DBMS > Apache IoTDB vs. GeoSpock vs. PostGIS vs. SAP HANA

System Properties Comparison Apache IoTDB vs. GeoSpock vs. PostGIS vs. SAP HANA

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be 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

Spatial and temporal data processing engine for extreme data scale

Spatial extension of PostgreSQL

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Secondary database models

Time Series DBMS

Relational DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

iotdb.apache.org

geospock.com

postgis.net

www.sap.com/products/hana.html

Technical documentation

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

postgis.net/documentation

help.sap.com/hana

Developer

Apache Software Foundation

GeoSpock

SAP

Initial release

2018

2005

2010

Current release

1.1.0, April 2023

2.0, September 2019

3.4.2, February 2024

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL v2.0

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, Javascript

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Appliance or cloud-service

Data scheme

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.

yes

Secondary indexes

yes

temporal, categorical

yes

SQL

Support of SQL

SQL-like query language

ANSI SQL for query only (using Presto)

yes

APIs and other access methods

JDBC
Native API

JDBC

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

yes

user defined functions

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Automatic sharding

yes

based on PostgreSQL

yes

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

yes

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

Access rights for users can be defined per table

yes

based on PostgreSQL

yes

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