Client-Side Agent for Real-Time Storage Indexing
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Solution Overview
Problem
Current storage systems face inefficiencies in indexing user data, particularly due to the time-consuming and resource-intensive process of file system crawls, which impede access and lead to inconsistent search index information, especially in environments with diverse data formats and large volumes of data containers.
Innovation Solution
A system and method that employs a client-side agent to detect changes in user data and parse modified data containers, transmitting the parsed data to a management module for updating a search database, allowing for real-time indexing and searching without the need for frequent file system crawls, and enabling access to data in various formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If file system crawls are performed to index user data, then search index information is obtained, but the process is time-consuming and resource-intensive, impeding data access
Solution Approach 1:
The system performs preliminary actions by having client agents continuously monitor and detect data changes in real-time, parsing modified data containers and transmitting parsed data to the management module immediately when changes occur, rather than waiting for periodic file system crawls. This preliminary detection and parsing action eliminates the need for time-consuming full crawls while maintaining up-to-date search index information.
Solution Approach 2:
The indexing process is segmented into distributed client agents that independently monitor their respective clients' data containers, rather than a centralized file system crawl processing all data sequentially. Each client agent parses only the modified data containers locally and transmits results to the management module, dividing the indexing workload into parallel, independent segments that reduce overall processing time and resource consumption.
2Reliability
If frequent file system crawls are performed to maintain up-to-date search index information, then data retrieval accuracy is improved, but resource consumption increases
Solution Approach 1:
The system implements feedback through client agents that continuously monitor data containers for modifications and immediately detect changes. When data changes are detected, the agents parse the modified containers and transmit the parsed data to the management module, which updates the search index in real-time. This feedback mechanism ensures search index consistency without requiring frequent full crawls, as the system responds dynamically to actual data changes.
Solution Approach 2:
The system replaces periodic file system crawls with event-driven periodic action, where client agents continuously monitor data containers and trigger parsing operations only when modifications are detected. This transforms the periodic full-crawl approach into targeted, on-demand parsing of only changed data containers, maintaining index reliability while dramatically reducing resource consumption by acting only when necessary.
3Measurement precision
If file system crawls are used for indexing, then all data containers are indexed, but the process impedes data access and reduces productivity
Solution Approach 1:
Client agents perform preliminary detection and parsing of modified data containers before search operations are initiated. By pre-processing and transmitting parsed data to the management module as changes occur, the system ensures all data containers are indexed without impeding access, as the indexing happens in the background through distributed agents rather than blocking full crawls.
Solution Approach 2:
Each client agent independently monitors its own client's data containers, detects modifications, parses changed containers, and transmits results to the management module. This self-service approach distributes the indexing workload across multiple clients, eliminating the need for centralized file system crawls that impede access, while ensuring complete indexing through autonomous client-side processing.
4Ease of operation
If traditional indexing methods are used, then data containers can be accessed, but search times are long and data retrieval efficiency is reduced
Solution Approach 1:
The system performs preliminary parsing of data containers by client agents when modifications occur, creating ready-to-search parsed data that is transmitted to the management module in advance. This preliminary action ensures that when search operations are initiated, the management module can quickly query the pre-parsed data without performing time-consuming full crawls, significantly reducing search times while maintaining ease of data access.
Solution Approach 2:
The management module acts as an intermediary between client agents and the search database. Client agents transmit parsed data to the management module, which maintains and queries the search database. This intermediary architecture enables efficient data retrieval by centralizing the parsed data in an optimized search structure, allowing fast queries without requiring direct access to raw data containers during search operations.
Data Source
AI summary
A system and method for indexing user data on storage systems is provided. A client of the storage system executes a novel client side agent that is configured to detect changes to data stored by the client on the storage system. In response to detecting that data has been modified, the agent examines modified data containers and parses the modified data to identify new and/or modified index terms or the creation/deletion of data containers. Once the data has been parsed by the agent to identify new/modified index terms, the parsed data is transmitted from the agent to a management module executing on a management server. The management module receives the parsed data and updates a search database using the received parsed data.