DBMS > Apache IoTDB vs. Atos Standard Common Repository vs. Postgres-XL vs. Prometheus vs. ReductStore

System Properties Comparison Apache IoTDB vs. Atos Standard Common Repository vs. Postgres-XL vs. Prometheus vs. ReductStore

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed 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

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Open-source Time Series DBMS and monitoring system

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Time Series DBMS

Document store
Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Document store
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	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Website

iotdb.apache.org

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.postgres-xl.org

prometheus.io

github.com/reductstore
www.reduct.store

Technical documentation

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

www.postgres-xl.org/documentation

prometheus.io/docs

www.reduct.store/docs

Developer

Apache Software Foundation

Atos Convergence Creators

ReductStore LLC

Initial release

2018

2016

2014

since 2012, originally named StormDB

2015

2023

Current release

1.1.0, April 2023

1703

10 R1, October 2018

1.9, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla public license

Open Source

Apache 2.0

Open Source

Business Source License 1.1

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++, Rust

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
macOS

Linux
Windows

Docker
Linux
macOS
Windows

Data scheme

yes

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

yes

optional

yes

Numeric data only

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

XML type, but no XML query functionality

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

distributed, parallel query execution

APIs and other access methods

JDBC
Native API

LDAP

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

RESTful HTTP/JSON API

HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages with LDAP bindings

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

C++
JavaScript (Node.js)
Python
Rust

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)

Sharding

cell division

horizontal partitioning

Sharding

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

by Federation

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 or Eventual Consistency depending on configuration

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

MVCC

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

LDAP bind authentication

fine grained access rights according to SQL-standard

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