DBMS > ArangoDB vs. Bangdb vs. HugeGraph vs. Realm

System Properties Comparison ArangoDB vs. Bangdb vs. HugeGraph vs. Realm

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Native multi-model DBMS for graph, document, key/value and search. All in one engine and accessible with one query language.

Converged and high performance database for device data, events, time series, document and graph

A fast-speed and highly-scalable Graph DBMS

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Primary database model

Document store
Graph DBMS
Key-value store
Search engine

Document store
Graph DBMS
Time Series DBMS

Graph DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	3.32
Rank	#90	Overall
	#15	Document stores
	#5	Graph DBMS
	#12	Key-value stores
	#10	Search engines

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Website

arangodb.com

bangdb.com

github.com/hugegraph
hugegraph.apache.org

realm.io

Technical documentation

docs.arangodb.com

docs.bangdb.com

hugegraph.apache.org/docs

realm.io/docs

Social network pages

Developer

ArangoDB Inc.

Sachin Sinha, BangDB

Baidu

Realm, acquired by MongoDB in May 2019

Initial release

2012

2018

2014

Current release

3.11.5, November 2023

BangDB 2.0, October 2021

0.9

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial license (Enterprise) available

Open Source

BSD 3

Open Source

Apache Version 2.0

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

ArangoDB Cloud –The Managed Cloud Service of ArangoDB. Provides fully managed, and monitored cluster deployments of any size, with enterprise-grade security. Get started for free and continue for as little as $0,21/hour.

Implementation language

C++

C, C++

Java

Server operating systems

Linux
OS X
Windows

Linux

Linux
macOS
Unix

Android
Backend: server-less
iOS
Windows

Data scheme

schema-free

automatically recognizes schema within a collection

schema-free

yes

Typing

predefined data types such as float or date

yes

string, double, boolean, list, hash

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL like support with command line tool

APIs and other access methods

AQL
Foxx Framework
Graph API (Gremlin)
GraphQL query language
HTTP API
Java & SpringData
JSON style queries
VelocyPack/VelocyStream

Proprietary protocol
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

C#
C++
Clojure
Elixir
Go
Java
JavaScript (Node.js)
PHP
Python
R
Rust

C
C#
C++
Java
Python

Groovy
Java
Python

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

JavaScript

asynchronous Gremlin script jobs

runs within the applications so server-side scripts are unnecessary

Triggers

yes, Notifications (with Streaming only)

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

since version 2.0

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

yes

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with configurable replication factor

selectable replication factor, Knob for CAP (enterprise version only)

yes

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

can be done with stored procedures in JavaScript

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

configurable per collection or per write
Immediate Consistency
OneShard (highly available, fault-tolerant deployment mode with ACID semantics)

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

yes

edges in graph

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

In-Memory realm

User concepts

Access control

yes

yes (enterprise version only)

Users, roles and permissions

yes

More information provided by the system vendor

Specific characteristics

Graph and Beyond. With more than 11,000 stargazers on GitHub, ArangoDB is the leading...
» more

Competitive advantages

Consolidation: As a native multi-model database, can be used as a full blown document...
» more

Typical application scenarios

Native multi-model in ArangoDB is being used for a broad range of projects across...
» more

Key customers

Cisco, Barclays, Refinitive, Siemens Mentor, Kabbage, Liaison, Douglas, MakeMyTrip,...
» more

Market metrics

ArangoDB is the leading native multi-model database with over 11,000 stargazers on...
» more

Licensing and pricing models

Very permissive Apache 2 License for Community Edition & commercial licenses are...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

The Weight of Relational Databases: Time for Multi-Model?
29 August 2017, Luca Olivari (guest author)

show all

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

show all

Recent citations in the news

GQL: A New ISO Standard for Querying Graph Databases
29 April 2024, The New Stack

ArangoGraphML: Simplifying the Power of Graph Machine Learning
11 October 2023, Datanami

ArangoDB Announces Release of ArangoDB 3.11 for Search, Graph and Analytics - High-Performance Computing ...
30 May 2023, insideHPC

How to Build Knowledge Graph Enhanced Chatbot with ChatGPT and ArangoDB
30 June 2023, DataDrivenInvestor

ArangoDB brings yet more money into graph database market with $27.8M round
6 October 2021, SiliconANGLE News

provided by Google News

Critical Apache HugeGraph Flaw Let Attackers Execute Remote Code
23 April 2024, GBHackers

provided by Google News

GDG North Jersey's February Meeting Covers Realm, A New Mobile Database
6 March 2016, njtechweekly.com

MongoDB aims to unify developer experience with launch of MongoDB Cloud
9 June 2020, diginomica

Danish CEO explains Silicon Valley learning curve for European entrepreneurs - San Francisco Business Times
6 October 2016, The Business Journals

Is Swift the Future of Server-side Development?
12 September 2017, Solutions Review

Kotlin Programming Language Will Surpass Java On Android Next Year
15 October 2017, Fossbytes

provided by Google News

Share this page