Remote Data Search Coordination for Low-Latency Observability Pipelines
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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 re-collection, which hinders efficient data analysis and resource utilization.
Innovation Solution
An observability pipeline system with integrated search functionality that allows querying remote storage locations using dynamic computing resources, selecting geolocations based on network efficiency, and supporting structured, semi-structured, and unstructured data types, without the need for additional infrastructure or data re-collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored across multiple geolocations for observability pipeline processing, then data accessibility and processing capability are improved, but network communication latency and search efficiency deteriorate
Solution Approach 1:
The patent introduces a search coordinator as an intermediary component that receives search queries, determines optimal compute geolocations, and coordinates execution across distributed storage locations. This mediator manages the complexity of cross geolocation data access and optimizes search latency by selecting appropriate compute nodes based on data location and query requirements.
Solution Approach 2:
The system dynamically selects compute geolocations based on real-time conditions such as data location, query complexity, and network latency. The search coordinator evaluates multiple factors and dynamically routes queries to the most appropriate compute node, making the system adaptable to changing network conditions and data distributions to minimize search latency.
2Productivity
If dynamic computing resources are deployed for remote data search, then search capability and data analysis efficiency are improved, but infrastructure complexity and resource cost increase
Solution Approach 1:
The search functionality is integrated into the existing observability pipeline system, allowing the same infrastructure to handle both data collection/processing and remote search operations. The search coordinator reuses existing compute resources and data storage locations, avoiding the need for separate dedicated search infrastructure and reducing overall system complexity.
Solution Approach 2:
The system automatically provisions and manages compute resources based on search query requirements. The search coordinator dynamically allocates compute geolocations and manages resource lifecycle without manual intervention, allowing the system to self-adjust infrastructure capacity based on actual search needs and reduce overhead.
3Productivity
If data is re-collected and stored locally for search operations, then search performance and accessibility are improved, but data volume and storage requirements increase
Solution Approach 1:
Instead of extracting and re-storing all data locally, the system extracts only the necessary search capabilities to remote storage locations. The search coordinator queries data directly from its original storage locations across geolocations, extracting only the required information for search results rather than duplicating entire datasets, thereby reducing storage requirements.
Solution Approach 2:
The system adds a virtual dimension to data access by enabling search operations to traverse across distributed storage locations without physical data movement. This virtual layer allows search queries to access data in its original locations while presenting a unified search interface, eliminating the need for physical data replication and reducing storage volume.
Data Source
AI summary
In some aspects, search functionality is provided in an observability pipeline system. In some implementations, a method of searching remotely-stored data includes receiving a search query at a computer node residing at a node geolocation, the search query representing a request to search data stored at a storage geolocation; identifying compute geolocations each including computing resources, each of the compute geolocations being distinct from the node geolocation and the storage geolocation; obtaining latency data including latency values for the compute geolocations; selecting one of the compute geolocations based on the latency data; by operation of a coordinator agent on the computer node, initiating a dynamic computing resource at the selected compute geolocation, and receiving search results identified by the dynamic computing resource.