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

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

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Primary database model

Relational DBMS

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

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	0.63
Rank	#241	Overall
	#112	Relational DBMS

Website

impala.apache.org

iotdb.apache.org

www.progress.com/marklogic

www.sequoiadb.com

sql.js.org

Technical documentation

impala.apache.org/impala-docs.html

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

MarkLogic Corp.

Sequoiadb Ltd.

Alon Zakai

enhancements implemented by others

Initial release

2013

2018

2001

2013

2012

Current release

4.1.0, June 2022

1.1.0, April 2023

11.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Server: AGPL; Client: Apache V2

Open Source

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++

JavaScript

Server operating systems

Linux

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

Linux

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

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 DML and DDL statements

SQL-like query language

yes

SQL92

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC

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

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

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

.Net
C++
Java
PHP
Python

JavaScript

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

via XQuery or JavaScript

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

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

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency

none

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

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, groups and roles

based on Apache Sentry and Kerberos

yes

Role-based access control at the document and subdocument levels

simple password-based access control

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More resources

DB-Engines blog posts

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