Federated Graph Query Branching for Bandwidth-Aware Data Access
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Solution Overview
Problem
Conventional systems face inefficiencies in querying federated data storage systems due to varying data store capabilities, network bandwidth limitations, and data store unavailability, leading to scalability issues and high computational costs.
Innovation Solution
An optimized query execution plan is generated by identifying patterns in initial plans and replacing operations with first and second execution branches, leveraging a query planning schema to efficiently fetch data from multiple data stores, reducing network bandwidth and improving robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is consolidated into a centralized data repository, then data access efficiency is improved, but computational cost and network bandwidth requirements increase significantly
Solution Approach 1:
The patent segments the query execution process into multiple execution branches, each targeting specific data stores. Instead of consolidating all data into one repository, the system divides the federated query into parallel sub-queries that can be executed independently across distributed data stores, reducing the computational burden on any single system while maintaining efficient data access.
Solution Approach 2:
The patent introduces a new dimension of parallel execution by creating multiple execution branches that operate simultaneously across different data stores. This transforms the traditional single-threaded query execution model into a multi-dimensional parallel processing approach, improving data access efficiency without requiring centralized data consolidation.
2Reliability
If data is replicated across multiple data stores, then data availability and reliability are improved, but network bandwidth consumption increases
Solution Approach 1:
The patent applies partial action by creating execution branches that fetch only the specific subsets of data needed from each data store, rather than replicating or transferring entire datasets. This selective data retrieval approach maintains data availability across multiple stores while minimizing unnecessary network bandwidth consumption.
3Productivity
If conditional execution branches are implemented, then query efficiency and robustness are improved, but plan complexity increases
Solution Approach 1:
The patent implements dynamic execution plans with conditional branches that can adapt to runtime conditions such as data store availability and data size. The query planner generates multiple execution branches with different logic paths, allowing the system to dynamically select the most efficient execution path based on actual conditions, thereby improving query efficiency while managing complexity through structured conditional logic.
Data Source
AI summary
Example solutions for optimizing an initial query execution plan for a federated database includes identifying a group of operations in the initial query execution plan that conform to a recognized pattern including fetching data from a data store. Based on detection of the pattern and of the presence of a condition, an optimized query execution plan is created from the initial query execution plan by replacing the group of operations with a first execution branch and a second execution branch, wherein the optimized query execution plan executes at least one of the first and second execution branches. The first execution branch includes first operations for fetching the data from a first data store and the second execution branch comprise second operations for fetching at least a subset of the data from a second data store.