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

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

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

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

Widely used RDBMS

Primary database model

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

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	0.36
Rank	#270	Overall
	#126	Relational DBMS

Score	50.58
Rank	#19	Overall
	#13	Relational DBMS

Score	1017.80
Rank	#2	Overall
	#2	Relational DBMS

Score	0.00
Rank	#386	Overall
	#41	Time Series DBMS

Score	1317.01
Rank	#1	Overall
	#1	Relational DBMS

Website

pinot.apache.org

cloud.google.com/bigquery

www.mysql.com

www.opengemini.org
github.com/openGemini

www.oracle.com/database

Technical documentation

docs.pinot.apache.org

cloud.google.com/bigquery/docs

dev.mysql.com/doc

docs.opengemini.org/guide

docs.oracle.com/en/database

Developer

Apache Software Foundation and contributors

Google

Oracle

since 2010, originally MySQL AB, then Sun

Huawei and openGemini community

Oracle

Initial release

2015

2010

1995

2022

1980

Current release

1.0.0, September 2023

9.0.0, July 2024

1.1, July 2023

23c, September 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

Open Source

Apache Version 2.0

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Server operating systems

All OS with a Java JDK11 or higher

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
Windows

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

Data scheme

yes

schema-free

yes

Schemaless in JSON and XML columns

Typing

predefined data types such as float or date

yes

Integer, Float, Boolean, String

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

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

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++
Go
Java
JavaScript (Node.js)
Python
Rust

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Multi-source replication
Source-replica replication

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

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

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

Administrators and common users accounts

fine grained access rights according to SQL-standard

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