Log Analysis System - Variations and Follow-ups
Common Variations
1. Security Information and Event Management (SIEM)
- Security-focused log analysis
- Threat detection
- Compliance reporting
- Incident response
2. Application Performance Monitoring (APM)
- Application logs with traces
- Performance metrics
- Error tracking
- User session tracking
3. Audit Logging
- Compliance-focused
- Immutable logs
- Long retention (7+ years)
- Access controls
4. IoT Log Analysis
- High volume sensor data
- Time-series focus
- Edge processing
- Bandwidth optimization
Follow-up Questions
Q: How do you handle log spikes? A: Kafka buffering, backpressure, rate limiting, auto-scaling, sampling during spikes.
Q: How do you ensure no log loss? A: At-least-once delivery, Kafka retention, acknowledgments, dead letter queue, monitoring.
Q: How do you handle unstructured logs? A: Grok patterns, regex parsing, ML-based parsing, fallback to raw storage.
Q: How do you optimize search performance? A: Index optimization, query caching, shard allocation, field data caching, doc values.
Q: How do you handle PII in logs? A: Redaction at ingestion, field-level encryption, access controls, audit logging.
Q: How do you correlate logs across services? A: Trace IDs, correlation IDs, distributed tracing integration, log enrichment.
Q: How do you reduce storage costs? A: Compression, tiered storage, lifecycle policies, sampling, field exclusion.
Q: How do you handle multi-tenancy? A: Index per tenant, field-level security, separate clusters, resource quotas.
Edge Cases
Log Storms
- Application logging too much
- Infinite loops
- Error cascades
- Handling: Rate limiting, circuit breakers, sampling, alerts
Parsing Failures
- Malformed logs
- Schema changes
- Encoding issues
- Handling: Fallback parsing, raw storage, monitoring, alerts
Clock Skew
- Incorrect timestamps
- Out-of-order logs
- Time zone issues
- Handling: Server-side timestamps, NTP sync, timestamp validation
These variations demonstrate comprehensive understanding of log analysis systems.