As a backend developer working with a tech stack that includes Java, Spring Boot, MariaDB, MySQL, MongoDB, Redis, Kafka, Docker, Ansible, etc., you are often asked in interviews: “Have you faced any challenges in production during or after deployment?”
Below is a comprehensive list of common production issues, along with their solutions. This serves as a quick reference guide for interview preparation:
1. Deployment Failures
Scenario:
During zero-downtime deployment using Docker, traffic was routed to containers before initialization, causing 502 errors.
Solution:
- Added health checks in Docker.
- Used Rolling Updates deployment strategy.
- Implemented Graceful Shutdown hooks in Spring Boot.
2. Database Connection Pool Exhaustion
Scenario:
High-traffic event led to connection pool exhaustion, causing DB connection failures.
Solution:
- Tuned HikariCP connection pool settings.
- Added indexes to optimize slow queries.
- Implemented retry logic with exponential backoff.
- Monitored connection metrics via Prometheus.
3. Performance Degradation After Deployment
Scenario:
A new query caused response time spikes due to full table scans.
Solution:
- Used Spring Boot Actuators to monitor performance.
- Added Redis Caching.
- Analyzed queries using EXPLAIN PLAN and added indexes.
- Used pagination for large data sets.
4. Redis Out of Memory
Scenario:
Redis ran out of memory, leading to key evictions.
Solution:
- Configured TTL for cache keys.
- Set eviction policies based on key importance.
- Implemented Cache Warming and Cache Fallback.
5. Kafka Message Lag or Loss
Scenario:
Consumers fell behind due to high message volume.
Solution:
- Tuned consumer poll timeout and max partition fetch bytes.
- Used multi-threaded consumers for parallel processing.
- Monitored lag using Confluent Metrics and Prometheus.
6. Docker Environment Variable Misconfiguration
Scenario:
Wrong environment variables caused DB connection failure.
Solution:
- Added entrypoint scripts to validate environment variables.
- Used Docker Secrets for secure configurations.
- Implemented rollback strategy for faulty deployments.
7. OutOfMemoryError / Memory Leaks
Scenario:
Batch job led to memory leaks due to unclosed ResultSets.
Solution:
- Analyzed heap dumps using Eclipse MAT.
- Ensured try-with-resources for closing DB connections.
- Set JVM memory limits in Docker containers.
8. Application Crash After Deployment
Scenario:
Edge case input caused the application to crash.
Solution:
- Implemented Circuit Breaker pattern (Resilience4j).
- Added validation and exception handling.
- Strengthened unit testing and introduced chaos testing.
9. YAML Configuration Errors
Scenario:
Configuration typo caused Redis connection failure.
Solution:
- Used Spring Profiles for environment-specific settings.
- Validated YAML during CI/CD.
- Leveraged Ansible for configuration management.
10. Rollback During Deployment Failure
Scenario:
New deployment caused issues; quick rollback required.
Solution:
- Used Blue-Green Deployment.
- Maintained previous stable Docker image version.
- Applied Feature Toggles to control features.
11. Inconsistent State Due to Ansible Playbook Failure
Scenario:
Ansible Playbook partially failed, causing system inconsistency.
Solution:
- Ensured idempotency in Ansible tasks.
- Tested playbooks with Dry Run mode.
- Created rollback playbooks for reversion.
12. Post-Deployment Monitoring
Approach:
- Spring Boot Actuators for application health and metrics.
- Prometheus and Grafana for real-time monitoring.
- ELK Stack for centralized logging.
- Set up alerts via Prometheus or PagerDuty.
13. Concurrency Issues
Scenario:
Race condition during Redis counter updates.
Solution:
- Used Redis atomic operations like INCR.
- Implemented distributed locks with Redis.
14. Biggest Production Challenge Example
Scenario:
Database connection pool exhaustion during a marketing campaign caused downtime.
Solution:
- Increased HikariCP pool size dynamically.
- Implemented Redis Caching.
- Optimized queries using indexes.
- Load tested with JMeter before future events.
Key Takeaways for Interviews:
- Focus on real-world production issues.
- Emphasize root cause analysis (RCA).
- Highlight proactive monitoring solutions.
- Discuss collaboration with DevOps, QA, and DBAs.
This guide will help you confidently answer production-related questions in interviews. Keep practicing and understanding these scenarios deeply!
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