Fan-out/fan-in is an orchestration pattern that splits a single task into multiple parallel subtasks, executes them simultaneously, and merges the results back together. It transforms sequential bottlenecks into parallel throughput. For businesses, this means batch operations that took hours complete in minutes. Without it, processing time scales linearly with volume.
Your team needs to verify 200 customer records against three different databases.
One at a time, that takes 6 hours. Nobody can do anything else until it finishes.
The work could run in parallel. But your systems only know how to work one thing at a time.
Sequential work is a bottleneck disguised as simplicity.
ORCHESTRATION LAYER - Splits work, runs it in parallel, and brings results back together.
Layer 4: Orchestration & Control | Category 4.1: Process Control
One task becomes many, then many become one
Fan-out/fan-in is an orchestration pattern that takes a single task, splits it into multiple parallel subtasks, executes them simultaneously, and then merges the results back into a unified output. Instead of processing 100 items one after another, you process all 100 at once and wait only for the slowest one.
The "fan-out" phase distributes work across multiple paths. The "fan-in" phase collects results and reassembles them. Think of it like a highway that expands to 8 lanes through a city, then merges back to 4 lanes on the other side. More lanes mean more throughput.
The limiting factor shifts from total work to the slowest individual task. If 100 items each take 1 second sequentially, that is 100 seconds. In parallel, it is closer to 1 second plus overhead.
Fan-out/fan-in solves a universal problem: how do you complete independent work faster by doing it simultaneously instead of sequentially? The same pattern appears anywhere multiple similar tasks can run without depending on each other.
Start with a collection of work items. Distribute them across parallel workers. Execute independently with no shared state. Collect all results. Merge into a single output.
Five batches of address verification. Sequential processes them one at a time. Parallel runs all five simultaneously. Click to see the difference.
Three phases: split, execute, merge
Divide work into parallel paths
Take the incoming work and distribute it across multiple workers. Each worker gets an independent subset. A list of 100 records becomes 10 batches of 10, each handled by a separate process.
Run all paths at once
Each worker processes its subset independently. No coordination needed during execution. Workers can be different services, threads, or even different machines. The key is that they do not wait for each other.
Combine results back together
Wait for all parallel paths to complete. Collect their outputs. Merge them into a single result that looks like it came from one operation. Handle any failures from individual paths.
"Verify 2,000 addresses before the quarterly mailing"
The ops team needs to validate customer addresses before a newsletter goes out. Sequential validation would take 3+ hours. Fan-out splits the work across 50 parallel workers, each validating 40 addresses. Fan-in collects all results and flags addresses that need attention.
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You fan 10,000 records out to 10,000 parallel workers. The downstream API rate-limits you. The database connection pool exhausts. Everything times out. More parallelism is not always better.
Instead: Set sensible concurrency limits based on downstream capacity. 50 parallel workers with rate limiting beats 10,000 that all fail.
You fan out 100 tasks. 98 succeed, 2 fail. Your fan-in phase treats this as complete success because "most of it worked." Users later find missing data and lose trust.
Instead: Track individual task status during fan-in. Report partial failures clearly. Decide whether partial success is acceptable for this workflow.
Worker A needs results from Worker B to complete. But they are running in parallel. Worker A waits. Worker B waits for Worker A. Deadlock. Nothing completes.
Instead: Parallel work must be truly independent. If tasks depend on each other, they belong in sequential chains, not fan-out patterns.
You process everything sequentially and have not explored parallelism.
First step: Identify one batch operation that takes more than 10 minutes. List its steps and mark which ones could run independently.
You have some parallel processing, but it is inconsistent or hard to debug.
First step: Audit your current parallel workflows for error handling. Check: does a single failure take down the entire batch, or can you recover partial results?
You are looking to scale parallel operations without hitting rate limits or resource exhaustion.
First step: Implement concurrency controls with backpressure. Start with a semaphore pattern that limits to 50 parallel workers and adjust based on downstream capacity.
You have learned how to split work across parallel paths and merge results. The natural next step is understanding how to orchestrate more complex workflows that combine sequential and parallel patterns.