Spark Stuctured Streaming(实时流计算：Structured Streaming) windows开发环境搭建

1:http://archive.apache.org/dist/hadoop/core/hadoop-2.7.0/  下载Hadoop安装包到本地并解压 

 2: https://github.com/srccodes/hadoop-common-2.2.0-bin  下载这包.

3: 将步骤2下载的bin包中的内容。解压缩到1步骤下载的hadoop包的bin目录下.

 4:配置环境变量

    添加HADOOP_HOME

 添加path

 修改classpath.添加winutils.exe

5： idea创建Maven工程文件. POM文件添加：

<dependencies>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.11</artifactId>
        <version>2.4.3</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-sql_2.11</artifactId>
        <version>2.4.3</version>
    </dependency>
</dependencies>

 修改在IDEA中配置Run Configuration，添加HADOOP_HOME变量

main目录下创建scala文件夹(记得Mark Directory as sources root)。 添加Scala支持 

选中scala复选框.

 

 6:添加log。在Resources目录下添加log4j.properties。这文件从spark conf目录下拷贝过来的。(log4j.properties.template)

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

# Set everything to be logged to the console
log4j.rootCategory=ERROR, console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# Set the default spark-shell log level to WARN. When running the spark-shell, the
# log level for this class is used to overwrite the root logger's log level, so that
# the user can have different defaults for the shell and regular Spark apps.
log4j.logger.org.apache.spark.repl.Main=ERROR

# Settings to quiet third party logs that are too verbose
log4j.logger.org.spark_project.jetty=ERROR
log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
log4j.logger.org.apache.parquet=ERROR
log4j.logger.parquet=ERROR

# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR

 7：添加wordcount源代码.(在scala目录下创建wordcount scala object)

package com.atguigu.structure.streaming

import org.apache.spark.sql.streaming.StreamingQuery
import org.apache.spark.sql.{DataFrame, SparkSession}

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

    import  spark.implicits._
    val lines: DataFrame = spark.readStream.format("socket")
      .option("host", "hadoop202")
      .option("port", 9999)
      .load()


    val result: StreamingQuery = lines.writeStream
      .format("console")
      .outputMode("update")   // 三种模式:complete append update
      .start()

    result.awaitTermination()
    spark.stop()
  }
}

8:启动netcat 程序 发送数据: 

nc -l -p 9999

启动 wordcount:

 9:常见错误：winutils.exe 错误

 步骤1,2,3,4,5就可以解决。