Diagnose slow Dataverse plug-ins before changing code
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Trace the full save path, compare repeatable baselines, and target Dataverse plug-in bottlenecks with focused changes.
TL;DR
Dataverse plug-in performance diagnosis starts by comparing the same operation with and without a step, separating synchronous wait time from background work, and recording a repeatable baseline. Plug-in Trace Logs, execution context, and the Plug-in Profiler help locate delays before queries, loops, external calls, registrations, or execution modes are changed.
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Why it matters
- Makers: Repeat the same save with comparable records and conditions, record a baseline, and change one major factor at a time so each improvement can be linked to a trace stage.
- Admins/IT: Enable Plug-in Trace Logs during focused troubleshooting, inspect registration and execution context, and disable logging afterward because trace logs use organization storage.
- Leadership/Business: Approve asynchronous execution only for work that does not need to finish before saving, because it moves the user wait but can introduce queue delays and later completion.
Frequently asked questions
How can Plug-in Trace Logs reveal a slow Dataverse plug-in?
Plug-in Trace Logs show event order and pauses when ITracingService records key stages before and after retrievals, external calls, and major branches.
When should a Dataverse plug-in move from synchronous to asynchronous execution?
Use asynchronous execution when work does not need to finish before the save succeeds. The user waits less, but completion can be delayed by queue processing.
Why should a Dataverse plug-in retrieve only required columns?
Retrieving only required columns reduces unnecessary data work. The guidance also recommends avoiding duplicate retrievals and limiting returned records to those needed.