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zbensisi's avatar
zbensisi
Copper Contributor
Jan 07, 2025

Azure Synapse Issue

Hi,

I have a question regarding the backend mechanism of Synapse Spark clusters when running pipelines.

I have a notebook that installs packages using !pip since %pip is disabled during pipeline execution. I understand that !pip is a shell command that installs packages at the driver level. I’m wondering if this will impact other pipelines that are running concurrently but do not require the same packages.

Thank you for your help.

 

 

  • NilendraFabric's avatar
    NilendraFabric
    Copper Contributor
    Jan 20, 2025

    Hi ,

     

    When using !pip to install packages in an Azure Synapse notebook during pipeline execution, there are a few important considerations:
    1. Driver-level installation: As you correctly noted, !pip installs packages only on the driver node of the Spark cluster. This means the packages are not automatically distributed to worker nodes.
    2. Isolation: Each Spark application (including pipeline-triggered notebooks) runs in its own isolated environment. The packages installed using !pip in one notebook will not affect other concurrently running pipelines or notebooks.
    3. Temporary nature: Packages installed using !pip are only available for the duration of that specific Spark session. Once the session ends, the installed packages are removed.
    4. Performance impact: Installing packages at runtime can introduce some overhead, especially if the packages are large or have many dependencies. This may slightly increase the startup time of your notebook execution.
    5. Reliability concerns: Using !pip in production pipelines is generally not recommended due to potential network issues or package availability problems that could cause pipeline failures.
    For more reliable and efficient package management in Azure Synapse Spark pools, consider these alternatives:
    1. Pool-level package management: Install required packages at the Spark pool level. This ensures packages are available to all notebooks and jobs using that pool.
    2. Workspace packages: Upload custom wheel files as workspace packages and attach them to your Spark pool.
    3. Requirements file: Use a requirements.txt file to specify packages and versions, then upload it to your Spark pool configuration.
     
    thanks

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