Hot Key-Value Pair Preprocessing in Distributed Data Processing
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Solution Overview
Problem
Distributed data processing systems, such as HADOOP, face inefficiencies in handling large-scale data processing tasks due to the need to process all key-value pairs, which can be time-consuming and unsuitable for time-sensitive services, as they do not effectively prioritize and pre-process 'hot' data that is frequently invoked.
Innovation Solution
The system identifies and pre-processes 'hot' key-value pairs based on invocation frequency, using machine learning algorithms to select and prioritize these pairs, reducing the amount of real-time processing needed and improving efficiency by having pre-processed data ready for immediate use, while only processing non-hot pairs as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all key-value pairs are processed in distributed data processing systems, then complete data processing is achieved, but processing time increases significantly and efficiency decreases for time-sensitive services
Solution Approach 1:
The patent applies preliminary action by pre-processing key-value pairs based on their invocation frequency before actual service requests arrive. Hot key-value pairs are identified and processed in advance, so when service requests come in, the processing is already complete or near-complete, significantly reducing the time needed for time-sensitive services while maintaining reliable data processing.
2Productivity
If distributed systems process all data in parallel, then data processing capacity increases, but the total amount of computing resources needed increases without reducing data processing amount
Solution Approach 1:
The patent applies local quality by differentiating between hot key-value pairs (high invocation frequency) and cold key-value pairs (low invocation frequency). Different processing strategies are applied to different parts of the data: hot pairs are pre-processed with higher priority and more resources, while cold pairs use standard processing. This localized quality approach optimizes resource allocation rather than uniformly processing all data, improving productivity without proportionally increasing total computing resources.
3Productivity
If machine learning algorithms are used to identify hot key-value pairs, then data processing efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies parameter changes by using machine learning algorithms to dynamically determine the invocation frequency and identify hot key-value pairs based on changing parameters rather than static rules. The system monitors invocation patterns and adjusts which key-value pairs are classified as hot based on these parameter changes. This approach improves data processing efficiency by accurately identifying relevant data while managing complexity through parameter-based dynamic classification rather than complex hard-coded logic.
Data Source
AI summary
A method for distributed data processing includes selecting a first number of candidate key-value pairs from input key-value pairs stored in a distributed file system and identifying an invocation frequency for each of the first number of candidate key-value pairs in a predetermined period of time. The invocation frequency is a number of times the corresponding candidate key-value pair was called by a service system in the predetermined period of time. The method further includes selecting a second number of hot key-value pairs from the candidate key-value pairs based on the invocation frequencies of the candidate key-value pairs, mapping the second number of hot key-value pairs to intermediate key-value pairs, and reducing the intermediate key-value pairs to resultant key-value pairs to be called by the service system.