Remote Data Search in Observability Pipelines Without Data Ingestion
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Solution Overview
Problem
Existing observability pipeline systems face inefficiencies in searching and processing large volumes of unstructured data across multiple geolocations, requiring costly infrastructure and complex data ingestion processes, which hinder efficient data analysis and resource utilization.
Innovation Solution
An observability pipeline system with integrated search functionality that allows querying of remote storage locations using dynamic computing resources, selecting optimal geolocations based on network efficiency, and performing searches at rest without moving or indexing data, utilizing Kusto Query Language or similar query languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data is stored at remote geolocations and searched using traditional methods, then data can be accessed, but network communication efficiency deteriorates and resource consumption increases
Solution Approach 1:
Instead of bringing search capabilities to remote data locations, the patent inverts the approach by bringing data to the compute node. The system retrieves data from remote storage locations and performs search operations locally at the compute node, eliminating the need for efficient remote search capabilities and reducing network communication overhead during search operations.
Solution Approach 2:
The patent introduces an intermediary data retrieval mechanism that acts as a mediator between remote storage and compute nodes. This intermediary layer fetches data from remote geolocations and makes it available locally, allowing search operations to be performed without direct remote access and thereby improving network communication efficiency.
2Adaptability or versatility
If dynamic computing resources are allocated for search operations, then search flexibility improves, but system complexity increases
Solution Approach 1:
The compute node is designed with multi-functionality, serving both as a data processing unit and a search execution environment. By containerizing the search engine and making it available as a universal service on the compute node, the system achieves search flexibility without requiring specialized dedicated search hardware, thereby avoiding increased system complexity.
Solution Approach 2:
The system implements self-service through automated container orchestration and dynamic resource allocation. The search engine containers are automatically deployed, managed, and scaled based on workload demands, eliminating the need for manual configuration and reducing operational complexity while maintaining high adaptability.
3Productivity
If search operations are performed on remote data, then data analysis capability improves, but network communication overhead increases
Solution Approach 1:
The system performs preliminary data retrieval and local caching before search operations. By fetching data from remote storage and storing it locally on the compute node beforehand, the system enables fast search operations without repeated network communications, thereby improving data analysis efficiency while reducing network overhead.
Solution Approach 2:
The patent segments the data processing workflow into distinct phases: data retrieval, local storage, and search execution. This segmentation allows network communication to occur only during the initial data retrieval phase, while subsequent search operations are performed locally, reducing overall network communication overhead and improving analytical productivity.
Data Source
AI summary
A search query for data at a storage geolocation is received from a computer node at a node geolocation by a dynamic computing resource. The dynamic computing resource is at a selected compute geolocation having a higher network communication efficiency with the storage geolocation relative to one or more other potential compute geolocations. In response, an observability pipeline process is configured based on the query and executed. Executing the process includes performing a remote read of the data to generate search results. The generated search results are then transmitted to the computer node at the node geolocation.