DBMS > Apache IoTDB vs. MarkLogic vs. Sequoiadb vs. SQL.JS vs. TimesTen

System Properties Comparison Apache IoTDB vs. MarkLogic vs. Sequoiadb vs. SQL.JS vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Operational and transactional Enterprise NoSQL database

NewSQL database with distributed OLTP and SQL

Port of SQLite to JavaScript

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

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

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	4.15
Rank	#70	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#8	Search engines

Score	0.42
Rank	#263	Overall
	#41	Document stores
	#121	Relational DBMS

Score	0.53
Rank	#246	Overall
	#115	Relational DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

iotdb.apache.org

www.progress.com/marklogic

www.sequoiadb.com

sql.js.org

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

www.progress.com/marklogic/documentation

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

sql.js.org/documentation/index.html

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Apache Software Foundation

MarkLogic Corp.

Sequoiadb Ltd.

Alon Zakai

enhancements implemented by others

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2018

2001

2013

2012

1998

Current release

1.1.0, April 2023

11.0, December 2022

Release 22.1

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Server: AGPL; Client: Apache V2

Open Source

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

Java

C++

JavaScript

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

Linux

server-less, requires a JavaScript environment (browser, Node.js)

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

yes

schema-free

Schema can be enforced

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL92

SQL-like query language

yes

SQL-92 is not fully supported

yes

APIs and other access methods

JDBC
Native 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

JavaScript API

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

.Net
C++
Java
PHP
Python

JavaScript

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

JavaScript

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency

none

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

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

Durability

Support for making data persistent

yes

except by serializing a db to a file

yes

by means of logfiles and checkpoints

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

yes

Role-based access control at the document and subdocument levels

simple password-based access control

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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more DBMS events

Progress MarkLogic World
Washington DC, 23-25 September 2024

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