Data Intake Query System Scalable Distributed Search
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Solution Overview
Problem
Current data intake and query systems face challenges in seamlessly searching and analyzing diverse data types from various data sources, as their capabilities are often limited to internal data stores, and they lack the ability to route data to different destinations, restricting comprehensive search and analysis across diverse data systems.
Innovation Solution
A data intake and query system that employs a search process master and query coordinators combined with a scalable network of distributed nodes to collect and process data from diverse data systems, enabling search and analytics operations across internal and external data sources, including MySQL, PostgreSQL, NoSQL data stores, cloud storage, and Hadoop distributed file systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data intake and query systems are limited to internal data stores only, then system complexity is reduced and ease of operation is improved, but adaptability and versatility are worsened as the system cannot search or analyze diverse data from external data sources
Solution Approach 1:
The system is divided into distinct functional components: external data source interfaces, data collection modules, data processing engines, and user interface layers. Each component handles specific tasks independently, allowing the system to manage external data sources without overwhelming complexity. The segmentation enables modular integration of different data sources while maintaining manageable system architecture.
Solution Approach 2:
The patent introduces intermediary components such as data collection agents, protocol translators, and interface adapters that mediate between external diverse data sources and the core query system. These intermediaries handle the complexity of connecting to different data sources, translating various data formats and protocols, while presenting a unified interface to users, thus resolving the contradiction between versatility and complexity.
2Adaptability or versatility
If the system integrates multiple external data sources and diverse data types, then adaptability and data coverage are improved, but ease of operation is worsened due to the complexity of managing and querying diverse data systems
Solution Approach 1:
The patent implements a universal query interface and standardized data representation layer that works across all connected external data sources. The system provides multi-functional capabilities to handle different data types (structured, semi-structured, unstructured) through a single unified interface, allowing users to search and analyze diverse data without needing to understand the underlying data source complexities, thus maintaining ease of operation while achieving broad adaptability.
Solution Approach 2:
Intermediary components including data normalization layers and unified query processors translate diverse data source protocols into a common internal representation. These intermediaries shield users from the complexity of different data sources by providing consistent access methods, making the system easy to operate while supporting extensive data source coverage.
3Measurement precision
If the system processes and stores large volumes of raw data from multiple sources, then measurement precision and analytical insights are improved, but use of energy and computational resources are worsened
Solution Approach 1:
The system performs preliminary data processing, filtering, and indexing actions as data is ingested from external sources. Data is pre-processed, validated, and organized into optimized storage structures before being stored, reducing the computational burden during query operations. This preliminary action enables the system to maintain high analytical precision while reducing energy consumption during data retrieval and analysis operations.
Solution Approach 2:
The patent implements selective data processing where only relevant portions of data are fully processed and stored in detail, while less critical data is processed at lower fidelity or aggregated. The system applies partial processing to large volumes of data, focusing computational resources on high-value data subsets, thereby achieving sufficient analytical precision without the excessive energy consumption that would result from processing every byte of data at maximum detail.
Data Source
AI summary
Systems and methods are disclosed for providing a multi-component application, including a first and second component, and a first and second server. The first component may be implemented at the first server, while a second component may be implemented at a client device. An end user of a client device may request access to metadata stored on the second server that is utilized by the second component to implement the multi-component application. The end user may authenticate with the first component. The first component may then communicate with the second server to authenticate the end user to the second server, thereby granting the end user access to the second server without having to reauthenticate to the second server.