Home Database Mysql Tutorial Hadoop2.4.1入门实例:MaxTemperature

Hadoop2.4.1入门实例:MaxTemperature

Jun 07, 2016 pm 03:07 PM
getting Started Example

注意:以下内容在2.x版本与1.x版本同样适用,已在2.4.1与1.2.0进行测试。 一、前期准备 1、创建伪分布Hadoop环境,请参考官方文档。或者http://blog.csdn.net/jediael_lu/article/details/38637277 2、准备数据文件如下sample.txt: 12345679867623119010123


注意:以下内容在2.x版本与1.x版本同样适用,已在2.4.1与1.2.0进行测试。

一、前期准备

1、创建伪分布Hadoop环境,请参考官方文档。或者http://blog.csdn.net/jediael_lu/article/details/38637277

2、准备数据文件如下sample.txt:

123456798676231190101234567986762311901012345679867623119010123456798676231190101234561+00121534567890356
123456798676231190101234567986762311901012345679867623119010123456798676231190101234562+01122934567890456
123456798676231190201234567986762311901012345679867623119010123456798676231190101234562+02120234567893456
123456798676231190401234567986762311901012345679867623119010123456798676231190101234561+00321234567803456
123456798676231190101234567986762311902012345679867623119010123456798676231190101234561+00429234567903456
123456798676231190501234567986762311902012345679867623119010123456798676231190101234561+01021134568903456
123456798676231190201234567986762311902012345679867623119010123456798676231190101234561+01124234578903456
123456798676231190301234567986762311905012345679867623119010123456798676231190101234561+04121234678903456
123456798676231190301234567986762311905012345679867623119010123456798676231190101234561+00821235678903456


二、编写代码

1、创建Map

package org.jediael.hadoopDemo.maxtemperature;

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 intwritable> {
	private static final int MISSING = 9999;

	@Override
	public 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) == '+') { // parseInt doesn't like leading plus
										// signs
			airTemperature = Integer.parseInt(line.substring(88, 92));
		} else {
			airTemperature = Integer.parseInt(line.substring(87, 92));
		}
		String quality = line.substring(92, 93);
		if (airTemperature != MISSING && quality.matches("[01459]")) {
			context.write(new Text(year), new IntWritable(airTemperature));
		}
	}
}
</longwritable>
Copy after login

2、创建Reduce
package org.jediael.hadoopDemo.maxtemperature;

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> {
	@Override
	public void reduce(Text key, Iterable<intwritable> values, Context context)
			throws IOException, InterruptedException {
		int maxValue = Integer.MIN_VALUE;
		for (IntWritable value : values) {
			maxValue = Math.max(maxValue, value.get());
		}
		context.write(key, new IntWritable(maxValue));
	}
}</intwritable></text>
Copy after login

3、创建main方法
package org.jediael.hadoopDemo.maxtemperature;

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);
		job.setJobName("Max temperature");
		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);
	}
}
</output>
Copy after login

4、导出成MaxTemp.jar,并上传至运行程序的服务器。


三、运行程序

1、创建input目录并将sample.txt复制到input目录

hadoop fs -put sample.txt /

2、运行程序

export HADOOP_CLASSPATH=MaxTemp.jar

 hadoop org.jediael.hadoopDemo.maxtemperature.MaxTemperature /sample.txt output10

注意输出目录不能已经存在,否则会创建失败。

3、查看结果

(1)查看结果

[jediael@jediael44 code]$  hadoop fs -cat output10/*
14/07/09 14:51:35 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
1901    42
1902    212
1903    412
1904    32
1905    102

