DBMS > Datomic vs. Faircom EDGE vs. H2GIS vs. NSDb vs. SWC-DB

System Properties Comparison Datomic vs. Faircom EDGE vs. H2GIS vs. NSDb vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

Spatial extension of H2

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Spatial DBMS

Time Series DBMS

Wide column store

Secondary database models

Relational DBMS

Time Series DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

www.datomic.com

www.faircom.com/products/faircom-edge

www.h2gis.org

nsdb.io

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.datomic.com

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

www.h2gis.org/docs/home

nsdb.io/Architecture

Developer

Cognitect

FairCom Corporation

CNRS

Alex Kashirin

Initial release

2012

1979

2013

2017

2020

Current release

1.0.6735, June 2023

V3, October 2020

0.5, April 2021

License

Commercial or Open Source

commercial

limited edition free

commercial

Restricted, free version available

Open Source

LGPL 3.0

Open Source

Apache Version 2.0

Open Source

GPL V3

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

Java, Clojure

ANSI C, C++

Java

Java, Scala

C++

Server operating systems

All OS with a Java VM

Android
Linux

ARM, x86
Raspbian
Windows

Linux
macOS

Linux

Data scheme

yes

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

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

yes: int, bigint, decimal, string

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 fields are automatically indexed

SQL

Support of SQL

yes

ANSI SQL queries

yes

SQL-like query language

APIs and other access methods

RESTful HTTP API

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

gRPC
HTTP REST
WebSocket

Proprietary protocol
Thrift

Supported programming languages

Clojure
Java

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

Java

Java
Scala

C++

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

.Net, JavaScript, C/C++

yes

based on H2

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

File partitioning

Customizable business rules for partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

based on H2

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

Using Apache Lucene

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

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

yes

based on H2

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