Multi-partition Data Intake System for Scalable Analytics
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Solution Overview
Problem
Current data intake and query systems face challenges in efficiently searching and analyzing large, diverse datasets across multiple data sources due to imbalanced processing loads, leading to delays in result generation and limited scalability.
Innovation Solution
A data intake and query system that employs a network of distributed nodes and a search process master to collect and process data from diverse data systems, enabling scalable analytics and search operations across various data sources, including external systems like MySQL, PostgreSQL, and cloud storage, by offloading search and analytics operations to distributed nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single processing core processes all data entries in an imbalanced partition, then complete processing is achieved, but processing time increases significantly
Solution Approach 1:
The patent divides the imbalanced partition into multiple sub-partitions and distributes them across multiple processing cores. This segmentation allows parallel processing of data entries, reducing the time required while ensuring complete processing through coordinated execution across all cores.
Solution Approach 2:
The patent introduces a new dimension of parallelism by utilizing multiple processing cores simultaneously. Instead of sequential processing on a single core, the system leverages multi-core architecture to execute multiple processing operations in parallel, thereby reducing overall processing time while maintaining completeness.
2Productivity
If multiple partitions are used to distribute data, then processing capacity increases, but partition imbalance occurs leading to inefficient resource utilization
Solution Approach 1:
The patent implements dynamic partitioning where processing assignments are adjusted based on real-time workload conditions. Processing cores can dynamically take on additional sub-partitions when other cores complete their work, ensuring balanced resource utilization while maintaining high processing capacity through flexible adaptation to changing conditions.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor processing progress and partition balance across multiple cores. Based on this feedback, the system dynamically redistributes sub-partitions to achieve more balanced workloads, improving resource utilization efficiency while preserving the enhanced processing capacity provided by multi-partition architecture.
3Speed
If distributed processing cores are used, then processing speed increases, but coordination complexity increases
Solution Approach 1:
The patent introduces a partition manager as an intermediary component that coordinates between multiple processing cores. This mediator handles the complex tasks of partition distribution, progress tracking, and result aggregation, thereby enabling high-speed parallel processing while abstracting away the coordination complexity from individual processing cores.
Solution Approach 2:
The patent combines the coordination functions (partition distribution, progress monitoring, result aggregation) into a unified partition manager that works in conjunction with multiple processing cores. This merging of coordination tasks into a single managed process simplifies the overall system architecture while maintaining the speed benefits of distributed processing.
Data Source
AI summary
In an environment where multiple datasets are to be combined, systems and methods are disclosed for allocating a group of data entries from at least one dataset into multiple partitions. For a particular partition, the subgroup in the partition can be combined with data entries from the other dataset. In some cases, groups of data entries from each dataset are assigned to different partitions. For a particular partition, a subgroup is duplicated, some of the data entries of the subgroup are reassigned to other partitions, the subgroup is reformed to include data entries from other partitions, and the reformed subgroup is combined with the subgroup from the other dataset(s).