Query Plan Service Dynamic Join Order Optimization
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Solution Overview
Problem
Modern virtual computing systems face performance issues due to configuration limitations and inefficient query execution, leading to increased latency, resource usage, and user experience degradation, particularly in distributed data centers with large datasets and resource contention.
Innovation Solution
A query plan service that detects contention and reorders join operations to minimize performance bottlenecks, and an alert executing service that proactively caches data to address CPU, I/O, and network usage inefficiencies, optimizing join orders and caching strategies based on user preferences and system load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If join operations are executed in a fixed order, then query execution is simple, but performance is degraded due to resource contention and increased latency
Solution Approach 1:
The patent implements dynamic join order selection by evaluating multiple possible join orders and selecting the optimal one based on real-time performance metrics. The system transitions from a static fixed join order to a dynamic adaptive approach where the join order can change based on system conditions, resource availability, and query characteristics, thereby resolving the contradiction between simplicity and performance.
Solution Approach 2:
The system changes the parameter of join order from a fixed constant to a variable that can be optimized. By introducing performance cost calculations and selecting join orders based on evaluated metrics, the system transforms the join order from a static configuration parameter to a dynamic optimization parameter, improving query execution efficiency while managing complexity through systematic evaluation.
2Loss of time
If data is cached proactively, then user experience is improved and latency is reduced, but resource usage increases
Solution Approach 1:
The patent implements preliminary action by proactively caching data before it is actually requested. The system anticipates future data access needs and performs caching operations in advance, thereby reducing latency when the data is eventually needed. This resolves the contradiction by performing the useful action (caching) beforehand, accepting increased resource usage during caching in exchange for reduced latency during data access.
Solution Approach 2:
The system applies beforehand cushioning by pre-caching data to cushion against future latency issues. By preparing data in advance and storing it in cache memory, the system creates a buffer that protects against slow data access later, effectively cushioning the impact of potential latency problems while managing resource usage through intelligent caching strategies.
3Loss of energy
If multiple entity data structures are joined using optimal join order, then performance cost is reduced, but query planning complexity increases
Solution Approach 1:
The patent implements feedback mechanisms by evaluating the performance cost of different join orders and using this feedback to select the optimal join order. The system calculates performance metrics for various join sequences and uses this feedback information to make informed decisions, thereby reducing performance cost while managing planning complexity through systematic evaluation and selection based on measured outcomes.
Data Source
AI summary
In accordance with some aspects of the present disclosure, an apparatus is disclosed. The apparatus includes a processor and a memory, wherein the memory includes programmed instructions that when executed by the processor, cause the apparatus to receive a request to join a plurality of entity data structures using a first join order, determine a first performance cost of the first join order, determine a second performance cost of a second join order, determine whether the second performance cost is lower than the first performance cost, in response to determining that the second performance cost is lower than or exceeds the first performance cost, select the second join order or the first join order, respectively, join the plurality of entity data structures using the selected join order, and send the joined plurality of entity data structures.