Change Data Capture (CDC) Performance Optimization - AI Knowledge Base

Critical Configuration Settings

PostgreSQL WAL Management

Failure Mode: WAL files accumulate until disk is full, causing complete database failure at 95% disk usage
Required Settings:

• max_slot_wal_keep_size=4GB - CRITICAL: Prevents disk space disasters
• max_replication_slots=10 - Default inadequate for production (limit hit with 3 connectors)
• wal_level=logical - Required but often reset to 'replica' accidentally
• shared_preload_libraries='wal2json' - Better performance than pgoutput
• max_connections=300+ - Default 100 causes "too many clients" errors

Monitoring Query (saves production systems):

SELECT slot_name, active,
       pg_size_pretty(pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn)) as lag_size
FROM pg_replication_slots 
ORDER BY pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn) DESC;

MySQL Binlog Configuration

Failure Mode: Lost binlog position = missing data or complete reprocessing
Required Settings:

SET GLOBAL binlog_format = 'ROW';
SET GLOBAL binlog_row_image = 'FULL'; 
SET GLOBAL expire_logs_days = 7;
SET GLOBAL max_binlog_size = 1073741824;

Performance Reality vs Marketing Claims

Tool	Marketing	Single-AZ Reality	Multi-AZ Reality	First Failure Point
Debezium + PostgreSQL	"Sub-millisecond"	~100ms	~2 seconds	WAL disk space
Confluent Cloud	"Real-time streaming"	~300ms	~1 minute	Budget constraints
AWS DMS	"Low latency CDC"	~5 seconds	~30 seconds	Complex data types
Airbyte CDC	"Near real-time"	~30 seconds	~5 minutes	Not actually streaming
Fivetran	"Instant data sync"	~3 minutes	~10 minutes	Custom logic breaks

Memory and Resource Requirements

Debezium Memory Configuration

Failure Mode: OutOfMemoryError during large transactions (2M+ rows)
Required Settings:

export KAFKA_HEAP_OPTS="-Xmx8g -Xms8g"
export KAFKA_JVM_PERFORMANCE_OPTS="-XX:+UseG1GC -XX:+HeapDumpOnOutOfMemoryError"

# Debezium connector tuning
max.queue.size=16000
max.batch.size=4096

TOAST Field Problem: PostgreSQL large JSON/TEXT fields load entirely into memory
Solution: Exclude large fields: column.exclude.list=user_profile.large_json_field

Resource Scaling Thresholds

• Tables <1000 changes/hour: Don't need optimization
• Tables >10K changes/day: Require dedicated connectors
• Bulk imports >1M rows: Will crash default configurations
• Single connector limit: ~10K events/hour regardless of downstream capacity

Network and Cross-AZ Performance Impact

AWS Multi-AZ Reality

• Cross-AZ latency: 2-3ms average, spikes to 50ms during peak hours
• Performance degradation:
  • Single AZ: 200-300ms CDC latency
  • Multi-AZ: 2-5 seconds average, 30+ seconds during AWS issues
• Cost: Data transfer $0.02-0.12 per GB between regions

Network Optimization Strategy

Deploy components in same AZ despite availability trade-offs - consistent low latency beats theoretical high availability for most CDC use cases.

Common Production Failures and Solutions

WAL Accumulation (Most Common 3AM Alert)

Cause: One slow table holds up all replication slots
Solution: Heartbeat table with regular updates

CREATE TABLE cdc_heartbeat (id BIGINT PRIMARY KEY, last_updated TIMESTAMP);
-- Schedule updates every minute via pg_cron

Kafka Connect Rebalancing

Cause: Network glitches trigger rebalancing, stopping all processing for 30+ seconds
Mitigation:

• Static consumer group membership
• Increase session timeouts: session.timeout.ms=30000
• Monitor rebalancing frequency

Schema Evolution Breaking Changes

Impact: Adding NOT NULL column causes full table scan, 3+ hour CDC lag
Testing Required: Always test schema changes with actual CDC load
Safe Patterns: Add nullable columns first, populate later

Scaling Patterns

Single Connector Bottleneck

Problem: One thread per connector regardless of downstream capacity
Solution: Split high-volume tables into separate connectors

connector-users:
  table.include.list: "users"
connector-orders:  
  table.include.list: "orders"

Deduplication Strategy

Problem: High-frequency tables generate 90% duplicate events
Solution: Buffer events in 30-60 second windows, keep latest per primary key
Result: 70-90% reduction in downstream writes

Critical Monitoring Setup

Essential Alerts

1. WAL lag > 1GB: Immediate disk space risk
2. Kafka consumer lag > 10K messages: Processing falling behind
3. Disk usage > 80%: Before complete failure
4. CDC latency > business SLA: Performance degradation

Monitoring Queries

-- PostgreSQL connection monitoring
SELECT count(*) FROM pg_stat_activity WHERE state = 'active';

-- Long-running queries during snapshots
SELECT pid, now() - query_start AS duration, query 
FROM pg_stat_activity 
WHERE duration > interval '5 minutes';

