1. 旧版API
-- 源代码
MaxTemperatureMapper.java
package com.hadoop.study.chap01;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
public class MaxTemperatureMapper extends MapReduceBase implements
Mapper<LongWritable, Text, Text, IntWritable> {
private static final int MISSING = 9999;
@Override
public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> output, Reporter report)
throws IOException {
String line = value.toString();
String year = line.substring(15, 19);
int airTemperature;
if (line.charAt(87) == '+') {
airTemperature = Integer.parseInt(line.substring(88, 92));
} else {
airTemperature = Integer.parseInt(line.substring(87, 92));
}
String quaility = line.substring(92, 93);
if (airTemperature != MISSING && quaility.matches("[01459]")) {
output.collect(new Text(year), new IntWritable(airTemperature));
}
}
}
MaxTemperatureReducer.java
package com.hadoop.study.chap01;
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
public class MaxTemperatureReducer extends MapReduceBase implements
Reducer<Text, IntWritable, Text, IntWritable> {
@Override
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> output, Reporter report)
throws IOException {
int maxAirTemperature = Integer.MIN_VALUE;
while (values.hasNext()) {
maxAirTemperature = Math.max(maxAirTemperature, values.next().get());
}
output.collect(key, new IntWritable(maxAirTemperature));
}
}
MaxTemperature.java
package com.hadoop.study.chap01;
import java.io.IOException;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
public class MaxTemperature {
public static void main(String[] args) throws IOException {
if (args.length != 2) {
System.err.println("Usage: MaxTemperature <input path> <output path>");
System.exit(-1);
}
JobConf conf = new JobConf(MaxTemperature.class);
conf.setJobName("Max Temperature");
FileInputFormat.addInputPath(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
conf.setMapperClass(MaxTemperatureMapper.class);
conf.setReducerClass(MaxTemperatureReducer.class);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
JobClient.runJob(conf);
}
}
-- 执行
1) 将程序打包成hadoop-study.jar
2) 将输入文件1901上传至hadoop的master节点的/home/hadoop/input目录下
3) 将输入文件导入HDFS中
hadoop fs -copyFromLocal /home/hadoop/input input
4) 将jar包上传至hadoop的master节点的/home/hadoop/task目录下
5) 运行代码
hadoop jar /home/hadoop/task/hadoop-study.jar com.hadoop.study.chap01.MaxTemperature input/1901 output
-- 控制台输出
14/02/24 23:03:20 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same. 14/02/24 23:03:20 INFO util.NativeCodeLoader: Loaded the native-hadoop library 14/02/24 23:03:20 WARN snappy.LoadSnappy: Snappy native library not loaded 14/02/24 23:03:20 INFO mapred.FileInputFormat: Total input paths to process : 1 14/02/24 23:03:21 INFO mapred.JobClient: Running job: job_201402241759_0005 14/02/24 23:03:22 INFO mapred.JobClient: map 0% reduce 0% 14/02/24 23:03:29 INFO mapred.JobClient: map 50% reduce 0% 14/02/24 23:03:31 INFO mapred.JobClient: map 100% reduce 0% 14/02/24 23:03:37 INFO mapred.JobClient: map 100% reduce 16% 14/02/24 23:04:15 INFO mapred.JobClient: Task Id : attempt_201402241759_0005_m_000001_0, Status : FAILED Too many fetch-failures 14/02/24 23:04:16 WARN mapred.JobClient: Error reading task outputConnection refused 14/02/24 23:04:16 WARN mapred.JobClient: Error reading task outputConnection refused 14/02/24 23:04:17 INFO mapred.JobClient: map 50% reduce 16% 14/02/24 23:04:20 INFO mapred.JobClient: map 100% reduce 16% 14/02/24 23:04:38 INFO mapred.JobClient: map 100% reduce 33% 14/02/24 23:04:40 INFO mapred.JobClient: map 100% reduce 100% 14/02/24 23:04:41 INFO mapred.JobClient: Job complete: job_201402241759_0005 14/02/24 23:04:41 INFO mapred.JobClient: Counters: 30 14/02/24 23:04:41 INFO mapred.JobClient: Job Counters 14/02/24 23:04:41 INFO mapred.JobClient: Launched