Compression Window Buffering for High-Throughput Data Streams
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Solution Overview
Problem
Existing data buffering systems face inefficiencies due to non-negligible CPU, memory, or I/O overheads when handling large data sets, particularly when the peak data throughput of a sender exceeds that of the receiver.
Innovation Solution
A computer-implemented method involving a buffering system with multiple compression windows, each containing a data region and a statistics region, where data is written, statistics are updated, and the data is compressed based on these statistics before being sent, allowing for efficient handling and compression of large data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is buffered and compressed using existing data buffering systems, then data throughput can be increased, but CPU, memory, and I/O overheads become non-negligible when handling large data sets
Solution Approach 1:
The buffer is divided into multiple compression windows, each independently managing compression for a segment of the data stream. This segmentation allows parallel processing of different data portions, reducing overall CPU overhead while maintaining high throughput. Each compression window can be processed independently, enabling better resource utilization and reducing memory access overhead.
Solution Approach 2:
Statistics for compression are updated in advance as data arrives in each compression window, before the actual compression operation. This preliminary action allows the compression algorithm to be better prepared and optimized, reducing the computational overhead during the critical compression phase and improving overall processing efficiency.
2Quantity of substance
If compression is performed on large data sets, then data size for storage and transfer is reduced, but processing time and computational resources increase
Solution Approach 1:
By dividing the large data set into multiple compression windows that can be compressed in parallel, the total processing time is reduced while still achieving significant compression ratios. Each window processes a manageable portion of the data, avoiding the bottleneck of processing the entire large data set sequentially.
Solution Approach 2:
The system dynamically adjusts compression parameters based on the statistics updated for each compression window. By optimizing compression parameters for each segment based on its specific characteristics, the system achieves efficient compression with minimized processing time, adapting to varying data patterns without requiring exhaustive analysis of the entire data set.
Data Source
AI summary
A method, computer program product, and apparatus for buffering data sets are provided. The method includes preparing at least one buffer including a plurality of compression windows. The method also includes receiving a data set. The method further includes writing the received data set into a data region in a first compression window among the plurality of compression windows included in a first buffer among the at least one buffer. The method also includes updating statistics of a statistics region in the first compression window based on the received data set. The method further includes compressing the data set from the data region of the first compression window based on the statistics in the statistics region of the first compression window. The method also includes sending the compressed data set to the data region of the first compression window to replace the received data set.