Verification
Prove the happy path
- Can explain image vs container
- Can build multi-stage image
- Can inject secrets at runtime
- Can diagnose ports/network/health failures
Phase 32 · Lesson 16
Mastery Capstone Lab
Learning contract
What you should be able to do after this
- Explain Mastery Capstone Lab without relying on framework buzzwords.
- Connect the lesson to the phase outcome: Containerize the production API/web artifacts reproducibly with secure runtime configuration, small images, health checks, networking, and graceful shutdown.
- Reproduce the relevant behavior and verify it with evidence.
- Diagnose at least one representative failure instead of guessing.
Figure 32.16 — TaskFlow boundary map
source + lockfile
build context
dependency/build stages
runtime image
container process
network/healthcheck
orchestrator
Instructor workflow: predict first → implement the smallest slice → run it → inspect evidence → break one assumption → diagnose → explain it back. Do not copy the whole code block before understanding the first failing boundary.
Mastery Capstone Lab
This is the transfer exercise for Phase 32: Docker. The detailed lessons are now closed-book reference material. Your task is to prove that the phase outcome can be reproduced from requirements, repository evidence, and debugging—not by copying a finished implementation.
Target artifact Dockerfiles and compose/dev verification that build and run TaskFlow services from clean inputs.
Acceptance criteria - Write the behavior and constraints before implementation. - Identify the owning layer and every boundary crossed. - Implement the smallest complete vertical slice. - Verify the happy path from outside the implementation. - Demonstrate at least one expected failure path. - Run the phase-relevant quality checks. - Review the diff for accidental duplication, leaked responsibilities, missing validation/authorization, and hidden environment assumptions.
Evidence packet Save the command/request used, expected result, actual result, one failure reproduction, the fix, and a short explanation of why the final responsibility belongs where it does. This packet is more valuable than a screenshot of a green page because it demonstrates repeatable engineering reasoning.
Stretch Change one requirement after the first implementation. Measure which files must change. If a tiny domain change causes unrelated layers to move, use that evidence to discuss coupling before adding another abstraction.
Executable lesson studio · docker
Now turn the explanation into engineering skill.
A guided lesson proves you can follow. A capstone proves you can transfer the model to a requirement with fewer cues.
By the end, you should be able to
- Containerize the production API/web artifacts reproducibly with secure runtime configuration, small images, health checks, networking, and graceful shutdown.
- Produce a reproducible evidence packet for success and failure.
- Defend the architecture and identify one improvement without overengineering.
Figure 32.16B — Mental model
source + lockfile
build context
dependency/build stages
runtime image
container process
network/healthcheck
orchestrator
Code-along sequence
One coherent change at a time
- 01
Write a .dockerignore first so local node_modules, Git history, secrets, and build output do not enter context.
- 02
Create a multi-stage API Dockerfile separating dependencies/build/runtime artifacts.
- 03
Build with frozen lockfile and inspect image layers/size.
- 04
Run the container with runtime env vars and map the service port explicitly.
- 05
Add healthcheck/readiness behavior and test startup plus graceful stop.
- 06
Create a small compose setup only for local integration needs and explain what remains externally managed.
- 07
Run the phase mastery checklist without looking at the provided implementation.
- 08
Perform a short architecture review and document one trade-off you consciously accepted.
txtCapstone evidence packet
Requirement -> implementation diff -> verification -> controlled failure -> diagnosis -> final proofDo not copy this as a finished answer. Predict what each boundary does, type the smallest useful slice, run it, then compare the result with your prediction.
Break it on purpose
Build debugging memory
- Copying .env into image
- Running development server in production container
SymptomInvestigation path
You can only continue by copying the reference
Return to the mental model and acceptance criteria. Rebuild one smaller slice from first principles before reopening the source.
Everything seems to work but proof is weak
Choose an external observer: HTTP client, browser, database query, test runner, health endpoint, or deployment log, and capture deterministic evidence.
Closed-reference challenge
Can you transfer the idea?
Extend the phase artifact with one requirement not used in the guided example while preserving the same ownership rules and verification strategy.
Explain-back
Answer these without reopening the solution
- What did you build without a transcript?
- Which boundary caused the most reasoning effort?
- Which failure gave the strongest evidence?
- What shortcut did you deliberately avoid?
- What would you automate before repeating this in a team?
Lesson studio
Turn the reading into skill
Do it
Run the container with runtime env vars and map the service port explicitly.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Break it safely
Container listens only on localhost internally
Predict the symptom before causing the failure. Then locate the earliest boundary where reality diverges from expectation.Explain it back
Why does this responsibility live here, what input does it trust, what output proves success, and what would go wrong if another layer owned it?
If your explanation is vague, repeat the boundary trace before moving on.Exit ticket
Before marking complete
Close the reference. Reproduce the core idea from memory or explain the exact request/data flow aloud. Then check your diff or notes for accidental shortcuts that bypass validation, authorization, error handling, or verification earned in earlier phases.