Collaborative Query Planning for Distributed Database Analytics
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Solution Overview
Problem
Current Big Data analytic platforms face challenges in optimizing query plans due to the lack of understanding of how table operators modify input data streams, leading to redundant operations and sub-optimal operator placement, as the optimizer treats table operators as black boxes and cannot infer their properties.
Innovation Solution
The Collaborative Planning method allows for a collaborative exchange of plan properties and context information between the optimizer and table operators, enabling the optimizer to generate more optimal query plans by understanding how table operators process data and modify input streams, and allowing the author of the table operator to optimize its logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional SQL optimization techniques are applied to table operators, then query execution efficiency can be improved, but the optimizer cannot understand how table operators modify input data streams because they appear as black boxes
Solution Approach 1:
The patent introduces a contract negotiation mechanism as an intermediary between the optimizer and table operators. The contract serves as a mediator that conveys data stream property information from table operators to the optimizer, enabling the optimizer to make informed decisions without directly accessing the internal logic of table operators. This resolves the black box problem while maintaining optimization capabilities.
Solution Approach 2:
The patent implements a feedback loop where table operators provide information about how they modify input data streams through contract negotiation. This feedback enables the optimizer to adjust query plans based on actual operator behavior, improving execution efficiency while maintaining accurate understanding of data stream transformations.
2Device complexity
If table operators are treated as black boxes by the optimizer, then implementation simplicity is maintained, but redundant operations and sub-optimal operator placement occur
Solution Approach 1:
The patent applies preliminary action by having table operators pre-specify their data stream modification properties through contract negotiation before the optimizer generates the final query plan. This allows the optimizer to anticipate operator behavior and avoid redundant operations without increasing implementation complexity significantly.
3Manufacturing precision
If cost-based optimization techniques are applied to table operators, then query plan quality improves, but the optimizer needs detailed understanding of operator properties which is currently unavailable
Solution Approach 1:
The contract negotiation mechanism acts as an intermediary that bridges the gap between the optimizer's need for detailed operator property information and the table operators' encapsulated implementation. The contract conveys necessary property information without requiring the optimizer to understand internal operator logic, enabling precise cost-based optimization.
Data Source
AI summary
A system and method for determining optimal query plans within distributed database system employing table operators for performing analytic operations for storing and processing multi-structured data. The optimization of a query plan proceeds through a collaborative exchange between a database system optimizer, or planner, and a table operator, wherein multiple communications between said optimizer and said table operator are conducted to exchange input and output information relevant to optimizing execution of the query and table operator.