Datapath Graph Update Detection Using Node Fingerprints
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Solution Overview
Problem
Data processing systems that handle both big and non-big data face challenges in maintaining robustness and updating processes between data sources and sinks, particularly in handling failures and changes in underlying processes, which can lead to data inconsistencies and inefficiencies.
Innovation Solution
A datapath graph system that uses operation nodes, storage nodes, and fingerprints (based on metadata) to detect and update data transformations, ensuring that only necessary parts of the graph are rerun, utilizing hashes and fingerprints to manage data consistency and reduce processing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire datapath graph is rerun to ensure data consistency, then data integrity is maintained, but processing time and computational resources are wasted on unchanged data
Solution Approach 1:
The patent divides the datapath graph into individual operation nodes and storage nodes, each with its own fingerprint. This segmentation allows the system to identify and update only the specific nodes that have changed, rather than rerunning the entire graph. The fingerprint comparison at each node enables granular detection of changes, resolving the contradiction between maintaining data integrity and avoiding unnecessary processing time.
Solution Approach 2:
The system implements a feedback mechanism where fingerprints of operation nodes and storage nodes are continuously compared. When a fingerprint mismatch is detected, the system triggers an update only for the affected node and its downstream dependencies. This feedback loop ensures data integrity is maintained while preventing wasteful rerunning of unchanged portions of the datapath graph.
2Reliability
If all operation nodes are executed to handle underlying process failures, then system robustness is improved, but processing efficiency decreases due to unnecessary reruns
Solution Approach 1:
The patent computes and stores fingerprints for all operation nodes and storage nodes in advance. Before executing any operation, the system performs a preliminary fingerprint comparison to determine whether the node has changed. This preliminary action allows the system to skip unchanged nodes entirely, maintaining robustness by detecting actual changes while improving productivity by avoiding unnecessary reruns.
Solution Approach 2:
Instead of executing all operation nodes (excessive action), the system performs partial action by only rerunning nodes whose fingerprints indicate they have changed or whose upstream dependencies have changed. This partial execution approach maintains system robustness by ensuring changed data is processed while significantly improving processing efficiency by skipping unchanged nodes.
3Measurement precision
If fingerprints are computed for every data transformation, then change detection accuracy is improved, but computational overhead increases
Solution Approach 1:
The patent extracts only the essential metadata needed for change detection into fingerprints, rather than computing comprehensive hashes of entire datasets. By taking out only the critical identification elements (such as data identifiers, version information, or key metadata fields) and creating compact fingerprints, the system achieves high change detection accuracy while minimizing computational overhead and energy consumption.
Data Source
AI summary
A current fingerprint that is a function of an operation node and an input storage node is generated. The current fingerprint is compared to a previously generated fingerprint associated with an output storage node and a task associated with performing the operation node on the input storage node is generated.