Production CDC Platform with Intelligent Query Routing

A complete production-ready Change Data Capture (CDC) platform that provides real-time data replication from MySQL to ClickHouse with intelligent query routing through ProxySQL.

🏗️ Architecture Overview

graph TB
subgraph "Data Sources"
APP[Applications]
API[APIs]
end
subgraph "OLTP Layer"
MYSQL[(MySQL 8.0<br/>Primary Database)]
BINLOG[Binary Logs<br/>ROW Format]
end
subgraph "CDC Pipeline"
DEBEZIUM[Debezium Connect<br/>CDC Connector]
REDPANDA[(Redpanda<br/>High-Performance Messaging)]
end
subgraph "OLAP Layer"
CLICKHOUSE[(ClickHouse<br/>Analytics Database)]
KAFKA_TABLES[Kafka Tables]
MAT_VIEWS[Materialized Views]
end
subgraph "Intelligent Routing"
PROXYSQL[ProxySQL<br/>Query Router]
end
subgraph "Clients"
OLTP_CLIENTS[OLTP Clients<br/>Transactions]
OLAP_CLIENTS[OLAP Clients<br/>Analytics]
end
APP --> PROXYSQL
API --> PROXYSQL
PROXYSQL -->|OLTP Queries| MYSQL
PROXYSQL -->|OLAP Queries| CLICKHOUSE
MYSQL --> BINLOG
BINLOG --> DEBEZIUM
DEBEZIUM --> REDPANDA
REDPANDA --> KAFKA_TABLES
KAFKA_TABLES --> MAT_VIEWS
MAT_VIEWS --> CLICKHOUSE
OLTP_CLIENTS --> PROXYSQL
OLAP_CLIENTS --> PROXYSQL
style MYSQL fill:#e1f5fe
style CLICKHOUSE fill:#f3e5f5
style REDPANDA fill:#e8f5e8
style PROXYSQL fill:#fff3e0

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🚀 Quick Start

Prerequisites

• Docker and Docker Compose
• 8GB+ RAM available
• 20GB+ disk space

💡 VS Code users: open the repo and pick Dev Containers: Reopen in Container — the full CDC stack starts in the background, ports are forwarded automatically, and Redpanda Console pops open on :8080.

1. Deploy the Platform

./setup-production-cdc.sh

2. Load Test Data (Optional)

./load-imdb-data.sh # Loads 194M+ IMDb records

3. Test the Setup

./test-cdc.sh

4. Connect and Query

# All queries through intelligent routing
mysql -h127.0.0.1 -P6033 -uproxyuser -pproxypass123
# OLAP queries automatically go to ClickHouse
SELECT titleType, COUNT(*) FROM imdb.title_basics GROUP BY titleType;# OLTP queries automatically go to MySQL 
INSERT INTO imdb.title_basics VALUES ('tt9999999', 'movie', 'Test', 'Test', 0, 2024, NULL, 120, 'Drama');

📊 Performance Results

Test Environment: MacBook Pro M2

• Dataset: 194M+ IMDb records across 7 tables
• Largest Table: 94M records (title_principals)
• Total Processing Time: ~45 minutes end-to-end

Key Metrics

Metric	Value
CDC Processing Speed	~200K records/second
Large Transaction Processing	94M records in 12 minutes
ClickHouse Query Performance	21x faster than MySQL on aggregations
OLAP Query Response	0.033s for COUNT with WHERE on 2M+ records
OLTP Query Response	0.080s for point lookups
Resource Usage	Max 1141% CPU during complex analytics

🔧 Components

Core Services

• MySQL 8.0: Primary OLTP database with CDC-enabled binary logging
• Debezium Connect: Real-time CDC connector for MySQL
• Redpanda: High-performance Kafka-compatible message broker
• ClickHouse: Column-oriented analytics database
• ProxySQL: Intelligent query routing based on query patterns

Networking

• MySQL: Port 3306
• ClickHouse: Ports 9000 (native), 8123 (HTTP), 9004 (MySQL protocol)
• Redpanda: Ports 9092 (external), 29092 (internal)
• ProxySQL: Port 6033 (application), 6032 (admin)
• Redpanda Console: Port 8080

🎯 Query Routing Rules

ProxySQL automatically routes queries based on patterns:

OLAP → ClickHouse

• COUNT(*), SUM(), AVG(), MAX(), MIN()
• GROUP BY operations
• Complex aggregations and analytics
• Time-series queries

OLTP → MySQL

• INSERT, UPDATE, DELETE
• Point lookups (WHERE id = ?)
• Transactional operations
• SELECT without aggregations

🐛 Lessons Learned & Production Caveats

1. Decimal Handling in CDC

Issue: Debezium's default decimal handling caused JSON parsing errors in ClickHouse.

Solution: Added "decimal.handling.mode": "double" to all Debezium connectors.

{
"config": {
"decimal.handling.mode": "double"
}
}

2. Large Transaction Processing

Issue: Single 94M record transaction created 42M+ Kafka messages, causing significant processing lag.

Observations:

• Peak lag reached 42M messages
• Processing completed in 12 minutes
• System remained stable throughout
• CPU usage peaked at 1141% on M2

Production Recommendations:

• Split large data loads into smaller batches (1M records)
• Use chunked loading with manual commits
• Consider CDC pause/resume for initial bulk loads
• Monitor Kafka topic retention and storage growth

3. ClickHouse Kafka Table Configuration

Issue: Initial setup failed with heredoc syntax errors in Kafka table creation.

