Mastering Parallel Processing In Powershell For Efficient Arm Template Execution
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- Learn how to supercharge your ARM template deployments with PowerShell jobs, running tasks in parallel for rapid resource provisioning. Explore the ins and outs of parallel processing with this comprehensive guide.
- The article discusses parallel processing in PowerShell and how to run multiple ARM templates in parallel using PowerShell jobs.
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This article delves into the world of parallel processing in PowerShell and its application to efficiently run multiple ARM templates in parallel. It begins by explaining the concept of PowerShell jobs and their benefits in automating repetitive tasks and improving performance. The article outlines the various PowerShell job commands, such as Start-Job, Get-Job, Receive-Job, Stop-Job, Wait-Job, and Remove-Job.
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The article demonstrates how to create PowerShell background jobs, showcasing their ability to execute commands in the background without interrupting the console or terminal. It illustrates how you can use script blocks to define multiple commands within a single job, making it a powerful tool for running scripts in parallel.
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A critical aspect of working with PowerShell jobs is passing parameters or data between the parent and child sessions. The article explains the use of argument lists to convey information from the parent session to the child session, ensuring that the job has access to the required variables.
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The real-world use case explored in the article involves provisioning Azure resources using ARM templates. By creating multiple parameter files during runtime and running separate jobs for each combination of parameter and template files, the article shows how you can significantly reduce the time required for creating resources, making it a highly efficient method for managing large-scale deployments.
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Readers are encouraged to try the example provided in the article, complete with sample PowerShell scripts, parameter files, and ARM templates, allowing them to experiment with parallel processing in their own Azure deployments.
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