MySql分組后隨機(jī)獲取每組一條數(shù)據(jù)的操作
思路:先隨機(jī)排序然后再分組就好了。
1、創(chuàng)建表:
CREATE TABLE `xdx_test` ( `id` int(11) NOT NULL, `name` varchar(255) DEFAULT NULL, `class` varchar(255) DEFAULT NULL, PRIMARY KEY (`id`) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
2、插入數(shù)據(jù)
INSERT INTO xdx_test VALUES (1, '張三-1','1'); INSERT INTO xdx_test VALUES (2, '李四-1','1'); INSERT INTO xdx_test VALUES (3, '王五-1','1'); INSERT INTO xdx_test VALUES (4, '張三-2','2'); INSERT INTO xdx_test VALUES (5, '李四-2','2'); INSERT INTO xdx_test VALUES (6, '王五-2','2'); INSERT INTO xdx_test VALUES (7, '張三-3','3'); INSERT INTO xdx_test VALUES (8, '李四-3','3'); INSERT INTO xdx_test VALUES (9, '王五-3','3');
3、查詢(xún)語(yǔ)句
SELECT * FROM (SELECT * FROM xdx_test ORDER BY RAND()) a GROUP BY a.class
4、查詢(xún)結(jié)果
3 王五-1 1
5 李四-2 2
9 王五-3 3
3 王五-1 1
4 張三-2 2
7 張三-3 3
2 李四-1 1
5 李四-2 2
8 李四-3 3
補(bǔ)充知識(shí):mysql實(shí)現(xiàn)隨機(jī)獲取幾條數(shù)據(jù)的方法(效率和離散型比較)
sql語(yǔ)句有幾種寫(xiě)法、效率、以及離散型 比較
1:SELECT * FROM tablename ORDER BY RAND() LIMIT 想要獲取的數(shù)據(jù)條數(shù);
2:SELECT *FROM `table` WHERE id >= (SELECT FLOOR( MAX(id) * RAND()) FROM `table` ) ORDER BY id LIMIT 想要獲取的數(shù)據(jù)條數(shù);
3:SELECT * FROM `table` AS t1 JOIN (SELECT ROUND(RAND() * (SELECT MAX(id) FROM `table`)) AS id) AS t2 WHERE t1.id >= t2.id
ORDER BY t1.id ASC LIMIT 想要獲取的數(shù)據(jù)條數(shù);
4:SELECT * FROM `table`WHERE id >= (SELECT floor(RAND() * (SELECT MAX(id) FROM `table`))) ORDER BY id LIMIT 想要獲取的數(shù)據(jù)條數(shù);
5:SELECT * FROM `table` WHERE id >= (SELECT floor( RAND() * ((SELECT MAX(id) FROM `table`)-(SELECT MIN(id) FROM `table`)) + (SELECT MIN(id) FROM `table`))) ORDER BY id LIMIT 想要獲取的數(shù)據(jù)條數(shù);
6:SELECT * FROM `table` AS t1 JOIN (SELECT ROUND(RAND() * ((SELECT MAX(id) FROM `table`)-(SELECT MIN(id) FROM `table`))+(SELECT MIN(id) FROM `table`)) AS id) AS t2 WHERE t1.id >= t2.id ORDER BY t1.id LIMIT 想要獲取的數(shù)據(jù)條數(shù);
1的查詢(xún)時(shí)間>>2的查詢(xún)時(shí)間>>5的查詢(xún)時(shí)間>6的查詢(xún)時(shí)間>4的查詢(xún)時(shí)間>3的查詢(xún)時(shí)間,也就是3的效率最高。
以上6種只是單純的從效率上做了比較;
上面的6種隨機(jī)數(shù)抽取可分為2類(lèi):
第一個(gè)的離散型比較高,但是效率低;其他5個(gè)都效率比較高,但是存在離散性不高的問(wèn)題;
怎么解決效率和離散型都滿(mǎn)足條件啦?
