DBMS > IBM Db2 Event Store vs. openGauss vs. Postgres-XL

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

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

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

Primary database model

Event Store
Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	1.08
Rank	#183	Overall
	#84	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

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

gitee.com/opengauss
opengauss.org

www.postgres-xl.org

Technical documentation

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

docs.opengauss.org/en
gitee.com/opengauss/docs

www.postgres-xl.org/documentation

Developer

IBM

Huawei and openGauss community

Initial release

2017

2019

2014

since 2012, originally named StormDB

Current release

2.0

3.0, March 2022

10 R1, October 2018

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Mozilla public license

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 and C++

C, C++, Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux

Linux
macOS

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

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

ANSI SQL 2011

yes

distributed, parallel query execution

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

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

Supported programming languages

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

C
C++
Java

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

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash)

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard

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