Probabilistic Data Structures for Cardinality Estimation
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Solution Overview
Problem
Service providers face computational inefficiencies in generating and maintaining large datasets of metrics associated with cloud-based services, such as tracking top contributors and cumulative values, due to the sheer size and unbounded nature of the data, making it infeasible with existing methods.
Innovation Solution
The use of probabilistic data structures, like count-min-sketch, combined with top-N lists and multi-tiered data structures (hash-table, linear-counter, and probabilistic data structures) to efficiently identify and store top contributors and their cumulative values, allowing for accurate tracking without excessive computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to track and store metrics data for large datasets, then data accuracy is maintained, but computational resources and time consumption increase excessively
Solution Approach 1:
The patent segments the metrics data tracking into two distinct components: a probabilistic data structure (such as a Bloom filter or HyperLogLog) for efficient cardinality estimation of unique contributors, and a top-N data structure for tracking the most frequent contributors. This segmentation allows the system to handle large datasets by dividing the tracking task into manageable parts, each optimized for its specific function, thereby maintaining data accuracy while improving computational efficiency.
Solution Approach 2:
The patent changes the parameter of data representation by using probabilistic data structures that estimate cardinality with bounded error rather than storing exact counts for all elements. This parameter change from exact to approximate representation enables the system to process large datasets with limited computational resources while maintaining acceptable accuracy levels for metrics generation.
2Loss of information
If all contributor data is stored in detail, then complete information is available, but storage requirements and processing time increase
Solution Approach 1:
The patent extracts only the most relevant information from the complete contributor data set. By using a probabilistic data structure to identify unique contributors and a top-N structure to capture the most frequent ones, the system extracts sufficient information to generate accurate metrics without storing or processing complete detailed information for all contributors, thereby reducing processing time while maintaining information completeness for reporting purposes.
3Measurement precision
If exact counting methods are used for all contributors, then precise metrics are obtained, but computational complexity increases
Solution Approach 1:
The patent employs dynamic data structures that adapt to the characteristics of the input data. The probabilistic data structure dynamically estimates cardinality based on the stream of contributors, while the top-N structure dynamically maintains the most frequent elements. This dynamic approach reduces computational complexity compared to static exact counting methods, as the structures can operate with bounded error margins that reduce the computational burden while maintaining acceptable precision for metrics generation.
Data Source
AI summary
This disclosure describes, in part, techniques for estimating frequency values of top contributors of a high cardinality data set in a computationally efficient manner. In one example, the techniques utilize a probabilistic data structure, such as a count-min-sketch, for identifying which contributors are the largest contributors to a particular metric. After identifying these largest contributors using the probabilistic data structure, the techniques may store the contributor IDs and the their respective estimated cumulative value (e.g., frequency counts) in a top-N list.