我們有一個(gè)思路就是: 寫(xiě)一個(gè)存儲(chǔ)過(guò)程;
select * FROM test t1 JOIN (SELECT ROUND(RAND() * ((SELECT MAX(id) FROM test)-(SELECT MIN(id) FROM test)) + (SELECT MIN(id) FROM test)) AS id) t2 where t1.id >= t2.id limit 1
每次取出一條,然后循環(huán)寫(xiě)入一張臨時(shí)表中;最后返回 select 臨時(shí)表就OK;
這樣既滿(mǎn)足了效率又解決了離散型的問(wèn)題;可以兼并二者的優(yōu)點(diǎn);
下面是具體存儲(chǔ)過(guò)程的偽代碼
DROP PROCEDURE IF EXISTS `evaluate_Check_procedure`; DELIMITER ;; CREATE DEFINER=`root`@`%` PROCEDURE `evaluate_Check_procedure`(IN startTime datetime, IN endTime datetime,IN checkNum INT,IN evaInterface VARCHAR(36)) BEGIN
-- 新建一張臨時(shí)表 ,存放隨機(jī)取出的數(shù)據(jù)
create temporary table if not exists xdr_authen_tmp ( `ID` bigint(20) NOT NULL AUTO_INCREMENT COMMENT '序號(hào)', `LENGTH` int(5) DEFAULT NULL COMMENT '字節(jié)數(shù)', `INTERFACE` int(3) NOT NULL COMMENT '接口', `XDR_ID` varchar(32) NOT NULL COMMENT 'XDR ID', `MSISDN` varchar(32) DEFAULT NULL COMMENT '用戶(hù)號(hào)碼', `PROCEDURE_START_TIME` datetime NOT NULL DEFAULT '0000-00-00 00:00:00' COMMENT '開(kāi)始時(shí)間', `PROCEDURE_END_TIME` datetime DEFAULT NULL COMMENT '結(jié)束時(shí)間', `SOURCE_NE_IP` varchar(39) DEFAULT NULL COMMENT '源網(wǎng)元IP', `SOURCE_NE_PORT` int(5) DEFAULT NULL COMMENT '源網(wǎng)元端口', `DESTINATION_NE_IP` varchar(39) DEFAULT NULL COMMENT '目的網(wǎng)元IP', `DESTINATION_NE_PORT` int(5) DEFAULT NULL COMMENT '目的網(wǎng)元端口', `INSERT_DATE` datetime DEFAULT NULL COMMENT '插入時(shí)間', `EXTEND1` varchar(50) DEFAULT NULL COMMENT '擴(kuò)展1', `EXTEND2` varchar(50) DEFAULT NULL COMMENT '擴(kuò)展2', `EXTEND3` varchar(50) DEFAULT NULL COMMENT '擴(kuò)展3', `EXTEND4` varchar(50) DEFAULT NULL COMMENT '擴(kuò)展4', `EXTEND5` varchar(50) DEFAULT NULL COMMENT '擴(kuò)展5', PRIMARY KEY (`ID`,`PROCEDURE_START_TIME`), KEY `index_procedure_start_time` (`PROCEDURE_START_TIME`), KEY `index_source_dest_ip` (`SOURCE_NE_IP`,`DESTINATION_NE_IP`), KEY `index_xdr_id` (`XDR_ID`) ) ENGINE = InnoDB DEFAULT CHARSET=utf8; BEGIN DECLARE j INT; DECLARE i INT; DECLARE CONTINUE HANDLER FOR NOT FOUND SET i = 1;
-- 這里的checkNum是需要隨機(jī)獲取的數(shù)據(jù)數(shù),比如隨機(jī)獲取10條,那這里就是10,通過(guò)while循環(huán)來(lái)逐個(gè)獲取單個(gè)隨機(jī)記錄;
