DBMS > Google Cloud Datastore vs. HugeGraph vs. Microsoft Azure AI Search

System Properties Comparison Google Cloud Datastore vs. HugeGraph vs. Microsoft Azure AI Search

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Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

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

Primary database model

Document store

Graph DBMS

Search engine

Secondary database models

Vector DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Website

cloud.google.com/datastore

github.com/hugegraph
hugegraph.apache.org

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

Technical documentation

cloud.google.com/datastore/docs

hugegraph.apache.org/docs

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

Developer

Google

Baidu

Microsoft

Initial release

2008

2018

2015

Current release

0.9

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

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

Server operating systems

hosted

Linux
macOS
Unix

hosted

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language (GQL)

APIs and other access methods

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

Java API
RESTful HTTP API
TinkerPop Gremlin

RESTful HTTP API

Supported programming languages

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

Groovy
Java
Python

C#
Java
JavaScript
Python

Server-side scripts

Stored procedures

using Google App Engine

asynchronous Gremlin script jobs

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

via hugegraph-spark

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.

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

edges in graph

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

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)

Users, roles and permissions

yes

using Azure authentication

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