Data Sharding via Classifier and Event Fields
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Solution Overview
Problem
Querying large datasets in data analytics is resource-intensive and often slow due to the inability to generate data aggregates and indices across all dimensions, leading to performance issues in data analysis.
Innovation Solution
A method for highly efficient data sharding that identifies classifier and event fields to structure data, allowing for improved query speed by distributing data across distributed computing systems, using techniques such as hashing and degraded-resolution shard copying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data aggregates and indices are generated across all dimensions, then query performance is improved, but resource requirements and system complexity become infeasible for large datasets
Solution Approach 1:
The patent divides the large dataset into multiple shards based on classifier field values, creating manageable segments that can be processed independently. Each shard contains a subset of the data organized by classifier categories, allowing queries to be executed on smaller, more efficient units rather than the entire dataset at once.
Solution Approach 2:
The patent introduces a new organizational dimension by structuring data hierarchically across multiple levels: classifier fields at the top level, followed by event fields, and then temporal ordering. This multi-dimensional structure enables efficient querying without requiring traditional indices across all dimensions, as the hierarchical organization itself provides rapid data location capabilities.
2Quantity of substance
If traditional data sampling techniques are used, then resource requirements are reduced, but query performance deteriorates without enabling data structuring
Solution Approach 1:
The patent performs preliminary data structuring and organization into shards before queries are executed. By pre-organizing data according to classifier and event fields, the system enables fast queries without needing to load entire datasets into memory, as the structured format allows for efficient data location and retrieval of only the necessary portions.
Solution Approach 2:
The patent applies different organizational qualities to different parts of the data structure. Classifier fields provide categorical organization for broad data grouping, while event fields provide event-type organization within each classifier category. This localized optimization of data organization allows efficient querying for specific data types without requiring uniform structuring of the entire dataset.
Data Source
AI summary
A method for highly efficient data sharding includes: receiving a dataset containing event data; identifying a classifier field of the dataset; identifying an event field of the dataset; generating a data structure for the dataset using the classifier field and the event field; and storing the dataset by partitioning the dataset into shards using the classifier field as a shard key and ordering data within each shard by the classifier field.