spark 流处理的几个实例

package org.example;

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.streaming.OutputMode;
import org.apache.spark.sql.streaming.StreamingQuery;
import org.apache.spark.sql.streaming.StreamingQueryException;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructType;

import java.util.concurrent.TimeoutException;

public class Main {
    /*
    例子：从文件中读取流， 被定义模式，生成dataset ，使用sql api 进行分析。
     */
    public static void main(String[] args) throws TimeoutException, StreamingQueryException {
        System.out.println("Hello world!");

        SparkSession spark = SparkSession.builder().appName("spark streaming").config("spark.master", "local")
                .config("spark.sql.warehouse.dir", "file:///app/")
                .getOrCreate();
        spark.sparkContext().setLogLevel("ERROR");

        StructType schema =
                new StructType().add("empId", DataTypes.StringType).add("empName", DataTypes.StringType)
                        .add("department", DataTypes.StringType);

        Dataset<Row> rawData = spark.readStream().option("header", false).format("csv").schema(schema)
                .csv("D:/za/spark_data/*.csv");

        rawData.createOrReplaceTempView("empData");

        Dataset<Row> result = spark.sql("select count(*), department from  empData group by department");

        StreamingQuery query = result.writeStream().outputMode("complete").format("console").start();  // 每次触发，全表输出

        query.awaitTermination();


    }
}

package org.example;

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;

import java.util.Arrays;
import java.util.concurrent.TimeoutException;

public class SocketStreaming_wordcount {
    /*
     * 从socket 读取字符流，并做word count分析
     *
     * */
    public static void main(String[] args) throws TimeoutException, StreamingQueryException {

        SparkSession spark = SparkSession
                .builder()
                .appName("JavaStructuredNetworkWordCount")
                .config("spark.master", "local")
                .getOrCreate();

        // dataframe 表示 socket 字符流
        Dataset<Row> lines = spark
                .readStream()
                .format("socket")
                .option("host", "localhost")
                .option("port", 9999)
                .load();

//  把一行字符串切分为 单词
        Dataset<String> words = lines
                .as(Encoders.STRING())
                .flatMap((FlatMapFunction<String, String>) x -> Arrays.asList(x.split(" ")).iterator(), Encoders.STRING());

//  对单词分组计数
        Dataset<Row> wordCounts = words.groupBy("value").count();
//  开始查询并打印输出到console
        StreamingQuery query = wordCounts.writeStream()
                .outputMode("complete")
                .format("console")
                .start();

        query.awaitTermination();


    }


}

package org.example

import org.apache.spark.sql.SparkSession

object Main {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession
      .builder
      .appName("streaming_socket_scala")
      .config("spark.master", "local")
      .getOrCreate()

    import spark.implicits._

    // 创建datafram 象征从网络socket 接收流
    val lines = spark.readStream
      .format("socket")
      .option("host", "localhost")
      .option("port", 9999)
      .load()

    // 切分一行成单词
    val words = lines.as[String].flatMap(_.split(" "))

    // 进行单词统计
    val wordCounts = words.groupBy("value").count()

    // 开始查询并输出
    val query = wordCounts.writeStream
      .outputMode("complete")
      .format("console")
      .start()

    query.awaitTermination()

  }


}