Raw Data Atomic Block Formatting for Streamed Disk Reconstruction
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Solution Overview
Problem
The process of formatting and reconstructing large raw data, such as disk images for bare-metal servers, is time-consuming and resource-intensive due to slow formatting and low compression ratios, particularly when dealing with sparse and non-sparse data segments.
Innovation Solution
A method is introduced to format raw data by generating atomic blocks from sparse and non-sparse data segments, using metadata files to track segment locations, and reconstructing data in parallel with formatting, allowing for efficient streaming and reconstruction without memory size limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If raw data is transferred and reconstructed using conventional methods, then the disk image can be deployed on bare-metal servers, but the process is time-consuming and resource-intensive
Solution Approach 1:
The raw data is divided into atomic blocks that can be independently processed and streamed. Each atomic block contains a portion of non-sparse data segments from the raw data, allowing parallel processing and incremental reconstruction without requiring the entire disk image to be transferred and stored before deployment begins
Solution Approach 2:
The system performs preliminary formatting of raw data into atomic blocks with associated metadata files before transfer. This pre-processing organizes the data structure in advance, enabling the reconstruction process to begin immediately upon receiving atomic blocks without requiring complete data transfer first
2Productivity
If conventional formatting methods are used on raw data, then data can be processed, but the formatting process is slow
Solution Approach 1:
The formatting process segments raw data into atomic blocks based on sparse and non-sparse data segments. This segmentation allows the system to process and stream smaller units independently, increasing overall formatting throughput by enabling parallel operations and avoiding bottlenecks associated with processing entire disk images sequentially
Solution Approach 2:
The system processes only the necessary portions of raw data (non-sparse segments) into atomic blocks while identifying and skipping sparse segments. This partial action approach formats only the essential data portions, significantly reducing formatting time while maintaining data integrity for reconstruction
3Ease of operation
If disk images are transferred over the network, then OS deployment is enabled, but the large size of disk images makes the process resource-consuming
Solution Approach 1:
The disk image is segmented into atomic blocks that can be transferred incrementally over the network. Each atomic block is self-contained with its metadata, allowing the reconstruction process to begin with partial data receipt. This reduces peak network bandwidth requirements and allows parallel transfer and reconstruction operations
Solution Approach 2:
The system extracts and transfers only the essential atomic blocks containing non-sparse data segments, leaving sparse segments to be handled locally through metadata-guided reconstruction. This extraction approach reduces the quantity of data that must be transferred over the network while maintaining complete reconstruction capability
4Productivity
If memory size limitations are imposed on the system, then resource management is simplified, but deployment of large disk images becomes impossible
Solution Approach 1:
The system segments the disk image into atomic blocks that can be processed and streamed independently. This segmentation eliminates the need to load entire large disk images into memory simultaneously, allowing deployment of images larger than available memory by processing blocks incrementally in a memory-efficient manner
Solution Approach 2:
The system transitions from a memory-centric approach (requiring sufficient RAM to hold the entire disk image) to a stream-centric approach where atomic blocks are processed in sequence through a pipeline. This dimensional change from batch processing to streaming processing removes memory size constraints on deployable image sizes
Data Source
AI summary
A method for formatting raw data comprises accessing the raw data, the raw data comprising sparse data segments, which are empty of any data, and non-sparse data segments, which comprise data, and generating a formatted data stream comprising one or more atomic blocks, each atomic block corresponding to a metadata file and to a portion of the non-sparse data segments of the raw data. Generating one atomic block comprises browsing the raw data and, upon locating one sparse data segment, populating the corresponding metadata file with offsets indicative of a beginning and an end of the located sparse data segment and populating the atomic block with a concatenation of at least portions of the non-sparse segments of the raw data located before and after the located sparse segment. If the atomic block exceeds a maximum size, another atomic block and another corresponding metadata file are populated.