DBMS > FatDB vs. Microsoft Azure Cosmos DB vs. OpenEdge vs. OrientDB vs. Solr

System Properties Comparison FatDB vs. Microsoft Azure Cosmos DB vs. OpenEdge vs. OrientDB vs. Solr

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Globally distributed, horizontally scalable, multi-model database service

Application development environment with integrated database management system

Multi-model DBMS (Document, Graph, Key/Value)

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store
Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Document store
Graph DBMS
Key-value store

Search engine

Secondary database models

Spatial DBMS

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

azure.microsoft.com/services/cosmos-db

www.progress.com/openedge

orientdb.org

solr.apache.org

Technical documentation

learn.microsoft.com/azure/cosmos-db

documentation.progress.com/output/ua/OpenEdge_latest

www.orientdb.com/docs/last/index.html

solr.apache.org/resources.html

Developer

FatCloud

Microsoft

Progress Software Corporation

OrientDB LTD; CallidusCloud; SAP

Apache Software Foundation

Initial release

2012

2014

1984

2010

2006

Current release

OpenEdge 12.2, March 2020

3.2.29, March 2024

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

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

Java

Server operating systems

Windows

hosted

AIX
HP-UX
Linux
Solaris
Windows

All OS with a Java JDK (>= JDK 6)

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

JSON types

yes

supports customizable data types and automatic typing

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

yes

All search fields are automatically indexed

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like query language

yes

close to SQL 92

SQL-like query language, no joins

Solr Parallel SQL Interface

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

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

JDBC
ODBC

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

Supported programming languages

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

Progress proprietary ABL (Advanced Business Language)

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

via applications

JavaScript

yes

Java, Javascript

Java plugins

Triggers

yes

via applications

JavaScript

yes

Hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

since Version 11.4

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

Source-replica replication

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

could be achieved with distributed queries

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

optimistic locking

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

Can implement custom security layer via applications

Access rights can be defined down to the item level

Users and groups

Access rights for users and roles; record level security configurable

yes

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