代写CS246语言、java设计编程代写

日期：2022-03-26 08:30

CS246: Mining Massive Datasets Winter 2016

Hadoop Tutorial

Due 11:59pm January 17, 2017

General Instructions

The purpose of this tutorial is (1) to get you started with Hadoop and (2) to get you

acquainted with the code and homework submission system. Completing the tutorial is

optional but by handing in the results in time students will earn 5 points. This tutorial is

to be completed individually.

Here you will learn how to write, compile, debug and execute a simple Hadoop program.

First part of the assignment serves as a tutorial and the second part asks you to write your

own Hadoop program.

Section 1 describes the virtual machine environment. Instead of the virtual machine, you

are welcome to setup your own pseudo-distributed or fully distributed cluster if you prefer.

Any version of Hadoop that is at least 1.0 will suffice. (For an easy way to set up a

cluster, try Cloudera Manager: http://archive.cloudera.com/cm5/installer/latest/

cloudera-manager-installer.bin.) If you choose to setup your own cluster, you are responsible

for making sure the cluster is working properly. The TAs will be unable to help

you debug configuration issues in your own cluster.

Section 2 explains how to use the Eclipse environment in the virtual machine, including how

to create a project, how to run jobs, and how to debug jobs. Section 2.5 gives an end-to-end

example of creating a project, adding code, building, running, and debugging it.

Section 3 is the actual homework assignment. There are no deliverable for sections 1 and 2.

In section 3, you are asked to write and submit your own MapReduce job

This assignment requires you to upload the code and hand-in the output for Section 3.

All students should submit the output via Gradescope and upload the code via snap.

Gradescope: To register for Gradescope,

? Create an account on Gradescope if you don’t have one already.

? Join CS246 course using Entry Code MBDY2M

Upload the code: Put all the code for a single question into a single file and upload it at

http://snap.stanford.edu/submit/. You must aggregate all the code in a single

file (one file per question), and it must be a text file.

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Questions

1 Setting up a virtual machine

? Download and install VirtualBox on your machine: http://virtualbox.org/wiki/

Downloads

? Download the Cloudera Quickstart VM at https://downloads.cloudera.com/demo_

vm/virtualbox/cloudera-quickstart-vm-5.5.0-0-virtualbox.zip.

? Uncompress the VM archive. It is compressed with 7-zip. If needed you can download

a tool to uncompress the archive at http://www.7-zip.org/.

? Start VirtualBox and click Import Appliance in the File dropdown menu. Click the

folder icon beside the location field. Browse to the uncompressed archive folder, select

the .ovf file, and click the Open button. Click the Continue button. Click the Import

button.

? Your virtual machine should now appear in the left column. Select it and click on Start

to launch it.

? To verify that the VM is running and you can access it, open a browser to the URL:

http://localhost:8088. You should see the resource manager UI. The VM uses port

forwarding for the common Hadoop ports, so when the VM is running, those ports on

localhost will redirect to the VM.

? Optional: Open the Virtual Box preferences (F ile → P references → Network) and

select the Adapter 2 tab. Click the Enable Network Adapter checkbox. Select Hostonly

Adapter. If the list of networks is empty, add a new network. Click OK. If you

do this step, you will be able to connect to the running virtual machine via SSH from

the host OS at 192.168.56.101. The username and password are ’cloudera’.

The virtual machine includes the following software

? CentOS 6.4

? JDK 7 (1.7.0 67)

? Hadoop 2.5.0

? Eclipse 4.2.6 (Juno)

The virtual machine runs best with 4096MB of RAM, but has been tested to

function with 1024MB. Note that at 1024MB, while it did technically function,

it was very slow to start up.

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2 Running Hadoop jobs

Generally Hadoop can be run in three modes.

1. Standalone (or local) mode: There are no daemons used in this mode. Hadoop

uses the local file system as an substitute for HDFS file system. The jobs will run as

if there is 1 mapper and 1 reducer.

2. Pseudo-distributed mode: All the daemons run on a single machine and this setting

mimics the behavior of a cluster. All the daemons run on your machine locally using

the HDFS protocol. There can be multiple mappers and reducers.

3. Fully-distributed mode: This is how Hadoop runs on a real cluster.

In this homework we will show you how to run Hadoop jobs in Standalone mode (very useful

for developing and debugging) and also in Pseudo-distributed mode (to mimic the behavior

of a cluster environment).

