Incident Response & System Engineering Case Studies

Production Problem Solving

Below are some of my top 3 detailed case studies of real production challenges I've tackled. Each demonstrates my systematic approach to diagnosing, solving, and preventing infrastructure issues at scale. All examples are from actual production environments, with sensitive details generalized.

Click any case study to expand and read the full details.

#01 Mitigating Network Saturation Under DDoS Attack ▼

Context

Multi-tenant platform serving 3,200+ concurrent users across 12 production environments. Platform hosted on VPS infrastructure with fixed bandwidth allocation. During peak evening hours, users experienced severe connection timeouts and service degradation.

Linux iptables Cloudflare Network Monitoring

The Problem

Symptoms: Sudden spikes in connection requests (5,000+ req/sec), network bandwidth saturation at 95%+, legitimate user connection failures, service response times exceeding 10 seconds.

Business Impact: User complaints increasing, potential revenue loss from service unavailability, reputation damage.

3,200 users affected 95%+ bandwidth usage 5,000+ req/sec

Investigation Process

Step 1: Analyzed network traffic patterns using netstat and tcpdump - identified massive SYN flood from distributed sources targeting port 80/443.

Step 2: Confirmed DDoS pattern - requests coming from 200+ unique IPs, no valid payloads, identical packet sizes.

Step 3: Evaluated mitigation options considering: cost, implementation time, and minimal legitimate user impact.

                            # Network analysis revealed the pattern

                            netstat -an | grep SYN_RECV | wc -l

                            # Output: 4,872 connections in SYN_RECV state

The Solution

Layer 1 - Cloudflare DDoS Protection:

Proxied traffic through Cloudflare's network
Enabled "I'm Under Attack" mode during active incidents
Configured rate limiting rules: max 100 requests per minute per IP
Set up WAF rules to block known attack patterns

Layer 2 - Server-Level Protection:

                            # iptables rate limiting configuration

                            iptables -A INPUT -p tcp --dport 80 -m state --state NEW -m recent --set

                            iptables -A INPUT -p tcp --dport 80 -m state --state NEW -m recent \

                              --update --seconds 60 --hitcount 20 -j DROP

                            # SYN cookie protection

                            sysctl -w net.ipv4.tcp_syncookies=1

                            sysctl -w net.ipv4.tcp_max_syn_backlog=2048

Layer 3 - Monitoring & Alerting:

Set up real-time alerts for connection spike patterns
Created dashboard tracking requests/sec and bandwidth usage
Automated response script to escalate Cloudflare protection level

Results & Impact

Immediate Impact:
✓ Blocked 98% of malicious traffic at Cloudflare edge
✓ Reduced server bandwidth usage from 95% to 35%
✓ Connection timeout errors dropped from 40% to <1%
✓ Service response time restored to <500ms average

Long-term Benefits:
✓ Maintained 99.8% uptime during subsequent attacks
✓ 3,200+ users experienced uninterrupted service
✓ Zero revenue loss from network-related downtime
✓ Established repeatable incident response playbook

Key Takeaways

Defense in depth: Multiple protection layers proved critical - when one fails, others maintain service
Cost-effective security: Cloudflare's free tier provided enterprise-level DDoS protection for smaller operations
Preparation matters: Having monitoring and automated responses in place reduced incident response time from hours to minutes
Document everything: Created runbook that enabled junior team members to handle similar incidents independently

#02 Eliminating Database Query Timeouts Through Strategic Optimization ▼

Context

MySQL database supporting a progression system for 1,800 active users. Database grew to 2.5M+ records across 45 tables. As user base expanded, critical queries began timing out during peak hours, causing interruptions and data loss.

MySQL 5.7 HeidiSQL Slow Query Log EXPLAIN Analysis

The Problem

Symptoms: User actions taking 8-15 seconds to complete, query timeout errors (30s limit exceeded), database CPU usage at 85%+ sustained, application threads blocking on database locks.

Business Impact: Poor user experience, data inconsistencies from failed transactions & increased support tickets.

1,800 users affected 15s+ response time 85% CPU usage

Investigation Process

Step 1: Enabled MySQL slow query log to capture all queries exceeding 2 seconds - identified 23 problematic queries accounting for 80% of database load.

                            -- Most expensive query (12.4s average)

                            SELECT u.*, i.*, s.* 

                            FROM users p

                            JOIN inventory i ON p.id = i.connection_id_id

                            JOIN statistics s ON p.id = s.connection_id_id

                            WHERE p.last_active > DATE_SUB(NOW(), INTERVAL 7 DAY);

                            -- EXPLAIN showed full table scans on all three tables

Step 2: Used EXPLAIN to analyze execution plans - discovered missing indexes, inefficient CONNECT orders, and unnecessary SELECT * operations pulling 100+ columns.

Step 3: Profiled query patterns to understand access frequencies - 70% of queries only needed 5-10 specific columns, not entire row data.

