Top-K Pushdown Through Inner Joins for Faster Query Plans
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Solution Overview
Problem
Conventional database systems face challenges in optimizing query execution plans for large tables, particularly those involving aggregation operations with joins, as traditional optimization approaches fail to effectively filter out rows early in the process, leading to excessive processing and resource waste.
Innovation Solution
Implement early row flush mechanisms and push down top-k information through inner join operators in the query plan, allowing aggregation operators to filter rows early and establish a boundary value, thereby reducing unnecessary processing and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional query optimization approaches are used for aggregation queries with joins, then the query execution follows conventional processing sequences, but excessive rows are processed and computational resources are wasted
Solution Approach 1:
The patent applies preliminary action by pushing top-k information (boundary values) down to aggregation operators before they perform their aggregation functions. This allows aggregation operators to filter out rows that cannot contribute to the top-k results early in the query execution plan, before expensive join operations occur. The boundary values are computed upfront and used to prune the input space, reducing the number of rows processed through subsequent operators.
Solution Approach 2:
The patent segments the query execution by introducing early row flush mechanisms that divide the aggregation process into stages. Instead of processing all rows through completion before filtering, the system flushes partial results early and uses top-k information to determine which groups need further processing. This segmentation allows independent optimization of different parts of the query plan.
2Measurement precision
If all rows are processed through aggregation operators without early filtering, then complete aggregation results are obtained, but query execution time increases significantly
Solution Approach 1:
The system performs preliminary computation of top-k boundary values and pushes this information down to aggregation operators. This preliminary action enables each aggregation operator to determine early which groups can contribute to the final top-k results and which can be discarded, maintaining accuracy while reducing execution time.
Solution Approach 2:
The patent implements feedback mechanisms where top-k information flows downward through the query plan from operators that can compute it early. This feedback allows upstream operators to adjust their processing behavior, filtering rows based on the boundary values received, thereby reducing execution time without sacrificing result accuracy.
3Device complexity
If top-k information is not pushed down through inner join operators, then the query plan maintains simple structure, but unnecessary rows pass through joins increasing processing overhead
Solution Approach 1:
The patent uses top-k information as an intermediary that mediates between the top-k operator and aggregation operators separated by inner join operators. This information flow acts as a signal that enables pruning at multiple points in the query plan without requiring structural reorganization. The intermediary information allows the system to maintain the original query plan structure while achieving optimization.
4Reliability
If early row flush is not implemented, then aggregation operators process complete data sets, but resource usage increases due to handling unnecessary rows
Solution Approach 1:
The system computes top-k boundary values in advance and pushes them down to aggregation operators, enabling early row flush. This preliminary action allows aggregation operators to discard rows that cannot contribute to top-k results before performing expensive aggregation operations, reducing resource consumption while maintaining result reliability.
Solution Approach 2:
The patent applies partial action by having aggregation operators process only the subset of rows that can potentially contribute to top-k results, as determined by pushed-down boundary values. Instead of processing the complete data set, operators perform partial aggregation on filtered inputs, reducing resource usage while ensuring the top-k results are complete and accurate.
Data Source
AI summary
Various example embodiments described herein provide for systems, methods, devices, instructions, and the like for optimizing a query plan to execute a query by using early row flush and pushing down top-k information in a query plan that includes an inner join, which can be used within a data platform environment. In particular, various example embodiments use early flush operations by one or more aggregation operators to eventually enable information from a top-k operator of a query plan to be pushed down the query plan and through one or more inner join operators of the query plan to one or more select operators (e.g., aggregation operators and table scan operators) positioned below the one or more inner join operators.