Spark--SpringBoot整合(测试API)

小鱼儿 2022-11-01 15:58 891阅读 0赞

原文网址：[Spark--SpringBoot整合(测试API)\_IT利刃出鞘的博客-CSDN博客][Spark--SpringBoot_API_IT_-CSDN]

# 其他网址 #

> [Springboot2.X 集成 spark2.X 实现WordCount\_李虹柏的博客-CSDN博客][Springboot2.X _ spark2.X _WordCount_-CSDN]  
> [Spring Boot集成Spark\_weixin\_33924770的博客-CSDN博客\_springboot集成spark][Spring Boot_Spark_weixin_33924770_-CSDN_springboot_spark]  
> [springboot集成spark，ETL demo\_MJ的博客-CSDN博客][springboot_spark_ETL demo_MJ_-CSDN]

# 简单测试API #

**简介**

> 本处采用接口的方式简单测下api的使用，源码地址：[https://gitee.com/shapeless/demo\_bigdata/tree/testAPI/][https_gitee.com_shapeless_demo_bigdata_tree_testAPI]

## 依赖及配置 ##

**application.yml**

>     custom:
>       spark:
>         appName: appName_demo
>         sparkHome: .
>         # master 可指定为：
>         #  1. Spark，Mesos 或 YARN 的 cluster URL。例：spark://127.0.0.1:7077
>         #  2. 本地模式(local mode)。例如：local；local[4]
>         master: local[4]

**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 https://maven.apache.org/xsd/maven-4.0.0.xsd">
>         <modelVersion>4.0.0</modelVersion>
>         <parent>
>             <groupId>org.springframework.boot</groupId>
>             <artifactId>spring-boot-starter-parent</artifactId>
>             <version>2.3.7.RELEASE</version>
>             <relativePath/> 
>         </parent>
>         <groupId>com.example</groupId>
>         <artifactId>demo</artifactId>
>         <version>0.0.1-SNAPSHOT</version>
>         <name>demo</name>
>         <description>Demo project for Spring Boot</description>
>         <properties>
>             <java.version>1.8</java.version>
>             <scala.version>2.12</scala.version>
>             <spark.version>3.0.1</spark.version>
>         </properties>
>         <dependencies>
>             <dependency>
>                 <groupId>org.springframework.boot</groupId>
>                 <artifactId>spring-boot-starter-web</artifactId>
>             </dependency>
>     
>             <dependency>
>                 <groupId>org.projectlombok</groupId>
>                 <artifactId>lombok</artifactId>
>                 <optional>true</optional>
>             </dependency>
>     
>             <dependency>
>                 <groupId>org.apache.spark</groupId>
>                 <artifactId>spark-core_${scala.version}</artifactId>
>                 <version>${spark.version}</version>
>                 <exclusions>
>                     <exclusion>
>                         <groupId>org.slf4j</groupId>
>                         <artifactId>slf4j-log4j12</artifactId>
>                     </exclusion>
>                 </exclusions>
>             </dependency>
>     
>             <dependency>
>                 <groupId>org.apache.spark</groupId>
>                 <artifactId>spark-streaming_${scala.version}</artifactId>
>                 <version>${spark.version}</version>
>                 <scope>provided</scope>
>             </dependency>
>     
>             <dependency>
>                 <groupId>org.apache.spark</groupId>
>                 <artifactId>spark-sql_${scala.version}</artifactId>
>                 <version>${spark.version}</version>
>             </dependency>
>     
>         </dependencies>
>     
>         <build>
>             <plugins>
>                 <plugin>
>                     <groupId>org.springframework.boot</groupId>
>                     <artifactId>spring-boot-maven-plugin</artifactId>
>                     <configuration>
>                         <excludes>
>                             <exclude>
>                                 <groupId>org.projectlombok</groupId>
>                                 <artifactId>lombok</artifactId>
>                             </exclude>
>                         </excludes>
>                     </configuration>
>                 </plugin>
>             </plugins>
>         </build>
>     
>     </project>

## 代码 ##

**普通API**

> 新建文件：src/main/java/com/example/file/TestFile.txt，内容如下：
> 
>     java python javascript java javascript java