Cost Reality Check

Budget Planning (3-Year Total Cost)

• Infrastructure: $5-15K/month (Kafka, monitoring, storage)
• Engineering: 1-2 FTE for operations
• Hidden costs: Data transfer, backups, disaster recovery
• Vendor licenses: $50-200K/year for managed services
• Total realistic budget: $500K - $1.5M over 3 years

Cost Optimization

• Spot instances: 50-70% reduction for Kafka brokers
• Reserved instances: 30-50% reduction for PostgreSQL RDS
• Deduplication: Eliminates AWS RDS IOPS bottlenecks

When NOT to Use CDC

Inappropriate Use Cases

• Tables <10K changes/day: Batch ETL simpler and cheaper
• Analytics with 5+ minute tolerance: No need for real-time
• Compliance reporting: Usually requires batch processing anyway

Optimization Threshold

Don't optimize for imaginary scale: Most CDC problems solved by proper database configuration, not complex architectures.

Disaster Recovery Patterns

Recovery Time Objectives

• RTO <15 minutes: Multi-region active-active (expensive)
• RTO <2 hours: Standby infrastructure with manual failover
• RTO <24 hours: Full rebuild from snapshots (cheapest)

Recovery Validation

-- Row count validation after recovery
SELECT COUNT(*) FROM source_table WHERE updated_at > '2025-09-01';
-- Checksum validation for critical data
SELECT SUM(HASH(primary_key, updated_at)) FROM critical_table;

Advanced Patterns (High Complexity)

Snapshot Management

Problem: Single-threaded snapshots take 18+ hours on large tables
Solution: Split by primary key ranges, parallel processing
Alternative: Lock-free snapshots (RisingWave approach)

Multi-Sink Architecture

Pattern: One Kafka topic serving multiple consumers
Benefit: Independent scaling without cross-impact
Implementation: Separate consumer groups with backpressure isolation

Cross-Region CDC

Active-Passive: Primary real-time, secondary batch every 5-15 minutes
Cost consideration: Cross-region transfer costs significant at scale

Technology-Specific Gotchas

Debezium Connector Limits

• Memory leaks: Versions before 2.x with large transactions
• Single-threaded processing: Cannot exceed source DB single-core performance
• TOAST field loading: Entire large fields loaded into memory

Kafka Connect Issues

• Default 1GB heap: Insufficient for production CDC workloads
• Rebalancing delays: All processing stops during consumer rebalancing
• GC tuning required: G1GC recommended for CDC workloads

AWS-Specific Problems

• Cross-AZ latency spikes: Unpredictable 50ms spikes during peak hours
• RDS connection limits: Default too low for CDC + application load
• Data transfer costs: Hidden cost that scales with volume

Performance Ceiling Recognition

Hard Limits

• Single-core database performance: Cannot exceed source DB capabilities
• Network bandwidth: Cross-region limited by WAN capacity
• Storage IOPS: WAL writes bounded by disk performance
• Memory constraints: Large transactions require proportional RAM

Architectural Solutions

Sometimes split databases rather than optimize CDC tools when hitting fundamental limits.

Essential Operational Knowledge

3AM Debugging Checklist

1. Check WAL disk usage first
2. Verify replication slot advancement
3. Monitor Kafka consumer group lag
4. Check for connector rebalancing
5. Validate network connectivity between components

Production Readiness Requirements

• Monitoring: WAL, consumer lag, latency, error rates
• Alerting: Disk space, performance degradation, failure detection
• Runbooks: Recovery procedures for common failure modes
• Capacity planning: 3x initial estimates for realistic budgeting

Team Requirements

• At least one engineer capable of Kafka debugging at 3AM
• Database administration expertise for WAL management
• Network troubleshooting skills for cross-AZ issues
• Budget authority for scaling infrastructure during incidents

Success Criteria Definition

Performance Targets (Realistic)

• Sub-second latency: Only achievable in single-AZ deployments
• Multi-AZ tolerance: Plan for 2-5 second average latency
• Bulk operation handling: System survives 50M row imports
• Schema evolution: Changes complete without multi-hour lag

Business Value Realization

• Data lag reduction: From hours to seconds enables real-time features
• ETL elimination: Engineers stop fighting batch schedules
• Feature enablement: Real-time fraud detection, live dashboards possible
• Operational improvement: Reduced manual data synchronization effort

Critical Decision Points

Build vs Buy Analysis

• Self-managed: High complexity, lower ongoing cost, full control
• Managed services: Lower complexity, higher cost, vendor lock-in
• Hybrid approach: Critical components self-managed, peripherals outsourced

Technology Selection Criteria

1. Existing team expertise: PostgreSQL vs MySQL knowledge
2. Infrastructure constraints: Available resources and budget
3. Performance requirements: Sub-second vs minute-level tolerance
4. Operational capacity: 24/7 support availability

Implementation Phases

1. Proof of concept: Single table, development environment
2. Production pilot: Critical table with full monitoring
3. Gradual rollout: Add tables based on business priority
4. Full deployment: Complete CDC coverage with optimization