DBMS > GridDB vs. MarkLogic vs. Sequoiadb vs. SQLite vs. SQream DB

System Properties Comparison GridDB vs. MarkLogic vs. Sequoiadb vs. SQLite vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

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

Operational and transactional Enterprise NoSQL database

NewSQL database with distributed OLTP and SQL

Widely used embeddable, in-process RDBMS

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Time Series DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store
Relational DBMS

Relational DBMS

Secondary database models

Key-value store
Relational DBMS

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

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

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

Technical documentation

docs.griddb.net

docs.marklogic.com

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

www.sqlite.org/docs.html

docs.sqream.com

Developer

Toshiba Corporation

MarkLogic Corp.

Sequoiadb Ltd.

Dwayne Richard Hipp

SQream Technologies

Initial release

2013

2001

2013

2000

2017

Current release

5.1, August 2022

11.0, December 2022

3.45.3 (15 April 2024), April 2024

2022.1.6, December 2022

License

Commercial or Open Source

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

Server: AGPL; Client: Apache V2

Open Source

Public Domain

commercial

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++

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux

Linux
OS X
Windows

Linux

server-less

Linux

Data scheme

yes

schema-free

Schema can be enforced

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

oid, date, timestamp, binary, regex

yes

not rigid because of 'dynamic typing' concept.

yes, ANSI Standard SQL Types

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

yes

SQL-92 is not fully supported

yes

APIs and other access methods

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

proprietary protocol using JSON

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

.Net
JDBC
ODBC

Supported programming languages

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

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

.Net
C++
Java
PHP
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

JavaScript

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

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

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID at container level

ACID

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

Document is locked during a transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

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

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