Quantile Estimation in Distributed Data Systems
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Solution Overview
Problem
Estimating quantiles in large distributed data systems is challenging due to the complexity of handling data across multiple nodes, where different components have access to different portions of the data, making it difficult to determine precise quantiles efficiently.
Innovation Solution
The system defines data bins ranging from the minimum to the maximum data value, determines the number of data values in each bin, and refines the bins until a specified quantile is converged upon, using either uniform or non-uniform grid points based on the quantile's location, allowing for precise estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in a distributed system with multiple nodes, then data storage capacity and scalability are improved, but quantile estimation complexity and difficulty increase
Solution Approach 1:
The patent segments the data distribution problem by having each node in the distributed system maintain its own data bins and quantile estimates locally. The overall quantile estimation is achieved by aggregating results from individual node estimates, transforming a complex global problem into simpler local problems that can be solved independently and then combined.
Solution Approach 2:
The patent introduces an intermediary coordination mechanism that enables nodes to exchange quantile estimate information without requiring full data sharing. This intermediary layer facilitates efficient quantile estimation across distributed nodes by mediating the aggregation of local estimates into a global result.
2Quantity of substance
If the number of data values increases, then data representativeness is improved, but quantile determination difficulty increases
Solution Approach 1:
The patent segments the large data set into bins grouped by nodes in the distributed system. Each node processes its local portion of the data independently by creating bins and calculating quantile estimates for its segment, then these local estimates are aggregated to determine the overall quantile for the complete data set, making the determination process manageable despite the large total number of values.
3Adaptability or versatility
If data is distributed across multiple nodes, then system scalability is improved, but measurement precision for quantiles deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where nodes receive quantile estimates from other nodes and adjust their own calculations accordingly. This iterative feedback process allows the system to refine quantile measurements across distributed nodes, improving precision while maintaining scalability through the coordinated aggregation of local estimates.
Data Source
AI summary
In accordance with the teachings described herein, systems and methods are provided for estimating quantiles for data stored in a distributed system. In one embodiment, an instruction is received to estimate a specified quantile for a variate in a set of data stored at a plurality of nodes in the distributed system. A plurality of data bins for the variate are defined that are each associated with a different range of data values in the set of data. Lower and upper quantile bounds for each of the plurality of data bins are determined based on the total number of data values that fall within each of the plurality of data bins. The specified quantile is estimated based on an identified one of the plurality of data bins that includes the specified quantile based on the lower and upper quantile bounds.