Spark实时（三）：Structured Streaming入门案例

文章目录

Structured Streaming入门案例

一、Scala代码如下

二、Java 代码如下

三、以上代码注意点如下

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Structured Streaming入门案例

我们使用Structured Streaming来监控socket数据统计WordCount。这里我们使用Spark版本为3.4.3版本，首先在Maven pom文件中导入以下依赖：

 <!-- 配置以下可以解决 在jdk1.8环境下打包时报错 “-source 1.5 中不支持 lambda 表达式” --><properties><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><maven.compiler.source>1.8</maven.compiler.source><maven.compiler.target>1.8</maven.compiler.target><spark.version>3.4.3</spark.version></properties><dependencies><!-- Spark-core --><dependency><groupId>org.apache.spark</groupId><artifactId>spark-core_2.12</artifactId><version>${spark.version}</version></dependency><!-- SparkSQL --><dependency><groupId>org.apache.spark</groupId><artifactId>spark-sql_2.12</artifactId><version>${spark.version}</version></dependency><!-- SparkSQL  ON  Hive--><dependency><groupId>org.apache.spark</groupId><artifactId>spark-hive_2.12</artifactId><version>${spark.version}</version></dependency><!--mysql依赖的jar包--><dependency><groupId>mysql</groupId><artifactId>mysql-connector-java</artifactId><version>5.1.47</version></dependency><!--SparkStreaming--><dependency><groupId>org.apache.spark</groupId><artifactId>spark-streaming_2.12</artifactId><version>${spark.version}</version></dependency><!-- Kafka 0.10+ Source For Structured Streaming--><dependency><groupId>org.apache.spark</groupId><artifactId>spark-sql-kafka-0-10_2.12</artifactId><version>${spark.version}</version></dependency><!-- 向kafka 生产数据需要包 --><dependency><groupId>org.apache.kafka</groupId><artifactId>kafka-clients</artifactId><version>2.8.0</version></dependency><!-- Scala 包--><dependency><groupId>org.scala-lang</groupId><artifactId>scala-library</artifactId><version>2.12.15</version></dependency><dependency><groupId>org.scala-lang</groupId><artifactId>scala-compiler</artifactId><version>2.12.15</version></dependency><dependency><groupId>org.scala-lang</groupId><artifactId>scala-reflect</artifactId><version>2.12.15</version></dependency><dependency><groupId>log4j</groupId><artifactId>log4j</artifactId><version>1.2.12</version></dependency><dependency><groupId>com.google.collections</groupId><artifactId>google-collections</artifactId><version>1.0</version></dependency></dependencies>

一、Scala代码如下

package com.lanson.structuredStreaming/***  Structured Streaming 实时读取Socket数据*/import org.apache.spark.sql.streaming.StreamingQuery
import org.apache.spark.sql.{DataFrame, Dataset, SparkSession}/*** Structured Streaming 读取Socket数据*/
object SSReadSocketData {def main(args: Array[String]): Unit = {//1.创建SparkSession对象val spark: SparkSession = SparkSession.builder().master("local").appName("StructuredSocketWordCount")//默认200个并行度，由于源头数据量少，可以设置少一些并行度.config("spark.sql.shuffle.partitions",1).getOrCreate()import spark.implicits._spark.sparkContext.setLogLevel("Error")//2.读取Socket中的每行数据,生成DataFrame默认列名为"value"val lines: DataFrame = spark.readStream.format("socket").option("host", "node3").option("port", 9999).load()//3.将每行数据切分成单词，首先通过as[String]转换成Dataset操作val words: Dataset[String] = lines.as[String].flatMap(line=>{line.split(" ")})//4.按照单词分组，统计个数，自动多一个列countval wordCounts: DataFrame = words.groupBy("value").count()//5.启动流并向控制台打印结果val query: StreamingQuery = wordCounts.writeStream//更新模式设置为complete.outputMode("complete").format("console").start()query.awaitTermination()}}

 

二、Java 代码如下

package com.lanson.structuredStreaming;import java.util.Arrays;
import java.util.Iterator;
import java.util.concurrent.TimeoutException;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.streaming.StreamingQuery;
import org.apache.spark.sql.streaming.StreamingQueryException;public class SSReadSocketData01 {public static void main(String[] args) throws StreamingQueryException, TimeoutException {SparkSession spark = SparkSession.builder().master("local").appName("SSReadSocketData01").config("spark.sql.shuffle.partitions", 1).getOrCreate();spark.sparkContext().setLogLevel("Error");Dataset<Row> lines = spark.readStream().format("socket").option("host", "node3").option("port", 9999).load();Dataset<String> words = lines.as(Encoders.STRING()).flatMap(new FlatMapFunction<String, String>() {@Overridepublic Iterator<String> call(String line) throws Exception {return Arrays.asList(line.split(" ")).iterator();}}, Encoders.STRING());Dataset<Row> wordCounts = words.groupBy("value").count();StreamingQuery query = wordCounts.writeStream().outputMode("complete").format("console").start();query.awaitTermination();}
}

 

以上代码编写完成之后，在node3节点执行“nc -lk 9999”启动socket服务器，然后启动代码，向socket中输入以下数据：

第一次输入：a b c
第二次输入：d a c
第三次输入：a b c

可以看到控制台打印如下结果：

-------------------------------------------
Batch: 1
-------------------------------------------
+-----+-----+
|value|count|
+-----+-----+
|    c|    1|
|    b|    1|
|    a|    1|
+-----+-----+-------------------------------------------
Batch: 2
-------------------------------------------
+-----+-----+
|value|count|
+-----+-----+
|    d|    1|
|    c|    2|
|    b|    1|
|    a|    2|
+-----+-----+-------------------------------------------
Batch: 3
-------------------------------------------
+-----+-----+
|value|count|
+-----+-----+
|    d|    1|
|    c|    3|
|    b|    2|
|    a|    3|
+-----+-----+

三、以上代码注意点如下

• SparkSQL 默认并行度为200，这里由于数据量少，可以将并行度通过参数“spark.sql.shuffle.partitions”设置少一些。
• StructuredStreaming读取过来数据默认是DataFrame，默认有“value”名称的列
• 对获取的DataFrame需要通过as[String]转换成Dataset进行操作
• 结果输出时的OutputMode有三种输出模式：Complete Mode、Append Mode、Update Mode。

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