--- title: "Postgres Watermarks for Parallel CDC & Backfill Sync" description: "Discover Postgres watermarks for seamless parallel CDC and backfill sync, ensuring data consistency and efficiency in database operations." canonical: https://www.stacksync.com/blog/postgres-watermarks-parallel-cdc-backfill-sync last_modified: 2026-03-22 --- # Postgres Watermarks for Parallel CDC & Backfill Sync Discover Postgres watermarks for seamless parallel CDC and backfill sync, ensuring data consistency and efficiency in database operations. Author Ruben Burdin · Founder & CEO Published September 6, 2025 Updated March 22, 2026 Read time 6 min read DATA ENGINEERING Database synchronization becomes complex when organizations need to run change data capture alongside table backfill operations. The fundamental challenge emerges from coordinating two concurrent data streams: the table reader capturing current state and the slot processor handling WAL changes. Without proper coordination, these streams create race conditions, message ordering failures, and data consistency issues that compromise operational integrity. Consider a scenario where row A undergoes updates (A₀ → A₁ → A₂) while both processes run simultaneously. Table Reader may capture A₂ during its scan, while Slot Processor later processes change events (A₀→A₁, A₁→A₂). The output buffer receives messages in incorrect sequence, causing downstream systems to process stale data as current. More critically, if Table Reader queries during an update transition, it may capture A₁ while Slot Processor processes A₁→A₂, leaving the system with permanently stale data. Building reliable CDC systems requires coordination strategies that maintain data consistency without operational trade-offs. The following analysis examines technical approaches to this challenge, with particular focus on watermark-based coordination for production environments. ## Solution Approaches for CDC Coordination ### Solution A: Serialized Capture Processes The simplest approach eliminates coordination complexity by processing streams sequentially: - 01 Create replication slot before table capture begins - 02 Execute complete table state capture - 03 Apply accumulated slot changes after table capture completion `-- Create slot for change accumulationSELECT pg_create_logical_replication_slot('cdc_slot', 'pgoutput');-- Execute table captureCOPY (SELECT * FROM target_table) TO output_stream;-- Process accumulated changesSELECT * FROM pg_logical_slot_get_changes('cdc_slot', NULL, NULL);` **Technical limitations:** Change capture halts during table operations, creating inflexibility for operational systems requiring continuous synchronization. Large table captures can stall change processing for extended periods. **Operational impact:** Unsuitable for frequent replay scenarios where downstream systems need rapid data recovery without interrupting live synchronization. ### Solution B: WAL-Unified Processing This approach forces all operations through the Write-Ahead Log for strict ordering guarantees: `-- Generate WAL entries for all rowsUPDATE target_table SET id = id WHERE condition;` By channeling everything through WAL, the system processes only one stream, eliminating coordination requirements. **Critical drawbacks:** Massive database load from artificial updates, performance degradation, and unsuitable resource consumption for production environments. ### Solution C: Complete Memory Buffering Buffer the entire table capture in memory with WAL coordination: - 01 Start slot processor for change tracking - 02 Table reader buffers complete dataset in memory - 03 Slot processor tracks modified primary keys - 04 Emit buffered data after filtering against WAL-seen records `-- Emit coordination messageSELECT pg_logical_emit_message(true, 'cdc', '{"backfill_id": 1, "marker": "end"}');` **Scalability constraints:** Requires sufficient memory for complete datasets, impractical for large tables, risk of memory exhaustion on enterprise-scale data. ### Solution D: Chunked Watermark Coordination The most sophisticated approach processes tables in memory-efficient chunks with watermark synchronization: - 01 **Low watermark emission:** Signals chunk processing start - 02 **Parallel processing:** Table reader accumulates chunk while slot processor tracks primary keys - 03 **High watermark coordination:** Triggers filtered output emission - 04 **Memory management:** Automatic cleanup prevents accumulation `-- Low watermarkSELECT pg_logical_emit_message(true, 'cdc:low-watermark', '{"table_oid": 1234, "batch_id": "batch_001"}');-- High watermark SELECT pg_logical_emit_message(true, 'cdc:high-watermark', '{"table_oid": 1234, "batch_id": "batch_001"}');` This approach delivers enterprise-scale performance while maintaining operational flexibility and memory efficiency. ## Implementation Architecture ### Watermark Protocol