2.1 Creating a Hadoop project in Eclipse

(There is a plugin for Eclipse that makes it simple to create a new Hadoop project and

execute Hadoop jobs, but the plugin is only well maintained for Hadoop 1.0.4, which

is a rather old version of Hadoop. There is a project at https://github.com/winghc/

hadoop2x-eclipse-plugin that is working to update the plugin for Hadoop 2.0. You can

try it out if you like, but your milage may vary.)

To create a project:

1. Open Eclipse. If you just launched the VM, you may have to close the Firefox window

to find the Eclipse icon on the desktop.

2. Right-click on the training node in the Package Explorer and select Copy. See Figure

1.

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Figure 1: Create a Hadoop Project.

3. Right-click on the training node in the Package Explorer and select Paste . See Figure

2.

Figure 2: Create a Hadoop Project.

4. In the pop-up dialog, enter the new project name in the Project Name field and click

OK. See Figure 3.

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Figure 3: Create a Hadoop Project.

5. Modify or replace the stub classes found in the src directory as needed.

2.2 Running Hadoop jobs in standalone mode

Once you’ve created your project and written the source code, to run the project in standalone

mode, do the following:

1. Right-click on the project and select Run As → Run Conf igurations. See Figure 4.

Figure 4: Run a Hadoop Project.

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2. In the pop-up dialog, select the Java Application node and click the New launch con-

figuration button in the upper left corner. See Figure 5.

Figure 5: Run a Hadoop Project.

3. Enter a name in the Name field and the name of the main class in the Main class field.

See Figure 6.

Figure 6: Run a Hadoop Project.

4. Switch to the Arguments tab and input the required arguments. Click Apply. See

Figure 7. To run the job immediately, click on the Run button. Otherwise click Close

and complete the following step.

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Figure 7: Run a Hadoop Project.

5. Right-click on the project and select Run As → Java Application. See Figure 8.

Figure 8: Run a Hadoop Project.

6. In the pop-up dialog select the main class from the selection list and click OK. See

Figure 9.

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Figure 9: Run a Hadoop Project.

After you have setup the run configuration the first time, you can skip steps 1 and

2 above in subsequent runs, unless you need to change the arguments. You can also

create more than one launch configuration if you’d like, such as one for each set of

common arguments.

2.3 Running Hadoop in pseudo-distributed mode

Once you’ve created your project and written the source code, to run the project in pseudodistributed

mode, do the following:

1. Right-click on the project and select Export. See Figure 10.

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Figure 10: Run a Hadoop Project.

2. In the pop-up dialog, expand the Java node and select JAR file. See Figure 11. Click

Next >

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Figure 11: Run a Hadoop Project.

3. Enter a path in the JAR file field and click Finish. See Figure 12.

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Figure 12: Run a Hadoop Project.

4. Open a terminal and run the following command:

hadoop jar path/to/file.jar input path output path

After modifications to the source files, repeat all of the above steps to run job again.

2.4 Debugging Hadoop jobs

To debug an issue with a job, the easiest approach is to run the job in stand-alone mode

and use a debugger. To debug your job, do the following steps:

1. Right-click on the project and select Debug As → Java Application. See Figure 13.

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Figure 13: Debug a Hadoop project.

2. In the pop-up dialog select the main class from the selection list and click OK. See

Figure 14.

Figure 14: Run a Hadoop Project.

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You can use the Eclipse debugging features to debug your job execution. See the additional

Eclipse tutorials at the end of section 2.6 for help using the Eclipse debugger.

When running your job in pseudo-distributed mode, the output from the job is logged in the

task tracker’s log files, which can be accessed most easily by pointing a web browser to port

8088 of the server, which will the localhost. From the job tracker web page, you can drill

down into the failing job, the failing task, the failed attempt, and finally the log files. Note

that the logs for stdout and stderr are separated, which can be useful when trying to isolate

specific debugging print statements.

2.5 Example project

In this section you will create a new Eclipse Hadoop project, compile, and execute it. The

program will count the frequency of all the words in a given large text file. In your virtual

machine, Hadoop, Java environment and Eclipse have already been pre-installed.

? Open Eclipse. If you just launched the VM, you may have to close the Firefox window

to find the Eclipse icon on the desktop.

