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Building reliable sync systems requires understanding a fundamental truth: exactly-once delivery is impossible. Yet many teams designing automated data sync between applications chase this ideal, creating brittle architectures that fail under real-world conditions.
At Stacksync, we've engineered bi-directional sync tools specifically to address these distributed systems challenges. According to a 2024 Gartner report, 72% of mid-market organizations plan to invest in real-time integration solutions within the next 18 months . As demand grows for real-time data synchronization across operational systems, understanding delivery semantics becomes critical for building reliable database synchronization.
At-most-once delivery systems treat messages as ephemeral. The publisher sends a message and forgets about it. If a subscriber isn't listening or there's a network hiccup, the message is lost forever. PostgreSQL's LISTEN/NOTIFY works this way - simple but unreliable for critical business data flowing between CRMs and databases.
At-least-once delivery systems guarantee delivery by persisting messages and tracking their state. They won't mark a message as delivered until receiving confirmation from the receiver. This approach powers Stacksync's bi-directional sync architecture, ensuring reliability for database synchronization scenarios across operational systems.
The key difference: at-least-once systems can deliver the same message multiple times during failure scenarios. A receiver might process a message but fail to acknowledge it before a timeout occurs, triggering redelivery. This is exactly why Stacksync implements field-level change detection - to handle these scenarios gracefully.
This introduces the two-phase commit problem. The system needs to atomically update two separate locations: mark the message as delivered AND confirm receipt with the receiver. If it updates the database first, the connection might fail. If it confirms with the receiver first, the database update might fail. Either scenario leaves the system inconsistent.
Exactly-once delivery sounds perfect but represents a platonic ideal you can only approach, never achieve. It allows operations to be safely retried in the face of network failures, without risking data corruption or inconsistencies [1], but true exactly-once delivery cannot guarantee 100% reliability across distributed systems.
The fundamental issue: you cannot transactionally update two bits in two different physical locations with absolute certainty. At Stacksync, we've seen teams spend months trying to build "perfect" sync systems, only to discover these theoretical limitations in production.
Consider a practical example: our platform syncs customer data between Salesforce and PostgreSQL with sub-second latency. The system updates a contact record in Salesforce, then propagates that change to PostgreSQL while confirming sync completion. Network partitions, process crashes, or timeouts can interrupt this flow at any point, potentially causing duplicate processing - which is why we built our conflict resolution engine from the ground up.
Modern ETL tools comparison reveals a crucial distinction between delivery (getting the message to the receiver) and processing (the complete message lifecycle including acknowledgment).
While exactly-once delivery remains impossible, exactly-once processing becomes achievable. Stacksync guarantees exactly-once processing by combining at-least-once delivery with sophisticated acknowledgment mechanisms and automated retry logic.
However, acknowledgments don't eliminate the two-phase commit problem. Our platform handles scenarios where a worker processes a sync operation (updating both Salesforce and PostgreSQL), sends notifications, but fails to acknowledge completion due to network errors. Without proper idempotency design, the system would retry the entire operation, creating duplicates.
This reality shapes how we architect reliable sync systems. These terms help distinguish between delivery mechanics and processing guarantees, but neither eliminates the fundamental challenges of distributed systems - which is precisely why Stacksync focuses on idempotent processing patterns.
Given the two-phase commit problem, teams building automated data sync between applications have three options:
Sometimes bugs from redeliveries are acceptable. For analytics pipelines or non-critical notifications, occasional duplicates might be tolerable compared to the engineering effort required for perfect deduplication.
This is Stacksync's preferred approach for operational systems. Idempotent APIs are designed to produce the same outcome regardless of how many times they are called with the same input. This means that if an operation is performed multiple times, the result will be identical to performing it just once [2].
Our bi-directional sync platform implements idempotency through:
Stacksync's architecture ensures that whether you're syncing 50,000 or 100 million records, duplicate processing scenarios are handled automatically without data corruption.
If you'd rather miss data than process it twice, at-most-once delivery might be appropriate. This works for scenarios where data loss is preferable to duplicate processing, though we rarely recommend this approach for operational database synchronization.
Configure Appropriate Timeouts: In Stacksync's platform, we configure visibility timeouts conservatively - long enough for normal processing but short enough to detect failures quickly. Our system sets hard timeouts on workers below the visibility timeout so we know definitively when processing has failed.
Design Atomic Sync Operations: Don't pack too many side effects into single sync operations. Our platform breaks complex workflows into appropriately sized units that can be safely retried as atomic operations across your data stack.
Implement Comprehensive Monitoring: They also enhance data accuracy and consistency, ensuring that all your systems are working with the most current and correct information. This real-time synchronization helps in making informed decisions faster [3]. Stacksync provides real-time monitoring dashboards, automated alerting, and detailed logging to detect sync failures and performance issues immediately.
At Stacksync, we've architected our entire platform around the reality that exactly-once delivery is impossible. Our bi-directional sync technology delivers exactly-once processing through:
This approach has enabled organizations to achieve reliable real-time data synchronization without the complexity and brittleness of custom-built solutions.
Exactly-once delivery is a myth that leads teams down expensive, complex paths. Real-time data synchronization systems must embrace at-least-once delivery with idempotent processing to achieve reliable results.
At Stacksync, we've learned that the messaging system can only get you so far. Success depends on designing sync operations that handle duplicates gracefully through idempotent patterns - which is exactly what our platform provides out of the box.
As distributed systems experts know: "Only you can prevent reprocessing issues" by building systems that work reliably in an imperfect world.
Ready to implement idempotent sync across your operational systems? Explore Stacksync's pricing and features to discover how purpose-built bi-directional sync tools eliminate the complexity of exactly-once delivery while ensuring data consistency across your entire stack.
