Parallel execution is an orchestration pattern that runs multiple independent operations at the same time instead of one after another. It works by identifying tasks that do not depend on each other and processing them concurrently. For businesses, this means workflows that take minutes instead of hours. Without it, every task waits in line behind every other task.
Your daily report pulls data from five different systems. One at a time.
The first query finishes. Then the second. Then the third. Twenty minutes later, you have a report.
Each system could have answered in 4 minutes. But you made them wait their turn.
When tasks do not depend on each other, there is no reason to make them wait in line.
ORCHESTRATION LAYER - Makes workflows faster by running independent tasks at the same time.
Parallel execution takes operations that do not depend on each other and runs them simultaneously. Instead of waiting for task A to finish before starting task B, both tasks start at the same time. The total time becomes the longest single task, not the sum of all tasks.
The key requirement is independence. If task B needs the result of task A, they must remain sequential. But if task A queries a CRM while task B queries a database while task C calls an API, all three can happen at once. Three 10-second operations complete in 10 seconds, not 30.
Parallel execution is not about working harder. It is about not waiting unnecessarily. The work stays the same. The waiting disappears.
Parallel execution solves a universal problem: why wait for something to finish when something else could be starting? The same pattern appears anywhere multiple independent tasks exist.
Identify tasks that do not depend on each other. Start them all at the same time. Wait for all to complete. Continue with results from all paths.
A weekly report needs data from four systems. Toggle between sequential and parallel execution, then run the simulation to see the difference.
Start tasks without waiting for results
Launch multiple operations and continue immediately. Used when you do not need the results to proceed. Notifications, logging, and analytics events often use this pattern.
Run tasks together, wait for all to complete
Launch multiple operations simultaneously and wait until every task finishes. Used when you need all results before proceeding. Report generation and data aggregation use this pattern.
Run tasks together, proceed when any finishes
Launch multiple operations and continue as soon as any one completes. Used for redundancy or finding the fastest provider. Cache checks and load balancing use this pattern.
Answer a few questions to get a recommendation tailored to your situation.
Do you need the results of the parallel tasks to continue?
The ops manager needs data from four different systems for the weekly report. Each query takes about 10 minutes. Running them sequentially takes 40 minutes. Running them in parallel takes 10 minutes. Same work, 75% less time.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
You run a task that writes to the database in parallel with a task that reads from the same database. Sometimes the read happens before the write finishes. Results are inconsistent. Debugging becomes a nightmare because the race condition only happens sometimes.
Instead: Map dependencies before parallelizing. If task B needs results from task A, they must remain sequential. Only parallelize truly independent operations.
You parallelize 100 API calls to an external service. All 100 fire at once. The service rate-limits you or times out. What should have been faster becomes slower as you hit retry logic and backoff delays.
Instead: Add concurrency limits. Run 10 tasks at a time instead of 100. Use semaphores or worker pools to control how many parallel operations can run simultaneously.
Four parallel tasks run. Three succeed. One fails. Your code continues as if everything worked. Now you have incomplete data and no one knows which piece is missing.
Instead: Define failure strategy upfront. Fail-fast stops everything when any task fails. Fail-safe continues and reports failures. Choose based on whether partial results are acceptable.
Parallel execution runs multiple tasks at the same time when those tasks do not depend on each other. Instead of processing items one by one in sequence, parallel execution splits work across multiple paths. A report that pulls data from five different systems can query all five simultaneously rather than waiting for each to finish before starting the next.
Use parallel execution when tasks are independent and do not need results from each other. Good candidates include: gathering data from multiple sources, sending notifications to multiple channels, enriching records with different data providers, or validating against multiple rule sets. If one task needs the output of another, those must remain sequential.
The most common mistake is parallelizing dependent tasks, which causes race conditions where results arrive out of order or incomplete. Another mistake is overwhelming external services by hitting rate limits when all parallel requests fire at once. A third mistake is ignoring partial failures, where some parallel paths succeed and others fail, leaving the system in an inconsistent state.
Parallel execution is the general concept of running tasks simultaneously. Fan-out/fan-in is a specific pattern where work splits into parallel paths (fan-out) and results merge back together (fan-in). All fan-out/fan-in uses parallel execution, but parallel execution can also describe simpler cases like firing two API calls at once without needing to merge their results.
Define a strategy before tasks start: fail-fast stops all parallel work when any task fails, fail-safe continues other tasks and reports failures at the end, and retry adds individual retry logic per parallel path. The right choice depends on whether partial results are useful. Enrichment can tolerate some failures. Payment processing usually cannot.
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Choose the path that matches your current situation
All your workflows run sequentially
Some parallel execution but inconsistent patterns
Parallel execution works but want better performance
You have learned how to run independent tasks simultaneously. The natural next step is understanding how to split work across paths and merge results back together.