一、市场份额

1.简介

Sphinx

优势：

1. Sphinx是一个基于SQL的C++开发的开源全文检索引擎，在1千万条记录情况下的查询速度为0.x秒（毫秒级）
2. 始于2001年，近20年的市场打磨（本文基于目前最新版3.0.3）
3. 搜索引擎市场份额占比排名第5
4. 阿里云RDS中有1款Mysql存储引擎：SphinxSE就是为此配套，支持SQL JOIN
5. 提供SphinxQL，像使用SQL一样使用搜索引擎
6. PHP官网文档目前收录了4款搜索引擎扩展，其中1种就是Sphinx

二、基础概念

1.搜索引擎

搜索引擎（Search Engine）是指根据一定的策略、运用特定的计算机程序从互联网上搜集信息，在对信息进行组织和处理后，为用户提供检索服务，将用户检索相关的信息展示给用户的系统。搜索引擎包括全文索引、目录索引、元搜索引擎、垂直搜索引擎、集合式搜索引擎、门户搜索引擎与免费链接列表等。

2.数据源

数据来源，目前系统支持一些主流存储产品的自动对接。 比如：mysql, pgsql, mssql, xmlpipe, xmlpipe2, odbc... 支持写SQL JOIN语句，作为数据来源。

3.分词

对推送上来的文档进行词组切分，本文使用的是一元分词法，并非中文分词、盘古分词等。 一元分词： 我爱中国 将会分成 我 爱 中 国

4.索引

1. 主索引：type=plain 通过SQL语句控制数据源范围
2. 增量索引：type=plain 通过SQL语句控制数据源范围
3. 实时索引：type=rt 在内存中CRUD进行搜索控制的类SQL操作
4. 分布式索引：type=distributed 上述3种的结合，且可夸服务器拼接数据

5.幽灵数据

场景

在主索引中，有篇文章：我要吃饭

后来更改为：我要喝酒，并建立增量索引

这时在增量索引中搜新数据 喝酒 可以搜到，搜旧数据 吃饭 还是能搜到。

如何确保主索引在大数据下文档更新的及时性？

三、实战演练

1.准备数据源

2个函数

DELIMITER $$ CREATE DEFINER=`root`@`localhost` FUNCTION `rand_num`(`start_number` INT(11) UNSIGNED, `end_number` INT(11) UNSIGNED) RETURNS int(11) BEGIN DECLARE i int default 0; set i = FLOOR(start_number+RAND() * (end_number-start_number+1)); return i; END$$ DELIMITER ; DELIMITER $$ CREATE DEFINER=`root`@`localhost` FUNCTION `rand_string`(`number` INT(11) UNSIGNED) RETURNS varchar(1024) CHARSET utf8 BEGIN DECLARE chars_str varchar(1024) DEFAULT 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789【买2免1】荣诚月饼纳福吉祥4口味月饼520g/袋中秋传统糕点点心配送范围送货范围仅限常州、扬州、苏州、盐城、徐州、宿迁、淮安、泰州、无锡、连云港、南通、镇江、南京地区(生鲜类别仅限部分地区)支付方式检测到您当前处于非安全网络环境，部分商品信息可能不准确，请在交易支付页面再次确认商品价格信息哈啊'; DECLARE return_str varchar(1024) DEFAULT ''; DECLARE i int DEFAULT 0; WHILE i < number DO set return_str = CONCAT(return_str,SUBSTRING(chars_str,FLOOR(1+RAND()*200),1)); set i=i+1; END while; RETURN return_str; END$$ DELIMITER ;

1个存储过程

DELIMITER $$ CREATE DEFINER=`root`@`localhost` PROCEDURE `insert_main`(IN `number` INT(10) UNSIGNED) BEGIN DECLARE i int default 0; # 设置自动提交为false set autocommit =0; # 开启循环 REPEAT set i = i+1; insert into main values(null,rand_num(0,999999999),rand_string(rand_num(0,1024))); UNTIL i=number END REPEAT; commit; END$$ DELIMITER ;

