Adaptive Aggregation Placement in Query Plans With Redundant Join Aggregates
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Solution Overview
Problem
Existing query plans in databases are costly to execute in terms of time, processing resources, and memory, often tying up computing resources and slowing down database operations.
Innovation Solution
Adaptive aggregation operators are inserted into query plans to remove duplicates dynamically during execution, monitoring runtime efficiency metrics to deactivate when inefficient, and conditionally continuing execution of query plans based on these metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional query plans are executed without adaptive aggregation operators, then query execution is simpler and faster, but duplicate values are not removed and processing overhead increases
Solution Approach 1:
The patent implements adaptive aggregation operators that dynamically adjust their behavior during query execution. The operators monitor runtime conditions and cost-efficiency metrics to determine when to activate or deactivate aggregation, allowing the query plan to adapt to actual data characteristics and execution conditions, thereby reducing processing overhead without permanently increasing structural complexity
Solution Approach 2:
The system changes execution parameters dynamically by modifying the state of aggregation operators based on monitored metrics. When cost-efficiency thresholds are met, the system transitions between different execution modes (aggregation active/inactive), effectively changing the query execution parameters to optimize performance without requiring complex static query plan redesign
2Loss of energy
If aggregation operators are inserted to remove duplicate values, then cost-efficiency improves, but query execution time may increase due to additional processing
Solution Approach 1:
The adaptive aggregation operators apply partial aggregation based on monitored conditions rather than always performing full aggregation. When cost-efficiency thresholds are not met or runtime conditions indicate poor performance, the operators partially execute or skip aggregation entirely, reducing time overhead while still achieving cost-efficiency improvements when beneficial
Solution Approach 2:
The system dynamically adjusts the aggregation operation intensity based on real-time monitoring of cost-efficiency metrics and runtime conditions. This allows the query execution to flex between aggressive aggregation (when cost-efficiency is high) and minimal processing (when time sensitivity is high), optimizing the balance between cost-efficiency and execution time
3Adaptability or versatility
If adaptive aggregation operators are implemented, then query performance adapts to runtime conditions, but the system complexity increases
Solution Approach 1:
The adaptive aggregation operators are designed to self-regulate based on monitored cost-efficiency metrics and runtime conditions. The operators automatically determine when to activate or deactivate without requiring external control systems, reducing the complexity burden while maintaining high runtime adaptability through self-based decision making
Solution Approach 2:
The system implements feedback mechanisms where aggregation operators monitor their own cost-efficiency performance and runtime conditions. This feedback loop enables automatic adaptation and self-adjustment, achieving high runtime versatility through simple feedback-driven control rather than complex external management systems
Data Source
AI summary
The subject technology receives a query plan, the query plan comprising a set of query operations, the set of query operations including at least one aggregation and at least one join operation. The subject technology analyzes the query plan to identify an aggregation that is redundant. The subject technology removes the aggregation based at least in part on the analyzing. The subject technology determines at least one aggregation property corresponding to at least one query operation of the query plan. The subject technology inserts at least one adaptive aggregation operator in the query plan based at least in part on the at least one aggregation property, the at least one aggregation property comprising a set of aggregation properties. The subject technology provides a modified query plan based at least in part on the inserted at least one adaptive aggregation operator in the query plan.