Design Effective watermark coordination requires structured messaging between processes: **Low Watermark Structure:** `{ "table_oid": 1234, "batch_id": "unique_identifier", "backfill_id": "session_id"}` **Process Coordination:** - Table reader emits low watermark before chunk selection - Slot processor begins primary key accumulation for specified table - Table reader completes chunk selection and emits high watermark - Slot processor retrieves chunk, applies PK filtering, emits filtered messages ### Error Handling and Recovery **Orphaned Watermark Management:** Slot processor validates batch existence with table reader, discarding invalid accumulations when processes restart unexpectedly. **Cursor Persistence:** Table reader maintains checkpoint positions, enabling recovery from exact locations after system failures. **Memory Pressure Handling:** Dynamic batch size adjustment based on available resources and processing capacity. ### Production Implementation Example Modern data integration platforms implement this coordination through process orchestration: `# Process coordination patternclass TableReader: def emit_low_watermark(self, table_oid, batch_id): self.emit_message("low-watermark", { "table_oid": table_oid, "batch_id": batch_id }) def process_chunk(self, chunk_size=100000): batch_id = self.generate_batch_id() self.emit_low_watermark(self.table_oid, batch_id) # Buffer chunk in memory chunk = self.select_chunk(chunk_size) self.emit_high_watermark(self.table_oid, batch_id) return chunkclass SlotProcessor: def handle_low_watermark(self, message): table_oid = message["table_oid"] self.tracked_pks[table_oid] = set() def handle_high_watermark(self, message): batch_id = message["batch_id"] chunk = self.table_reader.get_chunk(batch_id) if chunk: filtered_chunk = self.filter_chunk(chunk) self.emit_filtered_messages(filtered_chunk) self.cleanup_tracked_pks(message["table_oid"])` ### Performance Characteristics **Processing Rates:** Chunked coordination enables processing rates exceeding 1M records per minute for standard table captures. **Memory Efficiency:** 99%+ reduction in memory requirements compared to complete buffering approaches. **Latency Impact:** Sub-second coordination message processing with minimal overhead on source databases. **Scalability:** Linear scaling with chunk size optimization based on system resources and network capacity. ## Enterprise Integration Patterns ### Multi-System Synchronization Watermark coordination extends across multiple database types and business systems. Production deployments coordinate CDC across PostgreSQL, MySQL, and cloud databases while maintaining consistency with CRM and ERP systems. **Database Integration:** - PostgreSQL: Native logical replication with pg_logical_emit_message - MySQL: Binlog coordination with custom watermark tables - Oracle: LogMiner integration with watermark persistence **Business System Integration:** - CRM synchronization: Real-time coordination across API boundaries - ERP integration: Transactional consistency with financial systems - Data warehouse loading: Exactly-once delivery semantics ### Operational Reliability Features **Automatic Error Recovery:** Built-in retry logic, rollback capabilities, and failure isolation eliminate custom error handling implementation. **Conflict Resolution:** Primary key-based filtering with timestamp ordering prevents duplicate processing and maintains referential integrity. **Monitoring Integration:** Real-time visibility into watermark processing, batch status, and coordination health through operational dashboards. ## Production Deployment Considerations ### Resource Requirements **Memory Allocation:** Base requirement of 100MB plus 1KB per tracked primary key for optimal performance. **CPU Impact:** Less than 5% overhead on source database systems during normal operations. **Network Overhead:** Minimal - only coordination messages and filtered data transfer required. ### Security and Compliance **Data Protection:** Watermark messages contain only metadata, never sensitive data content. **Access Control:** Replication slot management requires appropriate PostgreSQL privileges. **Audit Requirements:** Complete message flow logging for compliance and troubleshooting. Two systems, one record, no batch window See your own stack synced live. Book a demo with the engineers who built it. [Book a demo](https://www.stacksync.com/book-a-demo) ## Replacing Custom CDC Solutions Organizations typically invest 3-6 months developing custom CDC coordination. Modern integration platforms eliminate this development cycle through: **Built-in Coordination:** Automated watermark messaging and primary key tracking without custom implementation. **Enterprise Security:** SOC2, GDPR, and HIPAA compliance integrated into coordination framework. **Operational Monitoring:** Comprehensive visibility into coordination