reduce tasks=1 14/02/24 23:04:41 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=16037 14/02/24 23:04:41 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 14/02/24 23:04:41 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 14/02/24 23:04:41 INFO mapred.JobClient: Launched map tasks=3 14/02/24 23:04:41 INFO mapred.JobClient: Data-local map tasks=3 14/02/24 23:04:41 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=69940 14/02/24 23:04:41 INFO mapred.JobClient: File Input Format Counters 14/02/24 23:04:41 INFO mapred.JobClient: Bytes Read=890559 14/02/24 23:04:41 INFO mapred.JobClient: File Output Format Counters 14/02/24 23:04:41 INFO mapred.JobClient: Bytes Written=9 14/02/24 23:04:41 INFO mapred.JobClient: FileSystemCounters 14/02/24 23:04:41 INFO mapred.JobClient: FILE_BYTES_READ=72210 14/02/24 23:04:41 INFO mapred.JobClient: HDFS_BYTES_READ=890763 14/02/24 23:04:41 INFO mapred.JobClient: FILE_BYTES_WRITTEN=305149 14/02/24 23:04:41 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=9 14/02/24 23:04:41 INFO mapred.JobClient: Map-Reduce Framework 14/02/24 23:04:41 INFO mapred.JobClient: Map output materialized bytes=72216 14/02/24 23:04:41 INFO mapred.JobClient: Map input records=6565 14/02/24 23:04:41 INFO mapred.JobClient: Reduce shuffle bytes=72216 14/02/24 23:04:41 INFO mapred.JobClient: Spilled Records=13128 14/02/24 23:04:41 INFO mapred.JobClient: Map output bytes=59076 14/02/24 23:04:41 INFO mapred.JobClient: Total committed heap usage (bytes)=412942336 14/02/24 23:04:41 INFO mapred.JobClient: CPU time spent (ms)=3780 14/02/24 23:04:41 INFO mapred.JobClient: Map input bytes=888190 14/02/24 23:04:41 INFO mapred.JobClient: SPLIT_RAW_BYTES=204 14/02/24 23:04:41 INFO mapred.JobClient: Combine input records=0 14/02/24 23:04:41 INFO mapred.JobClient: Reduce input records=6564 14/02/24 23:04:41 INFO mapred.JobClient: Reduce input groups=1 14/02/24 23:04:41 INFO mapred.JobClient: Combine output records=0 14/02/24 23:04:41 INFO mapred.JobClient: Physical memory (bytes) snapshot=333090816 14/02/24 23:04:41 INFO mapred.JobClient: Reduce output records=1 14/02/24 23:04:41 INFO mapred.JobClient: Virtual memory (bytes) snapshot=1122193408 14/02/24 23:04:41 INFO mapred.JobClient: Map output records=6564
2.新版API
-- 源代码
MaxTemperatureMapper.java
package com.hadoop.study.chap01.news;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class MaxTemperatureMapper extends
Mapper<LongWritable, Text, Text, IntWritable> {
private static final int MISSING = 9999;
@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
String year = line.substring(15, 19);
int airTemperature;
if (line.charAt(87) == '+') {
airTemperature = Integer.parseInt(line.substring(88, 92));
} else {
airTemperature = Integer.parseInt(line.substring(87, 92));
}
String quaility = line.substring(92, 93);
if (airTemperature != MISSING && quaility.matches("[01459]")) {
context.write(new Text(year), new IntWritable(airTemperature));
}
}
}
MaxTemperatureReducer.java
package com.hadoop.study.chap01.news;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class MaxTemperatureReducer extends
Reducer<Text, IntWritable, Text, IntWritable> {
@Override
protected void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int maxAirTemperature = Integer.MIN_VALUE;
for (IntWritable airTemperature : values) {
maxAirTemperature = Math.max(maxAirTemperature, airTemperature.get());
}
context.write(new Text(key), new IntWritable(maxAirTemperature));
}
}
MaxTemperature.java
package com.hadoop.study.chap01.news;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class MaxTemperature {
public static void main(String[] args) throws Exception {
if (args.length != 2) {
System.err.println("Usage: MaxTemperature <input path> <output path>");
System.exit(-1);
}
