DBMS > Apache IoTDB vs. MonetDB vs. PostgreSQL vs. SAP Advantage Database Server vs. SpaceTime

System Properties Comparison Apache IoTDB vs. MonetDB vs. PostgreSQL vs. SAP Advantage Database Server vs. SpaceTime

Editorial information provided by DB-Engines

Name

SAP Advantage Database Server 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 relational database management system that stores data in columns

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Low-cost RDBMS with access to ISAM and FoxPro data structures

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Time Series DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Relational DBMS

Spatial DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Website

iotdb.apache.org

www.monetdb.org

www.postgresql.org

www.sap.com/products/advantage-database-server.html

www.mireo.com/spacetime

Technical documentation

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

www.monetdb.org/Documentation

www.postgresql.org/docs

Developer

Apache Software Foundation

MonetDB BV

PostgreSQL Global Development Group

www.postgresql.org/developer

Sybase, SAP

Mireo

Initial release

2018

2004

1989

1989: Postgres, 1996: PostgreSQL

1993

2020

Current release

1.1.0, April 2023

Dec2023 (11.49), December 2023

16.3, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Mozilla Public License 2.0

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
OS X
Solaris
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux
Windows

Linux

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL 2003 with some extensions

yes

standard with numerous extensions

yes

A subset of ANSI SQL is implemented

APIs and other access methods

JDBC
Native API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Delphi
Perl
PHP

C#
C++
Python

Server-side scripts

Stored procedures

yes

yes, in SQL, C, R

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding via remote tables

partitioning by range, list and (since PostgreSQL 11) by hash

none

Fixed-grid hypercubes

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

none

Source-replica replication available in experimental status

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

Real-time block device replication (DRBD)

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

caching of (temporary) tables can be configured

User concepts

Access control

yes

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

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