SPHINX database index establishment and optimization technique

SPHINX is an open source search engine that can help developers conduct fast and efficient search on large -scale data sets.When using Sphinx to establish indexes and optimize databases, there are several key techniques to help improve the quality of search performance and search results. 1. Reasonably select the index field: It is very important to choose a field for establishing indexes, which directly affects the accuracy of search performance and results.Generally, the selected fields include title, content and labels.If there are multiple fields that need to be searched, you can create a combination field to index it, which can improve the search efficiency. 2. Use appropriate index type: Sphinx provides several different types of indexes, such as full -text indexes, blurring indexes and attribute indexes.The full -text index is suitable for searching large -scale text content, while blurred indexes are suitable for fuzzy matching.Attribute indexes are used for filtering and sorting.Choose suitable index types according to actual needs. 3. Analyze keywords: Before establishing indexes, analyze and process keywords to help improve the accuracy of search.SPHINX supports the use of various text processors, such as Chinese segmentors to handle keywords.Through the correct analysis and processing, you can better match the search and query. 4. Reasonable configuration index options: Reasonable configuration of SPHINX index options can improve query performance.Some important options include: set the appropriate weight, increase the position and distance weight of the matching word; use the Boolean computing symbols to filter the result; use fuzzy search, etc.Optimize the index options through reasonable configuration, you can get more accurate and efficient search results. The following is an example code and configuration using SPHINX to establish indexes and optimize databases: Example code (using PHP): php require('sphinxapi.php'); $cl = new SphinxClient(); $cl->SetServer("localhost", 9312); $cl->SetMatchMode(SPH_MATCH_EXTENDED); $cl->SetArrayResult(true); $ Result = $ Cl-> Query ("Keyword", 'Index_name'); // Process search results if ($result["total"] > 0) { foreach ($result["matches"] as $match) { // Get the matching information of the matching documentation and weight $docId = $match['id']; $weight = $match['weight']; // Find the specific content in the database according to the document ID // ... } } Example configuration file (sphinx.conf): source src1 { type = mysql sql_query = SELECT id, title, content FROM articles sql_attr_string = tags } index index_name { source = src1 path = /path/to/index charset_type = utf-8 docinfo = extern mlock = 0 } searchd { listen = 9312 listen = 9306:mysql41 log = /path/to/log/searchd.log query_log = /path/to/log/query.log } The above example code demonstrates the basic process of searching and processing results through Sphinx.In the configuration file example, key configurations such as data sources, indexes and search services are defined. In actual use, the establishment of indexes and optimization databases need to be adjusted and optimized according to specific conditions.The above is just some basic skills and examples, I hope to help you.