DBMS > Microsoft Azure AI Search vs. Quasardb vs. RocksDB

System Properties Comparison Microsoft Azure AI Search vs. Quasardb vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

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

Distributed, high-performance timeseries database

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Search engine

Time Series DBMS

Key-value store

Secondary database models

Vector DBMS

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

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

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

quasar.ai

rocksdb.org

Technical documentation

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

doc.quasar.ai/master

github.com/facebook/rocksdb/wiki

Developer

Microsoft

quasardb

Facebook, Inc.

Initial release

2015

2009

2013

Current release

3.14.1, January 2024

8.11.4, April 2024

License

Commercial or Open Source

commercial

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

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

hosted

BSD
Linux
OS X
Windows

Linux

Data scheme

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

RESTful HTTP API

HTTP API

C++ API
Java API

Supported programming languages

C#
Java
JavaScript
Python

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

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Source-replica replication with selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

yes

using Azure authentication

Cryptographically strong user authentication and audit trail

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