DBMS > Datomic vs. Graph Engine vs. SiteWhere

System Properties Comparison Datomic vs. Graph Engine vs. SiteWhere

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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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Graph DBMS
Key-value store

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	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

www.datomic.com

www.graphengine.io

github.com/sitewhere/sitewhere

Technical documentation

docs.datomic.com

www.graphengine.io/docs/manual

sitewhere1.sitewhere.io/index.html

Developer

Cognitect

Microsoft

SiteWhere

Initial release

2012

2010

Current release

1.0.6735, June 2023

License

Commercial or Open Source

commercial

limited edition free

Open Source

MIT License

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

.NET and C

Java

Server operating systems

All OS with a Java VM

.NET

Linux
OS X
Windows

Data scheme

yes

predefined scheme

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

APIs and other access methods

RESTful HTTP API

HTTP REST

Supported programming languages

Clojure
Java

C#
C++
F#
Visual Basic

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Users with fine-grained authorization concept

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