SET j = 0; WHILE j < checkNum DO set @sqlexi = concat( ' SELECT t1.ID,t1.LENGTH,t1.LOCAL_PROVINCE,t1.LOCAL_CITY,t1.OWNER_PROVINCE,t1.OWNER_CITY,t1.ROAMING_TYPE,t1.INTERFACE,t1.XDR_ID,t1.RAT,t1.IMSI,t1.IMEI,t1.MSISDN,t1.PROCEDURE_START_TIME,t1.PROCEDURE_END_TIME,t1.TRANSACTION_TYPE,t1.TRANSACTION_STATUS,t1.SOURCE_NE_IP,t1.SOURCE_NE_PORT,t1.DESTINATION_NE_IP,t1.DESTINATION_NE_PORT,t1.RESULT_CODE,t1.EXPERIMENTAL_RESULT_CODE,t1.ORIGIN_REALM,t1.DESTINATION_REALM,t1.ORIGIN_HOST,t1.DESTINATION_HOST,t1.INSERT_DATE', ' into @ID,@LENGTH,@LOCAL_PROVINCE,@LOCAL_CITY,@OWNER_PROVINCE,@OWNER_CITY,@ROAMING_TYPE,@INTERFACE,@XDR_ID,@RAT,@IMSI,@IMEI,@MSISDN,@PROCEDURE_START_TIME,@PROCEDURE_END_TIME,@TRANSACTION_TYPE,@TRANSACTION_STATUS,@SOURCE_NE_IP,@SOURCE_NE_PORT,@DESTINATION_NE_IP,@DESTINATION_NE_PORT,@RESULT_CODE,@EXPERIMENTAL_RESULT_CODE,@ORIGIN_REALM,@DESTINATION_REALM,@ORIGIN_HOST,@DESTINATION_HOST,@INSERT_DATE ', ' FROM xdr_authen t1 JOIN (SELECT ROUND(RAND() * ((SELECT MAX(id) FROM xdr_authen)-(SELECT MIN(id) FROM xdr_authen)) + (SELECT MIN(id) FROM xdr_authen)) AS id) t2', ' WHERE t1.PROCEDURE_START_TIME >= "',startTime,'"', ' AND t1.PROCEDURE_START_TIME < "',endTime,'"',' AND t1.INTERFACE IN (',evaInterface,')', ' and t1.id >= t2.id limit 1'); PREPARE sqlexi FROM @sqlexi; EXECUTE sqlexi; DEALLOCATE PREPARE sqlexi;
-- 這里獲取的記錄有可能會(huì)重復(fù),如果是重復(fù)數(shù)據(jù),我們則不往臨時(shí)表中插入此條數(shù)據(jù),再進(jìn)行下一次隨機(jī)數(shù)據(jù)的獲取。依次類(lèi)推,直到隨機(jī)數(shù)據(jù)取夠?yàn)橹梗?/p>
select count(1) into @num from xdr_authen_tmp where id = @ID; if @num > 0 or i=1 then SET j = j; ELSE insert into xdr_authen_tmp(ID,LENGTH,LOCAL_PROVINCE,LOCAL_CITY,OWNER_PROVINCE,OWNER_CITY,ROAMING_TYPE,INTERFACE,XDR_ID,RAT,IMSI,IMEI,MSISDN,PROCEDURE_START_TIME,PROCEDURE_END_TIME,TRANSACTION_TYPE,TRANSACTION_STATUS,SOURCE_NE_IP,SOURCE_NE_PORT,DESTINATION_NE_IP,DESTINATION_NE_PORT,RESULT_CODE,EXPERIMENTAL_RESULT_CODE,ORIGIN_REALM,DESTINATION_REALM,ORIGIN_HOST,DESTINATION_HOST,INSERT_DATE) VALUES(@ID,@LENGTH,@LOCAL_PROVINCE,@LOCAL_CITY,@OWNER_PROVINCE,@OWNER_CITY,@ROAMING_TYPE,@INTERFACE,@XDR_ID,@RAT,@IMSI,@IMEI,@MSISDN,@PROCEDURE_START_TIME,@PROCEDURE_END_TIME,@TRANSACTION_TYPE,@TRANSACTION_STATUS,@SOURCE_NE_IP,@SOURCE_NE_PORT,@DESTINATION_NE_IP,@DESTINATION_NE_PORT,@RESULT_CODE,@EXPERIMENTAL_RESULT_CODE,@ORIGIN_REALM,@DESTINATION_REALM,@ORIGIN_HOST,@DESTINATION_HOST,@INSERT_DATE); SET j = j + 1; end if; SET i=0; END WHILE;
