DBMS > Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. PlanetScale vs. RisingWave vs. Sphinx

System Properties Comparison Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. PlanetScale vs. RisingWave vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

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

Globally distributed, horizontally scalable, multi-model database service

Scalable, distributed, serverless MySQL database platform built on top of Vitess

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Search engine

Secondary database models

Vector DBMS

Spatial DBMS

Document store
Spatial DBMS

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

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.49
Rank	#155	Overall
	#72	Relational DBMS

Score	0.64
Rank	#238	Overall
	#110	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

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

azure.microsoft.com/services/cosmos-db

planetscale.com

www.risingwave.com/database

sphinxsearch.com

Technical documentation

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

learn.microsoft.com/azure/cosmos-db

planetscale.com/docs

docs.risingwave.com/docs/current/intro

sphinxsearch.com/docs

Developer

Microsoft

PlanetScale

RisingWave Labs

Sphinx Technologies Inc.

Initial release

2015

2014

2020

2022

2001

Current release

1.2, September 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

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

Rust

C++

Server operating systems

hosted

Docker
Linux
macOS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

yes

Standard SQL-types and JSON

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

All properties auto-indexed by default

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

yes

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP API

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

ADO.NET
JDBC
MySQL protocol
ODBC

JDBC
PostgreSQL wire protocol

Proprietary protocol

Supported programming languages

C#
Java
JavaScript
Python

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Go
Java
JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

JavaScript

yes

proprietary syntax

UDFs in Python or Java

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

User concepts

Access control

yes

using Azure authentication

Access rights can be defined down to the item level

Users with fine-grained authorization concept

no user groups or roles

Users and Roles

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