CDC Database Platform Implementation Guide: AI-Optimized Technical Reference

Configuration Requirements

PostgreSQL CDC Production Settings

Critical postgresql.conf settings:

• wal_level = logical - Required for CDC
• max_slot_wal_keep_size = 5GB - Prevents disk consumption during outages (WAL can grow to 200GB+ without this)
• max_replication_slots = 10 - Default limit of 5 causes failures with multiple connectors
• wal2json plugin - 30% better performance than default pgoutput, crashes on PostgreSQL 13.2.0 specifically

Debezium connector performance settings:

{
  "max.queue.size": 16000,
  "max.batch.size": 4096,
  "poll.interval.ms": 1000,
  "heartbeat.interval.ms": 60000
}

Database user permissions:

GRANT CONNECT, USAGE ON SCHEMA public, SELECT ON ALL TABLES, REPLICATION TO debezium_user;
CREATE PUBLICATION dbz_publication FOR TABLE public.orders, public.payments;

MySQL CDC Production Settings

Critical my.cnf configuration:

• binlog_format = ROW with binlog_row_image = FULL - Without FULL, CDC misses half the changes
• gtid_mode = ON with enforce_gtid_consistency = ON - Essential for position recovery, without GTID you're gambling with data on every restart
• expire_logs_days = 7 - Prevents binlog deletion during connector failures
• sync_binlog = 1 - Ensures durability but causes 40% write throughput drop

Position tracking failure modes:

• Without GTID: Binlog positions become invalid after rotation, requiring full re-snapshots
• During maintenance: WAL files can grow to 180GB and fill disk if connectors not paused
• Schema changes: Renaming/dropping columns destroys connectors with cryptic error messages

MongoDB CDC Production Settings

mongod.conf requirements:

• oplogSizeMB: 10240 (10GB minimum) - Size for 24+ hours of operations
• Replica set required for change streams
• Resume tokens expire if oplog doesn't retain enough history

Change stream configuration:

{
  "capture.mode": "change_streams_update_full",
  "mongodb.change.stream.full.document": "updateLookup"
}

Pre/post images setup:

db.runCommand({
  collMod: "orders",
  changeStreamPreAndPostImages: { enabled: true }
})

Performance Specifications

Platform Performance Impact

Platform	CDC Method	Overhead	Reliability	Operational Complexity
PostgreSQL	Logical replication	1-3%	Excellent	Medium
MySQL	Binary log parsing	3-8%	Good	High
MongoDB	Change streams	2-5%	Excellent	Low
SQL Server	Built-in CDC	5-10%	Good	Medium
Oracle	LogMiner/GoldenGate	2-15%	Excellent	Very High

Throughput Optimization Settings

High-volume connector tuning:

{
  "max.queue.size": 32000,
  "max.batch.size": 4096,
  "poll.interval.ms": 500,
  "database.connectionTimeoutInMs": 60000
}

Critical Failure Scenarios

WAL/Binlog Accumulation Disasters

PostgreSQL WAL explosion:

• Without max_slot_wal_keep_size: WAL can consume 500GB during weekend deployments
• During 4-hour connector outage: WAL grows to 200GB+ and fills disk
• Prevention: Always pause connectors before maintenance

MySQL binlog position corruption:

• Without GTID: Connectors lose 6 hours of data during routine restarts
• Binlog positions get corrupted and become unusable
• Recovery: Requires full re-snapshot of terabytes of data

MongoDB resume token expiration:

• Tokens expire during maintenance windows longer than oplog retention
• Forces full re-snapshots of 500GB collections
• Sizing: Oplog must cover longest expected outage plus buffer

Schema Evolution Breaking Points

Dangerous schema changes that break CDC:

• Renaming columns (destroys connectors with cryptic errors)
• Dropping columns (invalidates replication slots)
• Adding NOT NULL columns without defaults
• MySQL: ALTER TABLE statements lock tables and cause hours of CDC lag

Safe schema change procedure:

1. Test every change with CDC running in staging - no exceptions
2. Use pt-online-schema-change for large MySQL tables
3. Plan for connector restarts after most schema changes
4. Have rollback plan to drop and recreate connectors

Connection Exhaustion Reality

MySQL default max_connections = 151 is pathetic:

• CDC holds connections permanently
• Applications get "Too many connections" errors during peak load
• Solution: Increase max_connections or use pgbouncer for app connections

Resource Requirements

Time Investments

Initial setup time by platform:

• PostgreSQL: 2-4 hours (straightforward WAL configuration)
• MySQL: 8-16 hours (complex GTID setup and binlog tuning)
• MongoDB: 1-2 hours (native change streams)
• SQL Server: 4-8 hours (CDC enablement and job configuration)
• Oracle: 16-40 hours (complex licensing and LogMiner setup)

Ongoing operational overhead:

• PostgreSQL: Low (occasional WAL monitoring)
• MySQL: High (constant binlog position babysitting)
• MongoDB: Low (resume token monitoring)
• Enterprise databases: Very high (specialized DBA expertise required)

Licensing Costs (3-year total)

Oracle CDC:

• Enterprise Edition: $500K-1.2M per processor
• GoldenGate: $200K-500K additional
• Total: $1.6M-3.2M including infrastructure and operations

SQL Server CDC:

