DBMS > Faircom EDGE vs. InfinityDB vs. Manticore Search vs. TimescaleDB

System Properties Comparison Faircom EDGE vs. InfinityDB vs. Manticore Search vs. TimescaleDB

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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

A Java embedded Key-Value Store which extends the Java Map interface

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Key-value store
Relational DBMS

Key-value store

Search engine

Time Series DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Relational DBMS

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

Score	0.02
Rank	#370	Overall
	#55	Key-value stores
	#157	Relational DBMS

Score	0.00
Rank	#383	Overall
	#59	Key-value stores

Score	0.23
Rank	#301	Overall
	#21	Search engines

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Website

www.faircom.com/products/faircom-edge

boilerbay.com

manticoresearch.com

www.timescale.com

Technical documentation

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

boilerbay.com/infinitydb/manual

manual.manticoresearch.com

docs.timescale.com

Developer

FairCom Corporation

Boiler Bay Inc.

Manticore Software

Timescale

Initial release

1979

2002

2017

Current release

V3, October 2020

4.0

6.0, February 2023

2.15.0, May 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

GPL version 2

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

Java

C++

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

All OS with a Java VM

FreeBSD
Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

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

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

Fixed schema

yes

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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

yes

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

ANSI SQL queries

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

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

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

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

Java

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

none

Sharding

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

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

none

Synchronous replication based on Galera library

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

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.

fine grained access rights according to SQL-standard

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