Continuous Aggregation Data Grid Partitioning
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Solution Overview
Problem
Existing data aggregation methods, such as the Single-Threaded and Parallel Methods, are inefficient when dealing with large data sets and frequent changes, as they require recalculating the entire aggregate result upon any change in the underlying data, leading to significant performance and scalability issues.
Innovation Solution
The implementation of a continuous aggregation method that partitions data into subsets, maintains intermediate states for each partition, and updates the overall aggregate result only when necessary, using an event-driven model to process changes without revisiting the entire data set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional aggregation methods (Single-Threaded or Parallel) are used to handle data changes, then the aggregate result remains accurate, but the computational burden and processing time increase significantly due to complete recalculation
Solution Approach 1:
The patent segments the data set into multiple partitions, each with its own intermediate state. Instead of recalculating the entire aggregate result from all data, the system only recalculates the aggregate for changed partitions and combines it with unchanged partition results. This segmentation enables partial recalculation, dramatically improving processing speed while maintaining accuracy.
Solution Approach 2:
The patent pre-calculates and stores intermediate states for each data partition before changes occur. When data changes, these pre-computed intermediate states are reused for unchanged partitions, eliminating the need to recalculate them. This preliminary action stores valuable computational results that can be rapidly combined with minimal recalculation.
2Reliability
If complete data sets are processed for aggregation, then comprehensive results are obtained, but the system scalability deteriorates with large data volumes and frequent changes
Solution Approach 1:
The data set is divided into multiple independent partitions, each managed separately with its own intermediate state. This segmentation allows the system to scale horizontally by adding more partitions without increasing the complexity of individual partition processing. The modular architecture improves scalability while maintaining complete aggregation through systematic combination of partition results.
Solution Approach 2:
The system performs partial aggregation by processing only the necessary partitions rather than always processing the complete data set. When data changes are localized to specific partitions, only those partitions require recalculation and combination, reducing the computational scope while still achieving complete and accurate aggregation results through selective processing.
3Productivity
If intermediate states are maintained for all data partitions, then partial recalculation efficiency improves, but the memory requirements and system resources increase
Solution Approach 1:
Each data partition maintains its own intermediate state with properties optimized for that specific partition's characteristics and aggregation requirements. This local quality allows the system to store only the necessary intermediate information for each partition rather than uniform global state management, improving partial recalculation efficiency while optimizing memory usage through localized, targeted data storage.
Data Source
AI summary
A computer-readable medium, computer-implemented method, and apparatus are provided. In one embodiment, one or more events are received, a new intermediate state of a data partition is created based on the event, and the new intermediate state is stored. The new intermediate state is reduced into a form suitable for aggregation, and an aggregate value is created by aggregating the new intermediate state with other intermediate states of other data partitions.