3个表

CREATE TABLE `add` ( `type` int(10) unsigned NOT NULL, `id` int(10) unsigned NOT NULL, PRIMARY KEY (`type`) ) ENGINE=InnoDB DEFAULT CHARSET=utf8; CREATE TABLE `change` ( `type` int(10) unsigned NOT NULL, `id` int(10) unsigned NOT NULL ) ENGINE=InnoDB DEFAULT CHARSET=utf8; CREATE TABLE `main` ( `id` int(10) unsigned NOT NULL AUTO_INCREMENT, `type` int(10) unsigned NOT NULL DEFAULT '0', `beizhu` text NOT NULL, PRIMARY KEY (`id`) ) ENGINE=InnoDB AUTO_INCREMENT=1 DEFAULT CHARSET=utf8

生成1亿条测试数据：23.1GiB

mysql -uroot -p123456; use test; call insert_main(100000000); mysql> SELECT COUNT(*) FROM `main`; +----------+ | COUNT(*) | +----------+ | 100000000 | +----------+ 1 row in set (4 min 38.74 sec) 

2.安装Sphinx

wget -P ~/ http://sphinxsearch.com/files/sphinx-3.0.3-facc3fb-linux-amd64.tar.gz mkdir ~/sphinx cd ~/sphinx tar -xzvf ~/sphinx-3.0.3-facc3fb-linux-amd64.tar.gz -C ./ --strip-components 1 mkdir log/ data/ 

3.搜索配置

sudo vim ~/sphinx/etc/sphinx.conf

1/8.主数据源

source main { type = mysql sql_host = localhost sql_user = root sql_pass = 123456 sql_db = test sql_port = 3306 sql_query_pre = SET NAMES utf8 sql_query_pre = REPLACE INTO `add` SELECT 1,MAX(id) FROM `main` sql_query_pre = TRUNCATE `change` sql_query = SELECT `id`, `type`, `beizhu` FROM `main` WHERE `id`<=( SELECT `id` FROM `add` WHERE `type`=1) sql_attr_uint = type } 

2/8增量数据源

source zengliang:main { sql_query_pre = SET NAMES utf8 sql_query_pre = sql_query_pre = sql_query = SELECT `id`, `type`, `beizhu` FROM `main` WHERE `id`>( SELECT `id` FROM `add` WHERE `type`=1) UNION SELECT `id`, `type`, `beizhu` FROM `main` WHERE `id` IN(SELECT `id` FROM `change` WHERE `type`=1) sql_query_killlist = SELECT `id` FROM `change` WHERE `type`=1 } 

3/8主索引

其中ngram_chars配置在unicode官网可以查到，比如中文汉字通用的unicode编码范围。

index main { source = main path = /home/letwang/sphinx/data/main min_infix_len = 2 ngram_len = 1 ngram_chars = U+3000..U+2FA1F kbatch = main } 

4/8增量索引

index zengliang:main{ source = zengliang path = /home/letwang/sphinx/data/zengliang } 

5/8实时索引

index shishi { type = rt rt_mem_limit = 128M rt_attr_uint = type rt_field = beizhu path = /home/letwang/sphinx/data/shishi min_infix_len = 2 ngram_len = 1 ngram_chars = U+3000..U+2FA1F } 

6/8分布式索引

index fenbushi { type = distributed agent =127.0.0.1:9312:main #local = main agent =127.0.0.1:9312:zengliang #local = zengliang agent =127.0.0.1:9312:shishi #local = shishi } 

7/8索引器

indexer { mem_limit = 1024M } 

8/8守护服务

searchd { listen = 9312 listen = 9306:mysql41 log = /home/letwang/sphinx/log/searchd.log query_log = /home/letwang/sphinx/log/query.log read_timeout = 5 max_children = 30 pid_file = /home/letwang/sphinx/log/searchd.pid seamless_rotate = 1 preopen_indexes = 1 unlink_old = 1 workers = threads dist_threads = 4 binlog_path = /home/letwang/sphinx/data } 

