DBMS > Faircom EDGE vs. Manticore Search vs. Microsoft Azure Cosmos DB vs. Transwarp StellarDB

System Properties Comparison Faircom EDGE vs. Manticore Search vs. Microsoft Azure Cosmos DB vs. Transwarp StellarDB

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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

Multi-storage database for search, including full-text search.

Globally distributed, horizontally scalable, multi-model database service

A distributed graph DBMS built for enterprise-level graph applications

Primary database model

Key-value store
Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Spatial DBMS

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

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

Score	0.29
Rank	#302	Overall
	#21	Search engines

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

Score	0.07
Rank	#371	Overall
	#39	Graph DBMS

Website

www.faircom.com/products/faircom-edge

manticoresearch.com

azure.microsoft.com/services/cosmos-db

www.transwarp.cn/en/product/stellardb

Technical documentation

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

manual.manticoresearch.com

learn.microsoft.com/azure/cosmos-db

Developer

FairCom Corporation

Manticore Software

Microsoft

Transwarp

Initial release

1979

2017

2014

Current release

V3, October 2020

6.0, February 2023

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

GPL version 2

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++

C++

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

FreeBSD
Linux
macOS
Windows

hosted

Data scheme

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

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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

Can index from XML

Secondary indexes

yes

full-text index on all search fields

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

ANSI SQL queries

SQL-like query language

APIs and other access methods

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

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

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

OpenCypher

Supported programming languages

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

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Implicit feature of the cloud service

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

Synchronous replication based on Galera library

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

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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 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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