Dynamic Log Indexing for Data Processing Bottlenecks
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Solution Overview
Problem
Existing methods for partitioning system resources to process data streams are inefficient and inflexible, leading to underutilization of resources with small datasets and bottlenecks with large datasets, and are biased towards larger datasets at the expense of smaller but equally important ones.
Innovation Solution
A dynamic system that reorganizes log data into efficient bundles based on common properties, associates these bundles with dedicated log indices, and allocates processing resources based on the configuration and needs of each log index, allowing for flexible and unbiased allocation of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed partitioning of computing resources is used to process data streams, then resource allocation is simple and stable, but resource utilization is inefficient with small datasets and bottlenecks occur with large datasets
Solution Approach 1:
The patent implements dynamic resource partitioning where computing resources are not fixed but adaptively allocated based on real-time data characteristics. The system continuously monitors data stream properties and adjusts resource distribution accordingly, transforming the static resource allocation into a dynamic system that can respond to changing workload demands, thereby preventing both underutilization and bottlenecks
Solution Approach 2:
The system changes the allocation parameters of computing resources based on data characteristics such as dataset size, complexity, and processing requirements. By adjusting these parameters dynamically rather than using fixed values, the system optimizes processing throughput for varying data conditions without requiring manual reconfiguration
2Productivity
If proportional partitioning of resources is used based on dataset size, then large datasets receive adequate resources, but smaller but equally important data streams receive lesser processing resources
Solution Approach 1:
The patent applies different resource allocation strategies to different data streams based on their specific characteristics and importance. Rather than uniform proportional allocation, the system assigns computing resources according to local needs of each data stream, allowing small but critical datasets to receive sufficient resources while large datasets receive appropriate but not excessive allocation
Solution Approach 2:
The system segments the resource allocation decision into multiple independent evaluations based on different criteria (data size, importance, processing requirements). This segmentation allows the system to prevent any single factor like dataset size from dominating resource allocation, ensuring small important streams are not disadvantaged
Data Source
AI summary
The present disclosure relates to systems and methods that enhance log data processing, storage, and querying by dynamically allocating mapping connections between processing/storage components based on characteristics of incoming data and the processing/storage components. More particularly, incoming data from a data stream may be extracted and utilized according to dynamic component mapping to more efficiently process the data. Additionally, active processing/storage metrics may cause dynamic reallocation of the component mappings to balance resource loads.