Concurrent Summarization of Indexed Data Using Bucket Segmentation
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Solution Overview
Problem
Modern data centers face challenges in processing and indexing large volumes of machine-generated unstructured data due to its unstructured nature, making it difficult to apply semantic meaning and perform efficient searching operations.
Innovation Solution
The implementation of concurrent summarization processes by an indexer to generate summaries for subsets of indexed data, allowing multiple processes to execute in parallel without creating conflicting or redundant summaries, using summary identifiers and bucket IDs to manage data locking and parallel execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple summarization processes are executed concurrently to generate summaries for different subsets of indexed data, then productivity is improved, but the risk of creating conflicting or redundant summaries increases
Solution Approach 1:
The indexed data is divided into multiple distinct buckets, each identified by a unique bucket ID. Each summarization process is assigned to work on a specific bucket, ensuring that concurrent processes operate on separate data segments without interfering with each other. This segmentation allows parallel processing while maintaining data consistency.
Solution Approach 2:
A summary identifier acts as an intermediary mechanism to coordinate multiple summarization processes. The summary ID ensures that each process generates summaries for its assigned bucket without creating conflicts or redundancies with other processes. This intermediary enables safe concurrent execution while preserving summary data integrity.
2Reliability
If sequential summarization processes are used to ensure data consistency, then reliability is improved, but productivity deteriorates due to slower processing
Solution Approach 1:
By segmenting the indexed data into multiple buckets with unique bucket IDs, the system enables multiple summarization processes to execute concurrently on different segments. This segmentation transforms the sequential process into a parallel one, maintaining data consistency through clear separation of work assignments while dramatically improving processing speed.
Solution Approach 2:
The patent introduces a new dimension of parallelism by executing summarization processes across multiple buckets simultaneously. Instead of processing buckets one after another in a single dimension of time, the system adds a spatial dimension by distributing processes across multiple buckets, achieving both reliability and productivity.
3Productivity
If concurrent summarization processes are implemented without proper coordination, then productivity is improved, but device complexity increases due to locking and coordination mechanisms
Solution Approach 1:
The system performs preliminary assignment of buckets to summarization processes before execution begins. Each process is pre-assigned a specific bucket ID to work on, eliminating the need for complex runtime coordination and locking mechanisms. This preliminary action simplifies the coordination overhead while maintaining parallel processing capability.
Solution Approach 2:
Each summarization process independently manages its own execution on its assigned bucket without requiring complex inter-process coordination. The bucket ID serves as a self-identifying marker that allows each process to operate autonomously, reducing the need for external locking and coordination mechanisms.
Data Source
AI summary
Provided are systems and methods for concurrent summarization of indexed data. In some embodiments, two or more summary processes can be executed concurrently (e.g., in parallel) by an indexer to generate summaries for respective subsets of indexed data (e.g., partitions or buckets of indexed data) managed by the indexer.