Pipeline Dependent Tree Query Optimizer for Parallel Execution
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Solution Overview
Problem
Traditional query processing engines face inefficiencies due to extensive memory load and store operations, and lack of cost information, which limits optimization and parallel execution in query processing.
Innovation Solution
The method involves extracting multiple pipelines from a query plan tree, identifying dependencies between them, and generating a pipeline-dependent tree for execution by multiple processors, allowing for cost-based optimization and high inter-pipeline parallelism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional query plan trees are executed with multiple operators, then query processing can be performed, but extensive memory load and store operations occur consuming significant resources and time
Solution Approach 1:
The patent combines multiple operators into a single fused execution unit (pipeline) that processes data in one continuous pass. Instead of executing separate operators with intermediate memory operations, the join operator, filter operators, and other transformations are merged into one unified code block that processes rows continuously without materializing intermediate results, thereby eliminating extensive memory load and store operations.
Solution Approach 2:
The fused pipeline enables continuous processing of data rows through all operators in a single pass. The execution unit maintains continuous action by processing input rows through multiple operators sequentially without stopping to materialize intermediate results, keeping the data flow continuous and avoiding the stop-start nature of traditional operator execution with memory I/O between each operator.
2Loss of energy
If operators are fused into a single pipeline for efficient execution, then resource consumption is reduced, but cost information is lost making optimization difficult
Solution Approach 1:
The system performs preliminary cost analysis during the code generation phase, before the fused pipeline is executed. The code generator analyzes the query plan tree, estimates costs for different operator orderings and pipeline configurations, and uses this cost information to optimize the fusion strategy. This preliminary action preserves cost information availability while still enabling efficient fused execution.
Solution Approach 2:
The system incorporates feedback mechanisms where execution statistics and actual performance data from fused pipelines are collected and fed back to the code generator. This feedback loop allows the system to learn from actual runtime behavior and refine cost models, enabling better optimization decisions in subsequent code generation iterations while maintaining efficient fused execution.
3Productivity
If code generation creates native code for fused operators, then execution efficiency is improved, but execution is limited to a strictly bottom-up manner
Solution Approach 1:
The system dynamically determines execution strategies based on the specific query plan and cost considerations. Rather than being locked into a fixed bottom-up execution order, the code generator can produce fused pipelines that execute operators in optimized orders, and the system can adaptively choose between different execution modes (fused pipeline vs. traditional operator execution) based on query characteristics and available resources.
Solution Approach 2:
The query plan is segmented into logical pipelines that can be executed in flexible orders. The system identifies natural pipeline boundaries where data materialization is necessary, allowing independent pipelines to be executed in parallel or in different sequences. This segmentation enables execution flexibility while maintaining the efficiency benefits of fused operators within each pipeline segment.
4Adaptability or versatility
If traditional query processing uses multiple virtual function calls for each operator, then operator execution is flexible, but significant time is consumed due to extensive memory operations
Solution Approach 1:
Multiple operator executions that would traditionally require separate virtual function calls are merged into a single fused execution unit. The join operator, filter operators, and other transformations are combined into one continuous code block that processes data rows in a single pass, eliminating the overhead of repeated function calls and intermediate memory operations between each operator execution.
Solution Approach 2:
The fused pipeline maintains continuous data flow through all operators without interruption for memory I/O. Data rows are processed continuously through the join operation, filter operations, and other transformations in a single uninterrupted pass, eliminating the stop-start execution pattern of traditional operators that require materializing intermediate results to memory between each operation.
Data Source
AI summary
A method includes traversing a query plan tree having multiple nodes, each node representative of an operation on data that is the subject of a query, to extract multiple pipelines from the query plan tree, identify dependencies between the multiple extracted pipelines, and provide a pipeline dependent tree based on the dependencies between the multiple extracted pipelines for execution of the query by multiple processors.