DBMS > EsgynDB vs. IBM Db2 vs. Infobright vs. Spark SQL

System Properties Comparison EsgynDB vs. IBM Db2 vs. Infobright vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

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

Common in IBM host environments, 2 different versions for host and Windows/Linux

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

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

Primary database model

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.esgyn.cn

www.ibm.com/products/db2

ignitetech.com/softwarelibrary/infobrightdb

spark.apache.org/sql

Technical documentation

www.ibm.com/docs/en/db2

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

Developer

Esgyn

IBM

Ignite Technologies Inc.; formerly InfoBright Inc.

Apache Software Foundation

Initial release

2015

1983

host version

2005

2014

Current release

12.1, October 2016

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free version is available

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

Apache 2.0

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++, Java

C and C++

Scala

Server operating systems

Linux

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
Windows

Linux
OS X
Windows

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

Knowledge Grid Technology used instead

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

with separate tools (MQ, InfoSphere)

Source-replica replication

none

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

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