DBMS > EsgynDB vs. IBM Db2 vs. LeanXcale vs. Postgres-XL vs. PostgreSQL

System Properties Comparison EsgynDB vs. IBM Db2 vs. LeanXcale vs. Postgres-XL vs. PostgreSQL

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

Relational DBMS

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

Key-value store
Relational DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Secondary database models

Document store
RDF store

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

with Db2 Spatial Extender

Document store
Spatial DBMS

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Website

www.esgyn.cn

www.ibm.com/products/db2

www.leanxcale.com

www.postgres-xl.org

www.postgresql.org

Technical documentation

www.ibm.com/docs/en/db2

www.postgres-xl.org/documentation

www.postgresql.org/docs

Developer

Esgyn

IBM

LeanXcale

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2015

1983

host version

2015

2014

since 2012, originally named StormDB

1989

1989: Postgres, 1996: PostgreSQL

Current release

12.1, October 2016

10 R1, October 2018

16.3, May 2024

License

Commercial or Open Source

commercial

free version is available

commercial

Open Source

Mozilla public license

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java

C and C++

Server operating systems

Linux

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
macOS

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
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.

yes

XML type, but no XML query functionality

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

yes

through Apache Derby

yes

distributed, parallel query execution

yes

standard with numerous extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

C
Java
Scala

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

Java Stored Procedures

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

horizontal partitioning

partitioning by range, list and (since PostgreSQL 11) by hash

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

other methods possible by using 3rd party extensions

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

MVCC

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
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