DBMS > Fauna vs. Google BigQuery vs. Hyprcubd vs. MonetDB vs. RethinkDB

System Properties Comparison Fauna vs. Google BigQuery vs. Hyprcubd vs. MonetDB vs. RethinkDB

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

Large scale data warehouse service with append-only tables

Serverless Time Series DBMS

A relational database management system that stores data in columns

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

Primary database model

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	1.55
Rank	#151	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Website

fauna.com

cloud.google.com/bigquery

hyprcubd.com (offline)

www.monetdb.org

rethinkdb.com

Technical documentation

docs.fauna.com

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

rethinkdb.com/docs

Developer

Fauna, Inc.

Google

Hyprcubd, Inc.

MonetDB BV

The Linux Foundation

since July 2017

Initial release

2014

2010

2004

2009

Current release

Dec2023 (11.49), December 2023

2.4.1, August 2020

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

C++

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

string, binary, float, bool, date, geometry

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

SQL 2003 with some extensions

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

gRPC (https)

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in SQL, C, R

Triggers

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

consistent hashing

none

Sharding via remote tables

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Source-replica replication available in experimental status

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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.

User concepts

Access control

Identity management, authentication, and access control

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

Google Cloud Identity & Access Management (IAM)

token access

fine grained access rights according to SQL-standard

yes

users and table-level permissions

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