> 接口
> 
>     package com.example.controller;
>     
>     import com.example.WordCount;
>     import com.example.spark.CustomReceiver;
>     import com.google.common.base.Joiner;
>     import lombok.extern.slf4j.Slf4j;
>     import org.apache.spark.api.java.JavaPairRDD;
>     import org.apache.spark.api.java.JavaRDD;
>     import org.apache.spark.api.java.JavaSparkContext;
>     import org.apache.spark.storage.StorageLevel;
>     import org.apache.spark.streaming.Durations;
>     import org.apache.spark.streaming.api.java.JavaDStream;
>     import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
>     import org.apache.spark.streaming.api.java.JavaStreamingContext;
>     import org.springframework.beans.factory.annotation.Autowired;
>     import org.springframework.web.bind.annotation.PostMapping;
>     import org.springframework.web.bind.annotation.RequestMapping;
>     import org.springframework.web.bind.annotation.RestController;
>     import scala.Tuple2;
>     
>     import java.util.*;
>     
>     @RestController
>     @RequestMapping("rdd")
>     public class RDDController {
>         @Autowired
>         private transient JavaSparkContext javaSparkContext;
>     
>         private final String filePath = "src/main/java/com/example/file/TestFile.txt";
>     
>         @RequestMapping("wordCount1")
>         public Object wordCount1() {
>             // 获取本地文件 生成javaRDD
>             JavaRDD<String> file = javaSparkContext
>                     .textFile(filePath);
>     
>             // 按空格分解为数组 生成新的javaRDD
>             JavaRDD<String> words = file.flatMap(
>                     line -> Arrays.asList(line.split(" ")).iterator());
>     
>             // 统计每个词出现的次数 生成新的javaRDD
>             JavaRDD<WordCount> wordcount = words.map(
>                     word -> new WordCount(word, 1));
>     
>             // 将词与数转换为 key-value形式
>             JavaPairRDD<String, Integer> pair = wordcount.mapToPair(
>                     wordCount -> new Tuple2<>(wordCount.getWord(), wordCount.getCount())
>             );
>     
>             // 根据key进行整合
>             JavaPairRDD<String, Integer> wordcounts = pair.reduceByKey(
>                     (count1, count2) -> count1 + count2);
>     
>             // 将结果转换为 WordCount对象
>             JavaRDD<WordCount> map = wordcounts.map(
>                     (tuple2) -> new WordCount(tuple2._1, tuple2._2));
>             // 将结果转换为 list并返回
>             return map.collect();
>         }
>     
>         @RequestMapping("wordCount2")
>         public Object wordCount2() {
>             JavaRDD<String> lines = javaSparkContext.textFile(filePath).cache();
>     
>             System.out.println();
>             System.out.println("-------------------------------------------------------");
>             System.out.println(lines.count());
>     
>             JavaRDD<String> words = lines.flatMap(str -> Arrays.asList(str.split(" ")).iterator());
>     
>             JavaPairRDD<String, Integer> ones = words.mapToPair(str -> new Tuple2<String, Integer>(str, 1));
>     
>             JavaPairRDD<String, Integer> counts = ones.reduceByKey((Integer i1, Integer i2) -> (i1 + i2));
>     
>             JavaPairRDD<Integer, String> temp = counts.mapToPair(tuple -> new Tuple2<Integer, String>(tuple._2, tuple._1));
>     
>             JavaPairRDD<String, Integer> sorted = temp.sortByKey(false)
>                     .mapToPair(tuple -> new Tuple2<String, Integer>(tuple._2, tuple._1));
>     
>             System.out.println(sorted.count());
>     
>             List<Tuple2<String, Integer>> output = sorted.collect();
>             // List<Tuple2<String, Integer>> output = sorted.take(10);
>             // List<Tuple2<String, Integer>> output = sorted.top(10);
>     
>             Map<String, Object> result = new HashMap<String, Object>();
>             for (Tuple2<String, Integer> tuple : output) {
>                 result.put(tuple._1(), tuple._2());
>             }
>     
>             return result;
>         }
>     
>         @PostMapping("testAPI")
>         public Object testAPI() {
>             List<Integer> data = new ArrayList<>(Arrays.asList(1, 2, 3, 4, 5, 6));
>     
>             JavaRDD<Integer> rdd01 = javaSparkContext.parallelize(data);
>             rdd01 = rdd01.map(num -> {
>                 return num * num;
>             });
>             //data map:1,4,9,16,25,36
>             System.out.println("data map: " + Joiner.on(",").skipNulls().join(rdd01.collect()).toString());
>     
>             rdd01 = rdd01.filter(x -> x < 6);
>             //data filter: 1,4
>             System.out.println("data filter: " + Joiner.on(",").skipNulls().join(rdd01.collect()).toString());
>     
>             rdd01 = rdd01.flatMap(x -> {
>                 Integer[] test = {x, x + 1, x + 2};
>                 return Arrays.asList(test).iterator();
>             });
>             //flatMap: 1,2,3,4,5,6
>             System.out.println("flatMap : " + Joiner.on(",").skipNulls().join(rdd01.collect()).toString());
>     
>             JavaRDD<Integer> unionRdd = javaSparkContext.parallelize(data);
>     
>             rdd01 = rdd01.union(unionRdd);
>             //union: 1,2,3,4,5,6,1,2,3,4,5,6
>             System.out.println("union : " + Joiner.on(",").skipNulls().join(rdd01.collect()).toString());
>     
>             List<Integer> result = new ArrayList<>();
>             result.add(rdd01.reduce((Integer v1, Integer v2) -> {
>                 return v1 + v2;
>             }));
>             //reduce: 42
>             System.out.println("reduce : " + Joiner.on(",").skipNulls().join(result).toString());
>             result.forEach(System.out::print);
>     
>             JavaPairRDD<Integer, Iterable<Integer>> groupRdd = rdd01.groupBy(x -> {
>                 System.out.println("======grouby========： " + x);
>                 if (x > 10) return 0;
>                 else return 1;
>             });
>     
>             List<Tuple2<Integer, Iterable<Integer>>> resul = groupRdd.collect();
>             //group by==>  key: 1 value: 1,2,3,4,5,6,1,2,3,4,5,6
>             resul.forEach(x -> {
>                 System.out.println("group by==> key: " + x._1 + " value: "
>                         + Joiner.on(",").skipNulls().join(x._2).toString());
>             });
>     
>             return null;
>         }
>     }

