Penalty Box Key-Space Bounding for Stateful Stream Processing
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Solution Overview
Problem
Stateful streaming processing systems face inefficiencies due to unbounded key spaces consuming resources, with existing eviction strategies failing to prevent data re-entry and introducing complexity and performance degradation.
Innovation Solution
A processing device selectively removes parent nodes from key spaces based on leaf node counts exceeding a threshold, placing them in a blacklist container to prevent re-entry and reclaim resources, using statistical models to maintain resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If key spaces are allowed to grow unbounded to accommodate all event data, then data completeness is improved, but resource consumption increases until available resources are exhausted
Solution Approach 1:
The system changes the parameter of key space growth from unbounded to bounded by introducing a threshold mechanism. When the number of leaf nodes under a parent node exceeds the threshold, the parent node is removed from the key space, thereby limiting resource consumption while maintaining data completeness for beneficial patterns.
Solution Approach 2:
The system extracts and removes parent nodes that exceed the threshold from the key space structure. These removed parent nodes are placed in a blacklist container, effectively taking out the harmful element (excessive resource consumption) while preserving the beneficial data structures that fall within acceptable resource limits.
2Use of energy by moving object
If existing eviction strategies are used to remove key spaces, then resource consumption is reduced, but data re-entry is not prevented and system complexity increases
Solution Approach 1:
The invention extracts the problematic parent nodes from the key space and places them in a separate blacklist container. This simple extraction mechanism reduces resource consumption without introducing complex eviction strategies, as the blacklist container provides a straightforward mechanism to prevent re-entry of removed patterns.
Solution Approach 2:
The blacklist container serves as an intermediary structure between the key space and the removed parent nodes. It mediates the relationship by storing removed parent nodes and preventing their re-entry into the key space, thereby reducing resource consumption while maintaining system simplicity through a clear separation of concerns.
3Productivity
If parent nodes with excessive leaf nodes are removed from key spaces, then resource efficiency is improved, but data loss may occur if beneficial patterns are incorrectly removed
Solution Approach 1:
The system applies local quality by selectively removing only those parent nodes whose leaf node counts exceed the threshold. This localized approach ensures that resource efficiency is improved for problematic patterns while preserving beneficial patterns that fall within acceptable resource limits, thereby preventing unnecessary data loss.
Solution Approach 2:
The system uses feedback through the threshold mechanism to determine which parent nodes should be removed. By continuously monitoring the number of leaf nodes under each parent node and comparing against the threshold, the system provides feedback that guides selective removal, ensuring resource efficiency while maintaining data completeness for beneficial patterns.
Data Source
AI summary
The present disclosure provides techniques for selective removal of leaf nodes. A processing device tracks a count of leaf nodes associated with a parent node within a key space. The processing device identifies whether the count of the leaf nodes exceeds a threshold. The processing device removes the parent node within the key space in response to the count of leaf nodes exceeding the threshold. The processing device frees the resources utilized by the leaf node removed from the key space for use by other leaf nodes within the key space.