Candidate Projection Enumeration for Query Response Generation
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Solution Overview
Problem
Existing query response generation techniques in distributed databases face challenges in efficiently enumerating appropriate column sets, sort orders, and data distributions, leading to a large search space and a high number of ineffective projections, which increases storage requirements and query execution time.
Innovation Solution
The implementation of a tree-based search technique that strategically generates candidate projections using a benefit model to prune the search space and reduce the number of projections tested, focusing on projections with high potential benefits for query response, such as sort orders and segmentation, to optimize query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing query response generation techniques enumerate all possible column sets, sort orders, and data distributions, then query response completeness is improved, but search space size and storage requirements increase significantly
Solution Approach 1:
The patent extracts and enumerates only the most relevant column sets, sort orders, and data distributions by using a benefit model to score and select top-k candidates. This extraction approach filters out ineffective projections while maintaining query response completeness, directly reducing search space size without sacrificing reliability.
Solution Approach 2:
The patent changes the parameter of projection enumeration from exhaustive to selective by introducing a benefit model that scores projections based on query characteristics. This parameter change enables the system to generate a manageable subset of high-value projections, reducing storage requirements while maintaining response quality.
2Measurement precision
If a larger number of projections are generated and tested, then query response accuracy is improved, but query execution time increases
Solution Approach 1:
The patent performs preliminary action by pre-scoring and ranking projections using the benefit model before actual query execution. This preliminary evaluation identifies the most promising projections in advance, allowing the system to test and execute only the top-k candidates, thereby reducing query execution time while maintaining accuracy.
Solution Approach 2:
The benefit model provides feedback on projection effectiveness based on query characteristics, enabling the system to iteratively select and refine projection candidates. This feedback mechanism ensures that only the most accurate projections are tested, optimizing the balance between response accuracy and execution time.
3Adaptability or versatility
If all possible projections are stored and tested, then projection coverage is improved, but storage requirements increase
Solution Approach 1:
The patent extracts only the essential projections that provide meaningful coverage by using the benefit model to score and select top-k candidates. This extraction maintains projection coverage for the most important query patterns while significantly reducing storage requirements by excluding low-value projections.
Solution Approach 2:
The patent applies partial action by generating and storing only a subset (top-k) of projections rather than all possible projections. This partial approach provides sufficient coverage for practical query workloads while keeping storage requirements manageable, avoiding the excessive action of storing every conceivable projection.
Data Source
AI summary
In some examples, candidate projection enumeration based query response generation may include obtaining a query, and identifying, based on the query, a plurality of candidate columns from at least one table to respond to the query. Using a tree-based model, the candidate columns may be analyzed to generate a candidate set of projections to respond to the query. Each candidate projection of the candidate set of projections may include a sort order associated with at least one candidate column of the candidate columns, a segmentation key value associated with at least one candidate column of the candidate columns, and a replication value associated with the candidate projection. Based on the candidate set of projections, a response to the query may be generated.