第四部分-推荐系统-实时推荐
- <font color=#00cc66 size=4 face="黑体"> 本模块基于第4节得到的模型，开始为用户做实时推荐，推荐用户最有可能喜爱的5部电影。</font>
说明几点
1.数据来源是 testData 测试集的数据。这里面的用户，可能存在于训练集中，也可能是新用户。因此，这里要做处理。
-
SparkStreaming + kakfa
在这里插入图片描述
开始Coding
步骤一：在streaming 包下，新建PopularMovies2
package com.csylh.recommend.streaming
import com.csylh.recommend.config.AppConf
import org.apache.spark.sql.SaveMode
/**
* Description: 个性化推荐
*
* @Author: 留歌36
* @Date: 2019/10/18 17:42
*/
object PopularMovies2 extends AppConf{
def main(args: Array[String]): Unit = {
val movieRatingCount = spark.sql("select count(*) c, movieid from trainingdata group by movieid order by c")
// 前5部进行推荐
val Top5Movies = movieRatingCount.limit(5)
Top5Movies.registerTempTable("top5")
val top5DF = spark.sql("select a.title from movies a join top5 b on a.movieid=b.movieid")
// 把数据写入到HDFS上
top5DF.write.mode(SaveMode.Overwrite).parquet("/tmp/top5DF")
// 将数据从HDFS加载到Hive数据仓库中去
spark.sql("drop table if exists top5DF")
spark.sql("create table if not exists top5DF(title string) stored as parquet")
spark.sql("load data inpath '/tmp/top5DF' overwrite into table top5DF")
// 最终表里应该是5部推荐电影的名称
}
}
步骤二：在streaming 包下，新建SparkDirectStreamApp
package com.csylh.recommend.streaming
import com.csylh.recommend.config.AppConf
import kafka.serializer.StringDecoder
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
/**
* Description:
*
* @Author: 留歌36
* @Date: 2019/10/18 16:33
*/
object SparkDirectStreamApp extends AppConf{
def main(args:Array[String]): Unit ={
val ssc = new StreamingContext(sc, Seconds(5))
val topics = "movie_topic".split(",").toSet
val kafkaParams = Map[String, String](
"metadata.broker.list"->"hadoop001:9093,hadoop001:9094,hadoop001:9095",
"auto.offset.reset" -> "largest" //smallest :从头开始 largest：最新
)
// Direct 模式：SparkStreaming 主动去Kafka中pull拉数据
val modelPath = "/tmp/BestModel/0.8521581387523667"
val stream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)
def exist(u: Int): Boolean = {
val trainingdataUserIdList = spark.sql("select distinct(userid) from trainingdata")
.rdd
.map(x => x.getInt(0))
.collect() // RDD[row] ==> RDD[Int]
trainingdataUserIdList.contains(u)
}
// 为没有登录的用户推荐电影的策略：
// 1.推荐观看人数较多的电影，采用这种策略
// 2.推荐最新的电影
val defaultrecresult = spark.sql("select * from top5DF").rdd.toLocalIterator
// 创建SparkStreaming接收kafka消息队列数据的2种方式
// 一种是Direct approache,通过SparkStreaming自己主动去Kafka消息队
// 列中查询还没有接收进来的数据，并把他们拉pull到sparkstreaming中。
val model = MatrixFactorizationModel.load(ssc.sparkContext, modelPath)
val messages = stream.foreachRDD(rdd=> {
val userIdStreamRdd = rdd.map(_._2.split("|")).map(x=>x(1)).map(_.toInt)
val validusers = userIdStreamRdd.filter(userId => exist(userId))
val newusers = userIdStreamRdd.filter(userId => !exist(userId))
// 采用迭代器的方式来避开对象不能序列化的问题。
// 通过对RDD中的每个元素实时产生推荐结果，将结果写入到redis，或者其他高速缓存中，来达到一定的实时性。
// 2个流的处理分成2个sparkstreaming的应用来处理。
val validusersIter = validusers.toLocalIterator
val newusersIter = newusers.toLocalIterator
while (validusersIter.hasNext) {
val u= validusersIter.next
println("userId"+u)
val recresult = model.recommendProducts(u, 5)
val recmoviesid = recresult.map(_.product)
println("我为用户" + u + "【实时】推荐了以下5部电影：")
for (i <- recmoviesid) {
val moviename = spark.sql(s"select title from movies where movieId=$i").first().getString(0)
println(moviename)
}
}
while (newusersIter.hasNext) {
println("*新用户你好*以下电影为您推荐below movies are recommended for you :")
for (i <- defaultrecresult) {
println(i.getString(0))
}
}
})
ssc.start()
ssc.awaitTermination()
}
}
步骤三：将创建的项目进行打包上传到服务器
mvn clean package -Dmaven.test.skip=true
步骤四：先编写个性化推荐代码 shell 执行脚本
[root@hadoop001 ml]# vim PopularMovies2.sh
export HADOOP_CONF_DIR=/root/app/hadoop-2.6.0-cdh5.7.0/etc/hadoop
$SPARK_HOME/bin/spark-submit \
--class com.csylh.recommend.streaming.PopularMovies2 \
--master spark://hadoop001:7077 \
--name PopularMovies2 \
--driver-memory 10g \
--executor-memory 5g \
/root/data/ml/movie-recommend-1.0.jar
步骤五：执行sh PopularMovies2.sh
确保：
[root@hadoop001 ml]# spark-sql
19/10/20 22:59:28 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Spark master: local[*], Application Id: local-1571583574311
spark-sql> show tables;
default links false
default movies false
default ratings false
default tags false
default testdata false
default top5df false
default trainingdata false
default trainingdataasc false
default trainingdatadesc false
Time taken: 2.232 seconds, Fetched 9 row(s)
spark-sql> select * from top5df;
Follow the Bitch (1996)
Radio Inside (1994)
Faces of Schlock (2005)
Mág (1988)
"Son of Monte Cristo
Time taken: 1.8 seconds, Fetched 5 row(s)
spark-sql>
步骤六：再编写model实时推荐代码 shell 执行脚本
export HADOOP_CONF_DIR=/root/app/hadoop-2.6.0-cdh5.7.0/etc/hadoop
$SPARK_HOME/bin/spark-submit \
--class com.csylh.recommend.streaming.SparkDirectStreamApp \
--master spark://hadoop001:7077 \
--name SparkDirectStreamApp \
--driver-memory 10g \
--executor-memory 5g \
--total-executor-cores 10 \
--jars /root/app/kafka_2.11-1.1.1/libs/kafka-clients-1.1.1.jar \
--packages "mysql:mysql-connector-java:5.1.38,org.apache.spark:spark-streaming-kafka-0-8_2.11:2.4.2" \
/root/data/ml/movie-recommend-1.0.jar
步骤七：sh SparkDirectStreamApp.sh
// TODO...
有任何问题，欢迎留言一起交流~~
更多文章：基于Spark的电影推荐系统：https://blog.csdn.net/liuge36/column/info/29285
网友评论