HyperLogLog Data Structures for Large Scale Dataset Analysis
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Solution Overview
Problem
Existing methods for processing and storing large datasets of user interactions are inefficient, leading to significant system latency and resource consumption, especially when determining frequency distributions of objects in the dataset.
Innovation Solution
The use of enhanced HyperLogLog (HLL) data structures that include additional fields for storing leading zeros, trailing bits, and aggregation counters, allowing for efficient determination of frequency distributions without the need for storing entire user IDs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to store and process large datasets of user interactions, then complete accuracy in determining frequency distributions can be achieved, but significant system latency and resource consumption occur
Solution Approach 1:
The patent uses probabilistic data structures (HyperLogLog, LogLog, MinLogLog) that provide approximate rather than exact counting. These structures use minimal memory (e.g., 2KB for 64-bit integers) and can be discarded or reset easily, trading exactness for speed and resource efficiency. The approximation error is bounded and acceptable for large-scale analytics where exact counts are computationally prohibitive.
Solution Approach 2:
The patent changes the parameter of precision from exact to approximate by using probabilistic algorithms. Instead of storing complete user ID records, it uses hashed values and probabilistic counting methods that provide sufficiently accurate frequency distributions with dramatically reduced computational overhead and memory usage.
2Measurement precision
If complete user ID records are stored to determine frequency distributions, then accurate results can be obtained, but memory resources are excessively consumed
Solution Approach 1:
The patent extracts only the essential information needed for frequency distribution from complete user ID records. Instead of storing entire user IDs, it extracts hashed representations and uses probabilistic data structures to capture frequency patterns. This extraction reduces memory requirements from potentially gigabytes to kilobytes while maintaining acceptable accuracy.
Solution Approach 2:
The patent uses hashed copies of user IDs rather than the original complete records. The hashing function transforms potentially large user identifiers into compact fixed-size values that can be stored in probabilistic data structures. This copying approach preserves the ability to identify and count unique users while dramatically reducing storage requirements.
3Quantity of substance
If probabilistic data structures are used to reduce memory consumption, then resource efficiency improves, but measurement precision decreases
Solution Approach 1:
The patent implements dynamic adjustment of precision parameters in the probabilistic data structures. The system can adjust the number of registers, hash function parameters, and other configuration elements to optimize the trade-off between memory usage and accuracy based on specific query requirements and available resources. This dynamic tuning allows the system to adapt precision levels to match actual needs.
4Measurement precision
If large datasets are processed using traditional methods, then complete accuracy is maintained, but processing speed and productivity decrease
Solution Approach 1:
The patent employs probabilistic data structures that provide sufficiently accurate results with minimal computational overhead. These structures can be processed rapidly and are designed for high-throughput scenarios where exact precision is less critical than processing speed. The approximate nature of these structures enables real-time or near-real-time analytics on large datasets.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that facilitate resource and space efficient analysis of large scale datasets. Methods include obtaining activity data for objects in a dataset. For each data item in the dataset, a hashed parameter having a binary representation is generated using an identifier for the object. A register is identified from among a set of registers based on the hashed parameter. A determination is made that the hashed parameter for the object contributes to an aggregation amount that specifies a number of occurrences of the object in the dataset. Based on this determination, an aggregation amount stored in the register is updated. Based on aggregation amounts stored in the set of registers, a reporting output is generated that provides an aggregate distribution of the objects in the dataset based on the activity data for the objects.