DBMS > Google BigQuery vs. Oracle vs. Vitess vs. VoltDB

System Properties Comparison Google BigQuery vs. Oracle vs. Vitess vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Widely used RDBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Secondary database models

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

Document store
Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

cloud.google.com/bigquery

www.oracle.com/database

vitess.io

www.voltdb.com

Technical documentation

cloud.google.com/bigquery/docs

docs.oracle.com/en/database

vitess.io/docs

docs.voltdb.com

Developer

Google

Oracle

The Linux Foundation, PlanetScale

VoltDB Inc.

Initial release

2010

1980

2013

2010

Current release

23c, September 2023

15.0.2, December 2022

11.3, April 2022

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache Version 2.0, commercial licenses available

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

C and C++

Java, C++

Server operating systems

hosted

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

Docker
Linux
macOS

Linux
OS X

for development

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

yes

with proprietary extensions

yes

with proprietary extensions

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
JDBC
MySQL protocol
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

PL/SQL

also stored procedures in Java possible

yes

proprietary syntax

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

Sharding

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

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

FOREIGN KEY constraints are not supported

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Since BigQuery is designed for querying data

ACID

isolation level can be parameterized

ACID at shard level

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

Users and roles with access to stored procedures

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