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Level 5 / Project 06 - Metrics Summary Engine

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Quick Recall: This project uses numeric aggregation and statistics from structured data. Before starting, make sure you can: load a CSV into a list of dictionaries and compute a column's min, max, and average using a simple loop (Level 1, Project 05 - CSV First Reader).

Estimated time: 70 minutes

Focus

  • aggregate operational metrics

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-5/06-metrics-summary-engine
python project.py --input data/metrics.json --output data/metrics_summary.json
pytest -q

Expected terminal output

Aggregated 3 metrics: response_time, cpu_usage, error_rate
6 passed

Expected artifacts

  • data/metrics_summary.json
  • Passing tests
  • Updated notes.md

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

Alter it (required) — Extension

  1. Add a --window flag that computes moving averages over a configurable window size.
  2. Add standard deviation to the summary statistics for each metric.
  3. Add a --format flag that outputs either JSON or a human-readable table.
  4. Re-run script and tests.

Break it (required) — Core

  1. Add a metric with only one data point and check if percentile calculation breaks.
  2. Add a metric with an empty values list.
  3. Capture the first failing test or visible bad output.

Fix it (required) — Core

  1. Handle single-value metrics gracefully (p50 = p99 = the single value).
  2. Return zeros or nulls for empty metrics with a clear warning.
  3. Add tests for single-value and empty-metric edge cases.
  4. Re-run until output and tests are deterministic.

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

Explain it (teach-back)

  1. How does percentile compute p50 vs p99 from a sorted list?
  2. Why is moving average useful for smoothing noisy time-series data?
  3. What happens if you pass window=0 to moving_average?
  4. Where do you see metrics aggregation in production (Prometheus, Datadog, Grafana)?

Mastery check

You can move on when you can: - run baseline without docs, - explain one core function line-by-line, - break and recover in one session, - keep tests passing after your change.

Stuck? Ask AI

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

  • "I am working on Metrics Summary Engine. I got this error: [paste error]. Can you explain what this error means without giving me the fix?"
  • "I am trying to aggregate metrics (min, max, mean, percentiles) from a list of numeric values. Can you explain how to calculate the 95th percentile?"
  • "Can you explain the difference between mean, median, and mode with examples of when each is most useful?"
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