DBMS > Badger vs. HBase vs. IBM Db2 Event Store

System Properties Comparison Badger vs. HBase vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines

Name

Description

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

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

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Key-value store

Wide column store

Event Store
Time Series DBMS

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

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Score	0.23
Rank	#316	Overall
	#2	Event Stores
	#28	Time Series DBMS

Website

github.com/dgraph-io/badger

hbase.apache.org

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

Technical documentation

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

hbase.apache.org/book.html

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

Developer

DGraph Labs

Apache Software Foundation

Apache top-level project, originally developed by Powerset

IBM

Initial release

2017

2008

2017

Current release

2.3.4, January 2021

2.0

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

free developer edition available

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

Java

C and C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

Linux

Linux, macOS, Windows for the developer addition

Data scheme

schema-free

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.

Secondary indexes

SQL

Support of SQL

yes

through the embedded Spark runtime

APIs and other access methods

Java API
RESTful HTTP API
Thrift

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

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

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

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