DBMS > Dolt vs. Faircom EDGE vs. H2 vs. Microsoft Azure Cosmos DB

System Properties Comparison Dolt vs. Faircom EDGE vs. H2 vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

A MySQL compatible DBMS with Git-like versioning of data and schema

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

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

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store

Spatial DBMS

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

Score	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

github.com/dolthub/dolt
www.dolthub.com

www.faircom.com/products/faircom-edge

www.h2database.com

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.dolthub.com

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

www.h2database.com/html/main.html

learn.microsoft.com/azure/cosmos-db

Developer

DoltHub Inc

FairCom Corporation

Thomas Mueller

Microsoft

Initial release

2018

1979

2005

2014

Current release

V3, October 2020

2.2.220, July 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

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

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

Java

Server operating systems

Linux
macOS
Windows

Android
Linux

ARM, x86
Raspbian
Windows

All OS with a Java VM

hosted

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

yes

ANSI SQL queries

yes

SQL-like query language

APIs and other access methods

CLI Client
HTTP REST

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

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

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

Java

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

yes

currently in alpha release

yes

.Net, JavaScript, C/C++

Java Stored Procedures and User-Defined Functions

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning

Customizable business rules for partitioning

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

yes

Synchronous and asynchronous realtime replication based on transaction logs

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

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes, multi-version concurrency control (MVCC)

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

Only one user is configurable, and must be specified in the config file at startup

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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