Database B-Tree Pre-Fetching for Response Time
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Solution Overview
Problem
Existing database systems face performance issues due to time-consuming and resource-intensive search operations, leading to slowed response times and increased maintenance costs, as they often retrieve and process data sequentially without optimizing access patterns.
Innovation Solution
Implementing a b-tree structure with pre-fetching mechanisms that utilize volatile and non-volatile memory efficiently, where the database management system determines traversal patterns to pre-fetch data likely to be requested, based on the b-tree traversal path, and dynamically allocates memory for pre-fetching operations to enhance search efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential retrieval of database segments is used to determine satisfaction of search parameters, then comprehensive search coverage is achieved, but response time increases and system performance deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-fetching data segments into volatile memory before they are actually requested. The system monitors traversal patterns and proactively loads additional segments that are likely to be needed next, so that when search operations need this data, it is already available in fast memory, eliminating the need for sequential retrieval from non-volatile storage and significantly reducing response time while maintaining complete search coverage
2Speed
If more memory is allocated for data storage, then data access speed improves, but power consumption and resource utilization increase
Solution Approach 1:
The patent applies dynamics by making the memory allocation adaptive rather than static. The system dynamically adjusts the amount of volatile memory allocated for data storage based on real-time monitoring of access patterns, traversal depth, and current computational needs. When memory pressure is detected or access patterns change, the system adjusts its pre-fetching behavior and memory allocation accordingly, optimizing the balance between data access speed and power consumption rather than maintaining fixed high memory usage
Solution Approach 2:
The system changes operational parameters by adjusting memory allocation thresholds, pre-fetch distances, and caching strategies based on observed workload characteristics. These parameter changes allow the database to optimize for speed when resources are abundant while conserving power when resources are constrained, adapting to different operational conditions without sacrificing overall performance
3Productivity
If pre-fetching operations are implemented to load data into volatile memory, then search efficiency improves, but resource utilization and system complexity increase
Solution Approach 1:
The patent applies self-service by implementing automatic pre-fetching that monitors its own performance and adjusts without external intervention. The system includes feedback mechanisms that track which pre-fetching operations were beneficial and which wasted resources, automatically tuning its pre-fetching distance, timing, and target selection based on observed outcomes. This self-adjusting behavior reduces the need for complex external configuration and management while maintaining high search efficiency
Solution Approach 2:
The system implements feedback loops where the results of pre-fetching operations are monitored and fed back into the decision-making process. When pre-fetching consistently improves query performance, the system increases pre-fetching aggressiveness; when it causes unnecessary resource utilization, the system reduces pre-fetching activity. This feedback-driven approach automatically optimizes the balance between search efficiency and resource utilization without requiring complex manual configuration
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for an improved database. The systems, methods, and computer-readable media described herein may enhance the response time of databases and improve user experiences. In an example method described herein, a database may receive a search request for data stored in the database. The database may be stored in association with a b-tree to facilitate searching and retrieving data from the database. The method may include allocating a portion of a first memory for read ahead operations and performing the read ahead operations, wherein the read ahead operations comprise loading, into the portion of the first memory, data associated with one or more siblings of a current node, wherein the one or more siblings of the current node are one or more nodes that have a same number of parent nodes as the current node.