Skip to main content

Featured post

XM Cloud content sync from prod to uat or UAT to prod step by step

When working with Sitecore, it’s common to need content synchronization across environments. Today, I’ll walk you through the steps to sync content from Production to UAT/TEST and vice versa. Steps to Follow 1. Set Up Your Workspace Create a folder on your computer where you will manage the script files and exported data. Open the folder path in PowerShell to begin scripting. We need to run some scripts in PowerShell to update the folder with the basic requirements for syncing content. PS C:\Soft\ContentSync> dotnet new tool-manifest PS C:\Soft\ContentSync> dotnet nuget add source -n Sitecore https://nuget.sitecore.com/resources/v3/index.json PS C:\Soft\ContentSync> dotnet tool install Sitecore.CLI PS C:\Soft\ContentSync> dotnet sitecore cloud login If the above error occurs, you will need to run a different command to resolve the issue. PS C:\Soft\ContentSync> dotnet sitecore init now, Again run above command to open and authenticate with XM Cloud. It will be there a...

Sitecore pipeline implementation

 Sitecore pipeline implementation


Sitecore pipelines are a key concept in Sitecore architecture, allowing developers to add custom logic and process data at specific points during a request. Here's a general guide for implementing Sitecore pipelines:

1.       Create a custom class that inherits from the Sitecore.Pipelines.PipelineProcessor class.

2.       Override the Process method to add your custom logic.

3.       Register the pipeline processor in the Sitecore configuration file (usually the Web.config or Sitecore.config file).

4.       Determine the appropriate point in the pipeline to insert your custom logic. Sitecore provides many predefined pipelines, such as the httpRequestBegin pipeline, that you can use to insert your custom logic.

5.       Add a new node to the pipeline in the configuration file, specifying the class name and the order in which it should be executed.

Here is an example of a simple pipeline processor that logs a message before and after a request


using Sitecore.Pipelines;


public class LoggingProcessor : PipelineProcessor

{

    public override void Process(PipelineArgs args)

    {

        Sitecore.Diagnostics.Log.Info("Request started", this);


        try

        {

            // Your custom logic here

        }

        finally

        {

            Sitecore.Diagnostics.Log.Info("Request ended", this);

        }

    }

}



And here is an example of how to register the processor in the Sitecore configuration file:


<configuration xmlns:patch="http://www.sitecore.net/xmlconfig/">
  <sitecore>
    <pipelines>
      <httpRequestBegin>
        <processor type="MyNamespace.LoggingProcessor, MyAssembly" />
      </httpRequestBegin>
    </pipelines>
  </sitecore>
</configuration>

Note: This is just a simple example to demonstrate the basic steps involved in implementing a Sitecore pipeline. The exact implementation will depend on the specific requirements of your project.

Comments

Popular posts from this blog

Socket Programming in Python

  Example of socket programing in python. Here's a simple example of socket programming in Python: Server Side Code import socket # Create a socket object serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)  # Get local machine name host = socket.gethostname()                            port = 9999 # Bind to a port serversocket.bind((host, port))                                   # Listen to at most 1 connection at a time serversocket.listen(1) print("Server is ready to receive") while True:     # Establish a connection     clientsocket,addr = serversocket.accept()           print("Got a connection from", addr)     clientsocket.send(b"Thank you for connecting")     clientsocket.close() Client Side Code import socket # Create a socket obje...

Homework 3.3 MongoDB for DBAs

MongoDB Homework 3.3 for DBAs. She below image for the answer of homework 3.3.

How do I start learning on AI

To start learning AI, you can follow these steps: Choose a programming language: Python is the most popular language for AI and machine learning, but you can also use R or other languages. Get familiar with basic mathematics and statistics: You should have a basic understanding of linear algebra, calculus, and probability. Learn about artificial neural networks: Neural networks are the building blocks of deep learning and are essential to understanding AI. Get hands-on experience: The best way to learn AI is by working on projects. There are many online resources with tutorials and open-source projects to get you started. Participate in online communities: AI has a thriving online community where you can ask questions, share your work, and connect with others. Keep up with the latest developments: AI is a rapidly advancing field, and it's important to stay up-to-date with the latest developments and trends. Remember, learning AI requires time, effort, and practice, but it is a valu...

AngularJS Best Practice

Best Practice to write AngularJS Program code. This is very useful code to communicate with webApi or other any any services. You may learn here more about different services. var commonModule = angular.module('common', ['ngRoute']); var mainModule = angular.module('main', ['common']); commonModule.factory('viewModelHelper', function ($http, $q, $window, $location) { return MyApp.viewModelHelper($http, $q, $window, $location); }); commonModule.factory('validator', function () { return valJs.validator(); }); mainModule.controller("indexViewModel", function ($scope, $http, $q, $routeParams, $window, $location, viewModelHelper) { var self = this; $scope.sessionName = "ASP.NET MVC with Angular JS"; $scope.speakerName = "Shashi Keshar"; }); (function (myApp) { var viewModelHelper = function ($http, $q, $window, $location) { var self = this; self.modelIsValid = true...