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Overview
A study site built on top of a working Lambda + MinIO sandbox. Read the page, run the code, break things on purpose.
What this is
The repo at the root of this site (ethics/) holds a Python AWS Lambda function — lambda_function.py — that lists PDFs in an S3 bucket under a prefix, paginates, generates 15-minute presigned URLs, and writes a JSONL manifest. It runs locally against MinIO via docker compose, with the same handler signature as a real Lambda. This site explains the surrounding mental model in the order you'd want to study it before walking into a Lambda-heavy interview or production rotation.
How it's organised
The sidebar groups topics into four reading orders. Foundations is the picture in your head. Operating covers the day-to-day knobs. Production covers what changes when real users and real money are involved. Reference holds the must-know checklist (Pitfalls), brief orientations on adjacent tools (Glue, Prometheus/Grafana), the hands-on labs (Labs), and the repo tree (Repository).
How to use it
- Read top-to-bottom — the order in the sidebar is the recommended study path.
- Run the sandbox.
make install && make up && SOURCE_DIR=<dir> make seed && make invoke. The handler executes locally against MinIO; you can break it without burning AWS credit. - Do the labs. Each one mutates the existing app: deploy to real AWS, add an S3 trigger, switch to arm64, enable Provisioned Concurrency, fan out across prefixes with Step Functions, and so on.
- Skim Pitfalls the night before any interview or design review.
System overview
Caller → handler → MinIO/S3 → manifest write-back. The async producer/consumer overlaps S3 LIST calls with presigning + JSONL writes, so the manifest streams to
/tmprather than buffering in memory.
Legend:
- 🟢 Real / live
- 🟡 Ephemeral / caveat
- 🔵 Lambda boundary
- 🔴 Pitfall