? Right-click on the training node in the Package Explorer and select Copy. See Figure

15.

Figure 15: Create a Hadoop Project.

? Right-click on the training node in the Package Explorer and select Paste. See Figure

16.

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Figure 16: Create a Hadoop Project.

? In the pop-up dialog, enter the new project name in the Project Name field and click

OK. See Figure 17.

Figure 17: Create a Hadoop Project.

? Create a new package called edu.stanford.cs246.wordcount by right-clicking on the

src node and selecting New → P ackage. See Figure 18.

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Figure 18: Create a Hadoop Project.

? Enter edu.stanford.cs246.wordcount in the Name field and click Finish. See Figure

19.

Figure 19: Create a Hadoop Project.

? Create a new class in that package called WordCount by right-clicking on the edu.stanford.cs246.wordcount

node and selecting New → Class. See Figure 20.

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Figure 20: Create a Hadoop Project.

? In the pop-up dialog, enter WordCount as the Name. See Figure 21.

Figure 21: Create a Hadoop Project.

? In the Superclass field, enter Configured and click the Browse button. From the popup

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window select Configured ? org.apache.hadoop.conf and click the OK button. See

Figure 22.

Figure 22: Create a java file.

? In the Interfaces section, click the Add button. From the pop-up window select Tool ?

org.apache.hadoop.util and click the OK button. See Figure 23.

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Figure 23: Create a java file.

? Check the boxes for public static void main(String args[]) and Inherited abstract methods

and click the Finish button. See Figure 24.

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Figure 24: Create WordCount.java.

? You will now have a rough skeleton of a Java file as in Figure 25. You can now add

code to this class to implement your Hadoop job.

Figure 25: Create WordCount.java.

? Rather than implement a job from scratch, copy the contents from http://snap.

stanford.edu/class/cs246-data-2014/WordCount.java and paste it into the WordCount.java

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file. See Figure 26. The code in WordCount.java calculates the frequency of each word

in a given dataset.

Figure 26: Create WordCount.java.

? Download the Complete Works of William Shakespeare from Project Gutenberg at

http://www.gutenberg.org/cache/epub/100/pg100.txt. You can do this simply

with cURL, but you also have to be aware of the byte order mark (BOM). You can

download the file and remove the BOM in one line by opening a terminal, changing to

the ~/workspace/WordCount directory, and running the following command:

curl http://www.gutenberg.org/cache/epub/100/pg100.txt | perl -pe ’s/^\xEF\xBB

\xBF//’ > pg100.txt

If you copy the above command beware the quotes as the copy/paste will likely mistranslate

them.

? Right-click on the project and select Run As → Run Conf igurations. See Figure 27.

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Figure 27: Run WordCount.java.

? In the pop-up dialog, select the Java Application node and click the New launch con-

figuration button in the upper left corner. See Figure 28.

Figure 28: Run WordCount.java.

? Enter a name in the Name field and WordCount in the Main class field. See Figure 29.

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Figure 29: Run WordCount.java.

? Switch to the Arguments tab and put pg100.txt output in the Program arguments

field. See Figure 30. Click Apply and Close.

Figure 30: Run WordCount.java.

? Right-click on the project and select Run As → Java Application. See Figure 31.

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Figure 31: Run WordCount.java.

? In the pop-up dialog select WordCount - edu.stanford.cs246.wordcount from the selection

list and click OK. See Figure 32.

Figure 32: Export a hadoop project.

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You will see the command output in the console window, and if the job succeeds,

you’ll find the results in the ~/workspace/WordCount/output directory. If the job

fails complaining that it cannot find the input file, make sure that the pg100.txt file

is located in the ~/workspace/WordCount directory.

? Right-click on the project and select Export. See Figure 33.

Figure 33: Run WordCount.java.

? In the pop-up dialog, expand the Java node and select JAR file. See Figure 34. Click

Next >

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Figure 34: Export a hadoop project.

? Enter /home/cloudera/wordcount.jar in the JAR file field and click Finish. See

Figure 35.

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Figure 35: Export a hadoop project.

If you see an error dialog warning that the project compiled with warnings, you can

simply click OK.

