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We are excited to announce a new high concurrency mode in Fabric for Data Engineering and Data Science. This allows users to share Spark compute across multiple notebooks within a workspace which means that you can run multiple Spark notebooks simultaneously on the same Spark session without compromising performance or security when paying for a single session. High concurrency mode offers an instant run experience avoiding session start delays and ~30X faster session start experience for the shared notebooks when running on custom pools.
What is High Concurrency Mode?
High concurrency mode allows sharing of Spark compute across multiple notebooks and allows their queries to execute in parallel. In this shared mode, the resources and configurations of each notebook are isolated from each other. As the session sharing is always within a single user boundary, users cannot access or modify the data or variables of another user's high concurrency session.
High concurrency mode also leverages FAIR allocation to optimize the resource utilization and performance of the notebooks and ensures that each notebook gets a fair share of the executors available for the Spark application.
Why Use High Concurrency Mode?
High concurrency mode offers several benefits for Fabric Spark users, such as:
How to Enable High Concurrency Mode?
To enable high concurrency mode for your Fabric Spark workspace, you need to follow these steps:
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4. Save your changes.
Once you enable high concurrency mode, you can run your notebooks in High Concurrency mode from the notebook menu ribbon.
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To learn more about using high concurrency in notebooks read: Sharing Spark Compute across Notebooks with High Concurrency Mode in Fabric.
For more information on high concurrency mode, please read Overview of High Concurrency Mode in Microsoft Fabric
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