目录
20-05-Spark基于文件目录的单点恢复
root@bigdata00:~# start-all.sh
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh
Starting namenodes on [192.168.16.143]
192.168.16.143: starting namenode, logging to /root/training/hadoop-2.7.3/logs/hadoop-root-namenode-bigdata00.out
localhost: starting datanode, logging to /root/training/hadoop-2.7.3/logs/hadoop-root-datanode-bigdata00.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /root/training/hadoop-2.7.3/logs/hadoop-root-secondarynamenode-bigdata00.out
starting yarn daemons
starting resourcemanager, logging to /root/training/hadoop-2.7.3/logs/yarn-root-resourcemanager-bigdata00.out
localhost: starting nodemanager, logging to /root/training/hadoop-2.7.3/logs/yarn-root-nodemanager-bigdata00.out
root@bigdata00:~# jps
2964 ResourceManager
2807 SecondaryNameNode
2247 NameNode
2507 DataNode
3515 Jps
3199 NodeManager
root@bigdata00:~# cd /root/training/spark-2.1.0-bin-hadoop2.7
root@bigdata00:~/training/spark-2.1.0-bin-hadoop2.7# sbin/start-all.sh
starting org.apache.spark.deploy.master.Master, logging to /root/training/spark-2.1.0-bin-hadoop2.7/logs/spark-root-org.apache.spark.deploy.master.Master-1-bigdata00.out
192.168.16.143: starting org.apache.spark.deploy.worker.Worker, logging to /root/training/spark-2.1.0-bin-hadoop2.7/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-bigdata00.out
root@bigdata00:~/training/spark-2.1.0-bin-hadoop2.7# bin/spark-shel1 --master sproot@bigdata00:~/training/spark-2.1.0-bin-hadoop2.7# bin/spark-shel --master sparoot@bigdata00:~/training/spark-2.1.0-bin-hadoop2.7# bin/spark-shell --master sproot@bigdata00:~/training/spark-2.1.0-bin-hadoop2.7# bin/spark-shell --master spark://192.168.16.143:7077
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
20/10/27 18:17:08 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
20/10/27 18:17:08 WARN Utils: Your hostname, bigdata00 resolves to a loopback address: 127.0.1.1; using 192.168.16.143 instead (on interface eth0)
20/10/27 18:17:08 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
20/10/27 18:17:30 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
Spark context Web UI available at http://192.168.16.143:4040
Spark context available as 'sc' (master = spark://192.168.16.143:7077, app id = app-20201027181710-0000).
Spark session available as 'spark'.
Welcome to
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/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.1.0
/_/
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_144)
Type in expressions to have them evaluated.
Type :help for more information.
sbin/start-all.sh
bin/spark-shell --master spark://192.168.16.143:7077
20-06-基于ZooKeeper的Standby的Master
20-07-使用spark-submit
20-08-使用spark-shell
蒙特卡罗求PI（圆周率）.png
单步运行WordCount.png
20-09-在IDE中开发Scala版本的WordCount
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
/*
* 通过Spark Submit提交
* bin/spark-submit --master spark://bigdata111:7077 --class day1025.MyWordCount /root/temp/demo1.jar hdfs://bigdata111:9000/input/data.txt hdfs://bigdata111:9000/output/1025/demo1
*/
object MyWordCount {
def main(args: Array[String]): Unit = {
//创建任务的配置信息
//如果设置Master=local，表示运行在本地模式上
//如果运行集群模式上，不需要设置Master
//val conf = new SparkConf().setAppName("MyWordCount").setMaster("local")
val conf = new SparkConf().setAppName("MyWordCount")
//创建一个SparkContext对象
val sc = new SparkContext(conf)
//执行WordCount
val result = sc.textFile(args(0))
.flatMap(_.split(" "))
.map((_,1))
.reduceByKey(_+_)
//打印在屏幕上
//result.foreach(println)
//输出到HDFS
result.saveAsTextFile(args(1))
//停止SparkContext
sc.stop()
}
}
20-10-在IDE中开发Java版本的WordCount
import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
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.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import scala.Tuple2;
/*
* 使用spark submit提交
* bin/spark-submit --master spark://bigdata111:7077 --class demo.JavaWordCount /root/temp/demo2.jar hdfs://bigdata111:9000/input/data.txt
*/
public class JavaWordCount {
public static void main(String[] args) {
//运行在本地模式，可以设置断点
SparkConf conf = new SparkConf().setAppName("JavaWordCount").setMaster("local");
//运行在集群模式
//SparkConf conf = new SparkConf().setAppName("JavaWordCount");
//创建一个SparkContext对象： JavaSparkContext对象
JavaSparkContext sc = new JavaSparkContext(conf);
//读入HDFS的数据
JavaRDD<String> rdd1 = sc.textFile(args[0]);
/*
* 分词
* FlatMapFunction：接口，用于处理分词的操作
* 泛型:String 读入的每一句话
* U: 返回值 ---> String 单词
*/
JavaRDD<String> rdd2 = rdd1.flatMap(new FlatMapFunction<String, String>() {
@Override
public Iterator<String> call(String input) throws Exception {
//数据: I love Beijing
//分词
return Arrays.asList(input.split(" ")).iterator();
}
});
/*
* 每个单词记一次数 (k2 v2)
* Beijing ---> (Beijing,1)
* 参数：
* String：单词
* k2 v2不解释
*/
JavaPairRDD<String, Integer> rdd3 = rdd2.mapToPair(new PairFunction<String, String, Integer>() {
@Override
public Tuple2<String, Integer> call(String word) throws Exception {
return new Tuple2<String, Integer>(word, 1);
}
});
//执行Reduce的操作
JavaPairRDD<String, Integer> rdd4 = rdd3.reduceByKey(new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer a, Integer b) throws Exception {
//累加
return a+b;
}
});
//执行计算(Action)，把结果打印在屏幕上
List<Tuple2<String,Integer>> result = rdd4.collect();
for(Tuple2<String,Integer> tuple:result){
System.out.println(tuple._1+"\t"+tuple._2);
}
//停止JavaSparkContext对象
sc.stop();
}
}
