DBMS > Bangdb vs. Drizzle vs. GridDB vs. Kinetica vs. RDF4J

System Properties Comparison Bangdb vs. Drizzle vs. GridDB vs. Kinetica vs. RDF4J

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

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.

Scalable in-memory time series database optimized for IoT and Big Data

Fully vectorized database across both GPUs and CPUs

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

Key-value store
Relational DBMS

Spatial DBMS
Time Series DBMS

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

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

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

Technical documentation

docs.bangdb.com

docs.griddb.net

docs.kinetica.com

rdf4j.org/documentation

Developer

Sachin Sinha, BangDB

Drizzle project, originally started by Brian Aker

Toshiba Corporation

Kinetica

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2012

2008

2013

2012

2004

Current release

BangDB 2.0, October 2021

7.2.4, September 2012

5.1, August 2022

7.1, August 2021

License

Commercial or Open Source

Open Source

BSD 3

Open Source

GNU GPL

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C++

C, C++

Java

Server operating systems

Linux

FreeBSD
Linux
OS X

Linux

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

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

yes

numerical, string, blob, geometry, boolean, timestamp

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

SQL

Support of SQL

SQL like support with command line tool

yes

with proprietary extensions

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
C#
C++
Java
Python

C
C++
Java
PHP

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes, Notifications (with Streaming only)

hooks for callbacks inside the server can be used.

yes

triggers when inserted values for one or more columns fall within a specified range

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication
Source-replica replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate consistency within container, eventual consistency across containers

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID at container level

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

in-memory storage is supported as well

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

GPU vRAM or System RAM

User concepts

Access control

yes (enterprise version only)

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users can be defined per database

Access rights for users and roles on table level

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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