuery vs. MySQL vs. Oracle vs. Oracle Coherence

System Properties Comparison Apache Pinot vs. Google BigQuery vs. MySQL vs. Oracle vs. Oracle Coherence

Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Large scale data warehouse service with append-only tables

Widely used open source RDBMS

Widely used RDBMS

Oracles in-memory data grid solution

Primary database model

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Key-value store

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	0.40
Rank	#270	Overall
	#125	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	1.92
Rank	#130	Overall
	#22	Key-value stores

Website

pinot.apache.org

cloud.google.com/bigquery

www.mysql.com

www.oracle.com/database

www.oracle.com/java/coherence

Technical documentation

docs.pinot.apache.org

cloud.google.com/bigquery/docs

dev.mysql.com/doc

docs.oracle.com/en/database

docs.oracle.com/en/middleware/standalone/coherence

Developer

Apache Software Foundation and contributors

Google

Oracle

since 2010, originally MySQL AB, then Sun

Oracle

Initial release

2015

2010

1995

1980

2007

Current release

1.0.0, September 2023

8.4.0, April 2024

23c, September 2023

14.1, August 2023

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++

Java

Server operating systems

All OS with a Java JDK11 or higher

hosted

FreeBSD
Linux
OS X
Solaris
Windows

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

All OS with a Java VM

Data scheme

yes

Schemaless in JSON and XML columns

schema-free

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

SQL-like query language

yes

with proprietary extensions

yes

with proprietary extensions

APIs and other access methods

JDBC

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JCache
JPA
RESTful HTTP API

Supported programming languages

Go
Java
Python

.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

.Net
C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax

PL/SQL

also stored procedures in Java possible

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

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

Since BigQuery is designed for querying data

ACID

not for MyISAM storage engine

ACID

isolation level can be parameterized

configurable

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

optionally

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'

yes

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

authentification to access the cache via certificates or http basic authentication

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