Tiered Data Processing for Distributed Query Optimization
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Solution Overview
Problem
The complexity and cost of analyzing distributed data sets, which are often stored in diverse locations and formats, make it difficult to perform unified data processing, as existing solutions like federated data processing may not fully optimize execution performance.
Innovation Solution
Implementing tiered data processing to generate a centralized execution plan that leverages distributed processing performance by reassigned data processing operations between local and remote engines, minimizing data transmission and optimizing execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If federated data processing is used to analyze distributed data sets, then data accessibility is improved, but execution performance and complexity optimization is insufficient
Solution Approach 1:
The system segments data processing into two distinct layers: a coordination layer that manages query planning and optimization, and distributed execution layers that process data locally. This segmentation allows the system to maintain data accessibility across distributed locations while reducing execution complexity by handling coordination centrally and processing locally.
Solution Approach 2:
The patent introduces a centralized query planner as an intermediary component that mediates between the user's data access requests and the distributed data sources. This intermediary generates optimized execution plans that reduce complexity by pre-planning data retrieval strategies, avoiding the need for each distributed node to independently manage complex query coordination.
2Adaptability or versatility
If data is stored in diverse locations and formats, then data distribution flexibility is improved, but data transmission volume increases
Solution Approach 1:
The system applies local quality by allowing each distributed data source to maintain its native format and storage characteristics while the query planner generates location-specific execution plans. This enables data to be stored in diverse locations and formats (maintaining flexibility) while minimizing transmission by retrieving only the specific data needed from each location.
Solution Approach 2:
The query planner dynamically changes execution parameters based on data location and format characteristics. By analyzing the specific properties of distributed data sources (format, location, access patterns), the system optimizes retrieval parameters to minimize transmission volume while maintaining the ability to access diverse data distributions.
3Speed
If data processing operations are performed locally, then execution speed is improved, but data format compatibility decreases
Solution Approach 1:
The system implements universality through a standardized query interface and execution plan format that works across diverse data sources. The centralized query planner generates universal execution plans that can be executed locally at different distributed nodes, enabling fast local execution while maintaining compatibility with various data formats through the standardized planning layer.
Data Source
AI summary
Data processing engines implement tiered data processing for distributed data in local and remote data stores. Requests to access distributed data including a data object in a remote data store are received at a data processing engine. A query plan is generated to service the access request. Different operations in the query plan are identified and assigned to one or more remote query processing engines that may access the remote data object. Requests to perform the different operations are sent to the one or more remote query processing engines. A final result is generated for the request based on the results received for the different operations from the remote query processing engine and results from operations performed with respect to locally stored data.