DBMS > HBase vs. IBM Db2 Event Store vs. Postgres-XL vs. PostgreSQL

System Properties Comparison HBase vs. IBM Db2 Event Store vs. Postgres-XL vs. PostgreSQL

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

Distributed Event Store optimized for Internet of Things use cases

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

Wide column store

Event Store
Time Series 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
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	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Website

hbase.apache.org

www.ibm.com/products/db2-event-store

www.postgres-xl.org

www.postgresql.org

Technical documentation

hbase.apache.org/book.html

www.ibm.com/docs/en/db2-event-store

www.postgres-xl.org/documentation

www.postgresql.org/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

IBM

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2008

2017

2014

since 2012, originally named StormDB

1989

1989: Postgres, 1996: PostgreSQL

Current release

2.3.4, January 2021

2.0

10 R1, October 2018

16.3, May 2024

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free developer edition available

Open Source

Mozilla public license

Open Source

BSD

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux

Linux, macOS, Windows for the developer addition

Linux
macOS

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

Data scheme

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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 the embedded Spark runtime

yes

distributed, parallel query execution

yes

standard with numerous extensions

APIs and other access methods

Java API
RESTful HTTP API
Thrift

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

.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

yes

Coprocessors in Java

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

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
Source-replica replication

Active-active shard replication

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 or Eventual Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Single row ACID (across millions of columns)

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

More information provided by the system vendor

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