Solution: Switched from multiquery heredocs to individual command execution:

# Before (failed)
clickhouse-client --multiquery << 'EOF'CREATE TABLE kafka_table...CREATE MATERIALIZED VIEW...EOF# After (working)
clickhouse-client --query "CREATE TABLE kafka_table..."
clickhouse-client --query "CREATE MATERIALIZED VIEW..."

4. CDC DELETE Operation Handling

Issue: DELETE operations from MySQL don't replicate to ClickHouse by default.

Current Behavior: Materialized views filter for ('c', 'u', 'r') operations only.

Production Solution: Implement ReplacingMergeTree with CDC operation tracking:

CREATETABLEtable_name (
-- columns
cdc_operation String,
cdc_timestamp DateTime64,
is_deleted UInt8 DEFAULT 0
) ENGINE = ReplacingMergeTree(cdc_timestamp)
ORDER BY primary_key;

5. Resource and Storage Management

Observations:

• Kafka topic storage grew to 102GB during large transaction processing
• Memory usage peaked at 10.7GB for ClickHouse during complex queries
• Docker volume management is critical for production deployments

6. Database Naming Consistency

Issue: Mixed usage of imdb vs imdb_cdc database names caused routing confusion.

Solution: Standardized on imdb database name across all components for consistent ProxySQL routing.

7. Kafka Partition Scaling

Observation: Large tables (50M+ records) benefit from multiple partitions:

• title_principals: 5 partitions for 94M records
• title_akas: 3 partitions for 52M records
• Optimal partition count: ~10-20M records per partition

🔧 Troubleshooting

Check Service Health

docker compose ps

Monitor CDC Lag

./monitor-cdc-lag.sh

View Kafka Topics

docker exec redpanda-cdc rpk topic list
docker exec redpanda-cdc rpk group describe clickhouse-cdc-group

Check ProxySQL Routing

mysql -h127.0.0.1 -P6032 -uadmin -padmin -e "SELECT * FROM stats_mysql_query_rules;"

ClickHouse Query Performance

docker exec clickhouse-cdc clickhouse-client --password clickhouse123 --query "SELECT query, elapsed, memory_usage FROM system.query_log WHERE event_date = today() ORDER BY event_time DESC LIMIT 10;"

Check Volume Sizes

docker system df -v

Kafka Retention Management

# Check topic storage usage
docker exec redpanda-cdc du -sh /var/lib/redpanda/data/kafka/
# Clean up initial load data (reduces 100GB+ to ~7GB)
./configure-kafka-retention.sh
# Monitor CDC lag during cleanup
./monitor-cdc-lag.sh

🚀 Production Considerations

Scaling ClickHouse Horizontally

• Distributed Tables: Shard data across multiple ClickHouse nodes
• Replication: Use ClickHouse Keeper for automatic failover
• Mixed Clusters: Combine sharding and replication for ultimate performance

CDC Pipeline Optimization

• Parallel Processing: Increase Kafka partitions for high-throughput tables
• Batch Sizing: Tune Debezium connector batch settings
• Compression: Enable Kafka message compression for storage efficiency

Monitoring and Observability

• Metrics: Integrate with Prometheus/Grafana
• Alerting: Set up alerts for CDC lag, query performance, and resource usage
• Logging: Centralized logging for all components

🎯 Production Deployment Patterns

Chunked Loading Strategy

# Split large files
split -l 1000000 large_file.tsv chunk_
# Load in batches with progress trackingforchunkin chunk_*;doecho"Loading $chunk..."
mysql -h127.0.0.1 -P3306 -uroot -ppassword db -e " SET autocommit=0; LOAD DATA INFILE '/data/$chunk' INTO TABLE large_table; COMMIT;"# Monitor CDC lag before next batch
./check-cdc-lag.sh
done

CDC Connector Tuning

{
"config": {
"max.batch.size": "2048",
"max.queue.size": "8192", "poll.interval.ms": "1000",
"decimal.handling.mode": "double"
}
}

📈 Next Steps & TODO

Phase 2: Production Enhancements

1. Horizontal Scaling: Multi-node ClickHouse cluster configuration
2. DELETE Handling: Implement ReplacingMergeTree pattern for full CDC operations
3. Security: Add TLS encryption and proper authentication
4. Backup/Recovery: Automated backup strategies for both databases
5. Monitoring: Full observability stack integration

Performance Optimization

6. Chunked Loading: Implement batched data loading to avoid massive transactions
7. Resource Tuning: Optimize container resource allocation for production workloads
8. Storage Management: Implement Kafka topic retention policies

🏆 Success Metrics

This platform successfully demonstrated:

• ✅ Real-time CDC processing of 194M+ records
• ✅ Intelligent query routing with 21x performance improvement
• ✅ Stable handling of massive transactions (94M records)
• ✅ Production-grade architecture on consumer hardware
• ✅ Sub-second analytics on complex queries
• ✅ Sustained processing at 200K+ records/second

Tested on MacBook Pro M2 - Ready for production deployment and scaling! 🚀

🤝 Contributing

This platform was built and tested with real production workloads, processing 194M+ IMDb records with lessons learned from handling massive datasets, complex queries, and CDC pipeline optimization.

For issues, improvements, or questions about production deployment, please refer to the project repository.