DBMS > DataFS vs. GridDB vs. MarkLogic vs. RocksDB vs. Sequoiadb

System Properties Comparison DataFS vs. GridDB vs. MarkLogic vs. RocksDB vs. Sequoiadb

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.

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

Operational and transactional Enterprise NoSQL database

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

NewSQL database with distributed OLTP and SQL

Primary database model

Object oriented DBMS

Time Series DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Key-value store

Document store
Relational DBMS

Secondary database models

Graph DBMS

Key-value store
Relational DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Website

Technical documentation

dev.mobiland.com/Overview.xsp

docs.griddb.net

docs.marklogic.com

github.com/facebook/rocksdb/wiki

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Mobiland AG

Toshiba Corporation

MarkLogic Corp.

Facebook, Inc.

Sequoiadb Ltd.

Initial release

2018

2013

2001

2013

Current release

1.1.263, October 2022

5.1, August 2022

11.0, December 2022

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open Source

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

commercial

restricted free version is available

Open Source

BSD

Open Source

Server: AGPL; Client: Apache V2

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++

Server operating systems

Windows

Linux

Linux
OS X
Windows

Linux

Data scheme

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

yes

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

oid, date, timestamp, binary, regex

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

SQL92

SQL-like query language

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

C++ API
Java API

proprietary protocol using JSON

Supported programming languages

.Net
C
C#
C++
VB.Net

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

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

C
C++
Go
Java
Perl
Python
Ruby

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

JavaScript

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Source-replica replication

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

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID at container level

ACID

can act as a resource manager in an XA/JTA transaction

yes

Document is locked during a transaction

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

yes, with Range Indexes

yes

User concepts

Access control

Windows-Profile

Access rights for users can be defined per database

Role-based access control at the document and subdocument levels

simple password-based access control

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.