sphinx

I am trying to use Sphinx full-text search capability for my MySQL server. I have setup a local Sphinx service based on the installation manual and able to do text search. I can connect to mysql.exe --host=127.0.0.1 port=9306 Where the port 9306 is the port configured in sphinx.conf : searchd { ... listen = 9306:mysql41 ... } And do SphinxQL queries. I am using the default Sphinx example database shipped with the release package. However I want to integrate Sphinx with my MySQL server, such that all the clients connecting to my sql server can do SphinxQL and I want to try it out with the MySQL

I am current in planning on creating a big database (2+ million rows) with a variety of data from separate sources. I would like to avoid structuring the database around auto_increment ids to help prevent against sync issues with replication, and also because each item inserted will have a alphanumeric product code that is guaranteed to be unique - it seems to me more sense to use that instead. I am looking at a search engine to index this database with Sphinx looking rather appealing due to its design around indexing relational databases. However, looking at various tutorials and

I am trying to connect to SphinxQL server through Linux command-line this way: > mysql -P 9306 ERROR 1045 (28000): Access denied for user 'root'@'localhost' (using password: NO) My Sphinx config file has 2 listen entries: listen = 9312 listen = 9306:mysql41 searchd daemon is running: > ps ax | grep searchd 10727 ? S 0:00 /usr/local/sphinx/bin/searchd 10728 ? Sl 0:00 /usr/local/sphinx/bin/searchd Regular search queries work perfectly: > /usr/local/sphinx/bin/search StackOverflow | more Sphinx 2.0.4-release (r3135) Copyright (c) 2001-2012, Andrew Aksyonoff Copyright (c) 2008-2012, Sphinx

I'm looking for a complete guide to using Sphinx with PHP and MySQL. I'd like one that's a bit simpler and easygoing than the one provided on the site. I'm looking for a few concepts on how exactly it all works. I have a server with PHP, HTML, other data and a MySQL database. How would I go about setting up Sphinx to power the search and results being returned? I'd like to be able to pass my search terms to my PHP script and have it deal with Sphinx and return the data. P.S. I'm also open to suggestion regarding any other alternatives to Sphinx. I came across this post but didn't find an

推荐通过二进制包安装，简单快捷 1.如果你本机没有安装mysql5.6，会提示缺少 libmysqlclient.so.18 文件，这时候需要安装mysql-community-devel 先安装mysql yum仓库源 https://dev.mysql.com/downloads/repo/yum/ 然后修改 /etc/yum.repos.d/mysql-community.repo ，把5.7的enable设为0 ，5.6的设为1，因为默认5.7的mysql devel是没有libmysqlclient.so.18的，只有从5.6里面安装。 2.按照sphinx安装教程进行二进制的安装 =========== 编译安装 cd /usr/local/src/sphinx ./configure --prefix=/usr/local/sphinx && make && make install 问题记录： make报错： libsphinx.a(sphinx.o)：在函数‘xmlUnknownEncoding’中： /usr/local/src/sphinx-2.2.11-release/src/sphinx.cpp:29116：对‘libiconv_open’未定义的引用 解决： 安装 libiconv ,到 http://www.gnu.org/software

网上有视频在讲php+mysql+sphinx，以前只是听说过，心想今天终于能够学习一下涨涨见识了，一个小时的理论篇讲完，并没有预想中那种汲取大量知识的满足感，一来讲的理论多一些，二来都是在linux下的操作，没办法实践，先把今天的小笔记写一下，以后慢慢钻研这块内容。 sphinx生成索引数据，并为数据库提供更专业的搜索功能。 为什么要用sphinx？1.做搜索时当数据量很大单纯的mysql搜索比较慢（如果表进行了分表会更慢）2.搜索中文分词3.速度快 获取sphinx。 http://sphinxsearch.com sphinx如何工作。 1.修改sphinx的配置文件 2.利用indexer工具生成索引数据 /usr/bin/indexer - config /etc/sphinxsearch/sphinx.conf - all 生成索引数据 查看sphinx数据 通过命令进入 mysql -h0 -P9306 在查询的时候，from后要跟上索引名。同时拿到的数据的字段，都是在配置文件中定义好的。 今天就学了这么多，好无感(⊙﹏⊙)b 来源： oschina 链接： https://my.oschina.net/u/1423209/blog/509281

I have about 2000 rows in a mysql database. Each row is a max of 300 characters and contains a sentence or two. I use mysql's built in fulltext search to search these rows. I would like to add a feature so that typos and accidental mispellings are corrected, if possible. For example, if someone types "right shlder" into the searchbox, this would equate to "right shoulder" when performing the search. What are your suggestions on the simplest way to add this kind of functionality? Is it worth adding an external search engine of some kind, like lucene? (It seems like for such a small dataset,