DBMS > Datomic vs. EsgynDB vs. HEAVY.AI vs. SpatiaLite

System Properties Comparison Datomic vs. EsgynDB vs. HEAVY.AI vs. SpatiaLite

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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

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Spatial extension of SQLite

Primary database model

Relational DBMS

Spatial DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

www.datomic.com

www.esgyn.cn

github.com/heavyai/heavydb
www.heavy.ai

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

docs.datomic.com

docs.heavy.ai

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Cognitect

Esgyn

HEAVY.AI, Inc.

Alessandro Furieri

Initial release

2012

2015

2016

2008

Current release

1.0.6735, June 2023

5.10, January 2022

5.0.0, August 2020

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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

C++, Java

C++ and CUDA

C++

Server operating systems

All OS with a Java VM

Linux

server-less

Data scheme

yes

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

yes

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC
Thrift
Vega

Supported programming languages

Clojure
Java

All languages supporting JDBC/ODBC/ADO.Net

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

Java Stored Procedures

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

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication between multi datacenters

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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