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High Fabric utilization is not proof that requests are being throttled, and throttling is not proof that a report or job is inefficient. Check the Capacity Metrics app for throttling events, identify the operations driving demand, and then choose among optimization, scaling up, or distributing workloads across capacities. Smoothing spreads the accounting for compute over time; it does not make a slow operation run faster.
Understand utilization, smoothing, and throttling
Fabric accounts for compute in capacity units (CUs). Microsoft’s Capacity Metrics app represents activity in 30-second timepoints, with 2,880 timepoints across 24 hours. Because work is smoothed across time, a timepoint can show usage above 100% without proving that a request was delayed or rejected. Confirm throttling using the app’s throttling charts and system events, rather than treating utilization alone as the diagnosis.
Interactive operations are smoothed over a minimum of five minutes and up to 64 minutes, depending on CU consumption. Background operations are smoothed over 24 hours. Smoothing spreads consumption against capacity limits; it does not change execution time or correct inefficient item design. As Microsoft puts it, “Smoothing doesn’t change performance, it just spreads the accounting for consumed compute over a longer period, so that a larger SKU isn’t needed to handle the peak compute.” Microsoft Learn explains the throttling and smoothing policy, and its capacity optimization guidance covers performance and sizing.
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Microsoft documents a staged policy, but says the policy may change and that behavior can vary with workload. Treat these figures as the documented policy, not a guarantee that every workload will behave identically.
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| Stage | Documented behavior |
|---|---|
| Future-capacity overage protection | 10 minutes of protection against overage before throttling stages apply. |
| Interactive delay | After that protection, new interactive operations can be delayed by 20 seconds. |
| Interactive rejection | At the next-hour limit, new interactive operations can be rejected. |
| All-request rejection | At the next-24-hour limit, all new requests can be rejected. |
The stages describe capacity policy, not a diagnosis for any one slow report. Check the timepoint details and system events to determine whether throttling occurred, then trace contributing operations to a workspace and item. A slow query or job may instead reflect its design, even when the capacity is not throttling it.
Diagnose the source before changing capacity
- Open Capacity Metrics. Review the Compute page, throttling charts, system events, and individual timepoint details. Look for delays or rejections rather than inferring them from a utilization percentage alone.
- Find the contributors. Identify the workspace, item, and operation associated with the load. Determine whether demand is steady, bursty, driven by concurrency, linked to inefficient design, or affected by carryforward usage.
- Match the remedy to the cause. Item-level inefficiency calls for optimization; a sustained need for more compute may call for a larger SKU; workload separation may call for another capacity. Capacity needs depend on operation design and concurrency, so a SKU change alone may not fix a poorly designed item.
For scale, Microsoft’s worked example says a one-CU-hour background operation on F2 contributes approximately 2.1% to each timepoint. That is an illustration of smoothing, not a universal per-job impact: actual effects depend on the operation and applicable capacity policy.
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Choose between optimizing, scaling up, and scaling out
| Option | Best fit | Trade-off |
|---|---|---|
| Optimize items | High-compute reports, queries, or jobs whose design can be improved. | Requires engineering work; can reduce compute demand without simply buying more headroom. |
| Scale up | A workload needs additional compute and a larger capacity is the simplest way to provide it. | Adds capacity cost; does not create workload or administrative separation. |
| Scale out | Work needs distribution across capacities, distinct administration boundaries, or isolation for priority workloads. | Requires moving and governing work across more than one capacity. |
Before expanding, tune expensive items and consider workload-specific controls such as query timeouts, row limits, or Spark settings where appropriate. If measured demand still exceeds available compute, select a SKU increase for straightforward headroom or another capacity when separation is itself a requirement. Microsoft’s capacity optimization guidance discusses these approaches.
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Surge protection limits background compute to reduce the chance that background activity delays or rejects interactive work. Its trade-off is that background jobs can themselves be rejected. It is a control for competing workloads, not a substitute for optimizing items or sizing capacity appropriately.
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Decide how rejected background jobs will be detected and handled before enabling protection. Where mission-critical content requires predictable isolation, consider correctly sized dedicated capacity rather than relying solely on a shared capacity’s protection controls.
Govern shared capacity as demand grows
A larger SKU does not resolve unclear ownership, competing priorities, or unplanned growth. For decentralized self-service, define who is responsible for tenant-level and capacity-level decisions, and establish an equivalent center of excellence or governance group to make shared use understandable and enforceable.
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- Publish fair-use rules and explain what happens when a team repeatedly overloads shared resources.
- Assign administrator and workload responsibilities, and train users to build with capacity impact in mind.
- Report usage by team or workload; use showback or chargeback where it helps make consumption visible.
- Review top consumers, utilization, throttling and rejection events, and underused capacity on a regular schedule.
- Plan how workspaces will be distributed if one shared capacity cannot meet all needs.
In centralized enterprise environments, connect monitoring and scaling decisions to service-level expectations. Review growth trends and optimize before expanding; scale proactively before contention becomes sustained. A separate capacity may better serve a critical workload or administrative boundary than simply increasing the shared capacity. Microsoft’s capacity planning guide describes growth and governance practices.
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Plan capacity using measured usage
When future demand is uncertain, Microsoft recommends measuring real usage with a trial or pay-as-you-go F SKU before committing to reserved capacity. Start small and increase as observed needs require. This makes sizing a decision grounded in actual workload patterns rather than an assumed peak or an isolated utilization figure.
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If throttling is already occurring, Microsoft documents a temporary SKU increase and pause/resume as recovery options for F SKUs. Pause/resume can affect content availability and billing; check the current policy and billing terms before using either option. Consult the latest throttling documentation because service behavior can change.
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