开发环境:
系统:win 11 java : 1.8 scala:2.13 spark : 3.3.2
一, 使用 spark 结构化流读取文件数据,并做分组统计。
功能:spark 以结构化流形式从文件夹读取 csv 后缀数据文件,并进行连表分组统计。每次触发计算后,结果表输出到console控制板。
代码:
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(); } }
输出:
二, 使用 spark 结构化流读取socket流,做单词统计,使用Java编程
功能:spark 读取本地机器的网络流数据,并统计。
代码:
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(); } }
输出:
二, 使用 spark 结构化流读取socket流,做单词统计,使用scala 编程
功能:同上
代码:
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() } }
输出: