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DBMS > Google BigQuery vs. Oracle NoSQL vs. RethinkDB vs. SQL.JS

System Properties Comparison Google BigQuery vs. Oracle NoSQL vs. RethinkDB vs. SQL.JS

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Port of SQLite to JavaScript

Primary database model

Relational DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.53
Rank	#252	Overall
	#116	Relational DBMS

Website

cloud.google.com/bigquery

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

cloud.google.com/bigquery/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

rethinkdb.com/docs

sql.js.org/documentation/index.html

Developer

Google

Oracle

The Linux Foundation

since July 2017

Alon Zakai

enhancements implemented by others

Initial release

2010

2011

2009

2012

Current release

23.3, December 2023

2.4.1, August 2020

License

Commercial or Open Source

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Apache Version 2

Open Source

Cloud-based only

Only available as a cloud service

yes

no

no

no

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

JavaScript

Server operating systems

hosted

Linux
Solaris SPARC/x86

Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

yes

string, binary, float, bool, date, geometry

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.

no

no

no

no

Secondary indexes

no

yes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

no

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

JavaScript API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
Go
Java
JavaScript (Node.js)
Python

C

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

JavaScript

Server-side scripts

Stored procedures

user defined functions

in JavaScript

no

no

Triggers

no

no

Client-side triggers through changefeeds

no

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding

range based

none

Replication methods

Methods for redundantly storing data on multiple nodes

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

no

with Hadoop integration

yes

no

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

none

Foreign keys

Referential integrity

no

no

no

yes

Transaction concepts

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

no

Since BigQuery is designed for querying data

configurable

ACID within a storage node (=shard)

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes

yes

MVCC based

Durability

Support for making data persistent

yes

yes

yes

no

except by serializing a db to a file

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

no

yes

off heap cache

no

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

yes

users and table-level permissions

no

More information provided by the system vendor

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