DBMS > Apache IoTDB vs. EsgynDB vs. Faircom EDGE vs. Fauna vs. Percona Server for MongoDB

System Properties Comparison Apache IoTDB vs. EsgynDB vs. Faircom EDGE vs. Fauna vs. Percona Server for MongoDB

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Primary database model

Time Series DBMS

Relational DBMS

Key-value store
Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Document store

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

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

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Website

iotdb.apache.org

www.esgyn.cn

www.faircom.com/products/faircom-edge

fauna.com

www.percona.com/mongodb/software/percona-server-for-mongodb

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

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

docs.fauna.com

docs.percona.com/percona-distribution-for-mongodb

Developer

Apache Software Foundation

Esgyn

FairCom Corporation

Fauna, Inc.

Percona

Initial release

2018

2015

1979

2014

2015

Current release

1.1.0, April 2023

V3, October 2020

3.4.10-2.10, November 2017

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Restricted, free version available

commercial

Open Source

GPL Version 2

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

Java

C++, Java

ANSI C, C++

Scala

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux

Data scheme

yes

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

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

SQL

Support of SQL

SQL-like query language

yes

ANSI SQL queries

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC

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

RESTful HTTP API

proprietary protocol using JSON

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages supporting JDBC/ODBC/ADO.Net

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

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

Server-side scripts

Stored procedures

yes

Java Stored Procedures

yes

.Net, JavaScript, C/C++

user defined functions

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

File partitioning

Customizable business rules for partitioning

horizontal partitioning

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication between multi datacenters

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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

via In-Memory Engine

User concepts

Access control

yes

fine grained access rights according to SQL-standard

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

Identity management, authentication, and access control

Access rights for users and roles

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