DBMS > Apache IoTDB vs. Apache Pinot vs. Heroic vs. SpatiaLite

System Properties Comparison Apache IoTDB vs. Apache Pinot vs. Heroic vs. SpatiaLite

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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

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Spatial extension of SQLite

Primary database model

Secondary database models

Relational DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	1.63
Rank	#146	Overall
	#3	Spatial DBMS

Website

iotdb.apache.org

pinot.apache.org

github.com/spotify/heroic

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

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

docs.pinot.apache.org

spotify.github.io/heroic

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Apache Software Foundation

Apache Software Foundation and contributors

Spotify

Alessandro Furieri

Initial release

2018

2015

2014

2008

Current release

1.1.0, April 2023

1.0.0, September 2023

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java JDK11 or higher

server-less

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

JDBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Go
Java
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

User concepts

Access control

yes

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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