DBMS > Badger vs. Bangdb vs. IBM Cloudant vs. IBM Db2 Event Store vs. Microsoft Azure Cosmos DB

System Properties Comparison Badger vs. Bangdb vs. IBM Cloudant vs. IBM Db2 Event Store vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

Database as a Service offering based on Apache CouchDB

Distributed Event Store optimized for Internet of Things use cases

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Key-value store

Document store
Graph DBMS
Time Series DBMS

Document store

Event Store
Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

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

Score	2.68
Rank	#106	Overall
	#20	Document stores

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

github.com/dgraph-io/badger

bangdb.com

www.ibm.com/products/cloudant

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

azure.microsoft.com/services/cosmos-db

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.bangdb.com

cloud.ibm.com/docs/Cloudant

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

learn.microsoft.com/azure/cosmos-db

Developer

DGraph Labs

Sachin Sinha, BangDB

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

IBM

Microsoft

Initial release

2017

2012

2010

2017

2014

Current release

BangDB 2.0, October 2021

2.0

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD 3

commercial

free developer edition available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++

Erlang

C and C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

hosted

Linux

Linux, macOS, Windows for the developer addition

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

SQL like support with command line tool

yes

through the embedded Spark runtime

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

C
C#
C++
Java
Python

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

yes

JavaScript

Triggers

yes, Notifications (with Streaming only)

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Multi-source replication
Source-replica replication

Active-active shard replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

none

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

atomic operations within a document possible

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Optimistic locking

No - written data is immutable

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

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, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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