Memory profiling
P34.profiling.02 · Audience: guest, it-ml, language-pro · Prerequisites: Find the hot spot first
Speed is not the only cost. A program that allocates too much slows down — more
garbage collection, worse cache behaviour — and can run out of memory entirely.
The same measure-first discipline applies: tracemalloc profiles allocations
so you can see how much memory your code uses and prove which of two designs is
cheaper.
ⓘ Concept: tracemalloc records where memory comes from
tracemalloc.start() and the runtime records every allocation. tracemalloc.get_traced_memory() returns (current, peak) in bytes — current is what is still live, peak is the high-water mark reached while the code ran. For comparing two implementations, peak is the number to watch. Always stop() when you are done.Why it matters — Time profiling can't tell you a list is quietly eating a gigabyte — allocation profiling can.
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🎓 Practice ladder
1 graded rung · ~15 minMeasure the peak memory of a builder with tracemalloc, and use it
to prove the slots payoff.
Rung 1 — measure peak memory (tracemalloc)
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You can now find both the slow function and the memory hog. The optimisation
track is where you act on what the profile told you — starting with the change
that gives the biggest win: the algorithm.
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