How to Install and Uninstall python311-azure-monitor-query Package on openSuSE Tumbleweed

Last updated: December 25,2024

1. Install "python311-azure-monitor-query" package

Please follow the steps below to install python311-azure-monitor-query on openSuSE Tumbleweed


    
        
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                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                python311-azure-monitor-query
            
                    
    
    Copied

2. Uninstall "python311-azure-monitor-query" package

Here is a brief guide to show you how to uninstall python311-azure-monitor-query on openSuSE Tumbleweed:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                python311-azure-monitor-query
            
                    
    
    Copied

3. Information about the python311-azure-monitor-query package on openSuSE Tumbleweed

Information for package python311-azure-monitor-query:
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Repository : openSUSE-Tumbleweed-Oss
Name : python311-azure-monitor-query
Version : 1.2.1-1.4
Arch : noarch
Vendor : openSUSE
Installed Size : 1.1 MiB
Installed : No
Status : not installed
Source package : python-azure-monitor-query-1.2.1-1.4.src
Upstream URL : https://github.com/Azure/azure-sdk-for-python
Summary : Microsoft Azure Azure Monitor Query Client Library for Python
Description :
Azure Monitor helps you maximize the availability and performance of your applications and
services. It delivers a comprehensive solution for collecting, analyzing, and acting on
telemetry from your cloud and on-premises environments.
All data collected by Azure Monitor fits into one of two fundamental types, metrics and logs.
Metrics are numerical values that describe some aspect of a system at a particular point in
time. They are lightweight and capable of supporting near real-time scenarios. Logs contain
different kinds of data organized into records with different sets of properties for each
type. Telemetry such as events and traces are stored as logs in addition to performance data
so that it can all be combined for analysis.