Lazy Pipelines and Memory

Scenario: Millions of records need filtering, parsing, and aggregation with bounded memory.

New words, in plain English

• Pipeline - stages whose output feeds the next stage
• Materialize - turn lazy values into a stored collection
• itertools - standard tools for composing iterators

Chain generator stages and materialize only at the boundary. Useful itertools tools include islice, chain, count, repeat, and groupby. Laziness reduces memory, not necessarily CPU.

Analogy: A Python program is like a clear set of instructions for a careful helper: names label things, indentation groups steps, and errors explain where the helper became confused.

A small, real example

from itertools import islice
lines = (line.strip() for line in open("app.log"))
errors = (line for line in lines if "ERROR" in line)
for sample in islice(errors, 5):
    print(sample)

Tip: Do not call list() too early or the memory benefit disappears.

Goal: Practice this idea in the py-generators challenge at /labs/python. Fix the broken program, run it yourself, then use check to prove the real end state.