DBMS > Microsoft Azure Table Storage vs. OpenQM vs. RRDtool vs. SwayDB

System Properties Comparison Microsoft Azure Table Storage vs. OpenQM vs. RRDtool vs. SwayDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A Wide Column Store for rapid development using massive semi-structured datasets

QpenQM is a high-performance, self-tuning, multi-value DBMS

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

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

Score	4.92
Rank	#73	Overall
	#6	Wide column stores

Score	0.33
Rank	#286	Overall
	#10	Multivalue DBMS

Score	2.14
Rank	#124	Overall
	#10	Time Series DBMS

Score	0.04
Rank	#373	Overall
	#57	Key-value stores

Website

azure.microsoft.com/en-us/services/storage/tables

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

oss.oetiker.ch/rrdtool

swaydb.simer.au

Technical documentation

oss.oetiker.ch/rrdtool/doc

Developer

Microsoft

Rocket Software, originally Martin Phillips

Tobias Oetiker

Simer Plaha

Initial release

2012

1993

1999

2018

Current release

3.4-12

1.8.0, 2022

License

Commercial or Open Source

commercial

Open Source

GPLv2, extended commercial license available

Open Source

GPL V2 and FLOSS

Open Source

GNU Affero GPL V3.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Implementations in Java (e.g. RRD4J) and C# available

Scala

Server operating systems

hosted

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

HP-UX
Linux

Data scheme

schema-free

yes

with some exceptions

yes

schema-free

Typing

predefined data types such as float or date

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

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

APIs and other access methods

RESTful HTTP API

in-process shared library
Pipes

Supported programming languages

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
Basic
C
Java
Objective C
PHP
Python

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

optimistic locking

ACID

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

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

Access rights based on private key authentication or shared access signatures

Access rights can be defined down to the item level

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

show all

Recent citations in the news

Azure Cosmos DB Data Migration tool imports from Azure Table storage | Azure updates
5 May 2015, azure.microsoft.com

How to Use C# Azure.Data.Tables SDK with Azure Cosmos DB
9 July 2021, hackernoon.com

How to use Azure Table storage in .Net
14 January 2019, InfoWorld

Testing Precompiled Azure Functions Locally with Storage Emulator
8 March 2018, Visual Studio Magazine

How to write data to Azure Table Store with an Azure Function
14 April 2017, Experts Exchange

provided by Google News

SQLi vulnerability in Cacti could lead to RCE (CVE-2023-51448)
9 January 2024, Help Net Security

Critical IP spoofing bug patched in Cacti
15 December 2022, The Daily Swig

Cacti: Using Graphs to Monitor Networks and Devices
16 March 2011, Packt Hub

How to install Cacti SNMP Monitor on Ubuntu
24 November 2017, TechRepublic

Installation Guide for Collectd and Collectd-Web to Monitor Server Resources in Linux
29 November 2017, Linux.com

provided by Google News

Share this page