In-Memory Engine for Search Data Integration
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Solution Overview
Problem
Computer systems face performance degradation due to the significant difference in data retrieval times between disk storage and Random Access Memory (RAM), with disk storage being much slower, which affects search engine performance and data analysis efficiency.
Innovation Solution
A system that processes search queries by integrating structured and unstructured data, using a search server as a 'database' to handle queries and retrieve results, and an in-memory engine to process and provide dynamic search results, enabling faster data retrieval and analytics through incremental results retrieval and natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in disk storage, then larger data volumes can be managed, but data retrieval time increases significantly
Solution Approach 1:
The patent segments data into structured data (stored in disk databases) and unstructured data (stored in search indexes). The search server and in-memory engine process these segmented data types through different pathways, allowing simultaneous access to both disk-stored and memory-stored data without requiring all data to be loaded into RAM, thus resolving the contradiction between storage capacity and retrieval speed
Solution Approach 2:
The patent introduces an in-memory engine as an intermediary component between the disk storage system and the search server. This intermediary loads frequently accessed structured data into memory, acting as a buffer that speeds up data retrieval while allowing the system to maintain large disk storage capacity. The in-memory engine mediates between the slow disk storage and the fast search index, resolving the speed-capacity tradeoff
2Speed
If all data is loaded into RAM, then data retrieval speed improves, but memory capacity is limited
Solution Approach 1:
The system performs preliminary actions by pre-loading frequently accessed structured data into the in-memory engine before it is actually needed for query processing. This anticipatory loading ensures that hot data is already in memory when queries arrive, achieving fast retrieval speeds without requiring all data to be permanently resident in RAM
Solution Approach 2:
The patent applies local quality by storing different types of data in different locations with different access characteristics. Structured data that requires fast access is loaded into the in-memory engine, while less frequently accessed data remains in disk storage. This localized optimization allows the system to achieve high retrieval speeds for critical data without the cost of loading everything into memory
3Adaptability or versatility
If complex data analysis is performed, then analytical depth increases, but processing time increases
Solution Approach 1:
The patent merges structured data from databases with unstructured data from search indexes to perform comprehensive analytics. By combining these data sources in the in-memory engine, the system can execute complex analytical queries that leverage both types of data simultaneously, achieving deep analytical capabilities without the processing delays that would result from separate analysis of each data type
Solution Approach 2:
The system dynamically adjusts its processing approach based on query requirements. The in-memory engine can handle complex analytics operations in memory for fast results, while delegating less time-sensitive operations to disk-based processing. This dynamic resource allocation allows the system to maintain high analytical capabilities while optimizing processing time based on actual workload demands
Data Source
AI summary
Search integration is described. The actions include receiving, by one or more processors, a query. The actions include identifying search results that are responsive to the query. The actions include identifying, based on the query and the search results, structured data to supplement the search results, wherein the structured data is generated from binary table data that is received and deserialized by one or more processing modules. The actions include processing the structured data and the search results. The actions include providing, for output, the processed search results and the processed structured data results.