Distinct Aggregate Query Translation for Database Performance
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Solution Overview
Problem
Queries containing distinct aggregates in database systems often suffer from performance issues due to high memory usage and processing time, especially in distributed environments like the cloud, where each distinct aggregate must be computed separately for each category, leading to inefficiencies.
Innovation Solution
A system and method that translates queries with distinct aggregates into queries without distinct aggregates by creating records for each distinct aggregate attribute, allowing for non-distinct aggregation, thereby reducing memory usage and processing time by leveraging optimized base aggregation support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distinct aggregate operations are computed separately for each category in distributed environments, then accurate distinct aggregation results are obtained, but memory usage and processing time increase significantly
Solution Approach 1:
The patent segments the distinct aggregate computation into two phases: first computing non-distinct aggregates for all records, then performing a secondary aggregation to derive distinct values. This segmentation allows the system to leverage efficient non-distinct aggregation algorithms while still achieving accurate distinct aggregation results, thereby reducing processing time without sacrificing accuracy.
Solution Approach 2:
The patent performs preliminary non-distinct aggregation computations for all records before computing the final distinct aggregate results. By pre-computing these intermediate non-distinct aggregates, the system avoids the need to compute distinct aggregates separately for each category, significantly reducing overall processing time while maintaining result accuracy.
2Measurement precision
If distinct aggregate operations are computed separately for each category in distributed environments, then accurate distinct aggregation results are obtained, but memory usage increases significantly
Solution Approach 1:
The patent segments memory usage into two efficient phases: storing intermediate non-distinct aggregate results, then using these results to compute final distinct aggregates. This segmentation avoids the need to load all category data into memory simultaneously, reducing peak memory usage while preserving aggregation accuracy.
Solution Approach 2:
The patent uses intermediate non-distinct aggregate results as a copy or proxy for the actual distinct aggregation computation. By working with these intermediate results rather than the original large dataset, the system achieves accurate distinct aggregation with significantly reduced memory requirements.
3Adaptability or versatility
If multiple distinct aggregates are computed in a query, then comprehensive analytical results are obtained, but each aggregate must be computed separately leading to inefficiency
Solution Approach 1:
The patent merges the computation of multiple distinct aggregates by computing all necessary non-distinct aggregates in a single pass through the data. This unified approach allows the system to derive multiple distinct aggregate results from the same intermediate computations, significantly improving productivity while maintaining comprehensive query capabilities.
Solution Approach 2:
The intermediate non-distinct aggregate results serve multiple purposes: they are used to compute any number of distinct aggregates for different categories. This universal intermediate representation enables the system to efficiently respond to diverse query requirements without re-computing data, enhancing both versatility and efficiency.
4Adaptability or versatility
If distinct aggregates are computed in distributed environments, then queries can be executed across distributed data storage, but the plurality of data storage options exacerbates performance issues
Solution Approach 1:
The patent performs preliminary non-distinct aggregation computations across distributed data storage nodes before consolidating results to compute final distinct aggregates. This preliminary action at the distributed level reduces data transfer requirements and allows each node to contribute efficiently, improving overall query performance while maintaining distributed environment support.
Data Source
AI summary
Embodiments relate to aggregation operations. More specifically, embodiments relate to translating a query involving a distinct aggregate(s) into a query that does not involve a distinct aggregate. The translation involves a transformation effectively rewriting the original query containing the distinct aggregate including removal of the distinct aggregate from the query. The translated query enables the query to perform a non-distinct aggregation that effectively provides the results of the removed distinct aggregate(s).