(2)运行时输出

[jediael@jediael44 code]$  hadoop org.jediael.hadoopDemo.maxtemperature.MaxTemperature /sample.txt output10
14/07/09 14:50:40 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/07/09 14:50:41 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
14/07/09 14:50:42 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
14/07/09 14:50:43 INFO input.FileInputFormat: Total input paths to process : 1
14/07/09 14:50:43 INFO mapreduce.JobSubmitter: number of splits:1
14/07/09 14:50:44 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1404888618764_0001
14/07/09 14:50:44 INFO impl.YarnClientImpl: Submitted application application_1404888618764_0001
14/07/09 14:50:44 INFO mapreduce.Job: The url to track the job: http://jediael44:8088/proxy/application_1404888618764_0001/
14/07/09 14:50:44 INFO mapreduce.Job: Running job: job_1404888618764_0001
14/07/09 14:50:57 INFO mapreduce.Job: Job job_1404888618764_0001 running in uber mode : false
14/07/09 14:50:57 INFO mapreduce.Job:  map 0% reduce 0%
14/07/09 14:51:05 INFO mapreduce.Job:  map 100% reduce 0%
14/07/09 14:51:15 INFO mapreduce.Job:  map 100% reduce 100%
14/07/09 14:51:15 INFO mapreduce.Job: Job job_1404888618764_0001 completed successfully
14/07/09 14:51:16 INFO mapreduce.Job: Counters: 49
        File System Counters
                FILE: Number of bytes read=94
                FILE: Number of bytes written=185387
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=1051
                HDFS: Number of bytes written=43
                HDFS: Number of read operations=6
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=2
        Job Counters 
                Launched map tasks=1
                Launched reduce tasks=1
                Data-local map tasks=1
                Total time spent by all maps in occupied slots (ms)=5812
                Total time spent by all reduces in occupied slots (ms)=7023
                Total time spent by all map tasks (ms)=5812
                Total time spent by all reduce tasks (ms)=7023
                Total vcore-seconds taken by all map tasks=5812
                Total vcore-seconds taken by all reduce tasks=7023
                Total megabyte-seconds taken by all map tasks=5951488
                Total megabyte-seconds taken by all reduce tasks=7191552
        Map-Reduce Framework
                Map input records=9
                Map output records=8
                Map output bytes=72
                Map output materialized bytes=94
                Input split bytes=97
                Combine input records=0
                Combine output records=0
                Reduce input groups=5
                Reduce shuffle bytes=94
                Reduce input records=8
                Reduce output records=5
                Spilled Records=16
                Shuffled Maps =1
                Failed Shuffles=0
                Merged Map outputs=1
                GC time elapsed (ms)=154
                CPU time spent (ms)=1450
                Physical memory (bytes) snapshot=303112192
                Virtual memory (bytes) snapshot=1685733376
                Total committed heap usage (bytes)=136515584
        Shuffle Errors
                BAD_ID=0
                CONNECTION=0
                IO_ERROR=0
                WRONG_LENGTH=0
                WRONG_MAP=0
                WRONG_REDUCE=0
        File Input Format Counters 
                Bytes Read=954
        File Output Format Counters 
                Bytes Written=43


Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
3 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. Best Graphic Settings
3 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. How to Fix Audio if You Can't Hear Anyone
3 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
WWE 2K25: How To Unlock Everything In MyRise
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

A Diffusion Model Tutorial Worth Your Time, from Purdue University A Diffusion Model Tutorial Worth Your Time, from Purdue University Apr 07, 2024 am 09:01 AM

Diffusion can not only imitate better, but also "create". The diffusion model (DiffusionModel) is an image generation model. Compared with the well-known algorithms such as GAN and VAE in the field of AI, the diffusion model takes a different approach. Its main idea is a process of first adding noise to the image and then gradually denoising it. How to denoise and restore the original image is the core part of the algorithm. The final algorithm is able to generate an image from a random noisy image. In recent years, the phenomenal growth of generative AI has enabled many exciting applications in text-to-image generation, video generation, and more. The basic principle behind these generative tools is the concept of diffusion, a special sampling mechanism that overcomes the limitations of previous methods.

Generate PPT with one click! Kimi: Let the 'PPT migrant workers' become popular first Generate PPT with one click! Kimi: Let the 'PPT migrant workers' become popular first Aug 01, 2024 pm 03:28 PM

Kimi: In just one sentence, in just ten seconds, a PPT will be ready. PPT is so annoying! To hold a meeting, you need to have a PPT; to write a weekly report, you need to have a PPT; to make an investment, you need to show a PPT; even when you accuse someone of cheating, you have to send a PPT. College is more like studying a PPT major. You watch PPT in class and do PPT after class. Perhaps, when Dennis Austin invented PPT 37 years ago, he did not expect that one day PPT would become so widespread. Talking about our hard experience of making PPT brings tears to our eyes. "It took three months to make a PPT of more than 20 pages, and I revised it dozens of times. I felt like vomiting when I saw the PPT." "At my peak, I did five PPTs a day, and even my breathing was PPT." If you have an impromptu meeting, you should do it

All CVPR 2024 awards announced! Nearly 10,000 people attended the conference offline, and a Chinese researcher from Google won the best paper award All CVPR 2024 awards announced! Nearly 10,000 people attended the conference offline, and a Chinese researcher from Google won the best paper award Jun 20, 2024 pm 05:43 PM

