Parse logs and generate NFT attributes

Q: Parse logs and generate NFT attributes

This question evaluates parsing and log-processing skills alongside combinatorics and weighted-sampling competency, covering extraction and time-based grouping of timestamped records, handling malformed input policies, enumeration of Cartesian products, deduplication, and probabilistic generation.

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Question

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You are given two independent coding tasks.

Task 1: Parse and group a log file by thread ID

You are given a list of log lines (strings). Each line contains at least:

a timestamp (parseable and comparable; assume ISO-8601 or an integer epoch),
a thread ID (integer or string),
an arbitrary message .

Example format (you may assume a consistent format across all lines):

2026-02-13T10:15:30Z thread=42 msg=Starting work

Requirements

Parse each line to extract (timestamp, threadId, message) .
Group logs by threadId .
For each threadId , sort that thread’s log entries by timestamp ascending .
Return a structure like Map<threadId, List<LogEntry>> (or Map<threadId, List<String>> preserving original lines) where each list is time-sorted.

Edge cases to handle

Multiple logs with the same timestamp.
Malformed lines (state and apply a reasonable policy: skip, throw, or collect errors).

Task 2: Generate NFT attribute combinations (and follow-ups)

You are given a set of NFT attributes, where each attribute category has a list of possible values.

Example input:

Background: ["Red", "Blue"]
Ears: ["Pointy", "Wide"]
Hat: ["Cap", "None"]

Part A: Enumerate all combinations

Generate all possible NFTs as combinations of choosing exactly one value from each category (i.e., the Cartesian product). Return the list of combinations.

Follow-up 1: Deduplication

If the input may contain duplicates (e.g., Ears: ["Pointy", "Pointy", "Wide"]) or different paths could create the same final combination, ensure the output contains unique combinations only.

Follow-up 2: Weighted/random generation

Now assume each value has an associated weight/probability (not necessarily uniform). Example:

Ears: Pointy=0.6, Wide=0.4

Design an algorithm to randomly generate an NFT (one combination) according to the specified weights per attribute category. Explain how you would implement the sampling step efficiently and how (if required) you would handle avoiding duplicates when generating many NFTs.