Stream Processor Daisy Chain for Zero-Message Loss
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Solution Overview
Problem
Unreliable data sinks in data streaming systems cause impedance mismatches, leading to data loss due to internal processing backlogs and network outages, and existing solutions either require additional hardware or block continuous data streams, failing to effectively handle partially failed batch requests.
Innovation Solution
A method utilizing a daisy chain of sink nodes, where a primary sink node handles continuous data submission and asynchronous callback functions for successful and failed records, passing failed records to fallback sink nodes while continuing new submissions, ensuring zero-message loss without full-time fallback systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full-time fallback systems are deployed to prevent data loss, then reliability is improved, but device complexity and hardware requirements increase
Solution Approach 1:
The system dynamically activates fallback sink nodes only when the primary sink node experiences failures. The stream processor monitors callback results and automatically routes failed records to available fallback nodes, transitioning from a static full-time redundancy architecture to a dynamic on-demand fallback mechanism that maintains reliability while reducing complexity.
Solution Approach 2:
The invention extracts only the necessary fallback functionality from a complete redundant system. Instead of deploying full backup sink nodes continuously, the system extracts and implements minimal fallback capabilities that are activated selectively based on actual failure conditions, reducing hardware requirements while maintaining data loss prevention.
2Reliability
If continuous data streams are blocked for retransmission of failed records, then data loss is prevented, but productivity decreases
Solution Approach 1:
The system segments the data stream handling into separate paths: successful records continue flowing through the primary sink node without interruption, while only failed records are extracted and routed to fallback nodes for retransmission. This segmentation prevents blocking of the continuous data stream while ensuring complete message delivery.
Solution Approach 2:
The stream processor maintains continuous operation by allowing new batches of data to be submitted to the primary sink node concurrently with retransmission operations. The fallback mechanism operates in parallel without interrupting the continuous flow of successful data transmissions, preserving productivity while ensuring reliability.
3Reliability
If additional hardware is deployed for fallback systems, then reliability is improved, but cost and device complexity increase
Solution Approach 1:
The stream processor is designed with multi-functionality, serving both as the primary data submission endpoint and as the coordinator for fallback operations. The same stream processor instance manages continuous data intake, monitors primary sink node performance, and orchestrates retransmission to fallback nodes, eliminating the need for separate dedicated hardware components.
Solution Approach 2:
The system implements self-service through automatic failure detection and recovery. The stream processor monitors callback results from the primary sink node and autonomously routes failed records to fallback nodes without external intervention. This self-managing capability reduces the need for additional control hardware and simplifies the overall system architecture.
Data Source
AI summary
According to at least one embodiment, a method, a computer system, and a computer program product for handling an unreliable data sink is provided. The present invention may include reading, continuously, via a stream processor, a batch of data entering the stream processor via a reliable input stream, the stream processor comprising a daisy chain of sink nodes, wherein a first sink node is a primary sink node and a subsequent plurality of sink nodes in the daisy chain are fallback sink nodes; outputting processed data to a non-streaming target system; upon completion of the processed data to the non-streaming target system, invoking an asynchronous callback function listing successful and failed record IDs of the processed data; and passing the failed records to a first fallback sink within the daisy chain, while the primary sink node concurrently continues submitting new batches of data to the non-streaming target system.