• Standard Edition minimum: $200K-500K
• Total: $750K-1.4M including infrastructure and operations

Open source alternatives (PostgreSQL/MySQL/MongoDB):

• Software: $0
• Operations and infrastructure: $300K-600K

Critical Warnings

Production Deployment Gotchas

TOAST field disasters (PostgreSQL):

• Large JSONB/TEXT fields in TOAST tables crash connectors with OOM errors
• Won't show up until production load hits
• Solution: Exclude large fields with column.exclude.list

Large transaction failures (MySQL):

• Bulk imports create massive binlog events that crash connectors
• Mitigation: Increase binlog.buffer.size and max.queue.size

Sharded cluster nightmare (MongoDB):

• Shard rebalancing invalidates change streams mid-processing
• 50GB backlog processing fails during peak traffic
• Reality: MongoDB's "seamless" balancing isn't seamless with CDC

Enterprise Database Licensing Traps

Oracle licensing shock:

• LogMiner requires Enterprise Edition at $47,500 per processor
• GoldenGate adds $17,500 per processor for real-time features
• Compliance audits discover "free" implementations leading to $200K surprise bills

SQL Server CDC requirements:

• Built-in CDC requires Standard Edition minimum
• Cannot use Express or Web editions for CDC functionality

Disaster Recovery Procedures

Recovery by Outage Duration

Short outages (<1 hour):

• Connectors resume automatically
• Monitor lag and let catch up naturally

Medium outages (1-8 hours):

• PostgreSQL: WAL available, resume normally
• MySQL: Check binlog file existence, may need position reset
• MongoDB: Resume tokens likely valid

Long outages (>8 hours):

• PostgreSQL: May exceed max_slot_wal_keep_size, need fresh snapshot
• MySQL: Binlog files purged, definitely need position reset
• MongoDB: Resume tokens expired, falls back to timestamp-based resume

Nuclear option (complete failure):

1. Delete all connectors
2. Clean database artifacts (drop replication slots, disable/re-enable CDC)
3. Create fresh connectors with "snapshot.mode": "initial"
4. Accept hours-long re-snapshot time

Maintenance Window Procedures

Before maintenance (never skip):

curl -X PUT localhost:8083/connectors/postgres-connector/pause

Monitor lag drops to near zero before proceeding

After maintenance:

curl -X PUT localhost:8083/connectors/postgres-connector/resume

Monitoring and Alerting

Critical Metrics to Track

PostgreSQL:

• WAL lag size > 1GB (alert threshold)
• Replication slot status and active connections
• WAL generation rate during peak hours

MySQL:

• Binlog file count and total size
• GTID gaps and position tracking
• Connection count for CDC user

MongoDB:

• Oplog utilization > 80% (alert threshold)
• Resume token age > 1 hour (alert threshold)
• Change stream cursor count and memory usage

Essential Monitoring Queries

PostgreSQL WAL monitoring:

SELECT slot_name, active,
       pg_size_pretty(pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn)) as lag_size
FROM pg_replication_slots WHERE slot_name = 'debezium_slot';

MySQL binlog status:

SHOW MASTER STATUS;
SHOW BINARY LOGS;
SELECT ROUND(SUM(file_size)/1024/1024/1024,2) AS 'Binlog Size (GB)'
FROM information_schema.binary_log_files;

MongoDB oplog window:

db.runCommand("replSetGetStatus").optimes;
db.oplog.rs.stats();

Platform Selection Decision Matrix

Choose PostgreSQL When:

• Need most reliable CDC with lowest operational overhead
• Team has PostgreSQL expertise
• WAL-based replication meets requirements
• Budget constraints rule out enterprise databases

Choose MySQL When:

• Already deeply invested in MySQL ecosystem
• Have expert MySQL DBAs available
• Can tolerate higher operational complexity
• GTID setup and binlog management are acceptable

Choose MongoDB When:

• Document-based data model fits use case
• Want cleanest CDC API with minimal configuration
• Schema flexibility is important
• Change streams meet performance requirements

Choose Enterprise (SQL Server/Oracle) When:

• Enterprise features justify licensing costs
• Compliance requires enterprise database support
• Budget allows $750K-3.2M total investment
• Have specialized DBA expertise available

Best Practices Summary

Universal CDC Principles

1. Always test schema changes with CDC running in staging
2. Size WAL/oplog for longest expected outage plus 50% buffer
3. Monitor lag religiously with automated alerts
4. Pause connectors before any maintenance operations
5. Have documented disaster recovery procedures
6. Plan connector restart procedures for schema changes

Technology-Specific Recommendations

PostgreSQL: Use wal2json plugin, set appropriate max_slot_wal_keep_size, monitor replication slots
MySQL: Enable GTID, use pt-online-schema-change for large tables, increase connection limits
MongoDB: Size oplog appropriately, enable pre/post images, monitor resume token age
Enterprise: Budget for specialized expertise, understand licensing implications, test disaster recovery

Operational Readiness Checklist

• WAL/binlog/oplog sized for 24+ hour retention
• Monitoring and alerting configured for lag metrics
• Connector pause/resume procedures documented
• Schema change testing process established
• Disaster recovery procedures tested
• Connection limits increased appropriately
• Backup strategy includes CDC-specific considerations