Data Pipeline Rollback Recovery with Event Lineage Capture
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Solution Overview
Problem
Existing data pipeline systems face challenges in achieving accurate and repeatable rollback recovery and efficient fine-grained data lineage capture, particularly in distributed environments, where system failures can occur due to communication issues and data loss, requiring effective state restoration and lineage tracing without developer intervention.
Innovation Solution
The implementation of a method that involves operators in a data pipeline to log events with incomplete and complete status designations, allowing for resilient recovery and lineage capture by resending events upon failure, and using monitoring agents to manage alert events and preserve necessary logs for data lineage queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fine-grained data lineage capture is implemented in distributed data pipelines, then measurement precision and reliability are improved, but device complexity and storage requirements increase significantly
Solution Approach 1:
The patent segments the data pipeline into discrete operators with individually tracked input and output events. Each operator maintains its own event logs separately, allowing fine-grained lineage tracking without requiring centralized complex storage. This segmentation enables precise measurement of data flow relationships while keeping individual operator complexity manageable.
Solution Approach 2:
The patent adds a temporal dimension to data lineage tracking by implementing event-based logging with timestamps and sequence numbers. Instead of tracking only data relationships, the system tracks the evolution of events over time, enabling precise lineage capture through the addition of temporal metadata without fundamentally changing the pipeline structure.
2Reliability
If comprehensive state capture is performed for rollback recovery, then reliability is improved, but storage requirements and processing overhead increase
Solution Approach 1:
The patent extracts only the essential state information needed for recovery - specifically input and output events at operator boundaries - rather than capturing the complete internal state of each operator. This extraction approach maintains reliable rollback capability by preserving the minimum necessary state information while reducing storage overhead by excluding redundant internal state details.
Solution Approach 2:
The patent performs preliminary logging of input and output events as they occur during normal pipeline execution, rather than capturing state after failures occur. This preliminary action ensures that recovery state is already prepared and validated, improving reliability while avoiding the need for additional storage during recovery operations.
3Ease of operation
If automatic recovery protocols are implemented without developer intervention, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service recovery where the pipeline execution engine automatically detects failures, retrieves appropriate state from event logs, and resumes execution without developer intervention. Each operator independently manages its own recovery process using its logged events, eliminating the need for complex centralized recovery management while maintaining ease of operation.
Solution Approach 2:
The patent implements feedback mechanisms where operators monitor the completion status of their output events and automatically trigger re-execution when downstream operators indicate failure or incomplete processing. This feedback-driven approach enables automatic recovery through simple conditional logic rather than complex recovery protocols.
Data Source
AI summary
Computer-readable media, methods, and systems are disclosed for performing rollback recovery with data lineage capture for data pipelines. A middle operator receives ingested input events from a source operator reading data from an external input data source. The middle operator then logs information regarding middle input events to a middle operator input log, designating the logged middle input event information as incomplete. The middle operator then processes data associated with the middle input events and updates the middle input log entries setting them to a completed logging status designation for middle input events that were consumed to produce the one or more middle output events. The middle operator then transmits the middle output events to subsequent operators. Garbage collection is performed to remove completed entries from the middle operator output log. Finally, based on receiving a recovering message from a subsequent operator, corresponding middle output events are re-sent.