DBMS > Datomic vs. Elasticsearch vs. Newts vs. Stardog

System Properties Comparison Datomic vs. Elasticsearch vs. Newts vs. Stardog

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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, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Time Series DBMS based on Cassandra

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	134.78
Rank	#7	Overall
	#1	Search engines

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	2.05
Rank	#129	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.datomic.com

www.elastic.co/elasticsearch

opennms.github.io/newts

www.stardog.com

Technical documentation

docs.datomic.com

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

github.com/OpenNMS/newts/wiki

docs.stardog.com

Developer

Cognitect

Elastic

OpenNMS Group

Stardog-Union

Initial release

2012

2010

2014

2010

Current release

1.0.6735, June 2023

8.6, January 2023

7.3.0, May 2020

License

Commercial or Open Source

commercial

limited edition free

Open Source

Elastic License

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

Java

Server operating systems

All OS with a Java VM

Linux
OS X
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

schema-free and OWL/RDFS-schema support

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.

Import/export of XML data possible

Secondary indexes

yes

All search fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP/JSON API

HTTP REST
Java API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Clojure
Java

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

Java

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

By using transaction functions

yes

by using the 'percolation' feature

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

selectable replication factor

based on Cassandra

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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)

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

Memcached and Redis integration

yes

User concepts

Access control

Access rights for users and roles

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