DBMS > Faircom EDGE vs. IRONdb vs. PouchDB vs. Splice Machine vs. Tigris

System Properties Comparison Faircom EDGE vs. IRONdb vs. PouchDB vs. Splice Machine vs. Tigris

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

JavaScript DBMS with an API inspired by CouchDB

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A horizontally scalable, ACID transactional, document database available both as a fully managed cloud service and for deployment on self-managed infrastructure

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Document store

Relational DBMS

Document store
Key-value store
Search engine
Time Series 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	2.18
Rank	#114	Overall
	#21	Document stores

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.05
Rank	#359	Overall
	#50	Document stores
	#52	Key-value stores
	#23	Search engines
	#37	Time Series DBMS

Website

www.faircom.com/products/faircom-edge

www.circonus.com/solutions/time-series-database/

pouchdb.com

splicemachine.com

www.tigrisdata.com

Technical documentation

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

docs.circonus.com/irondb/category/getting-started

pouchdb.com/guides

splicemachine.com/how-it-works

www.tigrisdata.com/docs

Developer

FairCom Corporation

Circonus LLC.

Apache Software Foundation

Splice Machine

Tigris Data, Inc.

Initial release

1979

2017

2012

2014

2022

Current release

V3, October 2020

V0.10.20, January 2018

7.1.1, June 2019

3.1, March 2021

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

ANSI C, C++

C and C++

JavaScript

Java

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Data scheme

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

schema-free

yes

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

text, numeric, histograms

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

via views

yes

SQL

Support of SQL

yes

ANSI SQL queries

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

APIs and other access methods

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

HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
Native Spark Datasource
ODBC

CLI Client
gRPC
RESTful HTTP API

Supported programming languages

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

JavaScript

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Go
Java
JavaScript (Node.js)

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes, in Lua

View functions in JavaScript

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Automatic, metric affinity per node

Sharding

with a proxy-based framework, named couchdb-lounge

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

configurable replication factor, datacenter aware

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate consistency per node, eventual consistency across nodes

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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

yes, using FoundationDB

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 for users, groups and roles according to SQL-standard

Access rights for users and roles

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