Predictive Connection Request Shedding in Database Systems
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Solution Overview
Problem
In database management systems, connection requests often time out due to long wait queues, leading to negative effects on upstream components and end-user experiences, as the increased requests overwhelm available connections in the connection pool.
Innovation Solution
Implement predictive connection request shedding, where requests are proactively removed from the wait queue before timing out, using a method that determines whether a connection request will time out by analyzing the connection pool's status and predicted wait time, and sheds the request if it exceeds a threshold, thereby reducing the queue length and alleviating system load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If connection requests are placed in a wait queue to maintain system stability, then system stability is improved, but the queue length increases causing requests to timeout and negative effects on upstream components
Solution Approach 1:
The system performs preliminary action by proactively shedding connection requests from the wait queue before they timeout. The shedding mechanism predicts when requests will timeout and removes them in advance, preventing the negative effects of timeout on upstream components while maintaining system stability.
Solution Approach 2:
The system applies preliminary anti-action by preemptively removing connection requests that are likely to timeout. This counteracts the potential harmful effect of request accumulation and timeout before it occurs, protecting upstream components from negative impacts.
2Productivity
If the connection pool is expanded to handle more requests, then processing capacity is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system changes parameters by dynamically adjusting the connection pool size based on current system conditions and request patterns. Instead of maintaining a large fixed connection pool, the system optimizes the pool size dynamically, improving productivity while avoiding the complexity of managing excessively large pools.
Solution Approach 2:
The system applies dynamics by making the connection pool adaptive and flexible. The pool size and composition change dynamically based on real-time conditions, allowing the system to handle varying request loads efficiently without the complexity of a static large-scale pool.
3Productivity
If connection requests are allowed to accumulate in the wait queue, then all requests are processed, but end-user experience deteriorates due to timeouts and delays
Solution Approach 1:
The system extracts problematic connection requests from the wait queue by shedding requests that are likely to timeout. This removes the harmful elements (requests that would timeout and degrade user experience) while allowing the remaining requests to be processed reliably, improving both throughput and user experience reliability.
Solution Approach 2:
The system converts the potential harm of request accumulation into benefit by using the wait queue analysis to identify and shed requests that would timeout. This transforms the problematic accumulation into a useful mechanism for predicting and preventing timeouts, thereby improving end-user experience reliability.
Data Source
AI summary
According to one or more embodiments, a method may include obtaining a connection request to communicate with a database. The method may further include determining a predicted wait time for the connection request. The predicted wait time may include a predicted amount of time until a connection with the database will be available to communicate with the database. The method may further include determining whether to shed the connection request before a connection request timeout time. The determination as to whether to shed the connection request may be based on whether the predicted wait time exceeds the connection request timeout time.