4.重建全量索引

~/sphinx/bin/indexer -c ~/sphinx/etc/sphinx.conf --all --rotate Sphinx 3.0.3 (commit facc3fb) using config file '/home/letwang/sphinx/etc/sphinx.conf'... indexing index 'main'... collected 100000000 docs, 17421.4 MB sorted 6623.4 Mhits, 100.0% done total 100000000 docs, 17.42 Gb total 2819.8 sec, 6.178 Mb/sec, 35464 docs/sec indexing index 'zengliang'... collected 0 docs, 0.0 MB total 0 docs, 0.0 Kb total 0.0 sec, 0.0 Kb/sec, 0 docs/sec skipping non-plain index 'shishi'... skipping non-plain index 'fenbushi'... 

5.启动Sphinx

~/sphinx/bin/searchd -c ~/sphinx/etc/sphinx.conf Sphinx 3.0.3 (commit facc3fb) using config file '/home/letwang/sphinx/etc/sphinx.conf'... listening on all interfaces, port=9312 listening on all interfaces, port=9306 precaching index 'main' rotating index 'main': success precaching index 'zengliang' rotating index 'zengliang': success precaching index 'shishi' precached 3 indexes in 0.130 sec 停止服务： ~/sphinx/bin/searchd -c ~/sphinx/etc/sphinx.conf --stopwait 

6.SphinxQL查看搜索引擎状态

➜ ~ mysql -h127.0.0.1 -P9306 Welcome to the MySQL monitor. Commands end with ; or \g. Your MySQL connection id is 1 Server version: 3.0.3 (commit facc3fb) mysql> show databases; Empty set (0.00 sec) mysql> show tables; +-----------+-------------+ | Index | Type | +-----------+-------------+ | fenbushi | distributed | | main | local | | shishi | rt | | zengliang | local | +-----------+-------------+ 4 rows in set (0.00 sec) 

7.生成增量索引

➜ ~ mysql -uroot -p123456; mysql> call insert_main(1); ~/sphinx/bin/indexer -c ~/sphinx/etc/sphinx.conf zengliang --rotate Sphinx 3.0.3 (commit facc3fb) Copyright (c) 2001-2018, Andrew Aksyonoff Copyright (c) 2008-2016, Sphinx Technologies Inc (http://sphinxsearch.com) using config file '/home/letwang/sphinx/etc/sphinx.conf'... indexing index 'zengliang'... collected 1 docs, 0.0 MB sorted 0.0 Mhits, 100.0% done total 1 docs, 0.5 Kb total 0.1 sec, 4.8 Kb/sec, 10 docs/sec rotating indices: successfully sent SIGHUP to searchd (pid=11713). 

8.合并增量索引到主索引（可选操作）

mysql> select * from zengliang; +-----------+-----------+ | id | type | +-----------+-----------+ | 100000001 | 172620683 | +-----------+-----------+ 1 row in set (0.00 sec) ~/sphinx/bin/indexer -c ~/sphinx/etc/sphinx.conf --merge main zengliang --rotate Sphinx 3.0.3 (commit facc3fb) using config file '/home/letwang/sphinx/etc/sphinx.conf'... merging index 'zengliang' into index 'main'... merged 7233.8 Kwords merged in 1590.479 sec rotating indices: successfully sent SIGHUP to searchd (pid=7718). 

9.使用实时索引

➜ ~ mysql -h127.0.0.1 -P9306 mysql> DESC shishi; +--------+--------+------------+------+ | Field | Type | Properties | Key | +--------+--------+------------+------+ | id | bigint | | | | beizhu | field | indexed | | | type | uint | | | +--------+--------+------------+------+ 3 rows in set (0.00 sec) mysql> INSERT INTO `shishi` values (1, '我是中国人', 11); Query OK, 1 row affected (0.01 sec) mysql> INSERT INTO `shishi` values (2, '我要吃饭', 22); Query OK, 1 row affected (0.01 sec) mysql> select * from shishi WHERE MATCH('"*我*"'); | id | type | +------+------+ | 1 | 11 | | 2 | 22 | +------+------+ 2 rows in set (0.00 sec) Tips:你也可以近似疯狂的把主索引数据切换到实时索引中 mysql> TRUNCATE RTINDEX shishi; mysql> ATTACH INDEX main TO RTINDEX shishi; 

