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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →To enable high concurrency for Fabric notebooks, turn on the workspace setting, then choose New high concurrency session from a notebook’s Run menu. Compatible notebooks can reuse the running Spark application, avoiding a fresh session start for each one. Sharing depends on matching workspace, user, default lakehouse, Spark compute configuration, and library packages.
What high concurrency changes
In standard mode, each notebook or pipeline activity starts its own Spark session. High concurrency lets compatible workloads reuse a shared Spark application, while each workload has a separate REPL core for execution state and fair executor scheduling. Because the application is already running, later compatible workloads can acquire a session faster. Microsoft says starts on custom pools can be up to 36 times faster than standard Spark session starts; that is a stated maximum, not a guarantee for every pool or workload. Microsoft’s overview of high concurrency explains the behavior.
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High concurrency is not a way to bypass Fabric capacity limits or guarantee immediate job execution. Spark capacity is shared across Spark-based items, and concurrency or queueing depends on capacity and pool configuration. See Microsoft’s Spark compute and capacity documentation.
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Enable and start a high-concurrency notebook session
- Enable the workspace option. Open workspace settings and go to Data Engineering/Science > Spark settings > High concurrency. Ensure For notebooks is enabled. Microsoft says this is enabled by default in Fabric workspaces, though an administrator can turn it off.
- Choose the session type. Open the notebook, select the Run tab, open the session type dropdown (which defaults to Standard), and select New high concurrency session.
- Attach compatible notebooks. For another notebook that meets the sharing requirements, use the same session-type menu to attach it to the existing high-concurrency session.
- Check or change session details. The notebook status bar shows the session type and ID. If a notebook needs dedicated compute, detach it from the shared session to switch to a standard session.
For Microsoft’s notebook-specific setup and interface details, see Configure high concurrency mode for notebooks.
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When notebooks can share a session
Sharing requires all of the following to match:
- The notebooks run in the same workspace and under the same user.
- They use the same default lakehouse.
- Their Spark compute configuration matches.
- Their library package configuration matches.
Different inline library installations in notebook cells may still be compatible in the cases Microsoft documents. If the required settings do not match, expect a separate Spark session rather than assuming the notebook will attach to the existing one. The notebook configuration guide and pipeline guide describe these matching rules.
Standard and high-concurrency sessions compared
| Consideration | Standard session | High-concurrency session |
|---|---|---|
| Session startup | Each notebook or pipeline activity starts its own Spark session. | Compatible workloads can use an already-running shared Spark application. |
| Execution separation | Separate session for each notebook or activity. | Each workload uses a separate REPL core within the shared application. |
| Compatibility | No cross-notebook sharing requirement. | Sharing requires matching workspace, user, default lakehouse, Spark compute configuration, and library packages. |
| Capacity and billing | Subject to Fabric capacity and workload configuration. | Also subject to Fabric capacity and workload configuration; the initiating activity is billed for the shared Spark application. |
| Monitoring | Inspect that session’s run and job details. | Inspect session details and use notebook associations to identify the workload behind jobs and logs. |
Set the notebook limit for a shared session
Microsoft’s overview gives a default limit of five notebooks per high-concurrency session. To raise it, configure the Environment used by the notebook: in its Spark Properties, set spark.highConcurrency.max to a value from 2 through 50, then save and publish the Environment. The default does not automatically become 50, and the configured limit is a maximum rather than a reason to combine workloads that need isolation. See the high-concurrency overview.
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Understand billing before enabling it broadly
Microsoft attributes billing for a shared Spark application to the notebook or pipeline activity that starts it; later activities using that session are not billed individually. Capacity Metrics attribute the usage to the initiating notebook. This is a billing attribution rule, not proof that every workload’s total cost will fall: actual capacity use still depends on the work executed and how the shared capacity is used. Details are in Microsoft’s high-concurrency overview.
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Monitor runs and diagnose problems
- In the notebook, open Run > All Runs to view active and historic Spark sessions.
- Select a session to open its monitoring details, then inspect the Spark jobs.
- For high-concurrency runs, use the Related notebook tab to associate jobs and logs with the notebook that produced them. Use the session ID and notebook association to narrow down a slow or failed run.
Inline %pip install is not supported in high-concurrency mode for the execution paths described in Microsoft’s notebook limitations. Check that page for other current notebook constraints before moving a workload into a shared session.
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Pipeline notebook activities use related but separate controls
For pipeline runs, workspace settings include a separate option for high concurrency with multiple notebook activities. Pipeline activities can also use a session tag as a matching criterion; Microsoft says up to five notebooks are supported per session tag before another session is created. These pipeline controls are distinct from selecting a high-concurrency session in a notebook’s Run menu. See Configure high concurrency mode for pipeline runs.
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