**SparkStreaming**

>     package com.example.controller;
>     
>     import com.example.spark.CustomReceiver;
>     import org.apache.spark.api.java.JavaSparkContext;
>     import org.apache.spark.storage.StorageLevel;
>     import org.apache.spark.streaming.Durations;
>     import org.apache.spark.streaming.api.java.JavaDStream;
>     import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
>     import org.apache.spark.streaming.api.java.JavaStreamingContext;
>     import org.springframework.beans.factory.annotation.Autowired;
>     import org.springframework.web.bind.annotation.PostMapping;
>     import org.springframework.web.bind.annotation.RequestMapping;
>     import org.springframework.web.bind.annotation.RestController;
>     
>     @RestController
>     @RequestMapping("streaming")
>     public class StreamingController {
>         @Autowired
>         private transient JavaSparkContext javaSparkContext;
>     
>         @PostMapping("customReceiver")
>         public String customReceiver() throws InterruptedException{
>             // 第二个参数：批间隔时间（以此间隔时间分割数据流）
>             JavaStreamingContext jsc = new JavaStreamingContext(javaSparkContext, Durations.seconds(5));
>             JavaReceiverInputDStream<String> lines = jsc.receiverStream(new CustomReceiver(StorageLevel.MEMORY_AND_DISK_2()));
>             JavaDStream<Long> count =  lines.count();
>             count = count.map(x -> {
>                 System.out.println("这批一共这些条数据：" + x);
>                 return x;
>             });
>             System.out.println("count.print()");
>             count.print();
>             System.out.println("jsc.start()");
>             // 现在才是真正开始计算
>             jsc.start();
>             System.out.println("jsc.awaitTermination()");
>             // 等待计算结束
>             jsc.awaitTermination();
>             System.out.println("jsc.stop()");
>             jsc.stop();
>             return "success";
>         }
>     }

