Spark机器学习入门·编程(scala/java/python)实现分析商店购买记录
问题导读1.Scala是如何实现分析商店购买记录的？
2.对比Scala程序与Java程序实现差别？
3.三种语言各有什么特点？
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Spark安装目录
/Users/erichan/Garden/spark-1.4.0-bin-hadoop2.6
基本测试
./bin/run-example org.apache.spark.examples.SparkPi
MASTER=local ./bin/run-example org.apache.spark.examples.SparkPi
scala
import org.apache.spark.SparkContextimport org.apache.spark.SparkContext._
/**
* 用Scala编写的一个简单的Spark应用
*/
object ScalaApp {
在主函数里,我们要初始化所需的 SparkContext 对象,并且用它通过 textFile 函数来访问CSV数据文件。之后对每一行原始字符串以逗号为分隔符进行分割,提取出相应的用户名、产品和价格信息,从而完成对原始文本的映射:
def main(args: Array) {
val sc = new SparkContext("local", "First Spark App")
// 将CSV格式的原始数据转化为(user,product,price)格式的记录集
val data = sc.textFile("data/UserPurchaseHistory.csv")
.map(line => line.split(","))
.map(purchaseRecord => (purchaseRecord(0), purchaseRecord(1),
purchaseRecord(2)))
现在,我们有了一个RDD,其每条记录都由 (user, product, price) 三个字段构成。我们可以对商店计算如下指标:购买总次数 客户总个数 总收入1.4 最畅销的产品
// 求购买次数
val numPurchases = data.count()
// 求有多少个不同客户购买过商品
val uniqueUsers = data.map{ case (user, product, price) => user }.distinct().count()
// 求和得出总收入
val totalRevenue = data.map{ case (user, product, price) => price.toDouble }.sum()
// 求最畅销的产品是什么
val productsByPopularity = data
.map{ case (user, product, price) => (product, 1) }
.reduceByKey(_ + _)
.collect()
.sortBy(-_._2)
val mostPopular = productsByPopularity(0)
最后那段计算最畅销产品的代码演示了如何进行Map/Reduce模式的计算,该模式随Hadoop而流行。第一步,我们将 (user, product, price) 格式的记录映射为 (product, 1) 格式。然后,我们执行一个 reduceByKey 操作,它会对各个产品的1值进行求和。转换后的RDD包含各个商品的购买次数。有了这个RDD后,我们可以调用 collect 函数,这会将其计算结果以Scala集合的形式返回驱动程序。之后在驱动程序的本地对这些记录按照购买次数进行排序。(注意,在实际处理大量数据时,我们通常通过 sortByKey 这类操作来对其进行并行排序。) 最后,可在终端上打印出计算结果:
println("Total purchases: " + numPurchases)
println("Unique users: " + uniqueUsers)
println("Total revenue: " + totalRevenue)
println("Most popular product: %s with %d purchases".
format(mostPopular._1, mostPopular._2))
}
}
可以在项目的主目录下执行 sbt run 命令来运行这个程序。如果你使用了IDE的话,也可以从Scala IDE直接运行。最终的输出应该与下面的内容相似:
...
Compiling 1 Scala source to ...
Running ScalaApp
...
14/01/30 10:54:40 INFO spark.SparkContext: Job finished: collect at
ScalaApp.scala:25, took 0.045181 s
Total purchases: 5
Unique users: 4
Total revenue: 39.91
Most popular product: iPhone Cover with 2 purchases
build.sbt
name := "scala-spark-app"
version := "1.0"
scalaVersion := "2.11.6"
libraryDependencies += "org.apache.spark" %% "spark-core" % "1.4.0"
erichan:scala-spark-app/ $ sbt run
java 8
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.PairFunction;
import scala.Tuple2;
import java.util.List;
public class JavaApp {
public static void main(String[] args) {
JavaSparkContext sc = new JavaSparkContext("local", "First Spark App");
JavaRDD<String[]> data = sc.textFile("data/UserPurchaseHistory.csv").map(s -> s.split(","));
long numPurchases = data.count();
long uniqueUsers = data.map(strings -> strings).distinct().count();
double totalRevenue = data.mapToDouble(strings -> Double.parseDouble(strings)).sum();
List<Tuple2<String, Integer>> pairs = data.mapToPair(
new PairFunction<String[], String, Integer>() {
@Override
public Tuple2<String, Integer> call(String[] strings) throws Exception {
return new Tuple2(strings, 1);
}
}
).reduceByKey((i1, i2) -> i1 + i2).collect();
pairs.sort((o1, o2) -> -(o1._2() - o2._2()));
String mostPopular = pairs.get(0)._1();
int purchases = pairs.get(0)._2();
System.out.println("Total purchases: " + numPurchases);
System.out.println("Unique users: " + uniqueUsers);
System.out.println("Total revenue: " + totalRevenue);
System.out.println(String.format("Most popular product: %s with %d purchases", mostPopular, purchases));
sc.stop();
}
}
Maven pom.xml
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>java-spark-app</groupId>
<artifactId>java-spark-app</artifactId>
<version>1.0</version>
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>1.4.0</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
</configuration>
</plugin>
</plugins>
</build>
</project>
python
from pyspark import SparkContext
sc = SparkContext("local", "First Spark App")
data = sc.textFile("data/UserPurchaseHistory.csv").map(lambda line: line.split(",")).map(lambda record: (record, record, record))
numPurchases = data.count()
uniqueUsers = data.map(lambda record: record).distinct().count()
totalRevenue = data.map(lambda record: float(record)).sum()
products = data.map(lambda record: (record, 1.0)).reduceByKey(lambda a, b: a + b).collect()
mostPopular = sorted(products, key=lambda x: x, reverse=True)
print "Total purchases: %d" % numPurchases
print "Unique users: %d" % uniqueUsers
print "Total revenue: %2.2f" % totalRevenue
print "Most popular product: %s with %d purchases" % (mostPopular, mostPopular)
sc.stop()
cd /Users/erichan/Garden/spark-1.4.0-bin-hadoop2.6/bin
./spark-submit pythonapp.py
讲解不错，java8的lambda表达式还是很不错！ 要学习，要学习，努力
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