DBMS > ArcadeDB vs. Bangdb vs. EJDB vs. HBase

System Properties Comparison ArcadeDB vs. Bangdb vs. EJDB vs. HBase

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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

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Document store
Graph DBMS
Time Series DBMS

Document store

Wide column store

Secondary database models

Spatial DBMS

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

Score	0.10
Rank	#358	Overall
	#48	Document stores
	#38	Graph DBMS
	#52	Key-value stores
	#35	Time Series DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Website

arcadedb.com

bangdb.com

github.com/Softmotions/ejdb

hbase.apache.org

Technical documentation

docs.arcadedb.com

docs.bangdb.com

github.com/Softmotions/ejdb/blob/master/README.md

hbase.apache.org/book.html

Developer

Arcade Data

Sachin Sinha, BangDB

Softmotions

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2021

2012

2008

Current release

September 2021

BangDB 2.0, October 2021

2.3.4, January 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD 3

Open Source

GPLv2

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++

Java

Server operating systems

All OS with a Java VM

Linux

server-less

Linux
Unix
Windows

using Cygwin

Data scheme

schema-free

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

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

yes

string, integer, double, bool, date, object_id

options to bring your own types, AVRO

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

SQL

Support of SQL

SQL-like query language, no joins

SQL like support with command line tool

APIs and other access methods

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

Proprietary protocol
RESTful HTTP API

in-process shared library

Java API
RESTful HTTP API
Thrift

Supported programming languages

Java

C
C#
C++
Java
Python

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Read/Write Locking

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

User concepts

Access control

yes (enterprise version only)

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

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