最新版wordpress背景音乐,汉川seo推广,多合一网站建设,公司比较好1 准备数据 我们这次Spark-sql操作所有的数据均来自Hive#xff0c;首先在Hive中创建表#xff0c;并导入数据。一共有3张表#xff1a;1张用户行为表#xff0c;1张城市表#xff0c;1张产品表。 1#xff09;将city_info.txt、product_info.txt、user_visit_action.txt… 1 准备数据 我们这次Spark-sql操作所有的数据均来自Hive首先在Hive中创建表并导入数据。一共有3张表1张用户行为表1张城市表1张产品表。 1将city_info.txt、product_info.txt、user_visit_action.txt上传到/opt/module/data [atguiguhadoop102 module]$ mkdir data 2将创建对应的三张表 hive (default)
CREATE TABLE user_visit_action(date string,user_id bigint,session_id string,page_id bigint,action_time string,search_keyword string,click_category_id bigint,click_product_id bigint, --点击商品id没有商品用-1表示。order_category_ids string,order_product_ids string,pay_category_ids string,pay_product_ids string,city_id bigint --城市id
)
row format delimited fields terminated by \t;CREATE TABLE city_info(city_id bigint, --城市idcity_name string, --城市名称area string --区域名称
)
row format delimited fields terminated by \t;CREATE TABLE product_info(product_id bigint, -- 商品idproduct_name string, --商品名称extend_info string
)
row format delimited fields terminated by \t;3并加载数据 hive (default)
load data local inpath /opt/module/data/user_visit_action.txt into table user_visit_action;
load data local inpath /opt/module/data/product_info.txt into table product_info;
load data local inpath /opt/module/data/city_info.txt into table city_info; 4测试一下三张表数据是否正常 hive (default)
select * from user_visit_action limit 5;
select * from product_info limit 5;
select * from city_info limit 5;2 需求各区域热门商品Top3 2.1 需求简介 这里的热门商品是从点击量的维度来看的计算各个区域前三大热门商品并备注上每个商品在主要城市中的分布比例超过两个城市用其他显示。 例如 地区 商品名称 点击次数 城市备注 华北 商品A 100000 北京21.2%天津13.2%其他65.6% 华北 商品P 80200 北京63.0%太原10%其他27.0% 华北 商品M 40000 北京63.0%太原10%其他27.0% 东北 商品J 92000 大连28%辽宁17.0%其他 55.0% 2.2 思路分析 CREATE TABLE user_visit_action(date string,user_id bigint,session_id string,page_id bigint,action_time string,search_keyword string,click_category_id bigint,click_product_id bigint, --点击商品id没有商品用-1表示。order_category_ids string,order_product_ids string,pay_category_ids string,pay_product_ids string,city_id bigint --城市id
)
CREATE TABLE city_info(city_id bigint, --城市idcity_name string, --城市名称area string --区域名称
)
CREATE TABLE product_info(product_id bigint, -- 商品idproduct_name string, --商品名称extend_info string
)
city_remark
IN: 城市名称 String
BUFF: totalcnt总点击量Map[(cityName, 点击数量)]
OUT:城市备注 String
selectc.area, --地区c.city_name, -- 城市p.product_name, -- 商品名称v.click_product_id -- 点击商品id
from user_visit_action v
join city_info c
on v.city_id c.city_id
join product_info p
on v.click_product_id p.product_id
where click_product_id -1selectt1.area, --地区t1.product_name, -- 商品名称count(*) click_count, -- 商品点击次数city_remark(t1.city_name) --城市备注
from t1
group by t1.area, t1.product_nameselect*,rank() over(partition by t2.area order by t2.click_count desc) rank -- 每个区域内按照点击量倒序排行
from t2select*
from t3
where rank 3使用Spark-SQL来完成复杂的需求可以使用UDF或UDAF。 1查询出来所有的点击记录并与city_info表连接得到每个城市所在的地区与 Product_info表连接得到商品名称。 2按照地区和商品名称分组统计出每个商品在每个地区的总点击次数。 3每个地区内按照点击次数降序排列。 4只取前三名并把结果保存在数据库中。 5城市备注需要自定义UDAF函数。 2.3 代码实现 package com.atguigu.sparksql.demo;import lombok.Data;
import org.apache.spark.SparkConf;
import org.apache.spark.sql.*;
import org.apache.spark.sql.expressions.Aggregator;import java.io.Serializable;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.TreeMap;
import java.util.function.BiConsumer;
