DBMS > Apache IoTDB vs. FatDB vs. InfluxDB vs. Spark SQL vs. Yanza

System Properties Comparison Apache IoTDB vs. FatDB vs. InfluxDB vs. Spark SQL vs. Yanza

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

DBMS for storing time series, events and metrics

Spark SQL is a component on top of 'Spark Core' for structured data processing

Time Series DBMS for IoT Applications

Primary database model

Time Series DBMS

Document store
Key-value store

Time Series DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

with GEO package

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

iotdb.apache.org

www.influxdata.com/products/influxdb-overview

spark.apache.org/sql

yanza.com

Technical documentation

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

docs.influxdata.com/influxdb

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation

FatCloud

Apache Software Foundation

Yanza

Initial release

2018

2012

2013

2014

2015

Current release

1.1.0, April 2023

2.7.6, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

Apache 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Scala

Server operating systems

All OS with a Java VM (>= 1.8)

Windows

Linux
OS X

through Homebrew

Linux
OS X
Windows

Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

SQL-like query language

Via inetgration in SQL Server

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC
Native API

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

HTTP API
JSON over UDP

JDBC
ODBC

HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

Java
Python
R
Scala

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

via applications

Triggers

yes

via applications

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

in enterprise version only

yes, utilizing Spark Core

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

selectable replication factor

in enterprise version only

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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

Depending on used storage engine

User concepts

Access control

yes

Can implement custom security layer via applications

simple rights management via user accounts

More information provided by the system vendor

Specific characteristics

InfluxData is the creator of InfluxDB , the open source time series database. It...
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Competitive advantages

Time to Value InfluxDB is available in all the popular languages and frameworks,...
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Typical application scenarios

IoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
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Key customers

InfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
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Market metrics

Fastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
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Licensing and pricing models

Open source core with closed source clustering available either on-premise or on...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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