DBMS > Badger vs. Google Cloud Firestore vs. HugeGraph vs. IRONdb vs. RethinkDB

System Properties Comparison Badger vs. Google Cloud Firestore vs. HugeGraph vs. IRONdb vs. RethinkDB

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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 fast-speed and highly-scalable Graph DBMS

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Secondary database models

Spatial 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	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Website

github.com/dgraph-io/badger

firebase.google.com/products/firestore

github.com/hugegraph
hugegraph.apache.org

www.circonus.com/solutions/time-series-database/

rethinkdb.com

Technical documentation

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

firebase.google.com/docs/firestore

hugegraph.apache.org/docs

docs.circonus.com/irondb/category/getting-started

rethinkdb.com/docs

Developer

DGraph Labs

Google

Baidu

Circonus LLC.

The Linux Foundation

since July 2017

Initial release

2017

2018

2017

2009

Current release

0.9

V0.10.20, January 2018

2.4.1, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

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 and C++

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Linux
macOS
Unix

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

string, binary, float, bool, date, geometry

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 (Circonus Analytics Query Language: CAQL)

APIs and other access methods

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

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API

Supported programming languages

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

Groovy
Java
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

asynchronous Gremlin script jobs

yes, in Lua

Triggers

yes, with Cloud Functions

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

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

Automatic, metric affinity per node

Sharding

range based

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

configurable replication factor, datacenter aware

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

yes

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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.

Users, roles and permissions

yes

users and table-level permissions

More information provided by the system vendor

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More resources

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