DBMS > Apache Kylin vs. OpenTenBase vs. Spark SQL vs. Trafodion

System Properties Comparison Apache Kylin vs. OpenTenBase vs. Spark SQL vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

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

An enterprise-level distributed HTAP open source database based on PostgreSQL

Spark SQL is a component on top of 'Spark Core' for structured data processing

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	0.08
Rank	#370	Overall
	#156	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

kylin.apache.org

github.com/OpenTenBase/OpenTenBase
www.opentenbase.org

spark.apache.org/sql

trafodion.apache.org

Technical documentation

kylin.apache.org/docs

docs.opentenbase.org
docs.opentenbase.org/en

spark.apache.org/docs/latest/sql-programming-guide.html

trafodion.apache.org/documentation.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

OpenAtom Foundation, previously Tencent

Apache Software Foundation

Apache Software Foundation, originally developed by HP

Initial release

2015

2014

Current release

3.1.0, July 2020

2.5, January 2024

3.5.0 ( 2.13), September 2023

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD-3

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

C++, Java

Server operating systems

Linux

Linux
OS X
Windows

Linux

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

ANSI SQL for queries (using Apache Calcite)

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

C
Go
Java
PHP
Python

Java
Python
R
Scala

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

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

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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