DBMS > Badger vs. Google Cloud Firestore vs. Graph Engine vs. HugeGraph vs. Lovefield

System Properties Comparison Badger vs. Google Cloud Firestore vs. Graph Engine vs. HugeGraph vs. Lovefield

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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 distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A fast-speed and highly-scalable Graph DBMS

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Key-value store

Document store

Graph DBMS
Key-value store

Graph DBMS

Relational DBMS

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Website

github.com/dgraph-io/badger

firebase.google.com/products/firestore

www.graphengine.io

github.com/hugegraph
hugegraph.apache.org

google.github.io/lovefield

Technical documentation

godoc.org/github.com/dgraph-io/badger

firebase.google.com/docs/firestore

www.graphengine.io/docs/manual

hugegraph.apache.org/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

DGraph Labs

Google

Microsoft

Baidu

Google

Initial release

2017

2010

2018

2014

Current release

0.9

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

MIT License

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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

.NET and C

Java

JavaScript

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

.NET

Linux
macOS
Unix

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

tested with Chrome, Firefox, IE, Safari

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

via JavaScript builder pattern

APIs and other access methods

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

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

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

C#
C++
F#
Visual Basic

Groovy
Java
Python

JavaScript

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

asynchronous Gremlin script jobs

Triggers

yes, with Cloud Functions

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

yes

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

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.

Users, roles and permissions

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More resources

DB-Engines blog posts

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12 December 2019, Paul Andlinger

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