DBMS > Dgraph vs. Google Cloud Firestore vs. HBase vs. KeyDB vs. Snowflake

System Properties Comparison Dgraph vs. Google Cloud Firestore vs. HBase vs. KeyDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

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.

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

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

dgraph.io

firebase.google.com/products/firestore

hbase.apache.org

github.com/Snapchat/KeyDB
keydb.dev

www.snowflake.com

Technical documentation

dgraph.io/docs

firebase.google.com/docs/firestore

hbase.apache.org/book.html

docs.keydb.dev

docs.snowflake.net/manuals/index.html

Developer

Dgraph Labs, Inc.

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

EQ Alpha Technology Ltd.

Snowflake Computing Inc.

Initial release

2016

2017

2008

2019

2014

Current release

2.3.4, January 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache version 2

Open Source

BSD-3

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

C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
Unix
Windows

using Cygwin

Linux

hosted

Data scheme

schema-free

schema-free, schema definition possible

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

SQL

Support of SQL

yes

APIs and other access methods

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

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

Java API
RESTful HTTP API
Thrift

Proprietary protocol

RESP - REdis Serialization Protoco

CLI Client
JDBC
ODBC

Supported programming languages

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

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

Coprocessors in Java

Lua

user defined functions

Triggers

yes, with Cloud Functions

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

Single row ACID (across millions of columns)

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

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

simple password-based access control and ACL

Users with fine-grained authorization concept, user roles and pluggable authentication

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