DBMS > Apache Kylin vs. DataFS vs. Ehcache vs. Faircom DB vs. SAP HANA

System Properties Comparison Apache Kylin vs. DataFS vs. Ehcache vs. Faircom DB vs. SAP HANA

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

A widely adopted Java cache with tiered storage options

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

Object oriented DBMS

Key-value store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Graph DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	4.64
Rank	#68	Overall
	#8	Key-value stores

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

kylin.apache.org

newdatabase.com

www.ehcache.org

www.faircom.com/products/faircom-db

www.sap.com/products/hana.html

Technical documentation

kylin.apache.org/docs

dev.mobiland.com/Overview.xsp

www.ehcache.org/documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

help.sap.com/hana

Developer

Apache Software Foundation, originally contributed from eBay Inc

Mobiland AG

Terracotta Inc, owned by Software AG

FairCom Corporation

SAP

Initial release

2015

2018

2009

1979

2010

Current release

3.1.0, July 2020

1.1.263, October 2022

3.10.0, March 2022

V12, November 2020

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Restricted, free version available

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

ANSI C, C++

Server operating systems

Linux

Windows

All OS with a Java VM

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Appliance or cloud-service

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

schema free, schema optional, schema required, partial schema,

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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, ANSI SQL with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

.NET Client API
Proprietary client DLL
WinRT client

JCache

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

Java

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

SQLScript, R

Triggers

no, except callback-events from server when changes happened

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

by using Terracotta Server

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

Yes, tunable from durable to delayed durability to in-memory

yes

In-memory capabilities

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

yes

User concepts

Access control

Windows-Profile

Fine grained access rights according to SQL-standard with additional protections for files

yes

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