DBMS > DataFS vs. Dgraph vs. EsgynDB vs. GridDB vs. Warp 10

System Properties Comparison DataFS vs. Dgraph vs. EsgynDB vs. GridDB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Distributed and scalable native Graph DBMS

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Secondary database models

Graph DBMS

Key-value store
Relational DBMS

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

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

Technical documentation

dev.mobiland.com/Overview.xsp

dgraph.io/docs

docs.griddb.net

www.warp10.io/content/02_Getting_started

Developer

Mobiland AG

Dgraph Labs, Inc.

Esgyn

Toshiba Corporation

SenX

Initial release

2018

2016

2015

2013

2015

Current release

1.1.263, October 2022

5.1, August 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

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

Open Source

Apache License 2.0

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

C++, Java

C++

Java

Server operating systems

Windows

Linux
OS X
Windows

Linux

Linux
OS X
Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
C
C#
C++
VB.Net

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

All languages supporting JDBC/ODBC/ADO.Net

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

Server-side scripts

Stored procedures

Java Stored Procedures

yes

WarpScript

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

yes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication between multi datacenters

Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID at container level

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

Windows-Profile

Planned for future releases

fine grained access rights according to SQL-standard

Access rights for users can be defined per database

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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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