## 测试 ##

**启动服务**

> 后台提示可以访问spark的地址：[http://localhost:4040/][http_localhost_4040]
> 
> ![20210227135742772.png][]

> 访问：[http://localhost:4040/][http_localhost_4040]
> 
> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70][]
> 
> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 1][]

### **测试普通API** ###

> 计算每个单词出现的次数。
> 
> 访问：[http://localhost:8080/rdd/wordCount1][http_localhost_8080_rdd_wordCount1]
> 
> 前端结果：
> 
>     [
>         {
>             "word": "python",
>             "count": 1
>         },
>         {
>             "word": "java",
>             "count": 3
>         },
>         {
>             "word": "javascript",
>             "count": 2
>         }
>     ]
> 
> spark结果：
> 
> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 2][]
> 
> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 3][]

> 计算每个单词出现的次数。
> 
> 访问：[http://localhost:8080/rdd/wordCount2][http_localhost_8080_rdd_wordCount2]
> 
> 前端结果：
> 
>     {
>         "python": 1,
>         "java": 3,
>         "javascript": 2
>     }
> 
> spark结果
> 
> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 4][]
> 
> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 5][]

> 纯粹测API
> 
> 访问：[http://localhost:8080/rdd/testAPI][http_localhost_8080_rdd_testAPI]
> 
> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 6][]
> 
> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 7][]

### **测试SparkStreaming** ###

访问：[http://localhost:8080/streaming/customReceiver][http_localhost_8080_streaming_customReceiver]

前端结果：前端会一直等待，没有回复

spark结果：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 8][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 9][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 10][]

红色箭头所指处，我在Idea中暂停了应用，后来又恢复了。

[Spark--SpringBoot_API_IT_-CSDN]: https://knife.blog.csdn.net/article/details/114041087
[Springboot2.X _ spark2.X _WordCount_-CSDN]: https://blog.csdn.net/weixin_40461281/article/details/105381514
[Spring Boot_Spark_weixin_33924770_-CSDN_springboot_spark]: https://blog.csdn.net/weixin_33924770/article/details/91715383
[springboot_spark_ETL demo_MJ_-CSDN]: https://blog.csdn.net/qq_39506978/article/details/104729974
[https_gitee.com_shapeless_demo_bigdata_tree_testAPI]: https://gitee.com/shapeless/demo_bigdata/tree/testAPI/
[http_localhost_4040]: http://localhost:4040/
[20210227135742772.png]: /images/20221024/dbc9d5e312094632b4c4f7dd0385947f.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70]: /images/20221024/ba87e17d79fa44f19e8f7c847b71e551.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 1]: /images/20221024/ca06b9b6813c44909bc5d1c3c3829878.png
[http_localhost_8080_rdd_wordCount1]: http://localhost:8080/rdd/wordCount1
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 2]: /images/20221024/42fbce9a5c354d18a652a6c631578ef8.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 3]: /images/20221024/b19de8d983704e6fa651cb28a6547df4.png
[http_localhost_8080_rdd_wordCount2]: http://localhost:8080/rdd/wordCount2
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 4]: /images/20221024/9c9217f0fa36412cbffecacd971cad1f.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 5]: /images/20221024/51855d11ac9549138e8668583460f95d.png
[http_localhost_8080_rdd_testAPI]: http://localhost:8080/rdd/testAPI
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 6]: /images/20221024/167ff2ba5ebd4a95b09b3de0e8e3a806.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 7]: /images/20221024/0eacce74cd624005aa3ccd867abacfaa.png
[http_localhost_8080_streaming_customReceiver]: http://localhost:8080/streaming/customReceiver
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 8]: /images/20221024/29b2c903393d412da3c17667b46916bf.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 9]: /images/20221024/f73802a6a49e48929b872a08ea99b403.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlaXlpbmcwY2FuZ2xhbmc_size_16_color_FFFFFF_t_70 10]: /images/20221024/9b9b386311ee485298bbbf37f83e00c0.png