从搭建大数据环境说起，到执行WordCount所遇到的坑
[TOC]
背景阐明
最近(2020年12月20日)在理解大数据相干架构及技术体系。
尽管说只是理解，不须要亲自动手去搭建一个环境并执行相应的job。
然而，技术嘛。就是要靠下笨功夫，一点点的积攒。该入手的还是不能少。
所以，就从搭环境(基于docker)开始，始终到胜利执行了一个基于yarn调度的wordcount的job。
期间，遇到了不少坑点，一个一个填好，大略花了10个小时左右的工夫。
心愿能将这种血泪教训，分享给须要的人。花更少的工夫，去实现整个流程。
留神：集体本地环境为macOS Big Sur。
基于docker compose的大数据环境搭建
参考 docker-hadoop-spark-hive 疾速构建你的大数据环境 搭建了一个大数据环境，调整了局部参数，以实用于mac os。
次要是如下五个文件：
.├── copy-jar.sh # spark yarn反对├── docker-compose.yml # docker compose文件├── hadoop-hive.env # 环境变量配置├── run.sh # 启动脚本└── stop.sh # 进行脚本留神：mac os的docker有一个坑点就是无奈间接在宿主机拜访容器，我应用Docker for Mac 的网络问题及解决办法（新增办法四）中的办法四解决的。
留神：须要在宿主机配置好相应docker容器对应的ip，这能力保障job胜利执行，且各个服务在宿主机拜访的时候，跳转不会呈现问题。这坑很深，慎踩。
# switch_local172.21.0.3 namenode172.21.0.8 resourcemanager172.21.0.9 nodemanager172.21.0.10 historyserverdocker-compose.yml
version: '2' services: namenode: image: bde2020/hadoop-namenode:1.1.0-hadoop2.8-java8 container_name: namenode volumes: - ~/data/namenode:/hadoop/dfs/name environment: - CLUSTER_NAME=test env_file: - ./hadoop-hive.env ports: - 50070:50070 - 8020:8020 resourcemanager: image: bde2020/hadoop-resourcemanager:1.1.0-hadoop2.8-java8 container_name: resourcemanager environment: - CLUSTER_NAME=test env_file: - ./hadoop-hive.env ports: - 8088:8088 historyserver: image: bde2020/hadoop-historyserver:1.1.0-hadoop2.8-java8 container_name: historyserver environment: - CLUSTER_NAME=test env_file: - ./hadoop-hive.env ports: - 8188:8188 datanode: image: bde2020/hadoop-datanode:1.1.0-hadoop2.8-java8 depends_on: - namenode volumes: - ~/data/datanode:/hadoop/dfs/data env_file: - ./hadoop-hive.env ports: - 50075:50075 datanode2: image: bde2020/hadoop-datanode:1.1.0-hadoop2.8-java8 depends_on: - namenode volumes: - ~/data/datanode2:/hadoop/dfs/data env_file: - ./hadoop-hive.env ports: - 50076:50075 datanode3: image: bde2020/hadoop-datanode:1.1.0-hadoop2.8-java8 depends_on: - namenode volumes: - ~/data/datanode3:/hadoop/dfs/data env_file: - ./hadoop-hive.env ports: - 50077:50075 nodemanager: image: bde2020/hadoop-nodemanager:1.1.0-hadoop2.8-java8 container_name: nodemanager hostname: nodemanager environment: - CLUSTER_NAME=test env_file: - ./hadoop-hive.env ports: - 8042:8042 hive-server: image: bde2020/hive:2.1.0-postgresql-metastore container_name: hive-server env_file: - ./hadoop-hive.env environment: - "HIVE_CORE_CONF_javax_jdo_option_ConnectionURL=jdbc:postgresql://hive-metastore/metastore" ports: - "10000:10000" hive-metastore: image: bde2020/hive:2.1.0-postgresql-metastore container_name: hive-metastore env_file: - ./hadoop-hive.env command: /opt/hive/bin/hive --service metastore ports: - 9083:9083 hive-metastore-postgresql: image: bde2020/hive-metastore-postgresql:2.1.0 ports: - 5432:5432 volumes: - ~/data/postgresql/:/var/lib/postgresql/data spark-master: image: bde2020/spark-master:2.1.0-hadoop2.8-hive-java8 container_name: spark-master hostname: spark-master volumes: - ./copy-jar.sh:/copy-jar.sh ports: - 18080:8080 - 7077:7077 env_file: - ./hadoop-hive.env spark-worker: image: bde2020/spark-worker:2.1.0-hadoop2.8-hive-java8 depends_on: - spark-master environment: - SPARK_MASTER=spark://spark-master:7077 ports: - "18081:8081" env_file: - ./hadoop-hive.envhadoop-hive.env
HIVE_SITE_CONF_javax_jdo_option_ConnectionURL=jdbc:postgresql://hive-metastore-postgresql/metastoreHIVE_SITE_CONF_javax_jdo_option_ConnectionDriverName=org.postgresql.DriverHIVE_SITE_CONF_javax_jdo_option_ConnectionUserName=hiveHIVE_SITE_CONF_javax_jdo_option_ConnectionPassword=hiveHIVE_SITE_CONF_datanucleus_autoCreateSchema=falseHIVE_SITE_CONF_hive_metastore_uris=thrift://hive-metastore:9083HIVE_SITE_CONF_hive_metastore_warehouse_dir=hdfs://namenode:8020/user/hive/warehouseCORE_CONF_fs_defaultFS=hdfs://namenode:8020CORE_CONF_fs_default_name=hdfs://namenode:8020CORE_CONF_hadoop_http_staticuser_user=rootCORE_CONF_hadoop_proxyuser_hue_hosts=*CORE_CONF_hadoop_proxyuser_hue_groups=*HDFS_CONF_dfs_webhdfs_enabled=trueHDFS_CONF_dfs_permissions_enabled=falseYARN_CONF_yarn_log___aggregation___enable=trueYARN_CONF_yarn_resourcemanager_recovery_enabled=trueYARN_CONF_yarn_resourcemanager_store_class=org.apache.hadoop.yarn.server.resourcemanager.recovery.FileSystemRMStateStoreYARN_CONF_yarn_resourcemanager_fs_state___store_uri=/rmstateYARN_CONF_yarn_nodemanager_remote___app___log___dir=/app-logsYARN_CONF_yarn_log_server_url=http://historyserver:8188/applicationhistory/logs/YARN_CONF_yarn_timeline___service_enabled=trueYARN_CONF_yarn_timeline___service_generic___application___history_enabled=trueYARN_CONF_yarn_resourcemanager_system___metrics___publisher_enabled=trueYARN_CONF_yarn_resourcemanager_hostname=resourcemanagerYARN_CONF_yarn_timeline___service_hostname=historyserverYARN_CONF_yarn_resourcemanager_address=resourcemanager:8032YARN_CONF_yarn_resourcemanager_scheduler_address=resourcemanager:8030YARN_CONF_yarn_resourcemanager_resource__tracker_address=resourcemanager:8031YARN_CONF_yarn_resourcemanager_resource__tracker_address=resourcemanager:8031YARN_CONF_yarn_nodemanager_aux___services=mapreduce_shufflerun.sh
#!/bin/bash# 启动容器docker-compose -f docker-compose.yml up -d namenode hive-metastore-postgresqldocker-compose -f docker-compose.yml up -d datanode datanode2 datanode3 hive-metastoredocker-compose -f docker-compose.yml up -d resourcemanagerdocker-compose -f docker-compose.yml up -d nodemanagerdocker-compose -f docker-compose.yml up -d historyserversleep 5docker-compose -f docker-compose.yml up -d hive-serverdocker-compose -f docker-compose.yml up -d spark-master spark-worker# 获取ip地址并打印到控制台my_ip=`ifconfig | grep 'inet.*netmask.*broadcast' | awk '{print $2;exit}'`echo "Namenode: http://${my_ip}:50070"echo "Datanode: http://${my_ip}:50075"echo "Spark-master: http://${my_ip}:18080"# 执行脚本，spark yarn反对docker-compose exec spark-master bash -c "./copy-jar.sh && exit"copy-jar.sh
#!/bin/bashcd /opt/hadoop-2.8.0/share/hadoop/yarn/lib/ && cp jersey-core-1.9.jar jersey-client-1.9.jar /spark/jars/ && rm -rf /spark/jars/jersey-client-2.22.2.jarstop.sh
#!/bin/bashdocker-compose stop基于IDEA提交MapReduce至yarn
参考列表
- IDEA向hadoop集群提交MapReduce作业
- java操作hadoop hdfs，实现文件上传下载demo
- IDEA近程提交mapreduce工作至linux，遇到ClassNotFoundException: Mapper
留神：在提交至yarn的时候，要将代码打成jar包，否则会报错ClassNotFoundExeption。具体参考《IDEA近程提交mapreduce工作至linux，遇到ClassNotFoundException: Mapper》。
pom.xml
<?xml version="1.0" encoding="UTF-8"?><project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.switchvov</groupId> <artifactId>hadoop-test</artifactId> <version>1.0.0</version> <name>hadoop-test</name> <properties> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <maven.compiler.source>1.8</maven.compiler.source> <maven.compiler.target>1.8</maven.compiler.target> </properties> <dependencies> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>4.12</version> <scope>test</scope> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>2.8.0</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-common</artifactId> <version>2.8.0</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-hdfs</artifactId> <version>2.8.0</version> </dependency> </dependencies></project>log4j.properties
log4j.rootLogger=INFO, consolelog4j.appender.console=org.apache.log4j.ConsoleAppenderlog4j.appender.console.Target=System.outlog4j.appender.console.layout=org.apache.log4j.PatternLayoutlog4j.appender.console.layout.ConversionPattern=[%p] %d{yyyy-MM-dd HH:mm:ss,SSS} method:%l%m%nwords.txt
this is a teststhis is a teststhis is a teststhis is a teststhis is a teststhis is a teststhis is a teststhis is a teststhis is a testsHdfsDemo.java
