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Level 3 / Project 03 - Logging Baseline Tool

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Try in Browser: Practice similar concepts online — browser exercises cover Level 2 topics

Before You Start

Recall these prerequisites before diving in: - Can you use logging.basicConfig() to set up Python's logging module? - Can you explain the difference between print() and logging.info()?

Estimated time: 35 minutes

Learn Your Way

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— This project — — Flashcards — Browser

Focus

  • structured logs and run summaries

Why this project exists

This project gives you level-appropriate practice in a realistic operations context. Goal: run the baseline, alter behavior, break one assumption, recover safely, and explain the fix.

Run (copy/paste)

Use <repo-root> as the folder containing this repository's README.md.

cd <repo-root>/projects/level-3/03-logging-baseline-tool
python project.py parse data/sample_input.txt
python project.py summary data/sample_input.txt
python project.py parse data/sample_input.txt --min-level WARNING
pytest -q

Expected terminal output

[INFO    ] auth         | User admin logged in successfully
[WARNING ] db           | Query took 2.3s (threshold: 1.0s)
...
12 passed

Expected artifacts

  • Parsed log output on stdout
  • Passing tests
  • Updated notes.md

Design First

Before writing code, sketch your approach in notes.md: - What functions or classes do you need? - What data structures will you use? - What's the flow from input to output? - What could go wrong?

Checkpoint: Baseline code runs and all tests pass. Commit your work before continuing.

Alter it (required) — Extension

  1. What filtering or output options would make this tool more useful for a sysadmin?
  2. Try improving the output format — what information is missing?
  3. Can you redirect output to a file instead of the terminal?

Break it (required) — Core

  1. What happens when the input data does not match the expected format?
  2. Try passing invalid arguments — how does the tool respond?
  3. What edge case produces surprising output?

Fix it (required) — Core

  1. Add validation for the issue you found most confusing.
  2. Make the tool handle empty or minimal input gracefully.
  3. Improve parsing to handle messy real-world log data.

Checkpoint: All modifications done, tests still pass. Good time to review your changes.

Explain it (teach-back)

  1. What is logging.getLogger(__name__) and why use __name__?
  2. What is the difference between logging.basicConfig() and adding handlers manually?
  3. How does the level ordering (DEBUG < INFO < WARNING < ERROR < CRITICAL) work?
  4. Why use @dataclass for LogEntry instead of a plain dict?

Mastery check

You can move on when you can: - configure Python's logging module with handlers and formatters, - filter log entries by level and source programmatically, - explain the logging level hierarchy, - use dataclasses for structured data in a real tool.

Stuck? Ask AI

If you are stuck after trying for 20 minutes, use one of these prompts:

  • "I am working on Logging Baseline Tool. I got this error: [paste error]. Can you explain what this error means without giving me the fix?"
  • "I am trying to configure Python logging with different levels (DEBUG, INFO, WARNING). Can you explain the logging level hierarchy with examples?"
  • "Can you explain the difference between logging.getLogger(__name__) and logging.getLogger('myapp')?"
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