Statistical Caching Aggregation for High-Throughput Data Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems are inefficient in handling large volumes of data inputs, as they process data sequentially and require significant scaling of processing and storage capacity, which is often insufficient for rapid data accumulation in applications like online commerce.
Innovation Solution
The implementation of statistical caching systems that aggregate and compact data using cache keys, periodically synchronizing with a final data store, and maintaining a transaction log for fault tolerance, allowing for efficient event storage and reduced writes to the final storage location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is processed sequentially in FIFO manner, then processing simplicity is maintained, but processing speed deteriorates when handling large volumes of data
Solution Approach 1:
The patent segments the data processing system into multiple independent worker threads that can process different portions of the data stream simultaneously. Each worker thread maintains its own processing state and can operate independently, allowing parallel processing of large volumes of data while maintaining the simplicity of individual thread operations.
Solution Approach 2:
The patent transitions from single-threaded sequential processing to multi-threaded parallel processing by adding the dimension of concurrency. Multiple worker threads process data in parallel along the time dimension, dramatically increasing processing throughput while each individual thread maintains simple FIFO processing logic.
2Productivity
If processing and storage capacity is increased to handle large data volumes, then data handling capability is improved, but resource consumption increases
Solution Approach 1:
The patent merges multiple worker threads into a single coordinated processing system that shares common resources such as the output queue and synchronization mechanisms. This allows the system to handle large data volumes through parallel processing while consolidating resource usage, reducing the total resources needed compared to having separate processing systems.
Solution Approach 2:
The patent introduces an intermediary output queue that buffers data between multiple worker threads and the final processing stage. This intermediary structure allows workers to process data in parallel without requiring proportional increases in downstream processing capacity, efficiently managing resource flow through the system.
3Productivity
If data is stored for processing at a later time, then processing load is reduced, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by having worker threads process and validate data in parallel before final output, preparing data for downstream processing without blocking the main data flow. This allows continuous high-speed ingestion while maintaining data quality, reducing the need for reprocessing and enabling faster overall throughput.
Data Source
AI summary
The present invention relates to systems and methods for statistical caching. Inputs are captured via an appropriate network protocol. The input includes statistical data and a corresponding cache key. The values for each cache key within a cache are compacted using the input. The compacting involves determining if the corresponding cache key is already set within the cache, and if the cache key is present, aggregating the statistical data with the value stored within the cache to generate an updated value. The updated cache may be periodically synchronized with a final data store. Additionally, each operation performed by the statistical cache may be recorded in a transaction log for fault tolerance.