DBMS > Firebase Realtime Database vs. Google Cloud Datastore vs. Hazelcast vs. PouchDB vs. RocksDB

System Properties Comparison Firebase Realtime Database vs. Google Cloud Datastore vs. Hazelcast vs. PouchDB vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

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

A widely adopted in-memory data grid

JavaScript DBMS with an API inspired by CouchDB

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

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

firebase.google.com/products/realtime-database

cloud.google.com/datastore

hazelcast.com

pouchdb.com

rocksdb.org

Technical documentation

firebase.google.com/docs/database

cloud.google.com/datastore/docs

hazelcast.org/imdg/docs

pouchdb.com/guides

github.com/facebook/rocksdb/wiki

Developer

Google

acquired by Google 2014

Google

Hazelcast

Apache Software Foundation

Facebook, Inc.

Initial release

2012

2008

2012

2013

Current release

5.3.6, November 2023

7.1.1, June 2019

9.2.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

BSD

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

JavaScript

C++

Server operating systems

hosted

All OS with a Java VM

server-less, requires a JavaScript environment (browser, Node.js)

Linux

Data scheme

schema-free

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

the object must implement a serialization strategy

Secondary indexes

yes

via views

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

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

JCache
JPA
Memcached protocol
RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

C++ API
Java API

Supported programming languages

Java
JavaScript
Objective-C

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

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

JavaScript

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

limited functionality with using 'rules'

using Google App Engine

yes

Event Listeners, Executor Services

View functions in JavaScript

Triggers

Callbacks are triggered when data changes

Callbacks using the Google Apps Engine

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

with a proxy-based framework, named couchdb-lounge

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

Replicated Map

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

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 or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

yes

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

yes, based on authentication and database rules

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

Role-based access control

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