The Solution

Optimization 1 - Strategic Indexing:

                            -- Added composite indexes for common query patterns

                            CREATE INDEX idx_connection_active ON connections(last_active, id);

                            CREATE INDEX idx_inventory_connection ON inventory(connection_id, item_type);

                            CREATE INDEX idx_stats_connection ON statistics(connection_id_id, stat_type);

                            -- Result: Query time dropped from 12.4s to 2.1s

Optimization 2 - Query Refactoring:

Replaced SELECT * with explicit column lists (only what's needed)
Broke complex CONNECTIONs into smaller queries with application-level assembly
Added query result caching for frequently accessed static data

Optimization 3 - Database Configuration Tuning:

                            # my.cnf optimizations

                            innodb_buffer_pool_size = 2G  # Was 128M

                            query_cache_size = 256M        # Enable query caching

                            max_connections = 200          # Increased from 100

                            innodb_flush_log_at_trx_commit = 2  # Less fsync overhead

Results & Impact

Performance Improvements:
✓ Average query time reduced by 73% (12.4s → 3.4s → 0.9s after all optimizations)
✓ Database CPU usage dropped from 85% to 35%
✓ Query timeout errors eliminated completely (0 timeouts in 30 days post-fix)
✓ Application response time improved from 8-15s to 1-2s

Business Impact:
✓ 1,800 users experienced immediate performance improvement
✓ Support tickets related to performance dropped 90%
✓ User retention improved (prevented estimated 150+ user churn)
✓ Database can now handle 3x current load with room to grow

Key Takeaways

Measure first, optimize second: Slow query log pinpointed exact bottlenecks - no guessing required
Low-hanging fruit: Simple indexes provided 80% of improvement with minimal effort
Application-level optimization: Sometimes splitting queries is faster than complex database operations
Monitor continuously: Set up alerts for slow query log size to catch regressions early
Document decisions: Created index strategy guide for future schema changes

#03 Resolving Redis Cache Exhaustion and System Failures ▼

Context

Redis instance (2GB allocated) serving as session store and application cache for 2,100 concurrent users. Cache used for sessions, temporary state, and frequently accessed configuration data. System began experiencing cascading failures during peak load.

Redis 4.0 redis-cli INFO command Memory Profiling

The Problem

Symptoms: Redis memory usage at 100%, OOM (Out of Memory) errors in logs, cache write failures causing application errors, session data loss forcing user logouts, database overwhelmed by cache misses.

Root Cause Discovery: No eviction policy configured - Redis refused new writes when memory full, keys with no TTL accumulated indefinitely, memory fragmentation from inconsistent key sizes.

2,100 users affected 100% memory usage ~500 errors/hour

Investigation Process

Step 1: Connected to Redis and analyzed memory usage patterns:

                            redis-cli INFO memory

                            # used_memory: 2.05GB (exceeding 2GB limit)

                            # mem_fragmentation_ratio: 1.8 (high fragmentation)

                            # eviction_policy: noeviction (problem identified!)

Step 2: Analyzed key distribution and TTL settings:

                            redis-cli --bigkeys

                            # Found 45,000+ keys with no TTL (sessions from months ago)

                            # Largest keys: cached reports (12MB each), old states

Step 3: Categorized key types by importance and access patterns - identified 60% of keys as stale/unused data.

The Solution

Immediate Fix - Memory Cleanup:

                            # Cleaned up stale sessions (older than 7 days)

                            redis-cli KEYS "session:*" | xargs redis-cli DEL

                            # Removed old cached reports

                            redis-cli KEYS "report:*" | while read key; do

                              redis-cli DEL "$key"

                            done

                            # Result: Freed 1.2GB (60% of memory)

Long-term Solution - Eviction Policy:

                            # redis.conf configuration

                            maxmemory 2gb

                            maxmemory-policy allkeys-lru  # Evict least recently used keys

                            # Set appropriate TTLs for all key types

                            SET session:{id} {data} EX 86400     # 24 hour sessions

                            SET cache:{key} {data} EX 3600      # 1 hour cache

                            SET temp:{key} {data} EX 300        # 5 min temporary data

Optimization - Memory Allocation:

Increased Redis memory allocation from 2GB to 4GB (budget allowed)
Implemented key compression for large cached objects (JSON → MessagePack)
Set up separate Redis instance for sessions vs application cache
Configured memory usage alerts at 75% and 90% thresholds

Results & Impact

Technical Improvements:
✓ Memory usage stabilized at 45-60% (2.0GB → 2.4GB used of 4GB allocated)
✓ Zero OOM errors since implementation (30+ days incident-free)
✓ Cache hit rate improved from 72% to 94%
✓ Memory fragmentation reduced from 1.8 to 1.2

User Impact:
✓ Eliminated unexpected session logouts for 2,100+ users
✓ Application errors dropped from 500/hour to <5/hour
✓ Reduced database load by 40% (better cache performance)
✓ System now handles traffic spikes without cache failures

Key Takeaways

Default configurations aren't production-ready: Redis defaults to 'noeviction' which is dangerous for production
Always set TTLs: Every cache key should expire - infinite TTL = memory leak
Monitor memory proactively: Catching at 75% usage prevents cascading failures
Separate concerns: Different Redis instances for sessions vs cache prevents one from affecting the other
Plan for growth: Leave 40-50% memory headroom for traffic spikes and gradual growth

Aymen Bowran - Case Studies

Production Problem Solving