DBMS > Faircom DB vs. Fauna vs. H2 vs. HBase vs. RocksDB

System Properties Comparison Faircom DB vs. Fauna vs. H2 vs. HBase vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

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.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Wide-column store based on Apache Hadoop and on concepts of BigTable

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

Primary database model

Key-value store
Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Wide column store

Key-value store

Secondary database models

Spatial DBMS

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

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

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

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

Website

www.faircom.com/products/faircom-db

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

docs.fauna.com

www.h2database.com/html/main.html

hbase.apache.org/book.html

github.com/facebook/rocksdb/wiki

Developer

FairCom Corporation

Fauna, Inc.

Thomas Mueller

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Facebook, Inc.

Initial release

1979

2014

2005

2008

2013

Current release

V12, November 2020

2.2.220, July 2023

2.3.4, January 2021

8.11.4, April 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache version 2

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

ANSI C, C++

Scala

Java

C++

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

All OS with a Java VM

Linux
Unix
Windows

using Cygwin

Linux

Data scheme

schema free, schema optional, schema required, partial schema,

schema-free

yes

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

options to bring your own types, AVRO

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, ANSI SQL with proprietary extensions

yes

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

RESTful HTTP API

JDBC
ODBC

Java API
RESTful HTTP API
Thrift

C++ API
Java API

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

Java

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

Java Stored Procedures and User-Defined Functions

yes

Coprocessors in Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

horizontal partitioning

consistent hashing

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Multi-source replication

With clustering: 2 database servers on different computers operate on identical copies of a database

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

Single row ACID (across millions of columns)

yes

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

Identity management, authentication, and access control

fine grained access rights according to SQL-standard

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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