DBMS > Dgraph vs. Firebase Realtime Database vs. Google Cloud Bigtable vs. HBase vs. RocksDB

System Properties Comparison Dgraph vs. Firebase Realtime Database vs. Google Cloud Bigtable vs. HBase vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Wide-column store based on Apache Hadoop and on concepts of BigTable

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

Primary database model

Graph DBMS

Document store

Key-value store
Wide column store

Wide column store

Key-value store

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

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

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

Website

dgraph.io

firebase.google.com/products/realtime-database

cloud.google.com/bigtable

hbase.apache.org

rocksdb.org

Technical documentation

dgraph.io/docs

firebase.google.com/docs/database

cloud.google.com/bigtable/docs

hbase.apache.org/book.html

github.com/facebook/rocksdb/wiki

Developer

Dgraph Labs, Inc.

Google

acquired by Google 2014

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Facebook, Inc.

Initial release

2016

2012

2015

2008

2013

Current release

2.3.4, January 2021

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache version 2

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
Unix
Windows

using Cygwin

Linux

Data scheme

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

SQL

Support of SQL

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Java API
RESTful HTTP API
Thrift

C++ API
Java API

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
JavaScript
Objective-C

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

Coprocessors in Java

Triggers

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes

Atomic single-row operations

Single row ACID (across millions of columns)

yes

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

Planned for future releases

yes, based on authentication and database rules

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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