Top K Query Pruning via Runtime Boundary Feedback
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Solution Overview
Problem
Executing queries on large tables is inefficient due to the need to scan entire tables, consuming significant time and computing resources, and conventional top K query processing does not effectively utilize runtime information for pruning and filtering.
Innovation Solution
The table scan operator in the cloud data platform receives continuous runtime information from the top K operator to update its pruning and filtering, allowing for more selective processing of table portions, reducing the data load and processing requirements for both operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the table scan operator scans the entire table to identify data satisfying the query, then query completeness is ensured, but processing time and computing resources increase significantly
Solution Approach 1:
The system performs preliminary actions by using metadata (min/max values, null counts) to determine which table portions can be safely skipped before actual data scanning begins. This allows the table scan operator to prune portions of the table that cannot possibly contain matching rows, thereby reducing processing time while ensuring query completeness through systematic evaluation of all potentially relevant data.
Solution Approach 2:
The top K operator provides feedback about the current boundary (K-th smallest/largest value) back to the table scan operator. This feedback enables the table scan operator to dynamically adjust which table portions need to be scanned, using the boundary information to prune portions that cannot contain better matching rows, thus reducing processing time while maintaining query completeness.
2Reliability
If the table scan operator scans the entire table, then all potential matching data is retrieved, but computing resources are consumed excessively
Solution Approach 1:
The system uses metadata (min/max values, null counts) as preliminary information to determine which table portions can be skipped before actual data processing begins. This allows the table scan operator to identify and exclude portions of the table that cannot possibly contain matching rows, thereby reducing computing resource consumption while ensuring data retrieval completeness through systematic evaluation of potentially relevant data.
Solution Approach 2:
The top K operator continuously provides feedback about the current boundary to the table scan operator, enabling dynamic adjustment of the scanning strategy. This feedback mechanism allows the system to stop scanning portions of the table that cannot contain better matching rows, significantly reducing computing resource consumption while maintaining complete and accurate data retrieval.
3Device complexity
If conventional top K query processing is used without runtime information sharing, then operator independence is maintained, but pruning and filtering effectiveness is reduced
Solution Approach 1:
The system introduces a feedback mechanism where the top K operator continuously sends runtime information (current boundary) back to the table scan operator. This feedback enables the table scan operator to dynamically prune table portions based on the current K-th value, significantly improving pruning and filtering effectiveness while maintaining reasonable operator independence through structured information exchange.
Data Source
AI summary
A top K query directed at a table is received. The table is organized into multiple storage units. The top K query comprises a first clause to sort a result set in order and a second clause that specifies a limit on a number of results provided in response to the query. A table scan operator identifies a first set of rows from the table based on a scan set determined for the table and provides the first set of rows to a top K operator. The top K operator determines a current boundary based on the first set of rows and provides the current boundary to the table scan operator. The table scan operator prunes the scan set based on the current boundary and identifies a second set of rows from the table based on the pruning.