In the early morning of June 20th, Beijing time, CVPR2024, the top international computer vision conference held in Seattle, officially announced the best paper and other awards. This year, a total of 10 papers won awards, including 2 best papers and 2 best student papers. In addition, there were 2 best paper nominations and 4 best student paper nominations. The top conference in the field of computer vision (CV) is CVPR, which attracts a large number of research institutions and universities every year. According to statistics, a total of 11,532 papers were submitted this year, and 2,719 were accepted, with an acceptance rate of 23.6%. According to Georgia Institute of Technology’s statistical analysis of CVPR2024 data, from the perspective of research topics, the largest number of papers is image and video synthesis and generation (Imageandvideosyn

Five programming software for getting started with learning C language Five programming software for getting started with learning C language Feb 19, 2024 pm 04:51 PM

As a widely used programming language, C language is one of the basic languages ​​that must be learned for those who want to engage in computer programming. However, for beginners, learning a new programming language can be difficult, especially due to the lack of relevant learning tools and teaching materials. In this article, I will introduce five programming software to help beginners get started with C language and help you get started quickly. The first programming software was Code::Blocks. Code::Blocks is a free, open source integrated development environment (IDE) for

From bare metal to a large model with 70 billion parameters, here is a tutorial and ready-to-use scripts From bare metal to a large model with 70 billion parameters, here is a tutorial and ready-to-use scripts Jul 24, 2024 pm 08:13 PM

We know that LLM is trained on large-scale computer clusters using massive data. This site has introduced many methods and technologies used to assist and improve the LLM training process. Today, what we want to share is an article that goes deep into the underlying technology and introduces how to turn a bunch of "bare metals" without even an operating system into a computer cluster for training LLM. This article comes from Imbue, an AI startup that strives to achieve general intelligence by understanding how machines think. Of course, turning a bunch of "bare metal" without an operating system into a computer cluster for training LLM is not an easy process, full of exploration and trial and error, but Imbue finally successfully trained an LLM with 70 billion parameters. and in the process accumulate

A must-read for technical beginners: Analysis of the difficulty levels of C language and Python A must-read for technical beginners: Analysis of the difficulty levels of C language and Python Mar 22, 2024 am 10:21 AM

Title: A must-read for technical beginners: Difficulty analysis of C language and Python, requiring specific code examples In today's digital age, programming technology has become an increasingly important ability. Whether you want to work in fields such as software development, data analysis, artificial intelligence, or just learn programming out of interest, choosing a suitable programming language is the first step. Among many programming languages, C language and Python are two widely used programming languages, each with its own characteristics. This article will analyze the difficulty levels of C language and Python

AI in use | AI created a life vlog of a girl living alone, which received tens of thousands of likes in 3 days AI in use | AI created a life vlog of a girl living alone, which received tens of thousands of likes in 3 days Aug 07, 2024 pm 10:53 PM

Editor of the Machine Power Report: Yang Wen The wave of artificial intelligence represented by large models and AIGC has been quietly changing the way we live and work, but most people still don’t know how to use it. Therefore, we have launched the "AI in Use" column to introduce in detail how to use AI through intuitive, interesting and concise artificial intelligence use cases and stimulate everyone's thinking. We also welcome readers to submit innovative, hands-on use cases. Video link: https://mp.weixin.qq.com/s/2hX_i7li3RqdE4u016yGhQ Recently, the life vlog of a girl living alone became popular on Xiaohongshu. An illustration-style animation, coupled with a few healing words, can be easily picked up in just a few days.

Counting down the 12 pain points of RAG, NVIDIA senior architect teaches solutions Counting down the 12 pain points of RAG, NVIDIA senior architect teaches solutions Jul 11, 2024 pm 01:53 PM

Retrieval-augmented generation (RAG) is a technique that uses retrieval to boost language models. Specifically, before a language model generates an answer, it retrieves relevant information from an extensive document database and then uses this information to guide the generation process. This technology can greatly improve the accuracy and relevance of content, effectively alleviate the problem of hallucinations, increase the speed of knowledge update, and enhance the traceability of content generation. RAG is undoubtedly one of the most exciting areas of artificial intelligence research. For more details about RAG, please refer to the column article on this site "What are the new developments in RAG, which specializes in making up for the shortcomings of large models?" This review explains it clearly." But RAG is not perfect, and users often encounter some "pain points" when using it. Recently, NVIDIA’s advanced generative AI solution

See all articles