Job job = new Job();
job.setJarByClass(MaxTemperature.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(MaxTemperatureMapper.class);
job.setReducerClass(MaxTemperatureReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
-- 执行
参考旧版本执行步骤
-- 控制台输出
14/02/24 23:10:37 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same. 14/02/24 23:10:37 INFO input.FileInputFormat: Total input paths to process : 1 14/02/24 23:10:37 INFO util.NativeCodeLoader: Loaded the native-hadoop library 14/02/24 23:10:37 WARN snappy.LoadSnappy: Snappy native library not loaded 14/02/24 23:10:38 INFO mapred.JobClient: Running job: job_201402241759_0006 14/02/24 23:10:39 INFO mapred.JobClient: map 0% reduce 0% 14/02/24 23:10:45 INFO mapred.JobClient: map 100% reduce 0% 14/02/24 23:10:53 INFO mapred.JobClient: map 100% reduce 33% 14/02/24 23:10:55 INFO mapred.JobClient: map 100% reduce 100% 14/02/24 23:10:56 INFO mapred.JobClient: Job complete: job_201402241759_0006 14/02/24 23:10:56 INFO mapred.JobClient: Counters: 29 14/02/24 23:10:56 INFO mapred.JobClient: Job Counters 14/02/24 23:10:56 INFO mapred.JobClient: Launched reduce tasks=1 14/02/24 23:10:56 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=6900 14/02/24 23:10:56 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 14/02/24 23:10:56 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 14/02/24 23:10:56 INFO mapred.JobClient: Launched map tasks=1 14/02/24 23:10:56 INFO mapred.JobClient: Data-local map tasks=1 14/02/24 23:10:56 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=9502 14/02/24 23:10:56 INFO mapred.JobClient: File Output Format Counters 14/02/24 23:10:56 INFO mapred.JobClient: Bytes Written=9 14/02/24 23:10:56 INFO mapred.JobClient: FileSystemCounters 14/02/24 23:10:56 INFO mapred.JobClient: FILE_BYTES_READ=72210 14/02/24 23:10:56 INFO mapred.JobClient: HDFS_BYTES_READ=888304 14/02/24 23:10:56 INFO mapred.JobClient: FILE_BYTES_WRITTEN=252199 14/02/24 23:10:56 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=9 14/02/24 23:10:56 INFO mapred.JobClient: File Input Format Counters 14/02/24 23:10:56 INFO mapred.JobClient: Bytes Read=888190 14/02/24 23:10:56 INFO mapred.JobClient: Map-Reduce Framework 14/02/24 23:10:56 INFO mapred.JobClient: Map output materialized bytes=72210 14/02/24 23:10:56 INFO mapred.JobClient: Map input records=6565 14/02/24 23:10:56 INFO mapred.JobClient: Reduce shuffle bytes=72210 14/02/24 23:10:56 INFO mapred.JobClient: Spilled Records=13128 14/02/24 23:10:56 INFO mapred.JobClient: Map output bytes=59076 14/02/24 23:10:56 INFO mapred.JobClient: CPU time spent (ms)=2050 14/02/24 23:10:56 INFO mapred.JobClient: Total committed heap usage (bytes)=210173952 14/02/24 23:10:56 INFO mapred.JobClient: Combine input records=0 14/02/24 23:10:56 INFO mapred.JobClient: SPLIT_RAW_BYTES=114 14/02/24 23:10:56 INFO mapred.JobClient: Reduce input records=6564 14/02/24 23:10:56 INFO mapred.JobClient: Reduce input groups=1 14/02/24 23:10:56 INFO mapred.JobClient: Combine output records=0 14/02/24 23:10:56 INFO mapred.JobClient: Physical memory (bytes) snapshot=190836736 14/02/24 23:10:56 INFO mapred.JobClient: Reduce output records=1 14/02/24 23:10:56 INFO mapred.JobClient: Virtual memory (bytes) snapshot=748298240 14/02/24 23:10:56 INFO mapred.JobClient: Map output records=6564
MaxTemperatureWithCombiner.java
package com.hadoop.study.chap01.news;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class MaxTemperatureWithCombiner {
public static void main(String[] args) throws Exception {
if (args.length != 2) {
System.err.println("Usage: MaxTemperature <input path> <output path>");
System.exit(-1);
}
Job job = new Job();
job.setJobName("Max Temperature");
job.setJarByClass(MaxTemperatureWithCombiner.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(MaxTemperatureMapper.class);
