DBMS > Fauna vs. Google Cloud Datastore vs. IBM Db2 vs. RocksDB

System Properties Comparison Fauna vs. Google Cloud Datastore vs. IBM Db2 vs. RocksDB

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Editorial information provided by DB-Engines

Name

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.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Common in IBM host environments, 2 different versions for host and Windows/Linux

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Document store

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Key-value store

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

fauna.com

cloud.google.com/datastore

www.ibm.com/products/db2

rocksdb.org

Technical documentation

docs.fauna.com

cloud.google.com/datastore/docs

www.ibm.com/docs/en/db2

github.com/facebook/rocksdb/wiki

Developer

Fauna, Inc.

Google

IBM

Facebook, Inc.

Initial release

2014

2008

1983

host version

2013

Current release

12.1, October 2016

9.2.1, May 2024

License

Commercial or Open Source

commercial

free version is available

Open Source

BSD

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

Scala

C and C++

C++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes, details here

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

C++ API
Java API

Supported programming languages

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

consistent hashing

Sharding

only with Windows/Unix/Linux Version

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication using Paxos

yes

with separate tools (MQ, InfoSphere)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

yes

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

Identity management, authentication, and access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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