You've built workflows with sequential activities and learned to manage their lifecycle. Now a business requirement arrives: process a batch of 50 task items overnight. Each item needs validation, enrichment, and scoring. Running them one at a time would take hours. Running them in parallel would take minutes.
This is where workflow patterns become essential. The right pattern transforms a multi-hour batch job into a quick parallel operation. The wrong pattern creates race conditions or ignores critical dependencies.
Dapr Workflows provides two fundamental patterns that cover most orchestration needs:
Understanding when to use each pattern is the difference between an efficient workflow and a slow, brittle one.
Task chaining executes activities in sequence, passing each activity's output as the next activity's input. Think of it as an assembly line: raw material enters, gets transformed at station 1, the transformed output moves to station 2, and so on until the final product emerges.
Use task chaining when:
Here's a complete task processing pipeline that validates, enriches, and scores tasks in sequence:
Output:
Fan-out/fan-in schedules multiple activities simultaneously, waits for all to complete, then aggregates results. Think of it as a team working on independent tasks: everyone works in parallel, and you combine the results at the end.
Here's a batch processor that analyzes multiple tasks in parallel:
For a batch of 50 items averaging 2 seconds each:
You extended your dapr-deployment skill in Module 7.0. Does it explain workflow patterns and when to use each?
If your skill recommends task chaining and recognizes the sequential dependency, it's working correctly.
Prompt 2: Implement Parallel Processing
Prompt 3: Pattern Selection Challenge
Safety Note: When implementing fan-out patterns, be aware of resource limits. Scheduling 10,000 parallel tasks at once could overwhelm downstream services or exhaust workflow engine resources. For large batches, consider batching your fan-out (process 100 at a time).