Box·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

May 2026

Summary

Box system design round, basically one big scenario question about debugging a production outage with only SSH access, then pivoting to how you'd build something better. Not a lot of back-and-forth, more like they wanted to see how far you'd run with it.

Questions Asked (2)

A production service is down and you only have SSH access to the host. The log files are massive. Walk through how you'd find the relevant errors or narrow down the time window, and what specific commands or strategies you'd use.

Root Cause AnalysisTechnical Trade-offs

Author's notes

I started with grep and tail and they were clearly waiting for more.

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AI HintsAI Generated

Suggested Approach

Start by establishing a timeline and using log rotation or timestamps to narrow the search window, then apply targeted filtering with tools like grep, awk, and journalctl. Emphasize a systematic, iterative approach that balances speed with thoroughness, and mention how you'd avoid overwhelming the system.

Pro tip: Use `tail -f` on the most recent log while reproducing the issue, and leverage `grep -B`/`-A` for context around errors—this often reveals the root cause faster than scanning entire files.

1. Orient and define the time window

Check the current time, service start time, and recent log file timestamps to bound your search. Use `date`, `uptime`, and `ls -lt` on log directories to identify the most relevant files.

2. Filter for errors and anomalies

Use `grep -i` for keywords like 'error', 'exception', 'fail', 'timeout', and 'panic' across recent logs. Combine with `tail` or `head` to limit output and avoid overwhelming the terminal.

3. Narrow by time and correlate events

If logs have timestamps, use `awk` or `sed` to extract entries within a specific window. Cross-reference with system logs (`journalctl`, `dmesg`) and application logs to build a timeline.

4. Analyze patterns and context

Use `grep -B` and `-A` to see lines before and after errors, and `sort | uniq -c` to find frequent error messages. Look for stack traces or repeated failures that point to a root cause.

5. Mitigate and document

Once the issue is identified, apply a quick fix (e.g., restart service, rollback) and document findings for post-mortem. Use `history` and command outputs to create an audit trail.

Key Points to Mention

Log rotation and compression (e.g., .gz files) — use `zgrep` or `zcat` to search without decompressing.
Systemd journal: `journalctl -u service --since "10 min ago"` for time-bound queries.
Efficient filtering: `grep`, `awk`, `sed`, and `cut` to parse large files without loading them entirely.
Avoiding resource exhaustion: use `nice`, `ionice`, and limit output with `head`/`tail`.
Correlating multiple log sources (application, system, kernel) to pinpoint the failure.
Using `tail -f` in real-time while reproducing the issue to capture transient errors.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

If this kind of incident keeps happening, how would you design a centralized logging and search system, and what trade-offs would you think through?

System DesignTechnical Trade-offs

Author's notes

This is where it got more interesting.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (ingest volume, retention, query patterns, latency, compliance) before proposing a design. Then walk through a high-level architecture (collection, transport, storage, indexing, query/UI) and explicitly discuss trade-offs at each layer, tying choices back to the incident-recurrence problem.

Pro tip: Anchor trade-offs to concrete SLOs and cost: e.g., 'hot vs cold storage based on 7-day query SLA' or 'sampling vs full fidelity for high-volume debug logs'—this shows you optimize for real constraints, not just buzzwords.

1. Clarify requirements and scale

Ask about log volume (GB/day), sources, retention period, query latency needs, compliance (PII, GDPR), and budget. Define what 'incident keeps happening' means—what queries must be fast to detect and debug it.

2. Sketch high-level architecture

Propose a pipeline: agents (Fluentd/Filebeat) → message queue (Kafka) → processing/enrichment → storage (hot: Elasticsearch/OpenSearch; cold: S3) → query/UI (Kibana/Grafana). Mention indexing strategy (e.g., time-based indices).

3. Discuss trade-offs per component

For each layer, compare options: push vs pull collection, Kafka vs direct ingestion, full-text vs structured indexing, hot vs cold storage, self-managed vs managed service. Tie choices to latency, cost, and operational complexity.

4. Address reliability, scale, and cost

Explain how to handle backpressure, data loss, and scaling (partitioning, replication). Discuss cost controls: tiered storage, sampling, retention policies, and compression.

5. Summarize and validate

Recap key decisions and trade-offs, and note how you'd validate the design (e.g., load testing, chaos engineering) and iterate based on incident patterns.

Key Points to Mention

Ingestion pipeline: agents, Kafka for buffering/decoupling, backpressure handling
Storage tiers: hot (Elasticsearch) for recent/fast queries, cold (S3) for cheap long-term retention
Indexing and schema: structured vs unstructured logs, time-based indices, mapping explosion risks
Query performance: distributed search, caching, and optimizing for common incident queries
Cost and operational trade-offs: managed vs self-hosted, retention vs cost, sampling vs full fidelity
Security and compliance: PII redaction, access control, audit logging, GDPR/CCPA considerations

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.