Python/Bash And Linux Operational Scripting

What's being tested

Candidates must show practical mastery of Python and Bash scripting for operational tasks: parsing logs, automating workflows, and gluing system tools. Interviewers probe correctness, robustness (error handling, idempotency), and readable maintainable scripts you would check into a repo.

Patterns & templates

• Shebang-led executable scripts — use #!/usr/bin/env python3 or #!/bin/bash and include usage/help flags for operability.

• Robust subprocess handling — prefer subprocess.run(..., check=True) in Python; use set -e and || checks in bash for failures.

• Stream-processing with UNIX tools — combine grep, awk, sed, cut and pipes for line-oriented transforms; avoid large in-memory buffers.

• Idempotent file ops — write to a temp file then mv to target, use atomic renames to avoid partial-state races.

• Logging & exit codes — write structured logs, send errors to stderr, return meaningful nonzero exit codes for orchestration.

• Scheduling & services — prefer systemd timers or cron for simple periodic runs; ensure environment/PATH is explicit and use virtualenvs.

• Process control & signals — handle SIGINT/SIGTERM in Python (signal), trap in bash to clean up temp files and child processes.

Common pitfalls

Pitfall: Assuming the runtime environment — scripts that rely on implicit PATH entries or unstated PYTHONPATH break in CI or cron.

Pitfall: Silent failures — swallowing exceptions or ignoring stderr makes debugging production incidents much harder than printing one clear error and exiting nonzero.

Pitfall: Memory-scaling mistakes — reading whole large log files into memory instead of streaming (for line in file:) causes OOM on real-world datasets.

Practice these

The practice cards below cover the canonical variants — solve all of them and time yourself.