← कोर्सच्या मुख्य पानाकडे परत

🗓️ अभ्यास योजना — 12 धडे, 4 आठवडे

~3 sessions a week: read the lesson (~15 min) + run and change its section of perf/demo.py (~25 min). Tick lessons off — progress is saved in this browser.

🐢 स्थिर: आठवड्याला 3 सत्रे → 4 आठवड्यांत पूर्ण.
🐇 वेगवान: प्रत्येक भागासाठी एक weekend → 3 weekends मध्ये पूर्ण.
💰 खर्च: शून्य. तुमच्या लॅपटॉपवर Python 3.
0 / 12

WEEK 1 ⏱️ Stopwatches and honest heats

Latency and percentiles, benchmarking, profiling. Lab: change the share of slow runners; drop fewer warm-up runs; profile with more laps.

~35 मि.
~40 मि.
~40 मि.
🏁 Checkpoint: you can read p50/p95/p99, say whether a change beat the noise, and name the hot function from a profile.

WEEK 2 📈 Curves and memory

Complexity, memory, caching. Lab: double n again; compare list and generator peaks; change the cache size and the miss cost.

~40 मि.
~35 मि.
~40 मि.
🏁 Checkpoint: you can predict how work grows, what a program holds at once, and what a cache's hit ratio buys.

WEEK 3 🔁 Trips, batons and queues

I/O and N+1, concurrency and the GIL, queueing. Lab: grow the runner count; mix CPU and waiting; push utilisation towards 100%.

~40 मि.
~45 मि.
~40 मि.
🏁 Checkpoint: you can spot an N+1, choose threads, asyncio or processes, and explain why 90% busy is already slow.

WEEK 4 🏆 Plans, pages and budgets

Query plans, web performance, load tests and budgets. Lab: add and drop indexes; slow the network; tighten a budget until it fails.

~40 मि.
~45 मि.
~40 मि.
🏁 Checkpoint: 🏆 Capstone: take the sports-day results service end to end — profile it, fix one N+1 and one missing index, trim the page to a byte budget, load-test it to find the knee, and write the CI budget that keeps it there.

🎓 12 / 12 — every runner timed, the relay is faster!

Next: the Scaling school adds machines when one is not enough, the Observability school measures it in production, and the School portal has the rest.