Power Automate Dev Week Day 5: Performance Patterns – Child Flows, Concurrency, and Pagination

Scaling Power Automate Flows the Right Way

As your Power Automate workflows grow, performance becomes a critical factor in ensuring they remain reliable and efficient. Day 5 of Power Automate Dev Week focused on advanced performance patterns that help developers scale their automations without sacrificing maintainability.

Three core strategies stand out: child flows, which enable modular design and reuse; concurrency control, which prevents resource contention and race conditions; and pagination, which allows processing large datasets in manageable chunks. Together, these patterns create a strong foundation for high‑volume, enterprise‑grade automation.

Child Flows: Building Modular, Reusable Automations

Child flows in Power Automate act as separate workflows that a parent flow can call, much like functions in programming. This modular approach simplifies the design of large automations, encourages reuse, and makes debugging far easier.

There are two main types:

• Synchronous (Respond) child flows: These return output to the parent flow before it continues execution. They are best when the parent flow depends on results (e.g., a calculation or status update).
• Asynchronous (Run) child flows: These are “fire-and-forget” workflows that start running while the parent flow continues. They’re ideal for long‑running tasks like batch processing.

Best practices:

• Include structured inputs and outputs for clarity and easy data handling.
• Wrap child flow actions inside scopes to centralize error handling.
• Use versioning in your outputs so parent flows can check compatibility before proceeding.

By moving complex logic into child flows, you can reduce action counts, keep parent flows simple, and enforce consistency across multiple solutions.

Concurrency Control: Preventing Race Conditions and Resource Overload

When flows run at the same time, they can accidentally overwrite data or overload downstream systems. Power Automate’s concurrency control feature helps mitigate this risk by limiting the number of parallel flow instances.

To enable concurrency control, open the trigger settings of your flow and set the Degree of Parallelism to a specific number. For example:

• Set to 1 to ensure only one instance runs at a time (ideal for sensitive updates).
• Allow more parallel runs if your workflow is lightweight and can safely run simultaneously.

Key considerations:

• Concurrency can introduce delays if there’s a high volume of triggers, so monitor queued runs closely.
• For scenarios like Power Apps triggers, use a parent-child flow pattern: the parent responds quickly to the app, while the child processes requests sequentially to avoid timeouts.
• Always log or store execution results so upstream systems can query the status without waiting.

This pattern is crucial for maintaining data integrity, especially in systems where multiple users or apps could trigger the same flow at once.

Pagination: Efficiently Handling Large Data Sets

Flows that process large datasets can quickly hit action limits or experience performance bottlenecks. Pagination allows you to break large datasets into smaller, manageable chunks.

Most connector actions, like List Rows (Dataverse) or Get Items (SharePoint), include pagination settings. You can specify the number of records to retrieve per page (up to 5,000 items) and loop through each page individually.

Best practices:

• Apply OData filters or query parameters to narrow down results at the source before pagination begins.
• Process each page in a controlled loop (e.g., an Apply to each action) to avoid timeouts or memory issues.
• Combine pagination with child flows for even more robust error handling—if one batch fails, you can retry it without reprocessing everything.

This approach ensures your flows remain efficient, scalable, and less prone to timeouts or connector throttling.

Combining the Patterns: A Scalable Architecture for Enterprise Flows

While each of these strategies is valuable individually, their real power comes when you combine them.

• Parent-child flows can encapsulate pagination logic, ensuring each dataset batch is processed independently.
• Concurrency limits can be applied at the trigger level so that high-volume events don’t overwhelm the child flows or backend systems.
• Pagination keeps batch sizes predictable and error handling straightforward.

Example pattern:

1. A parent flow is triggered hourly to check for new records.
2. The parent flow calls a child flow and passes parameters such as a date filter.
3. The child flow retrieves data using pagination, processes each page in parallel-safe chunks, and logs the results.
4. The parent flow summarizes outcomes (successes, failures, counts) and sends notifications.

By integrating these techniques, you create flows that can handle large workloads without slowing down or failing unexpectedly.

Building Reliable, Performant Flows

Performance optimization in Power Automate is about more than just speed—it’s about creating flows that scale gracefully and remain maintainable over time. Child flows provide modularity and reuse, concurrency control ensures reliability, and pagination makes large data processing manageable.

Take time to evaluate your current flows and identify where these patterns can be applied. Whether it’s breaking complex logic into smaller pieces, controlling how many runs execute simultaneously, or handling big datasets more efficiently, these strategies will improve both the performance and reliability of your automations.