DBMS > Apache Kylin vs. EsgynDB vs. SWC-DB

System Properties Comparison Apache Kylin vs. EsgynDB vs. SWC-DB

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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

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

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	1.20
Rank	#175	Overall
	#80	Relational DBMS

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	0.01
Rank	#387	Overall
	#13	Wide column stores

Website

kylin.apache.org

www.esgyn.cn

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

kylin.apache.org/docs

Developer

Apache Software Foundation, originally contributed from eBay Inc

Esgyn

Alex Kashirin

Initial release

2015

2020

Current release

3.1.0, July 2020

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++, Java

C++

Server operating systems

Linux

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

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Proprietary protocol
Thrift

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C++

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

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

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

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