DBMS > ArcadeDB vs. HEAVY.AI vs. HugeGraph vs. WakandaDB

System Properties Comparison ArcadeDB vs. HEAVY.AI vs. HugeGraph vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A fast-speed and highly-scalable Graph DBMS

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Relational DBMS

Graph DBMS

Object oriented DBMS

Secondary database models

Spatial DBMS

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

Score	0.02
Rank	#366	Overall
	#50	Document stores
	#38	Graph DBMS
	#53	Key-value stores
	#36	Time Series DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

arcadedb.com

github.com/heavyai/heavydb
www.heavy.ai

github.com/hugegraph
hugegraph.apache.org

wakanda.github.io

Technical documentation

docs.arcadedb.com

docs.heavy.ai

hugegraph.apache.org/docs

wakanda.github.io/doc

Developer

Arcade Data

HEAVY.AI, Inc.

Baidu

Wakanda SAS

Initial release

2021

2016

2018

2012

Current release

September 2021

5.10, January 2022

0.9

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2.0

Open Source

AGPLv3, extended commercial license available

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.

Implementation language

Java

C++ and CUDA

Java

C++, JavaScript

Server operating systems

All OS with a Java VM

Linux

Linux
macOS
Unix

Linux
OS X
Windows

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

SQL

Support of SQL

SQL-like query language, no joins

yes

APIs and other access methods

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

JDBC
ODBC
Thrift
Vega

Java API
RESTful HTTP API
TinkerPop Gremlin

RESTful HTTP API

Supported programming languages

Java

All languages supporting JDBC/ODBC/Thrift
Python

Groovy
Java
Python

JavaScript

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

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

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

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship 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

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

fine grained access rights according to SQL-standard

Users, roles and permissions

yes

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