SQL Query Scheduling with Metadata Prediction
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Solution Overview
Problem
Existing methods for scheduling SQL operations on heterogeneous systems, comprising CPUs and accelerators like GPUs or MICs, fail to optimize memory usage and throughput effectively, leading to inefficiencies in executing SQL queries.
Innovation Solution
The proposed solution involves using compression schemes and prediction algorithms based on metadata analysis to determine the most suitable device for executing SQL operators, maximizing memory usage by estimating result set sizes and offloading calculations to accelerators, while employing splitters and markers for fine-grained data management and cache optimization to reduce memory allocation overhead and improve storage access patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If SQL operators are executed on heterogeneous systems with CPUs and accelerators, then computational power and throughput are improved, but memory usage efficiency deteriorates due to inadequate optimization
Solution Approach 1:
The system performs metadata analysis and prediction algorithms before query execution to estimate result set sizes and determine optimal device allocation. This preliminary action allows the system to pre-configure memory allocation and device assignment, avoiding inefficient runtime memory management and improving both throughput and memory efficiency.
Solution Approach 2:
The system dynamically changes execution parameters by selecting different devices (CPU vs. accelerator) based on predicted query characteristics and result set sizes. This parameter optimization ensures that queries are executed on the most appropriate hardware, maximizing throughput while efficiently utilizing available memory resources.
2Loss of time
If result set sizes are not estimated, then memory allocation overhead increases, but query execution speed may be improved by avoiding prediction overhead
Solution Approach 1:
The system performs metadata analysis and result set size prediction before query execution. This preliminary action provides accurate size estimates that enable efficient memory allocation, reducing allocation overhead during query execution while the prediction itself is performed using optimized algorithms that minimize overhead impact.
Solution Approach 2:
The system uses metadata copies and statistical representations to predict result set sizes without requiring full data analysis. This copying approach allows rapid estimation using compressed metadata rather than examining actual data, reducing prediction overhead while maintaining accurate memory allocation planning.
3Quantity of substance
If data is not organized with splitters and markers, then storage access simplicity is improved, but memory allocation overhead and storage access efficiency deteriorate
Solution Approach 1:
The system organizes data into segments marked by splitters and markers, dividing large datasets into manageable chunks that can be efficiently processed and allocated in memory. This segmentation enables precise control over memory allocation for each data segment, reducing overall allocation overhead while the structured organization is managed through specialized hardware or software components.
Solution Approach 2:
The splitters and markers act as intermediary structures between raw data and the query processing system. These intermediaries provide the necessary organizational framework for efficient memory allocation and storage access, mediating between simple data storage and complex query requirements without requiring complete reorganization of the underlying data.
Data Source
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AI summary
According to some embodiments is disclosed a method for controlling and scheduling operation of at least one SQL operator on data chunk. The method comprising the step of: receiving SQL query, accessing data chunk blocks, receive meta data statistics and SQL query, analyzing the query selectivity, result size and Frequency moments calculation during the query execution run-time and choosing the right device to execute the each operator of the query according to analysis and predict results size.