Real-Time Postgres CDC: Architecting for Exactly-Once Delivery

PostgreSQL Replication: The Foundation and Its Limitations

PostgreSQL's logical replication streams changes serially from server to client, with each message associated to a Log Sequence Number (LSN). PostgreSQL CDC is driven by its Write-Ahead Logging (WAL), which also supports its replication process. Anytime data is inserted, updated, or deleted, it is logged in the WAL. [1]

However, production implementations face three critical challenges:

Storage Management Problem: If clients cannot process messages fast enough, PostgreSQL retains messages indefinitely in replication slots, potentially causing disk usage to balloon on database servers.

Delivery Guarantee Problem: Clients must ensure message durability before advancing the confirmed flush LSN. If systems crash between message receipt and processing, messages can be lost or replayed.

Ordering Constraint Problem: While PostgreSQL guarantees serial delivery, maintaining this ordering when processing messages in parallel requires careful architectural design.

Stacksync's PostgreSQL CDC Architecture

Stacksync solves these PostgreSQL replication challenges through a four-stage processing pipeline designed for exactly-once delivery:

Stage 1: Non-Blocking Message Ingestion

Stacksync connects to PostgreSQL replication slots and buffers messages in-memory without immediately advancing the confirmed_flush_lsn. This approach prevents PostgreSQL storage accumulation while ensuring message persistence.

Messages are parsed and routed to dedicated buffers for each destination system. Each buffer maintains an ordered list of pending messages, creating the foundation for reliable delivery processing.

Stage 2: Ordered Parallel Message Delivery

Stacksync guarantees message ordering for related records while enabling parallel processing. The platform groups messages by primary key and maintains a tracking set of inflight_primary_keys to prevent simultaneous processing of related records.

The message delivery algorithm:

• 01Check messages in LSN order from the sorted buffer
• 02Verify primary keys are not already being processed
• 03If available, deliver message and track primary keys as in-flight
• 04If blocked, skip to next available message
• 05Remove primary keys from tracking upon completion

This approach maximizes throughput while preserving ordering guarantees for individual entities.

Stage 3: Failed Message Persistence and Retry

When message delivery fails, Stacksync persists messages to internal PostgreSQL tables rather than blocking the entire pipeline. Failed messages are retried with exponential backoff, with retry state persisting across system restarts.

This design prevents failed messages from consuming memory resources while ensuring eventual delivery. The system continues processing new messages while failed messages retry independently.

Stage 4: Safe LSN Advancement

Stacksync advances the PostgreSQL confirmed_flush_lsn only after messages are either successfully delivered or persisted to internal storage. This approach ensures PostgreSQL can safely remove replication slot messages without data loss.

The platform regularly calculates the minimum unflushed LSN across all destination systems and updates PostgreSQL accordingly, preventing replication slot storage accumulation.

Achieving Exactly-Once Delivery Through Deduplication

Stacksync implements exactly-once delivery semantics using Redis-backed deduplication. Before delivering message batches, workers query a Redis sorted set to identify previously processed messages and discard duplicates.

After successful delivery, message identifiers are added to the sorted set. The system regularly trims the set based on LSN boundaries—messages below the current replication slot LSN will never be redelivered by PostgreSQL, making them safe to remove.

This approach handles connection restarts gracefully. When PostgreSQL replays messages from the last confirmed LSN, Stacksync's deduplication layer prevents duplicate processing while ensuring no messages are lost.

Horizontal Scaling Through Message Partitioning

For maximum throughput, Stacksync partitions messages by primary key using consistent hashing. A single process manages the PostgreSQL connection and performs minimal message parsing to extract primary keys.

Messages are then routed to multiple processing pipelines, each handling a subset of primary keys. This maintains ordering guarantees for individual records while enabling parallel processing across the entire dataset.

The partitioning approach scales processing capacity linearly with the number of workers while preserving the ordering and delivery guarantees required for operational systems.

Real-Time Database Synchronization Implementation

Unlike analytical CDC systems that tolerate latency, operational systems require sub-second synchronization. Stacksync's in-memory message processing eliminates disk I/O bottlenecks for the primary data path.

Only failed messages touch persistent storage, keeping the majority of data flowing through high-performance memory buffers. This design delivers the real-time performance operational teams demand while maintaining durability guarantees.

Bi-Directional Sync Architecture

Bi-Directional Synchronization involves a two-way, reciprocal exchange of data between systems. True bi-directional sync tools are essential for use cases where multiple teams interact with the same data through different applications.

Stacksync extends this PostgreSQL CDC foundation to support bi-directional synchronization with automatic conflict resolution. Changes flow in both directions while the platform handles simultaneous updates using configurable resolution policies.

Automated Data Sync Between Applications

Stacksync's PostgreSQL CDC implementation enables automated data sync across operational applications without custom development. The platform handles:

• Schema mapping between PostgreSQL and destination systems
• Data type conversion and field-level transformations
• Connection management across REST, SOAP, and bulk APIs
• Error handling with comprehensive retry and fallback mechanisms

This eliminates the engineering overhead typically associated with maintaining CDC integrations while providing enterprise-grade reliability.

ETL Tools Comparison: CDC vs. Batch Processing

Traditional ETL platforms focus on batch processing for analytical workloads. ETL tools extract, transform, and then load data, typically transforming data outside the warehouse. ELT is more scalable and suited to modern data warehouses like Snowflake, BigQuery, and Databricks. [2]

For operational use cases requiring real-time consistency, CDC provides fundamental advantages:

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Batch ETL vs PostgreSQL CDC vs Stacksync CDC

Fivetran vs Airbyte vs Stitch: The Operational Gap

Analytical data integration platforms like Fivetran, Airbyte, and Stitch provide broad connector ecosystems but lack the operational focus required for real-time business processes:

• Fivetran: Batch-oriented with limited real-time capabilities
• Airbyte: Open-source flexibility but requires extensive custom development for operational reliability
• Stitch: Simplified setup but lacks bi-directional synchronization and exactly-once guarantees

Stacksync addresses the operational CDC requirements these platforms don't solve, providing purpose-built PostgreSQL integration for business-critical systems.

Implementation Architecture for Production Systems

Core CDC Pipeline Setup

Establish PostgreSQL replication connection with Stacksync's managed infrastructure. The platform handles replication slot creation, connection management, and message processing without database modifications.

Message Processing Configuration

Configure destination systems and field mappings through Stacksync's interface. The platform automatically handles schema detection, data type conversion, and API authentication.

Monitoring and Operational Management

Monitor CDC pipeline health through Stacksync's real-time dashboards showing message throughput, delivery status, and system performance. Automated alerting notifies operations teams of any processing issues.

Conclusion

PostgreSQL's CDC capabilities provide a foundation for real-time data integration, but production implementations require sophisticated architecture to ensure exactly-once delivery, maintain message ordering, and prevent database performance impact.

Building this architecture requires months of specialized development and ongoing maintenance overhead. Stacksync eliminates this complexity through proven PostgreSQL CDC patterns designed specifically for operational systems.

Organizations implementing Stacksync's PostgreSQL CDC solution achieve exactly-once delivery guarantees, sub-second synchronization latency, and bi-directional data flow without the engineering investment required for custom implementations.

Ready to implement production-ready PostgreSQL CDC without the architectural complexity? Explore Stacksync's real-time data synchronization platform and discover how leading organizations achieve exactly-once delivery at enterprise scale.

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