DBMS > Apache Pinot vs. Google Cloud Datastore vs. Microsoft Azure AI Search vs. MySQL

System Properties Comparison Apache Pinot vs. Google Cloud Datastore vs. Microsoft Azure AI Search vs. MySQL

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Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Vector DBMS

Document store
Spatial DBMS

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

Score	0.35
Rank	#274	Overall
	#128	Relational DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	5.75
Rank	#58	Overall
	#7	Search engines

Score	1029.49
Rank	#2	Overall
	#2	Relational DBMS

Website

pinot.apache.org

cloud.google.com/datastore

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

www.mysql.com

Technical documentation

docs.pinot.apache.org

cloud.google.com/datastore/docs

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

dev.mysql.com/doc

Developer

Apache Software Foundation and contributors

Google

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2015

2008

2015

1995

Current release

1.0.0, September 2023

9.0.0, July 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are 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

Java

C and C++

Server operating systems

All OS with a Java JDK11 or higher

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL

Support of SQL

SQL-like query language

SQL-like query language (GQL)

yes

with proprietary extensions

APIs and other access methods

JDBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Go
Java
Python

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
Java
JavaScript
Python

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

Server-side scripts

Stored procedures

using Google App Engine

yes

proprietary syntax

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

using Azure authentication

Users with fine-grained authorization concept

no user groups or roles

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