DBMS > Badger vs. DataFS vs. Microsoft Azure AI Search vs. Quasardb

System Properties Comparison Badger vs. DataFS vs. Microsoft Azure AI Search vs. Quasardb

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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.

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Search-as-a-service for web and mobile app development

Distributed, high-performance timeseries database

Primary database model

Secondary database models

Graph DBMS

Vector DBMS

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

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

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	5.75
Rank	#58	Overall
	#7	Search engines

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Website

github.com/dgraph-io/badger

newdatabase.com

azure.microsoft.com/en-us/services/search

quasar.ai

Technical documentation

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

dev.mobiland.com/Overview.xsp

learn.microsoft.com/en-us/azure/search

doc.quasar.ai/master

Developer

DGraph Labs

Mobiland AG

Microsoft

quasardb

Initial release

2017

2018

2015

2009

Current release

1.1.263, October 2022

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Windows

hosted

BSD
Linux
OS X
Windows

Data scheme

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

SQL-like query language

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP API

HTTP API

Supported programming languages

.Net
C
C#
C++
VB.Net

C#
Java
JavaScript
Python

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

none

Proprietary Sharding system

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

Windows-Profile

yes

using Azure authentication

Cryptographically strong user authentication and audit trail

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