Record Generation Estimate for Data Processing Tasks
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Solution Overview
Problem
Current data intake and query systems face challenges in seamlessly searching and analyzing large sets of diverse data from various sources, including structured, semi-structured, and unstructured data, due to limited scope and unidirectional processing flows that restrict access to external data systems.
Innovation Solution
A data intake and query system with a search process master and query coordinators, combined with a scalable network of distributed nodes, enables extended search and analytics capabilities across diverse data systems, allowing for the collection and processing of data from distributed systems and presenting harmonized search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data systems store and process only internal data stores, then system complexity is reduced, but search and analytics capabilities are limited in scope
Solution Approach 1:
The system segments data storage locations into multiple categories (internal data stores, external data systems, common storage, ingested data buffers) and processes queries by dividing work across these segments. Query coordinators distribute search tasks to appropriate data sources and aggregate results, enabling extended capabilities while managing complexity through structured segmentation.
Solution Approach 2:
The query coordinator serves multiple functions: it receives queries, determines data source locations, distributes search tasks, collects results, and returns harmonized outputs. This multi-functional component enables the system to handle diverse data sources without proportionally increasing overall system complexity.
2Productivity
If data systems implement unidirectional processing flows, then processing efficiency is improved, but access to external data systems is restricted
Solution Approach 1:
The system implements dynamic query processing where the flow direction adapts based on data source location. For external data systems, the flow becomes bidirectional with query coordinators distributing tasks outward and collecting results inward. For internal data stores, the traditional unidirectional flow is maintained, optimizing efficiency for each data source type.
Solution Approach 2:
Query coordinators act as intermediaries between the central system and external data systems. They receive queries from the system, translate and distribute them to appropriate external sources, collect results, and return harmonized data to the central system, enabling external access without disrupting internal processing efficiency.
3Loss of information
If systems search and analyze large sets of diverse data from multiple sources, then analytical insights are improved, but system complexity and coordination overhead increase
Solution Approach 1:
The system segments the complex task of searching diverse data sources into manageable components: query coordination, task distribution, result collection, and harmonization. Each query coordinator handles a specific subset of data sources, dividing the overall complexity into parallel, independently manageable segments that can be scaled.
Solution Approach 2:
The system implements feedback loops where query coordinators monitor query execution across distributed data sources, collect performance data, and adjust task distribution strategies. This feedback mechanism optimizes coordination efficiency by learning from past query patterns and adapting resource allocation to minimize overhead while maximizing analytical insights.
Data Source
AI summary
Systems and methods are described for determining a record generation estimate related to a particular processing task. The system obtains a sample set of data that includes multiple records. The system applies a processing task, such as a transform or regular expression rule to the sample set of data and determines how many records are generated by the processing task. Based on the number of records generated, the system determines a record generation estimate. The system can use the record generation estimate to allocate compute resources or determine a query execution time for at least a portion of the query based on the record generation estimate.