-- 最后我們將所有的隨機(jī)數(shù)查詢(xún)出來(lái),以結(jié)果集的形式返回給后臺(tái)
select ID,LENGTH,LOCAL_PROVINCE,LOCAL_CITY,OWNER_PROVINCE,OWNER_CITY,ROAMING_TYPE,INTERFACE,XDR_ID,RAT,IMSI,IMEI,MSISDN,PROCEDURE_START_TIME,PROCEDURE_END_TIME,TRANSACTION_TYPE,TRANSACTION_STATUS,SOURCE_NE_IP,SOURCE_NE_PORT,DESTINATION_NE_IP,DESTINATION_NE_PORT,RESULT_CODE,EXPERIMENTAL_RESULT_CODE,ORIGIN_REALM,DESTINATION_REALM,ORIGIN_HOST,DESTINATION_HOST,INSERT_DATE from xdr_authen_tmp; END; truncate TABLE xdr_authen_tmp; END ;; DELIMITER ;
以上這篇MySql分組后隨機(jī)獲取每組一條數(shù)據(jù)的操作就是小編分享給大家的全部?jī)?nèi)容了,希望能給大家一個(gè)參考,也希望大家多多支持腳本之家。
相關(guān)文章
真的了解MySQL中的binlog和redolog區(qū)別
MySQL的binlog和redolog都是用于記錄數(shù)據(jù)庫(kù)操作的日志文件,但是它們有不同的作用和特點(diǎn),今天給大家分享MySQL的binlog和redolog區(qū)別,感興趣的朋友一起看看吧2023-11-11Mysql遷移DM國(guó)產(chǎn)達(dá)夢(mèng)數(shù)據(jù)庫(kù)完整步驟記錄
最近工作中用到國(guó)產(chǎn)數(shù)據(jù)庫(kù)達(dá)夢(mèng),簡(jiǎn)稱(chēng)DM,下面這篇文章主要給大家介紹了關(guān)于Mysql遷移DM國(guó)產(chǎn)達(dá)夢(mèng)數(shù)據(jù)庫(kù)完整步驟的相關(guān)資料,文中通過(guò)圖文介紹的非常詳細(xì),需要的朋友可以參考下2024-07-07Windows服務(wù)器下MySql數(shù)據(jù)庫(kù)單向主從備份詳細(xì)實(shí)現(xiàn)步驟分享
將主服務(wù)器中的MySql數(shù)據(jù)庫(kù)同步到從服務(wù)器中,使得對(duì)主服務(wù)器的操作可以即時(shí)更新到從服務(wù)器,避免主服務(wù)器因環(huán)境或者網(wǎng)絡(luò)異常一時(shí)無(wú)法使用,達(dá)到備份效果,這篇文章整理的確實(shí)挺詳細(xì)的2012-05-05MySQL數(shù)據(jù)表索引命名規(guī)范的實(shí)現(xiàn)示例
索引是提高查詢(xún)性能的重要工具,本文主要介紹了MySQL數(shù)據(jù)表索引命名規(guī)范的實(shí)現(xiàn)示例,包括不同類(lèi)型索引的命名方法,具有一定的參考價(jià)值,感興趣的可以了解一下2024-05-05實(shí)現(xiàn)MySQL與elasticsearch的數(shù)據(jù)同步的代碼示例
MySQL 自身簡(jiǎn)單、高效、可靠,是又拍云內(nèi)部使用最廣泛的數(shù)據(jù)庫(kù),但是當(dāng)數(shù)據(jù)量達(dá)到一定程度的時(shí)候,對(duì)整個(gè) MySQL 的操作會(huì)變得非常遲緩,這個(gè)時(shí)候我們就需要MySQL與elasticsearch數(shù)據(jù)同步,接下來(lái)就給大家介紹如何實(shí)現(xiàn)數(shù)據(jù)同步2023-07-07MySQL日期時(shí)間函數(shù)知識(shí)匯總
這篇文章主要介紹了MySQL日期時(shí)間函數(shù)知識(shí)匯總,這不同數(shù)據(jù)庫(kù)之間基本相同,只會(huì)有個(gè)別函數(shù)的差異。下文詳細(xì)介紹,需要的小伙伴可以參考一下2022-03-03分析一條sql的性能的標(biāo)準(zhǔn)總結(jié)
在本篇文章里小編給各位分享了關(guān)于分析一條sql的性能的相關(guān)知識(shí)點(diǎn)總結(jié)內(nèi)容,有興趣的朋友們學(xué)習(xí)下。2019-07-07MySQL中count(distinct?col...)組合使用的注意要點(diǎn)詳解
@count()是一個(gè)聚合函數(shù),返回指定匹配條件的行數(shù),開(kāi)發(fā)中常用來(lái)統(tǒng)計(jì)表中數(shù)據(jù)、全部數(shù)據(jù)、不為null數(shù)據(jù)或者去重?cái)?shù)據(jù),這篇文章主要給大家介紹了關(guān)于MySQL中count(distinct?col...)組合使用的注意要點(diǎn),需要的朋友可以參考下2024-08-08