Cache Lock Manager for Concurrent Data Request Deduplication
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Solution Overview
Problem
Highly concurrent data requests in distributed cache systems lead to duplicate writes, degrading service availability and causing inefficiencies, particularly during peak events like sales, where millions of redundant requests overwhelm the system, resulting in potential service downtime.
Innovation Solution
Implementing a data filter, such as a bloom filter, to determine if a requested document exists in the cache, reducing duplicate writes by allowing only one service module to update the cache and using aging and decaying keys to manage data expiry, thereby optimizing system performance and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple service modules concurrently access the cache to fulfill data requests, then the system can handle high traffic volume, but duplicate writes occur causing service degradation and potential downtime
Solution Approach 1:
The patent introduces a coordination mechanism (lock manager or coordination service) as an intermediary between service modules and the cache. This mediator manages concurrent access by granting exclusive locks to selected service modules, preventing duplicate writes while maintaining high traffic handling capacity. The intermediary resolves conflicts without requiring changes to the cache structure itself.
Solution Approach 2:
The system performs preliminary actions by acquiring locks or permissions before accessing the cache. Service modules must obtain exclusive access rights before writing to the cache, preventing duplicate writes from occurring. This preliminary coordination ensures that only one module writes at a time, maintaining service availability during high traffic periods.
2Speed
If the cache stores frequently accessed data to improve retrieval speed, then read performance increases, but memory consumption increases during peak events like sales
Solution Approach 1:
The patent implements dynamic cache management where the cache size and content adapt based on system conditions. During peak events, the cache dynamically adjusts its capacity and eviction policies to balance retrieval speed and memory consumption. The system can expand cache usage when memory is available and contract when resources are constrained, maintaining performance across varying loads.
Solution Approach 2:
The system changes operational parameters such as cache eviction thresholds, memory allocation limits, and data retention policies based on system state. During sales events, parameters are adjusted to optimize for either speed or memory consumption depending on available resources. This allows the cache to maintain effective retrieval speed while adapting memory consumption to system capacity.
3Reliability
If the system processes all concurrent requests individually to ensure data accuracy, then data integrity is maintained, but system response time increases causing service unavailability
Solution Approach 1:
The patent merges multiple concurrent requests for the same data into a single processing operation. When duplicate requests are detected, the system combines them and processes them together through a single cache write operation. This maintains data integrity by ensuring all requests receive consistent data while significantly reducing the time loss compared to processing each request individually.
Solution Approach 2:
The system applies preliminary anti-action by detecting and preventing duplicate request processing before it can cause delays. Through request deduplication mechanisms and lock-based coordination, the system identifies redundant requests upfront and consolidates them, preventing the time loss that would result from processing each request separately while maintaining data accuracy.
Data Source
AI summary
In some embodiments, systems, methods, and apparatuses are provided herein useful for managing a plurality of concurrent and nearly concurrent data requests within a computer system. The systems have a main data storage for storing source data, and a high speed, and/or remote data storage for storing computed data. In some embodiments, a combination of data filters and distributed mutex processes are used to eliminate or limit duplicate reads and writes into the high speed data storage units by ensuring only a single service module gets a lock to do the read and update of the cache; and makes it possible for keys to expire and be removed from the data filter. The systems and methods herein have various applications including retail sales environments where the requested data is related to product sales, product availability and the like.