job.setReducerClass(MaxTemperatureReducer.class);
job.setCombinerClass(MaxTemperatureReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
-- 控制台输出
14/02/24 23:12:16 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same. 14/02/24 23:12:17 INFO input.FileInputFormat: Total input paths to process : 1 14/02/24 23:12:17 INFO util.NativeCodeLoader: Loaded the native-hadoop library 14/02/24 23:12:17 WARN snappy.LoadSnappy: Snappy native library not loaded 14/02/24 23:12:17 INFO mapred.JobClient: Running job: job_201402241759_0007 14/02/24 23:12:18 INFO mapred.JobClient: map 0% reduce 0% 14/02/24 23:12:30 INFO mapred.JobClient: map 100% reduce 0% 14/02/24 23:12:41 INFO mapred.JobClient: map 100% reduce 33% 14/02/24 23:12:43 INFO mapred.JobClient: map 100% reduce 100% 14/02/24 23:12:44 INFO mapred.JobClient: Job complete: job_201402241759_0007 14/02/24 23:12:44 INFO mapred.JobClient: Counters: 29 14/02/24 23:12:44 INFO mapred.JobClient: Job Counters 14/02/24 23:12:44 INFO mapred.JobClient: Launched reduce tasks=1 14/02/24 23:12:44 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=10591 14/02/24 23:12:44 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 14/02/24 23:12:44 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 14/02/24 23:12:44 INFO mapred.JobClient: Launched map tasks=1 14/02/24 23:12:44 INFO mapred.JobClient: Data-local map tasks=1 14/02/24 23:12:44 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=13038 14/02/24 23:12:44 INFO mapred.JobClient: File Output Format Counters 14/02/24 23:12:44 INFO mapred.JobClient: Bytes Written=9 14/02/24 23:12:44 INFO mapred.JobClient: FileSystemCounters 14/02/24 23:12:44 INFO mapred.JobClient: FILE_BYTES_READ=17 14/02/24 23:12:44 INFO mapred.JobClient: HDFS_BYTES_READ=888304 14/02/24 23:12:44 INFO mapred.JobClient: FILE_BYTES_WRITTEN=108261 14/02/24 23:12:44 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=9 14/02/24 23:12:44 INFO mapred.JobClient: File Input Format Counters 14/02/24 23:12:44 INFO mapred.JobClient: Bytes Read=888190 14/02/24 23:12:44 INFO mapred.JobClient: Map-Reduce Framework 14/02/24 23:12:44 INFO mapred.JobClient: Map output materialized bytes=17 14/02/24 23:12:44 INFO mapred.JobClient: Map input records=6565 14/02/24 23:12:44 INFO mapred.JobClient: Reduce shuffle bytes=17 14/02/24 23:12:44 INFO mapred.JobClient: Spilled Records=2 14/02/24 23:12:44 INFO mapred.JobClient: Map output bytes=59076 14/02/24 23:12:44 INFO mapred.JobClient: CPU time spent (ms)=4460 14/02/24 23:12:44 INFO mapred.JobClient: Total committed heap usage (bytes)=210173952 14/02/24 23:12:44 INFO mapred.JobClient: Combine input records=6564 14/02/24 23:12:44 INFO mapred.JobClient: SPLIT_RAW_BYTES=114 14/02/24 23:12:44 INFO mapred.JobClient: Reduce input records=1 14/02/24 23:12:44 INFO mapred.JobClient: Reduce input groups=1 14/02/24 23:12:44 INFO mapred.JobClient: Combine output records=1 14/02/24 23:12:44 INFO mapred.JobClient: Physical memory (bytes) snapshot=191209472 14/02/24 23:12:44 INFO mapred.JobClient: Reduce output records=1 14/02/24 23:12:44 INFO mapred.JobClient: Virtual memory (bytes) snapshot=748470272 14/02/24 23:12:44 INFO mapred.JobClient: Map output records=6564
3. 对比
--Mapper，Reducer的实现由实现接口变成继承类
--使用Job类来控制作业，而不是JobClient
--控制台输出，旧版本Launched map tasks=3(除去FAILED,数量为2)，新版本Launched map tasks=1，使用combiner之后Reduce input records=1
4. 参考资料
Hadoop权威指南
相关推荐
【Hadoop第一次培训材料】是针对初学者设计的一份详细教程，旨在介绍分布式计算框架Hadoop的核心概念和技术。Hadoop是Apache软件基金会开发的一个开源项目，它为处理和存储大量数据提供了可扩展的、可靠的解决方案。...
Hadoop单机伪分布式搭建和运行第一个WordCount程序 Hadoop是Apache基金会下的一个开源的大数据处理框架，它广泛应用于数据处理、数据分析和机器学习等领域。下面是关于Hadoop单机伪分布式搭建和运行第一个WordCount...
- **ZooKeeper**：一种分布式的协调服务，用于分布式应用程序之间的同步，比如配置维护、域名服务、分布式同步等。 - **Sqoop**：用于在Hadoop和关系型数据库之间高效传输数据的工具。 - **Flume**：一个高可靠、高...
