DBMS > ArcadeDB vs. Bangdb vs. Drizzle vs. Stardog

System Properties Comparison ArcadeDB vs. Bangdb vs. Drizzle vs. Stardog

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Graph DBMS
RDF 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	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

arcadedb.com

bangdb.com

www.stardog.com

Technical documentation

docs.arcadedb.com

docs.bangdb.com

docs.stardog.com

Developer

Arcade Data

Sachin Sinha, BangDB

Drizzle project, originally started by Brian Aker

Stardog-Union

Initial release

2021

2012

2008

2010

Current release

September 2021

BangDB 2.0, October 2021

7.2.4, September 2012

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD 3

Open Source

GNU GPL

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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++

C++

Java

Server operating systems

All OS with a Java VM

Linux

FreeBSD
Linux
OS X

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

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.

Import/export of XML data possible

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language, no joins

SQL like support with command line tool

yes

with proprietary extensions

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

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

JDBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Java

C
C#
C++
Java
Python

C
C++
Java
PHP

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes, Notifications (with Streaming only)

hooks for callbacks inside the server can be used.

yes

via event handlers

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

Multi-source replication
Source-replica replication

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationship in graphs

yes

relationships in graphs

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

User concepts

Access control

yes (enterprise version only)

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users and roles

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