Calculation Engine Filter Pushdown for Join Performance
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Solution Overview
Problem
Hierarchical joins in database systems consume significant processing power, leading to slowdowns in calculation scenarios due to increased computational complexity and inefficiencies in filtering operations.
Innovation Solution
A calculation engine optimizes join operations by recognizing and utilizing attributes with semantically similar data, even if they have different names, to reduce the number of join keys and filter data more efficiently, thereby minimizing unnecessary computations and speeding up processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hierarchical join operations are executed to combine data from multiple database tables, then complete and accurate query results are obtained, but computational complexity increases and processing time extends
Solution Approach 1:
The system performs preliminary filtering operations before executing join operations. The calculation engine identifies filter conditions in the query and applies them to reduce the data sets in one or more tables prior to the join operation, thereby reducing the computational complexity of the subsequent join while ensuring accurate results are obtained from the reduced data sets
2Reliability
If hierarchical join operations are executed to combine data from multiple database tables, then complete and accurate query results are obtained, but computational complexity increases
Solution Approach 1:
The system performs preliminary filtering operations before executing join operations. The calculation engine identifies filter conditions in the query and applies them to reduce the data sets in one or more tables prior to the join operation, thereby reducing the computational complexity of the subsequent join while ensuring accurate results are obtained from the reduced data sets
Solution Approach 2:
The calculation engine segments the query processing into distinct phases: filtering phase and joining phase. By separating these operations and applying filters before joins, the system breaks down the complex hierarchical join operation into manageable segments that can be executed more efficiently with reduced computational overhead
3Measurement precision
If data from multiple database tables is joined using multiple join keys, then accurate joining of semantically similar attributes is achieved, but the number of join operations increases and processing efficiency decreases
Solution Approach 1:
The calculation engine merges semantically similar attributes from different tables by identifying attributes that represent the same real-world concept even if they have different names. Instead of creating separate join operations for each attribute pair, the system combines these attributes into a single join operation using semantic equivalence classes, thereby maintaining join accuracy while reducing the total number of join operations required
Data Source
AI summary
Described herein includes a calculation scenario of a calculation engine that efficiently filters and joins data for processing. The calculation engine enhances the performance of the join operations by allowing join inputs to be pre-filtered more effectively. Such join operations can allow for faster processing times, and a reduction in the amount of data to be joined, resulting in more efficient processing.