Stream Re-Sharding Reduces Data Entropy
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Solution Overview
Problem
Managing and processing large, dynamically fluctuating streams of semi-structured data with varying keys and values is challenging due to high data entropy, making it difficult to organize and utilize efficiently for downstream systems.
Innovation Solution
A stream re-sharding system that collects statistics on individual stream shards, selects high-frequency, low-cardinality keys as re-sharding keys, and redistributes data items to reduce data entropy by grouping similar data items together, allowing for efficient organization and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is randomly partitioned and stored into storage units without regard to structure, then data storage capacity and flexibility are improved, but data entropy increases making it difficult to use and manage
Solution Approach 1:
The system performs preliminary actions by collecting statistics on incoming data streams and pre-computing optimal partitioning strategies before data is stored. This allows the system to organize data with low entropy from the beginning, making it easier to manage while maintaining storage flexibility
Solution Approach 2:
The patent introduces an intermediary component (the stream re-sharding system with statistics collection) that sits between data ingestion and storage. This intermediary analyzes data characteristics and transforms raw high-entropy data into organized low-entropy storage units, resolving the contradiction between flexibility and manageability
2Ease of operation
If data is organized with low entropy for efficient downstream use, then data usability is improved, but the complexity of data organization and processing increases
Solution Approach 1:
The system employs self-service mechanisms where data automatically organizes itself into low-entropy structures through statistical analysis and automated re-sharding. The data stream service autonomously determines optimal partitioning based on collected statistics, reducing the apparent complexity for users while maintaining high usability
Solution Approach 2:
The patent changes key parameters of data organization by dynamically adjusting partitioning strategies based on statistical analysis of data characteristics. By changing parameters like partition keys and storage groupings based on observed data patterns, the system achieves low entropy organization without manual complexity
3Productivity
If statistics are collected and re-sharding is performed to reduce data entropy, then data processing efficiency is improved, but computational overhead and processing time are increased
Solution Approach 1:
The system applies partial action by collecting statistics on only the most relevant data characteristics and performing re-sharding on subsets of data that would benefit most from organization. This selective approach reduces overall processing time while still achieving significant entropy reduction and efficiency gains for critical data streams
Data Source
AI summary
Systems and methods are disclosed to implement a re-sharding technique that redistributes data in a first set of shards of a first stream into a second set of shards of a second stream to reduce the entropy of the data. In embodiments, individual ones of a number of re-sharding nodes determine statistics on an individual shard. The statistics include, for individual data keys in the shard, a frequency of the key and the cardinality of the key. The re-sharding node then selects a list of high-frequency, low-cardinality keys to be used generate a re-sharding scheme that is used to redistribute data items in the first set of shards to the second set. The re-sharding scheme assigns data items having the same key value combinations to the same range of shards in the second set. Accordingly, upon redistribution, data entropy in the second set of shards is reduced.