10.搜索分布式索引

mysql> SELECT * FROM `fenbushi` WHERE MATCH('"*鲜中交货淮州*"') LIMIT 10; | id | type | | 100000001 | 172620683 | 1 row in set, 1 warning (1.01 sec) mysql> select count(*) from fenbushi; | count(*) | | 100000003 | 1 row in set (1.01 sec) mysql> select count(*) from main; | count(*) | | 100000000 | 1 row in set (0.95 sec) mysql> select count(*) from zengliang; | count(*) | | 1 | 1 row in set (0.00 sec) mysql> select count(*) from shishi; | count(*) | | 2 | 1 row in set (0.00 sec) 

11.定时任务

crontab -e */1 * * * * /bin/sh ~/sphinx/bin/indexer -c ~/sphinx/etc/sphinx.conf zengliang --rotate */720 * * * * /bin/sh ~/sphinx/bin/indexer -c ~/sphinx/etc/sphinx.conf --merge main zengliang --rotate 30 1 * * * /bin/sh ~/sphinx/bin/indexer -c ~/sphinx/etc/sphinx.conf --all --rotate 每1分钟执行一遍增量索引 每720分钟执行一遍合并索引 每天1:30执行整体索引 

12.准备搜索

从主索引里搜索数据 mysql> SELECT * FROM `main` WHERE MATCH('"*仅非确息类*"'); 1 row in set (0.95 sec) 从增量索引里搜索数据 mysql> SELECT * FROM `zengliang` WHERE MATCH('"*仅非确息类*"'); 0 row in set (0.01 sec) 从实时索引里搜索数据 mysql> SELECT * FROM `shishi` WHERE MATCH('"*仅非确息类*"'); 0 row in set (0.02 sec) 从分布式索引里搜索数据 mysql> SELECT * FROM `fenbushi` WHERE MATCH('"*仅非确息类*"'); 1 row in set (0.80 sec) 搜索调试 mysql> SHOW META; | Variable_name | Value | | total | 1 | | total_found | 1 | | time | 0.80 | | keyword[0] | 仅 | | docs[0] | 24152970 | | hits[0] | 74754214 | | keyword[1] | 非 | | docs[1] | 16617532 | | hits[1] | 37394418 | | keyword[2] | 确 | | docs[2] | 23187798 | | hits[2] | 49207648 | | keyword[3] | 息 | | docs[3] | 23188209 | | hits[3] | 49235777 | | keyword[4] | 类 | | docs[4] | 16628887 | | hits[4] | 37414147 | 18 rows in set (0.00 sec) 

13.总结

性能指标

total 100000000 docs, 17.42 Gb Ubuntu 14.04 64bit Intel:registered: Core:tm: i5-6500 CPU @ 3.20GHz × 4 Intel:registered: HD Graphics 530 (Skylake GT2) 2*4G 2133 MHz ATA Disk Seagate 976.0 GB 属性筛选：300-400 毫秒 全文检索：1秒左右 

搜索引擎Sphinx亿级数据大并发实时搜索通用架构设计方案

1. 客户搜索【分布式索引】，其已包含：【主索引】、【增量索引】、【实时索引】
2. 定时任务每分钟更新 【增量索引】，解决幽灵数据问题，达到准实时搜索
3. 当用户操作数据时，同步到实时索引中，达到实时搜索；实时索引重启不会丢失数据

四、附录

PPT中所用的文件地址

1. https://baike.baidu.com/item/Sphinx/14627
2. http://sphinxsearch.com/
3. http://sphinxsearch.com/wiki/doku.php?id=third_party
4. http://php.net/manual/zh/book.sphinx.php
5. https://db-engines.com/en/ranking/search+engine