Temporal Data Retrieval via Distributed Search and Host Groups
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Solution Overview
Problem
Conventional data management techniques are inefficient, expensive, and difficult to scale, leading to slow and costly data retrieval from large data networks, particularly for smaller enterprises, and do not facilitate rapid and accurate searching and retrieval of specific data.
Innovation Solution
A system for temporal optimization of data operations using distributed search and server management, involving host groups, shards, and manifest files to manage data storage and retrieval based on time characteristics, optimizing storage and retrieval efficiency by reallocating data across different server classes based on aging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data management techniques are used to store large amounts of data, then data storage capacity is increased, but data retrieval speed and accuracy deteriorate
Solution Approach 1:
The patent divides the data storage system into multiple distributed servers organized in host groups, with data further segmented into shards across these servers. This segmentation allows the system to maintain large storage capacity while enabling parallel search operations across segments, thereby preserving retrieval speed despite increased data volume.
Solution Approach 2:
The patent introduces manifest files as intermediary structures that contain metadata about data locations, timestamps, and organizational information. These manifest files enable the search system to quickly locate and retrieve specific data without scanning entire data stores, thus maintaining retrieval speed while supporting large-scale storage.
2Quantity of substance
If more data storage servers are added to increase storage capacity, then data storage capability is improved, but system complexity and deployment difficulty increase
Solution Approach 1:
The patent creates a universal manifest file format that can describe data organized in various ways (by time, by type, by location) using the same structural framework. This universality allows the system to scale to multiple servers without requiring different management approaches for each configuration, thereby reducing deployment complexity despite increased storage capability.
Solution Approach 2:
The patent implements dynamic host groups that can be configured and reconfigured based on operational needs. The system allows flexible assignment of servers to host groups and dynamic adjustment of shard distributions, enabling the system to adapt to changing requirements without complex redeployment, thus managing complexity while scaling storage capacity.
3Stability of the object's composition
If conventional partitioning techniques such as striping are used, then data distribution is improved, but scalability deteriorates
Solution Approach 1:
The patent replaces static striping partitions with dynamic shard assignments that can be flexibly distributed across host groups. This dynamic approach allows the system to maintain stable data distribution patterns while easily scaling by adding or removing servers from host groups, as shards can be rebalanced without fixed partition constraints, thereby achieving both distribution stability and scalability.
4Speed
If faster performance servers are used, then data retrieval performance is improved, but cost increases
Solution Approach 1:
The patent applies local quality by assigning different performance characteristics to different host groups based on their intended workload. Not all servers need to be high-performance; the system allows mixing of server types within and across host groups, optimizing cost by using appropriate performance levels for specific functions while maintaining overall system performance through strategic placement of data and queries.
Data Source
AI summary
The disclosure describes temporal optimization of data operations using distributed search and server management, including configuring one or more host groups, determining one or more stripes associated with one or more shards distributed among the one or more host groups, receiving a query to retrieve data, evaluating the query to identify a time characteristic associated with the data, identifying a location from which to retrieve the data, and rewriting the query to run on at least one of the one or more host groups at the location using a distributed search platform, the another query being targeted at a host group associated with the class.