DBMS > Google Cloud Firestore vs. Hazelcast vs. HugeGraph vs. Microsoft Azure Cosmos DB

System Properties Comparison Google Cloud Firestore vs. Hazelcast vs. HugeGraph vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

A widely adopted in-memory data grid

A fast-speed and highly-scalable Graph DBMS

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store

Key-value store

Graph DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

firebase.google.com/products/firestore

hazelcast.com

github.com/hugegraph
hugegraph.apache.org

azure.microsoft.com/services/cosmos-db

Technical documentation

firebase.google.com/docs/firestore

hazelcast.org/imdg/docs

hugegraph.apache.org/docs

learn.microsoft.com/azure/cosmos-db

Developer

Google

Hazelcast

Baidu

Microsoft

Initial release

2017

2008

2018

2014

Current release

5.3.6, November 2023

0.9

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

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

All OS with a Java VM

Linux
macOS
Unix

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

the object must implement a serialization strategy

Secondary indexes

yes

also supports composite index and range index

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

APIs and other access methods

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

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

Supported programming languages

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Groovy
Java
Python

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

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

Event Listeners, Executor Services

asynchronous Gremlin script jobs

JavaScript

Triggers

yes, with Cloud Functions

yes

Events

JavaScript

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

yes

Replicated Map

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

Using Cloud Dataflow

yes

via hugegraph-spark

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

yes

one or two-phase-commit; repeatable reads; read commited

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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. Security Rules for 3rd party authentication using Firebase Auth.

Role-based access control

Users, roles and permissions

Access rights can be defined down to the item level

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