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Web Workers · 17 Worker Pool
Series contents · Technology map
Stage: Queues and Error Recovery · Editable example · Storage ID: web-workers-step-17
Goals and learning outcomes
- Learning outcome: explain the role of Parallelism. Input cases: 6 tasks of different sizes.
- Deliverable: per-worker task intervals and total completion time. Explain the rationale for handling Idle assignment, linking it to the code.
Prerequisites
Prepare the experiment results for Backpressure and State transitions from Unit 16, “A Bounded Task Queue”.
Key concepts and explanations
Parallelism
Multiple Workers can divide independent tasks. CPU contention and creation costs also increase.
Idle assignment
Assign tasks only to idle Workers. Do not confuse completion order with submission order.
Practice setup
First check the execution scope guidance on the example screen. The following describes the environment needed to extend the assignment in a separate project. The existing web-workers-01·02·03 are starting examples. Implement the new assignments in main.js and worker.js in a separate local HTTP project. Preserve CSP and iframe isolation. For SharedArrayBuffer, check COOP and COEP requirements on an external server you control; use the transfer path if they are not met. Measure performance yourself.
The linked example is a starting point. It does not implement every task below. Complete work beyond its supported scope in a separate project.
Practice tasks
- In src/day-17.js in a separate practice project, create an idle list and task dispatcher for each pool size. Refer to the existing Unit 1·14·28 examples only as starting points.
- Input conditions: 6 tasks of different sizes. Baseline: 1 Worker. Run under the baseline conditions, then record observations and errors in notes/day-17.md.
- Variable: Pool size; changed value: 2 Workers. Run again through the same input path. Deliverable to save: per-worker task intervals and total completion time. Hypothesis to check: Concurrency increases, but measurements determine whether speed improves. Compare it with the actual results, and record whether they agree and why they differ.
Variables to observe
| Variable | Baseline | Change | Expected change / hypothesis |
|---|---|---|---|
| Pool size | 1 Worker | 2 Workers | Concurrency increases, but measurements determine whether speed improves. |
Predictions in this table are not measured results. Change one condition at a time and record why the actual result differs.
Completion criteria
- Required deliverable: per-worker task intervals and total completion time. Results are available for both the baseline and changed conditions, allowing comparison of 2 conditions; input values and the execution environment are recorded together.
- The explanation of differences between the two conditions connects both concepts: Parallelism and Idle assignment. For unexpected items, at least 1 cause or reason they remain unresolved is recorded.
Self-check
- Which processing stage does Parallelism affect in this exercise? Input cases: 6 tasks of different sizes.
- Variable: Pool size. Hypothesis to check: Concurrency increases, but measurements determine whether speed improves. Did the actual results agree with the hypothesis? Explain the evidence in terms of Idle assignment.
Official references
Linked example and execution scope
Example source · Runtime environments and limits
Interactive Worker Pool experiment with resource cleanup.
Source code and records are not overwritten automatically. Resetting an existing example also preserves your notes.
Next learning
Previous: A Bounded Task Queue · Next: Restoring Result Order