DBMS > Bangdb vs. IBM Db2 Event Store vs. OushuDB

System Properties Comparison Bangdb vs. IBM Db2 Event Store vs. OushuDB

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Editorial information provided by DB-Engines

Name

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

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Primary database model

Document store
Graph DBMS
Time Series DBMS

Event Store
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

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

Score	0.10
Rank	#355	Overall
	#154	Relational DBMS

Website

bangdb.com

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

www.oushu.com/product/oushuDB

Technical documentation

docs.bangdb.com

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

www.oushu.com/documentation

Developer

Sachin Sinha, BangDB

IBM

Oushu

Initial release

2012

2017

Current release

BangDB 2.0, October 2021

2.0

4.0.1, August 2020

License

Commercial or Open Source

Open Source

BSD 3

commercial

free developer edition available

commercial

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++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux

Data scheme

schema-free

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.

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

Full-featured ANSI SQL support

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

C
C++

Server-side scripts

Stored procedures

yes

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

Replication methods

Methods for redundantly storing data on multiple nodes

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

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Foreign keys

Referential integrity

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

Kerberos, SSL and role based access

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