Search Template Analysis for LDAP Performance Optimization
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Solution Overview
Problem
LDAP server vendors lack direct assistance in determining which search attributes to index for optimal search performance, leading to inefficient data retrieval due to large data storage needs and high human intervention in logging performance data.
Innovation Solution
A method for statistically tracking search performance by correlating search requests to templates, calculating time parameters, and automatically updating and sorting statistical records to identify efficient search templates, reducing the need for manual intervention and optimizing search efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If logging features are implemented to record performance data on each individual search, then search performance can be analyzed, but large amounts of data storage are needed and time is lost for logging performance data
Solution Approach 1:
The patent extracts only the essential performance metrics (search time, template identification) from the logging process, storing only what is necessary for performance analysis rather than logging complete search data. This reduces data storage requirements while maintaining the ability to analyze search performance patterns.
Solution Approach 2:
The system pre-identifies and caches search template patterns before performance analysis is needed. By having templates预先 prepared and indexed, the system can quickly match incoming searches to templates without extensive logging and analysis of raw search data, reducing both storage needs and analysis time.
2Measurement precision
If logging features are implemented to record performance data on each individual search, then search performance can be analyzed, but high level of human intervention is required
Solution Approach 1:
The system automatically performs template identification, performance metric calculation, and analysis without requiring human intervention. The server autonomously monitors search performance, identifies slow search templates, and generates recommendations, eliminating the need for administrators to manually scan logs and analyze performance data.
Solution Approach 2:
The system implements automated feedback loops where performance data is continuously collected, analyzed, and used to generate actionable insights. This closed-loop system automatically adjusts and recommends optimizations based on observed performance patterns, reducing manual intervention while maintaining high measurement precision.
3Productivity
If all possible search attributes are indexed, then search efficiency is maximized, but it is not practical for large information directories
Solution Approach 1:
Instead of uniformly indexing all attributes across the entire directory, the system applies indexing selectively based on local performance needs. By identifying specific search templates that exhibit slow performance, the system indexes only the attributes relevant to those particular search patterns, optimizing search efficiency for critical queries while avoiding the complexity of indexing everything.
Solution Approach 2:
The system dynamically adjusts indexing parameters based on observed search performance. Rather than using a static all-or-nothing indexing approach, it modifies indexing parameters (which attributes to index) based on performance measurements and template analysis, achieving high search efficiency with manageable complexity.
4Ease of operation
If administrators manually scan logs to identify repeatedly slow searches, then search templates can be tuned, but time is lost and large amounts of data storage are needed
Solution Approach 1:
The patent replaces the manual mechanical process of scanning and analyzing logs with an automated computational system. The server automatically performs template identification, performance measurement, and analysis using algorithms rather than human operators, dramatically reducing the time required while maintaining ease of operation through automated reporting and recommendations.
Data Source
AI summary
Statistical information related to performing information searches based on search templates may be automatically generated and stored in statistical records. The statistical records may be automatically updated. The statistical records may be sorted to indicate which information searches are most efficient.