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To enable high concurrency for Fabric notebooks, turn on the notebook option in workspace Spark settings, then choose New high concurrency session from a notebook’s Run menu. Compatible notebooks in the same workspace can reuse the running Spark application, reducing repeated session startup; they must meet specific matching rules, and the feature does not guarantee faster job execution or lower total capacity use.
What high concurrency changes
In standard mode, each notebook or pipeline activity starts its own Spark session. High concurrency lets compatible workloads share a running Spark application, with a separate REPL core for each workload. Microsoft says execution state is isolated between those cores and executors are scheduled fairly across them. Reusing an already-running session can speed up session acquisition for later workloads.
Microsoft states that high-concurrency session start on custom pools can be up to 36 times faster than a standard Spark session. That is an upper-bound claim specific to custom pools, not a benchmark or a guarantee for every workspace. Actual job runtime still depends on the workload and available capacity.
Enable high concurrency for notebooks
- In the Fabric workspace, open Workspace settings > Data Engineering/Science > Spark settings > High concurrency.
- Make sure For notebooks is enabled. Microsoft’s setup documentation says this is enabled by default in Fabric workspaces, but an administrator can turn it off.
- Open a notebook and select the Run tab.
- Open the session type dropdown, which shows Standard by default, and choose New high concurrency session.
- For another compatible notebook, use the same dropdown to attach it to the existing high-concurrency session.
The notebook status bar shows the session type and ID. If a notebook needs dedicated compute instead, detach it from the shared session and switch it to a standard session.
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Which notebooks can share a session?
Notebooks share only when their configurations match. Microsoft’s notebook and pipeline guidance identifies these requirements:
- They run as the same user in the same workspace.
- They use the same default lakehouse.
- They have matching Spark compute configurations.
- They use matching library packages. Different inline library installations in notebook cells may still be compatible in the cases Microsoft describes, but do not assume that differing package configurations will share.
If a requirement does not match, expect Fabric to create a separate Spark session rather than attach the notebook to the existing one.
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Standard sessions and high-concurrency sessions compared
| Factor | Standard session | High-concurrency session |
|---|---|---|
| Session startup | Starts a separate Spark session for each notebook or pipeline activity. | Compatible workloads can reuse a running Spark application, avoiding repeated session acquisition. |
| Execution state | Each workload has its own session. | Each workload uses a separate REPL core within the shared application; Microsoft describes execution-state isolation between cores. |
| Compatibility | Does not require matching another notebook’s configuration to get its own session. | Sharing depends on matching user, workspace, default lakehouse, Spark compute configuration, and library packages. |
| Capacity and billing | Subject to Fabric capacity allocation and workload configuration. | Also subject to capacity and workload configuration; the activity that starts the shared application is billed for it. |
Understand the sharing limit and raise it if needed
Microsoft’s current overview states that a high-concurrency session supports up to five notebooks by default. You can explicitly configure the limit up to 50 for the Environment used by the notebook or pipeline:
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- Open the relevant Environment and go to Spark Properties.
- Set
spark.highConcurrency.maxto a value from 2 through 50. - Save and publish the Environment, then use it for the notebook or pipeline.
Changing the Environment property does not automatically raise the default for other Environments. Choose a limit that fits the workload and its isolation needs rather than increasing it simply because the setting allows it.
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Pipeline notebook activities have separate controls
For notebook activities in pipelines, workspace Spark settings provide a separate high-concurrency option for pipeline runs with multiple notebooks. Pipeline activities can also use a session tag as a matching criterion to group notebooks. Microsoft’s pipeline guide describes a limit of five notebooks per session tag before another session is created. Apply the pipeline-specific setting and matching behavior rather than assuming the notebook Run-menu selection alone controls pipeline sharing.
What high concurrency means for billing and capacity
Microsoft says billing for a shared Spark application is attributed to the notebook or pipeline activity that starts it; later activities using that same session are not billed individually. Capacity Metrics attribute the use to the initiating notebook. This describes how usage is accounted for, not a promise that a workload’s total cost will fall: capacity consumption and queueing still depend on the work being run and the Fabric capacity available.
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Fabric capacity is shared across Spark-based items in its workspaces. High concurrency can reduce repeated session creation, but it does not remove capacity limits or guarantee that every job starts immediately. Job concurrency and queueing also depend on capacity and pool configuration.
Monitor a session and troubleshoot common problems
- In the notebook, open Run > All Runs to see active and historical Spark sessions.
- Select the relevant session to open its monitoring details and inspect Spark jobs.
- In high-concurrency mode, use the Related notebook tab to associate jobs and logs with the notebook that produced them. The session ID and notebook association help narrow down slow or failed runs.
A notebook starts a separate session instead of sharing
Check that it uses the same user and workspace as the other notebook, and confirm its default lakehouse, Spark compute configuration, and library packages match. A mismatch can prevent reuse.
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An inline package installation causes trouble
Microsoft’s known-limitations documentation says inline %pip install is not supported in high-concurrency mode for the execution paths it covers. Check the current limitations page for other notebook-specific constraints before moving a workload to shared sessions.
A job is still slow or queued
Session reuse reduces repeated session acquisition; it does not ensure that the Spark job itself runs faster. Review session and job details, then consider capacity availability and queueing as well as the notebook’s compute configuration.
Quick Recap
Microsoft documentation
- Configure high concurrency mode for notebooks
- High concurrency mode in Apache Spark compute for Fabric
- Configure high concurrency mode for pipeline runs
- Notebook limitations in Microsoft Fabric
- Spark compute in Microsoft Fabric
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