DBMS > Apache Kylin vs. Datomic vs. EsgynDB vs. Oracle Coherence

System Properties Comparison Apache Kylin vs. Datomic vs. EsgynDB vs. Oracle Coherence

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Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

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

Oracles in-memory data grid solution

Primary database model

Relational DBMS

Key-value store

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Website

kylin.apache.org

www.datomic.com

www.esgyn.cn

www.oracle.com/java/coherence

Technical documentation

kylin.apache.org/docs

docs.datomic.com

docs.oracle.com/en/middleware/standalone/coherence

Developer

Apache Software Foundation, originally contributed from eBay Inc

Cognitect

Esgyn

Oracle

Initial release

2015

2012

2015

2007

Current release

3.1.0, July 2020

1.0.7075, December 2023

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

limited edition free

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, Clojure

C++, Java

Java

Server operating systems

Linux

All OS with a Java VM

Linux

All OS with a Java VM

Data scheme

yes

schema-free

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

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

ADO.NET
JDBC
ODBC

JCache
JPA
RESTful HTTP API

Supported programming languages

Clojure
Java

All languages supporting JDBC/ODBC/ADO.Net

.Net
C++
Java

Server-side scripts

Stored procedures

yes

Transaction Functions

Java Stored Procedures

Triggers

By using transaction functions

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

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

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

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

optionally

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

authentification to access the cache via certificates or http basic authentication

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