DBMS > Badger vs. IBM Db2 Event Store vs. jBASE vs. Microsoft SQL Server vs. Riak KV

System Properties Comparison Badger vs. IBM Db2 Event Store vs. jBASE vs. Microsoft SQL Server vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

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

Distributed Event Store optimized for Internet of Things use cases

A robust multi-value DBMS comprising development tools and middleware

Microsofts flagship relational DBMS

Distributed, fault tolerant key-value store

Primary database model

Key-value store

Event Store
Time Series DBMS

Multivalue DBMS

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.14
Rank	#328	Overall
	#48	Key-value stores

Score	0.18
Rank	#315	Overall
	#2	Event Stores
	#26	Time Series DBMS

Score	1.36
Rank	#157	Overall
	#3	Multivalue DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Website

github.com/dgraph-io/badger

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

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

www.microsoft.com/en-us/sql-server

Technical documentation

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

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

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

learn.microsoft.com/en-US/sql/sql-server

www.tiot.jp/riak-docs/riak/kv/latest

Developer

DGraph Labs

IBM

Rocket Software (formerly Zumasys)

Microsoft

OpenSource, formerly Basho Technologies

Initial release

2017

1991

1989

2009

Current release

2.0

5.7

SQL Server 2022, November 2022

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free developer edition available

commercial

restricted free version is available

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

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Implementation language

C and C++

C++

Erlang

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

Linux, macOS, Windows for the developer addition

AIX
Linux
Windows

Linux
Windows

Linux
OS X

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

optional

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

restricted

SQL

Support of SQL

yes

through the embedded Spark runtime

Embedded SQL for jBASE in BASIC

yes

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP API
Native Erlang Interface

Supported programming languages

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

.Net
Basic
Jabbascript
Java

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

none

Active-active shard replication

yes

yes, but depending on the SQL-Server Edition

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

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

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 can be defined down to the item level

fine grained access rights according to SQL-standard

yes, using Riak Security

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