【IT十八掌徐培成】Hadoop第01天-05.hadoop伪分布式2.zip 这个压缩包文件主要涵盖了Hadoop技术中的一个重要概念——伪分布式模式。在深入讲解这个主题之前，我们先来理解一下Hadoop是什么。Hadoop是一个开源的、基于...
1. **Hadoop概述**：Hadoop是由Apache基金会开发的一个开源项目，基于Google的MapReduce编程模型和GFS分布式文件系统。它的设计目标是高容错性、可扩展性和高效的数据处理。 2. **Hadoop组件**： - **HDFS（Hadoop...
Hadoop的I/O、MapReduce应用程序开发；MapReduce的工作机制；MapReduce的类型和格式；MapReduce的特性；如何构建Hadoop集群，如何管理Hadoop；Pig简介；Hbase简介；Hive简介；ZooKeeper简介；开源工具Sqoop，最后还...
1. **hadoop.dll**：这是一个动态链接库文件，它是Hadoop在Windows系统上的一个组件，用于实现某些特定功能，如JNI（Java Native Interface）调用，使得Java代码可以与本地系统交互。如果没有这个文件，Hadoop可能...
这个“细细品味Hadoop_Hadoop集群（第9期）_MapReduce初级案例”主题聚焦于Hadoop集群的核心组件——MapReduce，这是一种分布式计算模型，对于处理海量数据具有重要作用。 MapReduce的工作原理可以分为两个主要阶段...
在Hadoop第四版中，作者们对Hadoop生态系统的最新发展进行了全面更新。这一版本涵盖了Hadoop 2.x系列的诸多改进，包括YARN（Yet Another Resource Negotiator），这是一种新的资源管理器，将作业调度与集群资源管理...
《Hadoop实战第二版》是由陆嘉恒编著的一本深度解析Hadoop技术的专业书籍，其源码的提供为读者提供了亲自动手实践Hadoop的机会，增强了学习效果。Hadoop作为大数据处理领域的基石，它的核心在于分布式存储系统HDFS...
第一天 hadoop的基本概念 伪分布式hadoop集群安装 hdfs mapreduce 演示 01-hadoop职位需求状况.avi 02-hadoop课程安排.avi 03-hadoop应用场景.avi 04-hadoop对海量数据处理的解决思路.avi 05-hadoop版本选择和...
《Hadoop权威指南》第三版是Hadoop领域的经典著作，为读者全面解析了这个分布式计算框架的核心原理与实际应用。本书旨在帮助读者深入理解Hadoop的架构、设计思想以及如何在实际项目中有效地运用它。以下是对这本书...
1. 下载Hadoop Eclipse插件：通常，你可以从Apache Hadoop官方网站或第三方插件库找到适用于Eclipse的Hadoop插件。确保下载的插件版本与你的Eclipse和Hadoop版本兼容。 2. 安装插件：将下载的插件文件（通常是.zip...
1. **验证下载**：首先，检查Hadoop的下载源，确保是从官方或信誉良好的第三方网站获取的。有时，不完整的下载或损坏的文件会导致缺失关键组件。 2. **重新下载**：如果确认下载无误，尝试重新下载并重新安装Hadoop...
在IT行业中，Hadoop是一个广泛使用的开源框架，主要用于大数据处理和分布式存储。标题"hadop2.7.x_winutils_exe&&hadoop_dll"暗示我们关注的是Hadoop 2.7.x版本在Windows环境下的两个关键组件：`winutils.exe`和`...
- Hadoop MapReduce：一个Java软件框架，用于编写应用程序，以并行方式处理大数据集。 - Hadoop Ozone：为Hadoop提供一个通用的、可扩展的和高可用的存储平台。 Hadoop的使用场景非常广泛，包括但不限于： - 网络...
本资源"【IT十八掌徐培成】Hadoop第02天-01.Hadoop基础回顾-进程初识-webui.zip"聚焦于Hadoop的基础知识，特别是对Hadoop进程的理解以及如何通过Web UI进行监控。这里我们将深入探讨Hadoop的核心组件、进程架构以及...
在大数据处理领域，Hadoop是一个不可或缺的开源框架，主要用于...总之，正确配置和使用Hadoop的JAR包是开发Hadoop应用程序的关键。通过Eclipse这样的IDE，可以方便地管理这些依赖，加速大数据应用的开发和调试过程。
6. **最佳实践**：作者分享了他们在开发和应用Hadoop过程中的经验和技巧，帮助读者避免常见的陷阱，提高Hadoop应用的效率和性能。 通过阅读《Hadoop权威指南》，无论是初学者还是有经验的开发者，都能对Hadoop有...