package com.switchvov.hadoop.hdfs;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IOUtils;import java.io.InputStream;/** * @author switch * @since 2020/12/18 */public class HdfsDemo { /** * hadoop fs的配置文件 */ private static final Configuration CONFIGURATION = new Configuration(); static { // 指定hadoop fs的地址 CONFIGURATION.set("fs.default.name", "hdfs://namenode:8020"); } /** * 将本地文件(filePath)上传到HDFS服务器的指定门路(dst) */ public static void uploadFileToHDFS(String filePath, String dst) throws Exception { // 创立一个文件系统 FileSystem fs = FileSystem.get(CONFIGURATION); Path srcPath = new Path(filePath); Path dstPath = new Path(dst); long start = System.currentTimeMillis(); fs.copyFromLocalFile(false, srcPath, dstPath); System.out.println("Time:" + (System.currentTimeMillis() - start)); System.out.println("________筹备上传文件" + CONFIGURATION.get("fs.default.name") + "____________"); fs.close(); } /** * 下载文件 */ public static void downLoadFileFromHDFS(String src) throws Exception { FileSystem fs = FileSystem.get(CONFIGURATION); Path srcPath = new Path(src); InputStream in = fs.open(srcPath); try { // 将文件COPY到规范输入(即控制台输入) IOUtils.copyBytes(in, System.out, 4096, false); } finally { IOUtils.closeStream(in); fs.close(); } } public static void main(String[] args) throws Exception { String filename = "words.txt";// uploadFileToHDFS(// "/Users/switch/projects/OtherProjects/bigdata-enviroment/hadoop-test/data/" + filename,// "/share/" + filename// ); downLoadFileFromHDFS("/share/output12/" + filename + "/part-r-00000"); }}WordCountRunner.java
package com.switchvov.hadoop.mapreduce.wordcount;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import java.io.IOException;/** * @author switch * @since 2020/12/17 */public class WordCountRunner { /** * LongWritable 行号 类型 * Text 输出的value 类型 * Text 输入的key 类型 * IntWritable 输入的value 类型 * * @author switch * @since 2020/12/17 */ public static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> { /** * @param key 行号 * @param value 第一行的内容 如 this is a tests * @param context 输入 * @throws IOException 异样 * @throws InterruptedException 异样 */ @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); // 以空格宰割获取字符串数组 String[] words = line.split(" "); for (String word : words) { context.write(new Text(word), new IntWritable(1)); } } } /** * Text 输出的key的类型 * IntWritable 输出的value的类型 * Text 输入的key类型 * IntWritable 输入的value类型 * * @author switch * @since 2020/12/17 */ public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> { /** * @param key 输出map的key * @param values 输出map的value * @param context 输入 * @throws IOException 异样 * @throws InterruptedException 异样 */ @Override protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int count = 0; for (IntWritable value : values) { count += value.get(); } context.write(key, new IntWritable(count)); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); // 跨平台，保障在 Windows 下能够提交 mr job conf.set("mapreduce.app-submission.cross-platform", "true"); // 配置yarn调度 conf.set("mapreduce.framework.name", "yarn"); // 配置resourcemanager的主机名 conf.set("yarn.resourcemanager.hostname", "resourcemanager"); // 配置默认了namenode拜访地址 conf.set("fs.defaultFS", "hdfs://namenode:8020"); conf.set("fs.default.name", "hdfs://namenode:8020"); // 配置代码jar包，否则会呈现ClassNotFound异样，参考：https://blog.csdn.net/qq_19648191/article/details/56684268 conf.set("mapred.jar", "/Users/switch/projects/OtherProjects/bigdata-enviroment/hadoop-test/out/artifacts/hadoop/hadoop.jar"); // 工作名 Job job = Job.getInstance(conf, "word count"); // 指定Class job.setJarByClass(WordCountRunner.class); // 指定 Mapper Class job.setMapperClass(WordCountMapper.class); // 指定 Combiner Class，与 reduce 计算逻辑一样 job.setCombinerClass(WordCountReducer.class); // 指定Reucer Class job.setReducerClass(WordCountReducer.class); // 指定输入的KEY的格局 job.setOutputKeyClass(Text.class); // 指定输入的VALUE的格局 job.setOutputValueClass(IntWritable.class); //设置Reducer 个数默认1 job.setNumReduceTasks(1); // Mapper<Object, Text, Text, IntWritable> 输入格局必须与继承类的后两个输入类型统一 String filename = "words.txt"; String args0 = "hdfs://namenode:8020/share/" + filename; String args1 = "hdfs://namenode:8020/share/output12/" + filename; // 输出门路 FileInputFormat.addInputPath(job, new Path(args0)); // 输入门路 FileOutputFormat.setOutputPath(job, new Path(args1)); System.exit(job.waitForCompletion(true) ? 0 : 1); }}分享并记录所学所见