? Open a terminal in your VM and traverse to the folder /home/cloudera and run the

following commands:

hadoop fs -put workspace/WordCount/pg100.txt

hadoop jar WordCount.jar edu.stanford.cs246.wordcount.WordCount pg100.txt

output

? Run the command: hadoop fs -ls output

You should see an output file for each reducer. Since there was only one reducer for

this job, you should only see one part-* file. Note that sometimes the files will be

called part-NNNNN, and sometimes they’ll be called part-r-NNNNN. See Figure 36.

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Figure 36: Run WordCount job.

? Run the command:

hadoop fs -cat output/part\* | head

You should see the same output as when you ran the job locally, as shown in Figure

37

Figure 37: Run WordCount job.

? To view the job’s logs, open the browser in the VM and point it to http://localhost:

8088 as in Figure 38

Figure 38: Run WordCount job.

? Click on the link for the completed job. See Figure 39.

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Figure 39: View WordCount job logs.

? Click the link for the map tasks. See Figure 40.

Figure 40: View WordCount job logs.

? Click the link for the first attempt. See Figure 41.

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Figure 41: View WordCount job logs.

? Click the link for the full logs. See Figure 42.

Figure 42: View WordCount job logs.

2.6 Using your local machine for development

If you’d rather use your own development environment instead of working in the IDE, follow

these steps:

1. Make sure that you have an entry for localhost.localdomain in your /etc/hosts

file, e.g.

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127.0.0.1 localhost localhost.localdomain

2. Install a copy of Hadoop locally. The easiest way to do that is to simply download

the archive from http://archive.cloudera.com/cdh5/cdh/5/hadoop-latest.tar.

gz and unpack it.

3. In the unpacked archive, you’ll find a etc/hadoop directory. In that directory, open

the core-site.xml file and modify it as follows:

<?xml version=” 1. 0 ”?>

<?xml?s t y l e s h e e t type=” t e x t / x s l ” h r ef=” c o nf i g u r a t i o n . x s l ”?>

<!?? Put s i t e ?s p e c i f i c p r o p e r t y o v e r r i d e s in t h i s f i l e . ??>

<c o nf i g u r a t i o n>

<p ro p e r t y>

<name>f s . d ef a u l t . name</name>

<val u e>h df s : / / 1 9 2. 1 6 8. 5 6. 1 0 1 :8020</ val u e>

</ p ro p e r t y>

</ c o nf i g u r a t i o n>

4. Next, open the yarn-site.xml file in the same directory and modify it as follows:

<?xml version=” 1. 0 ”?>

<?xml?s t y l e s h e e t type=” t e x t / x s l ” h r ef=” c o nf i g u r a t i o n . x s l ”?>

<!?? Put s i t e ?s p e c i f i c p r o p e r t y o v e r r i d e s in t h i s f i l e . ??>

<c o nf i g u r a t i o n>

<p ro p e r t y>

<name>yarn . re sou rcemanage r . hostname</name>

<val u e>1 9 2. 1 6 8. 5 6. 1 0 1</ val u e>

</ p ro p e r t y>

</ c o nf i g u r a t i o n>

You can now run the Hadoop binaries located in the bin directory in the archive, and

they will connect to the cluster running in your virtual machine.

Further Hadoop tutorials

? Yahoo! Hadoop Tutorial: http://developer.yahoo.com/hadoop/tutorial/

? Cloudera Hadoop Tutorial:

http://www.cloudera.com/content/www/en-us/training/library/tutorials.html

? How to Debug MapReduce Programs:

http://wiki.apache.org/hadoop/HowToDebugMapReducePrograms

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Further Eclipse tutorials

? Genera Eclipse tutorial:

http://www.vogella.com/articles/Eclipse/article.html.

? Tutorial on how to use the Eclipse debugger:

http://www.vogella.com/articles/EclipseDebugging/article.html.

3 Task: Write your own Hadoop Job

Now you will write your first MapReduce job to accomplish the following task:

? Write a Hadoop MapReduce program which outputs the number of words that start

with each letter. This means that for every letter we want to count the total number

of words that start with that letter. In your implementation ignore the letter case, i.e.,

consider all words as lower case. You can ignore all non-alphabetic characters.

? Run your program over the same input data as above.

What to hand-in: Submit the printout of the output file to Gradescope (https://gradescope.com),

and upload the source code at http://snap.stanford.edu/submit/.