The Memory-Hungry CSV Parser
You are the data engineer on call for this production path. A Python ingestion job reads a 40 GB CSV into pandas and crashes despite running on a large VM. What would you investigate, how would you fix it safely, and how would you prove the issue is resolved?
Scenario context
The incident centers on bounded-memory ingestion and streaming parsing. The current implementation or operating process does not make that contract explicit, so the team needs a diagnosis supported by evidence rather than a tool or configuration guess.
Business requirement
Identify the most likely failure mechanism, propose a reversible production-safe fix, and explain validation, trade-offs, monitoring, and recovery.
Schema
Python Ingestion evidence is shown below. Treat it as a production review artifact rather than a toy exercise.Broken logic / code
import pandas as pd
# Broken: materializes the complete 40 GB file in one process.
orders = pd.read_csv(input_path)
orders = orders.drop_duplicates(subset=['order_id'])
orders.to_parquet(output_path)Logs / error
[Production review] Scenario 121: The Memory-Hungry CSV Parser
Observed symptom: A Python ingestion job reads a 40 GB CSV into pandas and crashes despite running on a large VM. What would you investigate, how would you fix it safely, and how would you prove the issue is resolved?
Core contract at risk: bounded-memory ingestion and streaming parsing.
Evidence to collect: Track requests, latency, status codes, retry count, throttle time, checkpoint age, duplicate rate, and rejected records. Add reconciliation between source-reported counts and landed records. Run failure tests for timeout, replay, malformed payloads, and partial publication.Actual output
A Python ingestion job reads a 40 GB CSV into pandas and crashes despite running on a large VM. What would you investigate, how would you fix it safely, and how would you prove the issue is resolved?Expected output / expected logic
A strong response should define the contract, rank likely causes, propose a safe fix, and prove correctness with monitoring and reconciliation.Your attempt
Write your answer
Think before revealing the answer. A partial but honest attempt is better practice than reading the model solution first.
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Interview-style explanation
Now explain your solution as if you are in an interview: symptom, root cause, fix, edge cases, trade-offs, monitoring, and prevention.