DBMS > Bangdb vs. IBM Db2 Event Store vs. OpenQM vs. Trafodion

System Properties Comparison Bangdb vs. IBM Db2 Event Store vs. OpenQM vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Converged and high performance database for device data, events, time series, document and graph

Distributed Event Store optimized for Internet of Things use cases

QpenQM is a high-performance, self-tuning, multi-value DBMS

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store
Graph DBMS
Time Series DBMS

Event Store
Time Series DBMS

Multivalue DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

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

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Website

bangdb.com

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

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

trafodion.apache.org

Technical documentation

docs.bangdb.com

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

trafodion.apache.org/documentation.html

Developer

Sachin Sinha, BangDB

IBM

Rocket Software, originally Martin Phillips

Apache Software Foundation, originally developed by HP

Initial release

2012

2017

1993

2014

Current release

BangDB 2.0, October 2021

2.0

3.4-12

2.3.0, February 2019

License

Commercial or Open Source

Open Source

BSD 3

commercial

free developer edition available

Open Source

GPLv2, extended commercial license available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C and C++

C++, Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux

Data scheme

schema-free

yes

with some exceptions

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

through the embedded Spark runtime

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

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

.Net
Basic
C
Java
Objective C
PHP
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

Java Stored Procedures

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Active-active shard replication

yes

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

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, optimistic concurrency control

No - written data is immutable

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

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, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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