Parallel Computing File Storage Using Parser Metadata Extraction
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Solution Overview
Problem
Current data analytic techniques in parallel computing environments require complete searches of large data sets, leading to inefficiencies in processing and bandwidth usage, as only a small percentage of data blocks satisfy specific criteria, while the majority are unnecessarily read and processed.
Innovation Solution
Implementing a parser function in a parallel computing system that extracts metadata from files as they are written to storage nodes, allowing for selective storage and querying, thereby reducing unnecessary reads and optimizing storage and processing by identifying relevant data blocks through metadata queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete search of entire data set is performed using map-reduce, then all data blocks are processed to ensure no matching data is missed, but processing time and bandwidth consumption increase significantly when only a small percentage of data blocks are needed
Solution Approach 1:
The parser function performs preliminary processing of data blocks as they are written to storage nodes, extracting metadata and evaluating filter criteria before the data is fully stored. This preliminary action allows the system to identify relevant data blocks in advance, avoiding the need to read and process the entire data set later during query operations.
Solution Approach 2:
The system extracts only the necessary metadata from each data block during the write operation, separating the essential information (metadata) from the complete data. This extraction allows subsequent queries to operate on the compact metadata rather than requiring full data block reads, significantly reducing processing time and bandwidth when only a subset of data is needed.
2Measurement precision
If all data blocks are read and processed through map-reduce, then accurate filtering results are achieved, but transfer bandwidth costs increase due to unnecessary data transfer
Solution Approach 1:
The parser function extracts and stores only the essential metadata from each data block during the write operation, including information needed for filter criteria evaluation. This extraction enables subsequent queries to accurately identify relevant data blocks by searching only the compact metadata, eliminating the need to transfer and process complete data blocks, thus significantly reducing bandwidth consumption while maintaining filtering accuracy.
Solution Approach 2:
The metadata acts as an intermediary between the stored data and query operations. Instead of queries directly accessing and reading complete data blocks, the system uses metadata as an intermediate layer to filter and identify relevant data blocks first, then transfers only those specific blocks for processing. This intermediary approach maintains accurate filtering while minimizing unnecessary data transfer.
3Quantity of substance
If traditional storage tiering is used without parser functions, then storage capacity requirements are met, but data processing efficiency decreases due to unnecessary reads of non-matching data blocks
Solution Approach 1:
The parser function performs preliminary evaluation of filter criteria during the data write operation, before data is committed to storage nodes. This preliminary action allows the system to pre-identify which data blocks will be needed for subsequent queries, enabling more efficient storage placement and avoiding unnecessary reads of non-matching data blocks during processing operations.
Solution Approach 2:
The parser function provides feedback about the contents and characteristics of data blocks during the write operation, allowing the storage system to adjust its behavior accordingly. This feedback mechanism enables the system to optimize storage tiering decisions and query processing based on the actual data characteristics, improving overall processing efficiency while maintaining adequate storage capacity.
Data Source
AI summary
Techniques are provided for storing files in a parallel computing system based on a user-specified parser function. A plurality of files generated by a distributed application in a parallel computing system are stored by obtaining a parser from the distributed application for processing the plurality of files prior to storage; and storing one or more of the plurality of files in one or more storage nodes of the parallel computing system based on the processing by the parser. The plurality of files comprise one or more of a plurality of complete files and a plurality of sub-files. The parser can optionally store only those files that satisfy one or more semantic requirements of the parser. The parser can also extract metadata from one or more of the files and the extracted metadata can be stored with one or more of the plurality of files and used for searching for files.