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
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count
val 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>() {
@Override
public 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。
- 📢博客主页：https://lansonli.blog.csdn.net
- 📢欢迎点赞 👍 收藏 ⭐留言 📝 如有错误敬请指正！
- 📢本文由 Lansonli 原创，首发于 CSDN博客🙉
- 📢停下休息的时候不要忘了别人还在奔跑，希望大家抓紧时间学习，全力奔赴更美好的生活✨
