DBMS > Apache Drill vs. Cubrid vs. EsgynDB vs. Kinetica

System Properties Comparison Apache Drill vs. Cubrid vs. EsgynDB vs. Kinetica

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

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

Fully vectorized database across both GPUs and CPUs

Primary database model

Document store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Website

drill.apache.org

cubrid.com (korean)
cubrid.org (english)

www.esgyn.cn

www.kinetica.com

Technical documentation

drill.apache.org/docs

cubrid.org/manuals

docs.kinetica.com

Developer

Apache Software Foundation

CUBRID Corporation, CUBRID Foundation

Esgyn

Kinetica

Initial release

2012

2008

2015

2012

Current release

1.20.3, January 2023

11.0, January 2021

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

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

C, C++, Java

C++, Java

C, C++

Server operating systems

Linux
OS X
Windows

Linux
Windows

Linux

Data scheme

schema-free

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

SQL SELECT statement is SQL:2003 compliant

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

ADO.NET
JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Supported programming languages

C++

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

All languages supporting JDBC/ODBC/ADO.Net

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

Java Stored Procedures

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

GPU vRAM or System RAM

User concepts

Access control

Depending on the underlying data source

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

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More resources

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