JSON Query Joins Across Cloud Resources Using Dynamic Indexing
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Solution Overview
Problem
Existing methods for joining JSON queries across cloud resources are inefficient and do not effectively utilize dynamic indexing to optimize join operations, leading to high computational overhead and large search spaces.
Innovation Solution
An optimized join method that employs dynamic indexing of query results, parsing JSON rules to generate first-order predicates, and using a search engine to merge pairwise joins based on cloud resource fingerprints, reducing the search space and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional join operations are used to merge results across cloud resources, then join functionality is achieved, but computational overhead and search space become excessively large
Solution Approach 1:
The system extracts and evaluates JSON rules from cloud resources before performing join operations. By pre-processing the data and identifying matching patterns in advance, the system reduces the search space and computational complexity during the actual join operation, thereby improving query processing efficiency and reducing computational time
Solution Approach 2:
The join operation is divided into multiple stages: first extracting JSON rules from individual cloud resources, then evaluating these rules to identify potential matches, and finally performing the actual join on the reduced result set. This segmentation allows each stage to focus on a specific task, improving overall efficiency and reducing the time required for complete query processing
2Productivity
If traditional join operations are used to merge results across cloud resources, then join functionality is achieved, but storage costs increase due to large search spaces
Solution Approach 1:
The system extracts only the necessary JSON rules and relevant data portions from cloud resources before joining. By taking out only the essential matching criteria and corresponding data, the system minimizes the amount of data that needs to be stored and processed, thereby reducing storage costs while maintaining query processing efficiency
Solution Approach 2:
By pre-evaluating JSON rules and identifying matching patterns before the join operation, the system reduces the size of the search space that needs to be stored in memory during processing. This preliminary filtering action significantly decreases storage requirements while preserving the ability to efficiently process queries
3Loss of time
If dynamic indexing is applied to optimize join operations, then search space is reduced, but system complexity increases
Solution Approach 1:
The system introduces an intermediary component that extracts and evaluates JSON rules as a mediator between the raw cloud resource data and the join operation. This intermediary layer simplifies the overall system by handling the complexity of rule evaluation and search space reduction in a dedicated module, making the system more manageable while achieving reduced computational time
Data Source
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AI summary
A cloud resource join query for join operations across cloud resources is parsed to extract join rules and queries to each cloud resource in the cloud resource join query. Results from the individual cloud queries are dynamically indexed based on pairs of cloud resources indicated in the join rules. A search engine applies first order predicates in the join rules using the dynamic indexes to generate pairwise join results corresponding to the query. A result for the cloud resource join query comprises the pairwise join results after merging.