Large Scale Data Join Service Using Bitset XOR Operations
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Solution Overview
Problem
Existing methods for performing large-scale data joins and anti-joins on billions or trillions of data files are inefficient, requiring extensive processing time, large memory allocation, and significant CPU resources, leading to performance issues and potential garbage collection delays.
Innovation Solution
The approach involves partitioning data files using a first identifier, encoding second identifiers into bitsets, and comparing these bitsets to determine discrepancies, which reduces memory usage and processing time by reusing pre-allocated memory locations and employing XOR operations for anti-join operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anti-join operations are performed on billions or trillions of data files to ensure consistency between object content files and index files, then data consistency is detected, but the operation takes weeks or even months to complete
Solution Approach 1:
The patent partitions the large set of data files into multiple smaller subsets based on a partitioning key (e.g., prefix of the file key). Each subset is then processed independently through anti-join operations. This segmentation transforms an infeasible full-scale operation into multiple manageable parallel operations, reducing overall processing time from weeks/months to a practical duration while maintaining complete data consistency coverage.
Solution Approach 2:
The patent performs preliminary partitioning of data files into subsets before executing the anti-join operations. By pre-organizing the data structure based on partitioning keys, the system prepares the data in advance for efficient parallel processing, avoiding the need to handle all files simultaneously and thus reducing the operational time significantly.
2Reliability
If traditional anti-join operations are performed on large datasets, then data consistency is verified, but large amounts of memory must be allocated
Solution Approach 1:
The patent divides the large dataset into multiple partitions, each fitting within available memory constraints. By processing one partition at a time rather than loading all data simultaneously, the system verifies complete data consistency across billions or trillions of files without requiring prohibitive memory allocation. Each partition is independently validated and then aggregated with others.
3Reliability
If traditional anti-join operations are performed on large datasets, then data consistency is detected, but significant CPU time is consumed and performance is negatively affected
Solution Approach 1:
The patent partitions the data processing task into multiple independent subset operations that can be executed in parallel. This segmentation enables efficient utilization of CPU resources across multiple cores or nodes, transforming a single sequential CPU-bound operation into parallel operations that significantly improve overall processing throughput and system performance.
Solution Approach 2:
The patent processes data partitions in periodic batches rather than attempting to process all data continuously. By periodically processing completed partitions and moving to the next batch, the system maintains steady CPU utilization without overwhelming the system, improving overall productivity through controlled periodic execution.
4Ease of manufacture
If data files are partitioned based on a partitioning key that is relatively very large with respect to the number of files, then partitioning is performed, but the subsets are not sufficiently small for efficient data join operations
Solution Approach 1:
The patent applies different partitioning strategies to different portions of the data based on local characteristics. When the partitioning key is large, the system uses prefix-based partitioning or hierarchical partitioning to create appropriately sized subsets. This local adaptation ensures that each partition is of suitable size for efficient join operations while maintaining the ability to handle the overall large dataset.
Data Source
AI summary
Technologies are disclosed for providing a large scale data join service within a service provider network. A data set includes first and second sets of files that correspond to each other. Each file includes a first identifier (ID) and a second ID. The first set of files is partitioned based at least in part upon the first ID into a plurality of first subsets of files and the second set of files is partitioned based at least in part upon the first ID into a plurality of second subsets of files. Files within a first group of the plurality of first subsets and files within a second group of the plurality of second subsets are encoded into first and second bitsets, respectively, based at least in part upon the second IDs. An exclusive-or operation is performed on the first and second bitsets to find discrepancies between the data files.