DBMS > Apache IoTDB vs. LeanXcale vs. PostGIS vs. STSdb

System Properties Comparison Apache IoTDB vs. LeanXcale vs. PostGIS vs. STSdb

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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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Spatial extension of PostgreSQL

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Time Series DBMS

Key-value store
Relational DBMS

Spatial DBMS

Key-value store

Secondary database models

Relational DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

iotdb.apache.org

www.leanxcale.com

postgis.net

github.com/STSSoft/STSdb4

Technical documentation

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

postgis.net/documentation

Developer

Apache Software Foundation

LeanXcale

STS Soft SC

Initial release

2018

2015

2005

2011

Current release

1.1.0, April 2023

3.4.2, February 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL v2.0

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

through Apache Derby

yes

APIs and other access methods

JDBC
Native API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

.NET Client API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
Java
Scala

C#
Java

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

yes

based on PostgreSQL

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

based on PostgreSQL

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

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

based on PostgreSQL

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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