DBMS > Faircom EDGE vs. Microsoft Azure Cosmos DB vs. Newts vs. Solr vs. TinkerGraph

System Properties Comparison Faircom EDGE vs. Microsoft Azure Cosmos DB vs. Newts vs. Solr vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

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

Globally distributed, horizontally scalable, multi-model database service

Time Series DBMS based on Cassandra

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

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Key-value store
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Search engine

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

www.faircom.com/products/faircom-edge

azure.microsoft.com/services/cosmos-db

opennms.github.io/newts

solr.apache.org

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

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

learn.microsoft.com/azure/cosmos-db

github.com/OpenNMS/newts/wiki

solr.apache.org/resources.html

Developer

FairCom Corporation

Microsoft

OpenNMS Group

Apache Software Foundation

Initial release

1979

2014

2006

2009

Current release

V3, October 2020

9.5.0, February 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2

Open Source

Apache 2.0

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++

Java

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux
OS X
Windows

All OS with a Java VM

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

Data scheme

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

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

JSON types

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

All properties auto-indexed by default

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

ANSI SQL queries

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

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

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

HTTP REST
Java API

Java API
RESTful HTTP/JSON API

TinkerPop 3

Supported programming languages

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

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

Java

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

Groovy
Java

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

JavaScript

Java plugins

Triggers

yes

JavaScript

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

Implicit feature of the cloud service

Sharding

based on Cassandra

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

Implicit feature of the cloud service

selectable replication factor

based on Cassandra

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

none

Foreign keys

Referential integrity

yes

when using SQL

yes

Relationships in graphs

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

optional

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

Access rights can be defined down to the item level

yes

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