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Cross-reference logs to flag spam numbers | Pinterest Coding Question

Cross-reference logs to flag spam numbers

Company: Pinterest

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: Medium

Interview Round: Onsite

You are given two data sets: ( 1) a call log containing phone numbers observed in calls and ( 2) a list of user reports where each entry is a phone number reported as spam (potentially with duplicates). Parse and cross-reference these inputs to output each spam phone number present in the call log and the number of times it was reported. Describe the algorithm, data structures (e.g., hash maps), handling of invalid numbers, and time/space complexity. Provide code in your preferred language.

Quick Answer: This question evaluates proficiency with data structures (such as hash maps), parsing and deduplication, frequency counting, input validation, and analysis of time and space complexity.

You are given two datasets: a call log containing raw phone numbers observed in calls, and a list of user reports where each entry is a raw phone number reported as spam. Phone numbers may contain formatting characters. Normalize each phone number by removing spaces, hyphens, and parentheses. After normalization, a phone number is valid only if it contains exactly 10 digits and no other characters appeared in the raw input. Invalid numbers should be ignored. Return every spam phone number that appears in the valid call log, along with the number of valid times it was reported as spam. Each phone number should appear at most once in the output, ordered by its first valid occurrence in the call log.

Constraints

0 <= len(call_log) <= 100000
0 <= len(reports) <= 100000
Each raw phone number string has length at most 100
Only spaces, hyphens, and parentheses are allowed as formatting characters
After removing formatting characters, a valid phone number must contain exactly 10 digits
Duplicate reports count multiple times
Duplicate call log entries should not create duplicate output rows

Examples

Input: (['555-123-4567', '(212) 555-0000', '5551234567', '999-999-9999'], ['5551234567', '555-123-4567', '2125550000', '0000000000'])

Expected Output: [['5551234567', 2], ['2125550000', 1]]

Explanation: 5551234567 appears in the call log and is reported twice. 2125550000 appears in the call log and is reported once. 0000000000 was reported but does not appear in the call log.

Input: (['123-45-6789', 'abc-555-1234', '1112223333', '(111) 222-3333'], ['111-222-3333', '1112223333x', '1112223333', '22233344444', '2223334444'])

Expected Output: [['1112223333', 2]]

Explanation: 123-45-6789 has only 9 digits and abc-555-1234 contains invalid letters, so they are ignored. 1112223333 appears in the call log and has two valid reports. Entries with an x or 11 digits are invalid.

Input: ([], ['5551234567', '5551234567'])

Expected Output: []

Explanation: There are valid spam reports, but the call log is empty, so no reported number can be cross-referenced.

Input: (['5551234567'], [])

Expected Output: []

Explanation: The phone number appears in the call log, but there are no spam reports.

Input: (['12', 'not a number', '555#123#4567'], ['5551234567', 'bad'])

Expected Output: []

Explanation: All call log entries are invalid, so nothing is returned even though one report is valid.

Input: (['3333333333', '2222222222', '333-333-3333', '1111111111'], ['1111111111', '2222222222', '222-222-2222', '3333333333', '3333333333', '4444444444'])

Expected Output: [['3333333333', 2], ['2222222222', 2], ['1111111111', 1]]

Explanation: Results follow the first valid occurrence order in the call log. Duplicate call log entries do not create duplicate output rows, while duplicate valid reports increase the count.

Hints

Normalize both datasets into the same canonical 10-digit representation before comparing them.
Use a hash map to count valid spam reports, then scan the call log while tracking which valid numbers have already been output.

Quick Overview

This question evaluates proficiency with data structures (such as hash maps), parsing and deduplication, frequency counting, input validation, and analysis of time and space complexity.