Distributed Execution Models for Untrusted Command Processing
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Solution Overview
Problem
Existing data systems struggle to efficiently search and analyze large volumes of diverse data types across various data sources, including structured, semi-structured, and unstructured data, due to limitations in search and analytics capabilities, and the processing flow is often unidirectional, lacking the ability to route data to different destinations.
Innovation Solution
A data intake and query system that extends search and analytics capabilities by employing a search process master and query coordinators combined with a scalable network of distributed nodes, enabling seamless data processing across diverse data systems, including MySQL, PostgreSQL, Oracle databases, NoSQL data stores, cloud storage, and Hadoop systems, and providing big data open stack integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data is pre-processed and stored in data systems, then retrieval efficiency is improved, but data flexibility and analysis capability are reduced
Solution Approach 1:
The system performs preliminary actions by collecting and storing raw data in its original form without pre-processing, enabling future analysis flexibility while maintaining efficient retrieval through the distributed execution model that processes queries directly against stored raw data
2Adaptability or versatility
If storage capacity is increased to retain all raw data, then data flexibility is improved, but system complexity and cost increase
Solution Approach 1:
The system segments the storage architecture into distributed data lakes across multiple nodes, allowing raw data to be stored in its original form without centralized pre-processing, reducing system complexity while maintaining data flexibility through distributed access capabilities
3Loss of information
If search capabilities are extended to diverse data sources, then analytical insight is improved, but search and processing efficiency is reduced
Solution Approach 1:
The system introduces an intermediary layer of distributed execution nodes that translate and execute search queries across diverse data sources, harmonizing partial results into comprehensive answers while maintaining efficiency through parallel processing and intelligent query routing
4Device complexity
If unidirectional processing flow is used, then system simplicity is maintained, but data routing flexibility is reduced
Solution Approach 1:
The system transforms the static unidirectional processing flow into a dynamic multi-directional architecture where data can be routed flexibly to different destinations based on query requirements, while maintaining simplicity through standardized interfaces and protocols that abstract the underlying complexity
Data Source
AI summary
Systems and methods are disclosed for generating a distributed execution model with untrusted commands. The system can receive a query, and process the query to identify the untrusted commands. The system can use data associated with the untrusted command to identify one or more files associated with the untrusted command. Based on the files, the system can generate a data structure and include one or more identifiers associated with the data structure in the distributed execution model. The system can distribute the distributed execution model to one or more nodes in a distributed computing environment for execution.