spark集群

https://blog.csdn.net/boling_cavalry/article/details/86747258

https://www.cnblogs.com/xuliangxing/p/7234014.html

第二个链接较为详细，但版本较旧

　　注意spark 7077端口URL，如果hostname没配置正确，spark-submit会报错

jps看了两个slaves是有worker进程的。

spark安装完毕，启动hadoop集群：./sbin/./start-all.sh

jps可查看

spark提交任务的三种的方法

https://www.cnblogs.com/itboys/p/9998666.html

虚拟机分配内存不足，解决方案参考：https://blog.csdn.net/u012848709/article/details/85425249

最后终于跑完了，把输出结果get下来

在master输入以下命令，最后三项为入参，9000为hadoop端口：

/usr/cloud/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--master spark://192.168.91.111:7077 \
--class WordCount \
--executor-memory 512m \
--total-executor-cores 2 \
/usr/cloud/spark-2.4.5-bin-hadoop2.7/jars/SparkWordCount-1.0-SNAPSHOT.jar \
192.168.91.111 \
9000 \
ihavaadream.txt

=====================WordCount代码如下：======================

import org.apache.commons.lang3.StringUtils;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import scala.Tuple2;

import java.text.SimpleDateFormat;
import java.util.Arrays;
import java.util.Date;
import java.util.List;

public class WordCount {

    private static final Logger logger = LoggerFactory.getLogger(WordCount.class);

    public static void main(String[] args) {
        if(null==args
                || args.length<3
                || StringUtils.isEmpty(args[0])
                || StringUtils.isEmpty(args[1])
                || StringUtils.isEmpty(args[2])) {
            logger.error("invalid params!");
        }

        String hdfsHost = args[0];
        String hdfsPort = args[1];
        String textFileName = args[2];

        SparkConf sparkConf = new SparkConf().setAppName("Spark WordCount Application (java)");

        JavaSparkContext javaSparkContext = new JavaSparkContext(sparkConf);

        String hdfsBasePath = "hdfs://" + hdfsHost + ":" + hdfsPort;
        //文本文件的hdfs路径
        String inputPath = hdfsBasePath + "/input/" + textFileName;

        //输出结果文件的hdfs路径
        String outputPath = hdfsBasePath + "/output/"
                + new SimpleDateFormat("yyyyMMddHHmmss").format(new Date());

        logger.info("input path : {}", inputPath);
        logger.info("output path : {}", outputPath);

        logger.info("import text");
        //导入文件
        JavaRDD<String> textFile = javaSparkContext.textFile(inputPath);

        logger.info("do map operation");
        JavaPairRDD<String, Integer> counts = textFile
                //每一行都分割成单词，返回后组成一个大集合
                .flatMap(s -> Arrays.asList(s.split(" ")).iterator())
                //key是单词，value是1
                .mapToPair(word -> new Tuple2<>(word, 1))
                //基于key进行reduce，逻辑是将value累加
                .reduceByKey((a, b) -> a + b);

        logger.info("do convert");
        //先将key和value倒过来，再按照key排序
        JavaPairRDD<Integer, String> sorts = counts
                //key和value颠倒，生成新的map
                .mapToPair(tuple2 -> new Tuple2<>(tuple2._2(), tuple2._1()))
                //按照key倒排序
                .sortByKey(false);

//        logger.info("take top 10");
        //取前10个
        List<Tuple2<Integer, String>> top10 = sorts.collect();
//        List<Tuple2<Integer, String>> top10 = sorts.take(10);

        StringBuilder sbud = new StringBuilder("top 10 word :\n");

        //打印出来
        for(Tuple2<Integer, String> tuple2 : top10){
            sbud.append(tuple2._2())
                    .append("\t")
                    .append(tuple2._1())
                    .append("\n");
        }

        logger.info(sbud.toString());

        logger.info("merge and save as file");
        //分区合并成一个，再导出为一个txt保存在hdfs
        javaSparkContext.parallelize(top10).coalesce(1).saveAsTextFile(outputPath);

        logger.info("close context");
        //关闭context
        javaSparkContext.close();
    }
}

done！