health and performance metrics. **API Management:** Intelligent rate limiting and connection handling across multiple systems. ## Conclusion Watermark-based coordination represents the definitive technical solution for parallel CDC and backfill operations. The chunked approach delivers enterprise performance while maintaining operational simplicity and memory efficiency. This coordination strategy eliminates traditional trade-offs between operational flexibility and data consistency, enabling reliable CDC implementation without extensive custom development. The technical architecture scales from thousands to millions of records while providing the reliability guarantees required for mission-critical operational systems. For organizations requiring robust CDC coordination, evaluate platforms that implement advanced watermark synchronization with built-in error handling and enterprise security. The combination of technical sophistication and operational simplicity represents the current state-of-the-art for production CDC systems. Start with one sync and see it hold Connect two systems, watch a record move both ways, then decide. [Start syncing](https://app.stacksync.com/) FAQ ## Frequently asked questions What does this guide cover? This guide covers postgres watermarks for parallel cdc & backfill sync, including key concepts, implementation strategies, and best practices for enterprises. You will learn how to leverage Stacksync's real-time bidirectional sync platform to solve common data integration challenges and maintain consistency across your business systems. How does Stacksync help with this? Stacksync provides a no-code platform for real-time bidirectional data synchronization between 1,000+ connectors including CRMs, ERPs, databases, and SaaS applications. The platform eliminates manual data entry, prevents data drift, and ensures all systems stay aligned with sub-second sync latency and enterprise-grade security. Is Stacksync secure for enterprise use? Yes. Stacksync is SOC 2 Type II certified, ISO 27001 certified, and HIPAA compliant. Data is encrypted in transit with TLS 1.2+ and at rest with AES-256. The platform uses zero-persistent-storage architecture, meaning your data is not retained after sync operations. Enterprise security features include SSO, SCIM, IP whitelisting, and full audit logging. How long does implementation take? Most Stacksync integrations go live within 3 to 7 business days. The no-code visual interface handles authentication, field mapping, and data transformation without engineering resources. Complex multi-system architectures may take 2 to 3 weeks. Stacksync provides pre-built connectors and templates that accelerate setup compared to custom development. What pricing model does Stacksync use? Stacksync uses flat pricing based on active sync connections and monthly record volume, starting at $1,000 per month. There are no per-row fees, no hidden charges for data volume, and no separate costs for bidirectional sync. Volume discounts are available for enterprise deployments. A 14-day free trial is available to evaluate the platform. About the author Ruben Burdin Founder & CEO Ruben Burdin is the Founder and CEO of Stacksync, the first real-time and two-way sync for enterprise data at scale. Ruben is a Y Combinator alumni with a strong background in software engineering and business. [All posts by Ruben Burdin](https://www.stacksync.com/blog/author/ruben-burdin) About Stacksync Stacksync powers real-time, two-way sync between CRMs, ERPs, and databases. Engineers sync data at scale and automate workflows, not dirty API plumbing. Stacksync ## Explore these integrations and topics - [connector PostgreSQL integrations](https://www.stacksync.com/connectors/postgresql) - [platform Database synchronization](https://www.stacksync.com/database) - [Database synchronization guides Work through database change capture, application writes, and reliable record reconciliation.](https://www.stacksync.com/blog#topic-database-sync) Keep reading ## Related articles [All articles](https://www.stacksync.com/blog) [Data engineering The AI Agent Data Layer: Per-App MCP Servers vs a Synced Postgres Database 15 min read read](https://www.stacksync.com/blog/ai-agent-data-layer-mcp-vs-synced-database) [Data engineering Acumatica–Postgres Sync for Beverage Operations: Match Case and Pallet Records Across Warehouses 5 min read read](https://www.stacksync.com/blog/acumatica-postgres-sync-for-beverage-operations-match-case-and-pallet-records-across-warehouses) [Data engineering NetSuite–Postgres Sync for Food Brands: Reconcile Co-Packer Receipts and Finished-Goods Records 5 min read read](https://www.stacksync.com/blog/netsuite-postgres-sync-for-food-brands-reconcile-co-packer-receipts-and-finished-goods-records) ## You just read how it should work. 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