import static org.apache.spark.sql.functions.udaf;public class Test01_Top3 {public static void main(String[] args) {// 1. 创建sparkConf配置对象SparkConf conf new SparkConf().setAppName(sql).setMaster(local[*]);// 2. 创建sparkSession连接对象SparkSession spark SparkSession.builder().enableHiveSupport().config(conf).getOrCreate();// 3. 编写代码// 将3个表格数据join在一起DatasetRow t1DS spark.sql(select \n \tc.area,\n \tc.city_name,\n \tp.product_name\n from\n \tuser_visit_action u\n join\n \tcity_info c\n on\n \tu.city_idc.city_id\n join\n \tproduct_info p\n on\n \tu.click_product_idp.product_id); t1DS.createOrReplaceTempView(t1); spark.udf().register(cityMark,udaf(new CityMark(),Encoders.STRING()));// 将区域内的产品点击次数统计出来DatasetRow t2ds spark.sql(select \n \tarea,\n \tproduct_name,\n \tcityMark(city_name) mark,\n \tcount(*) counts\n from\t\n \tt1\n group by\n \tarea,product_name);// t2ds.show(false);t2ds.createOrReplaceTempView(t2);// 对区域内产品点击的次数进行排序 找出区域内的top3spark.sql(select\n \tarea,\n \tproduct_name,\n \tmark,\n \trank() over (partition by area order by counts desc) rk\n from \n \tt2).createOrReplaceTempView(t3);// 使用过滤 取出区域内的top3spark.sql(select\n \tarea,\n \tproduct_name,\n \tmark \n from\n \tt3\n where \n \trk 4).show(50,false);// 4. 关闭sparkSessionspark.close();} Datapublic static class Buffer implements Serializable {private Long totalCount;private HashMapString,Long map;public Buffer() {}public Buffer(Long totalCount, HashMapString, Long map) {this.totalCount totalCount;this.map map;}}public static class CityMark extends AggregatorString, Buffer, String {public static class CityCount {public String name;public Long count;public CityCount(String name, Long count) {this.name name;this.count count;}public CityCount() {}}public static class CompareCityCount implements ComparatorCityCount {/*** 默认倒序* param o1* param o2* return*/Overridepublic int compare(CityCount o1, CityCount o2) {if (o1.count o2.count) {return -1;} else return o1.count.equals(o2.count) ? 0 : 1;}} Overridepublic Buffer zero() {return new Buffer(0L, new HashMapString, Long());}/*** 分区内的预聚合** param b map(城市,sum)* param a 当前行表示的城市* return*/Overridepublic Buffer reduce(Buffer b, String a) {HashMapString, Long hashMap b.getMap();// 如果map中已经有当前城市 次数1// 如果map中没有当前城市 01hashMap.put(a, hashMap.getOrDefault(a, 0L) 1); b.setTotalCount(b.getTotalCount() 1L);return b;}/*** 合并多个分区间的数据** param b1 (北京,100),(上海,200)* param b2 (天津,100),(上海,200)* return*/Overridepublic Buffer merge(Buffer b1, Buffer b2) {b1.setTotalCount(b1.getTotalCount() b2.getTotalCount());HashMapString, Long map1 b1.getMap();HashMapString, Long map2 b2.getMap();// 将map2中的数据放入合并到map1map2.forEach(new BiConsumerString, Long() {Overridepublic void accept(String s, Long aLong) {map1.put(s, aLong map1.getOrDefault(s, 0L));}});return b1;}/*** map {(上海,200),(北京,100),(天津,300)}** param reduction* return*/Overridepublic String finish(Buffer reduction) {Long totalCount reduction.getTotalCount();HashMapString, Long map reduction.getMap();// 需要对map中的value次数进行排序ArrayListCityCount cityCounts new ArrayList();// 将map中的数据放入到treeMap中 进行排序map.forEach(new BiConsumerString, Long() {Overridepublic void accept(String s, Long aLong) {cityCounts.add(new CityCount(s, aLong));}}); cityCounts.sort(new CompareCityCount());ArrayListString resultMark new ArrayList();Double sum 0.0;// 当前没有更多的城市数据 或者 已经找到两个城市数据了 停止循环while (!(cityCounts.size() 0) resultMark.size() 2) {CityCount cityCount cityCounts.get(0);resultMark.add(cityCount.name String.format(%.2f,cityCount.count.doubleValue() / totalCount * 100) %);cityCounts.remove(0);}// 拼接其他城市if (cityCounts.size() 0) {resultMark.add(其他 String.format(%.2f, 100 - sum) %);}StringBuilder cityMark new StringBuilder();for (String s : resultMark) {cityMark.append(s).append(,);}return cityMark.substring(0, cityMark.length() - 1);} Overridepublic EncoderBuffer bufferEncoder() {return Encoders.javaSerialization(Buffer.class);} Overridepublic EncoderString outputEncoder() {return Encoders.STRING();}}
}
