Change Data Capture (CDC) pipelines face an inevitable challenge: duplicate messages. While exactly-once delivery is theoretically impossible, network partitions and crashes make it impossible to ensure a downstream system saw an event precisely once. [1] Traditional CDC tools accept these duplicates as an unavoidable consequence, but organizations requiring mission-critical data reliability need better solutions.

This technical guide examines why CDC systems generate duplicates, their operational impact, and how purpose-built bi-directional synchronization platforms eliminate these issues through advanced architectural approaches.

Understanding the CDC Duplicate Problem

How WAL-Based CDC Creates Duplicates

Postgres' logical replication is driven by Postgres' write-ahead log (WAL). Subscribers create a replication slot on a Postgres database. They then receive an ordered stream of changes that occurred in the database: every create, update, and delete. [1]

The fundamental issue stems from the commit architecture:

1. Batch Processing: CDC subscribers pull batches of change messages from the WAL
2. Sink Delivery: Messages are shipped to downstream systems (Kafka, SQS, databases)
3. Offset Advancement: Log Sequence Number (LSN) positions are periodically advanced in the database

At any given time, a change data capture pipeline is in a partial commit state: it has pulled in many messages, some of them have been written to the sink, but the LSN/offset has not yet been advanced. If the connector crashes while in that partial commit state, Postgres will replay every message after the restart LSN on reconnect. [1]

Traditional CDC Limitations

Debezium follows this pattern. It relies on a restart LSN to track which messages have been processed, both in Postgres and its own internal store. When Debezium pulls a batch of changes from the WAL, it doesn't mark the LSN as processed until after it has successfully written those changes to its configured sink (like Kafka). [1]

This means effectively every time you restart Debezium or Debezium's connection to Postgres is cycled, you'll get some number of duplicate messages. For high-throughput databases, these events can easily cause tens of thousands of duplicate deliveries. [1]

The Operational Cost of CDC Duplicates

Database Replication Issues

Even with primary key-based upserts, replays create operational problems:

• Flapping: Source row changes from A→B→C, but restarts cause A→B replays, temporarily reverting destination data to older states
• Eventual Consistency Problems: Temporary data inconsistencies during replay windows
• Performance Overhead: Unnecessary processing of duplicate change events

Audit and Compliance Violations

While CDC systems aim for idempotent processing, ensuring that duplicate changes do not result in unintended side effects or data inconsistencies [2], audit systems require precision:

• Compliance Gaps: Multiple "user promoted to admin" records corrupt compliance timelines
• Financial Reconciliation Issues: Double-logged transactions complicate regulatory reporting
• Data Integrity Violations: Audit trails lose their single source of truth properties

Side Effect Amplification

Duplicate CDC messages trigger unintended operational consequences:

• Double Billing: Payment processors receive multiple charge events
• Communication Spam: Multiple password reset emails or notifications
• Workflow Corruption: Business process automation triggers multiple times

Stacksync's Bi-Directional Architecture Solution

Advanced Idempotency Framework

Unlike traditional CDC systems that accept duplicates, Stacksync implements comprehensive idempotency tracking at the message level:

Real-Time Change Messages:

• Each WAL transaction receives a unique LSN identifier
• Messages within transactions get commit_idx sequence numbers
• Generated idempotency_key combines commit_lsn:commit_idx for guaranteed uniqueness

Backfill Operations: Stacksync uses a combination of the backfill's ID and the source row's primary keys to produce its idempotency_key for a message. That produces a stable key that ensures consumers only process a given read message for a row once per backfill. [1]

Leaf-Level Filtering Architecture

Stacksync uses its idempotency keys to filter "at the leaf", right before delivering to the destination. Whenever Stacksync delivers a batch of messages to a sink, it writes the idempotency keys for each message in that batch to a sorted set in Redis. Therefore, before it delivers a batch of messages to a sink, it can filter out any messages that were already delivered against that sorted set. [1]

This approach provides:

• Pre-Delivery Deduplication: Messages are filtered before reaching destination systems
• Atomic Tracking: Redis sorted sets maintain delivery state with high availability
• Minimal Replay Windows: Only edge cases between Redis availability and message delivery can cause replays

True Bi-Directional Synchronization

Stacksync eliminates CDC duplicate issues through architectural superiority:

Unified Sync Engine:

• Single, centralized mechanism manages data flow in both directions
• Built-in conflict resolution prevents update wars and infinite loops
• Transactional integrity across bi-directional operations

Field-Level Change Detection:

• Non-invasive CDC captures granular field modifications
• Event-driven architecture processes changes in real-time
• Intelligent state management prevents synchronization loops

Implementation Recommendations

For CDC Duplicate Mitigation

Organizations currently using traditional CDC tools should implement:

1. Consumer-Level Idempotency: Include idempotency keys in message metadata. Some destinations allow deduplication keys on write – making delivery models exactly-once. Otherwise, the field is available as a last layer of idempotency protection. [1]
2. Monitoring and Alerting: Track replay frequency and duplicate volumes to quantify operational impact
3. Downstream Deduplication: Implement application-level duplicate detection where possible

For Mission-Critical Operations

Organizations with audit, compliance, or financial requirements should consider purpose-built solutions:

1. True Bi-Directional Platforms: Eliminate root causes of duplicates rather than managing symptoms
2. Field-Level Synchronization: Avoid batch-based approaches that create replay windows
3. Enterprise Security: SOC 2, GDPR, HIPAA compliance for regulated environments

Conclusion

The difference between traditional CDC and advanced bi-directional synchronization comes down to how they handle the inevitable reality of duplicates. While traditional CDC tools accept duplicates as a natural consequence of at-least-once delivery, purpose-built solutions actively work to minimize them through idempotency tracking and filtering. For use cases where duplicates are particularly costly (audit logs, financial transactions, or systems that trigger expensive side effects), tracking message delivery at the individual message level provides significant value. [1]

Organizations operating mission-critical systems requiring guaranteed data consistency should evaluate bi-directional synchronization platforms like Stacksync. These solutions eliminate the architectural limitations that cause CDC duplicates while providing real-time, two-way data flow with enterprise-grade reliability and security.

Ready to eliminate CDC duplicates from your data architecture? Explore how Stacksync's purpose-built bi-directional synchronization delivers guaranteed data consistency without the operational overhead of traditional integration maintenance.

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