Built-in operation costs

Opening your lesson

A list search compares values until it finds a match. A dictionary lookup uses a different data structure, and deleting the first list item shifts the remaining references. These details matter when a small helper sits inside a busy loop.

You count operations here instead of timing a browser. A count is reproducible and makes the growth visible. It also forces you to say what you counted: comparisons, shifted references, input visits or unordered pairs. Hash-table lookups have average constant cost under ordinary conditions; that is a model with assumptions, not a guarantee about every object or collision pattern. Preserve results while changing the container.

import json

def process_payload(payload: str) -> str:
    data = json.loads(payload)
    items = data['items']
    counts = []
    for query in data['queries']:
        count = 0
        for item in items:
            count += 1
            if item == query:
                break
        counts.append(count)
    return json.dumps(counts,separators=(',',':'))

Count the successful comparison too; a missing query scans every item. Use a fresh comparison counter for each query. Walk the list until the first equal value. For n items and q queries, the worst-case comparison count grows as n times q.

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