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Hadoop Fundamentals

Unit 2: Hadoop Architecture

© Copyright IBM Corporation, 2015

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Contents

LAB 1 HADOOP ARCHITECTURE ............................................................................................................................... 4 1.1 GETTING STARTED .................................................................................................................................................4 1.2 LOGIN TO THE VM ..................................................................................................................................................7 1.3 START ALL SERVICES WITH THE AMBARI WEB CONSOLE........................................................................................9 1.4 BASIC HDFS INTERACTIONS USING THE COMMAND LINE.....................................................................................10 1.5 SUMMARY.............................................................................................................................................................15

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Lab 1 Hadoop Architecture

The overwhelming trend towards digital services, combined with cheap storage, has generated massive amounts of data that enterprises need to effectively gather, process, and analyze. Data analysis techniques from the data warehouse and high-performance computing communities are invaluable for many enterprises, however often times their cost or complexity of scale-up discourages the accumulation of data without an immediate need. As valuable knowledge may nevertheless be buried in this data, related scaled-up technologies have been developed. Examples include Google’s MapReduce, and the open-source implementation, Apache Hadoop.

Hadoop is an open-source project administered by the Apache Software Foundation. Hadoop’s contributors work for some of the world’s biggest technology companies. That diverse, motivated community has produced a collaborative platform for consolidating, combining and understanding data. After completing this hands-on lab, you’ll be able to:

• Use Hadoop commands to explore HDFS on the Hadoop system

• Use the BigInsights Console to explore HDFS on the Hadoop system

Allow 60 minutes to 90 minutes to complete this lab.

This version of the lab was designed using the IBM BigInsights 4.0 Quick Start Edition (QSE). Throughout this lab you will be using the following account login information. The assumptions are that the passwords for root and virtuser are as follows. If, when you setup the Quick Start Edition image, you specified different passwords, then you will have to make the appropriate mental translations throughout the exercise.

Username Password

VM image setup screen root password

Linux virtuser password (but you choose your own password)

With the IBM BigInsights v4.0 QSE, you will be working with user virtuser.

1.1 Getting Started First start your Quick Start image. If you are asked whether you moved your VMware Image or copied it,

select Copied It. You need to have 10.5GB of memory to successfully work with this VMware image. You must run in an environment that supports 64-bit VMs and has at least 12GB of real, physical memory (some memory is required for the host operating system).

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When you connect, you should login as root and use password password at the following screen to complete the setup required to run the virtual machine (VM) on your system.

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Further set up is required to get your VM working. Select English (USA) (or, another language, if you keyboard is different), hit Tab, and then Enter.

You must accept the license. Hit Enter twice.

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Choose a password for the user id that you will be working with (virtuser), e.g., password (but you may select any other password, but have to remember that password for later use).. Hit Tab and Enter. Enter the password a second time when requested, as confirmation.

1.2 Login to the VM

__1. The VM will now start a graphical window (GUI) and present you with the standard login id, i.e., virtuser. Enter the password that you chose.

Once the password has been entered, you can use the mouse to select Log In.

__2. You need to start the BigInsights components. With this VM, you have two options. One option is to use the icon that was placed on the desktop.

―

But that icon is unique to this VM and may not be available on other systems / clusters for you in the future..

__3. The other approach is to use the Ambari Web Console, which may be already started (you would see a web page in front of you). If you need to start the Ambari Web Console, use the Firefox icon on the top-left window border (indicated here by the arrow):

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The URL needed is: http://localhost:8080 or substitute your hostname for localhost.

__4. The following screen will be shown. This is the Ambari Web Console where you configure the IBM Open Platform for Apache Hadoop v4 software. The default user id and password for Ambari are admin / admin.

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1.3 Start all services with the Ambari Web Console

__5. When you are signed in, you will see the following Dashboard page. If the service on the left-hand side shows a red-triangle with exclamation point, the particular service is not currently running; if the individual service has a green-circle with check mark, the particular service is running.

__6. To start all services, click Actions at the bottom of the left-hand side, and then Start All:

__7. Confirm with OK:

__8. Once all components have started successfully as shown on the Ambari Web Console, you can minimize this webpage as you will not need it further during this Lab Exercise.

If you later close down this VM and restart it, you will need to Start All services again.

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1.4 Basic HDFS interactions using the Command Line

The Hadoop Distributed File System (HDFS) allows user data to be organized in the form of files and directories. It provides a command line interface called FS shell that lets a user interact with the data in HDFS accessible to Hadoop MapReduce programs.

There are two ways that you can interact with HDFS at the command line:

1. hadoop fs command options

2. hdfs dfs command options

Where command is the particular command (ls, rm, mkdir, …) and options are variations on the particular command and may be followed by a list of files or a list of directories. The command is preceded by a single dash (“-“) and the options may be preceded by a single dash.

__9. Open a terminal window by clicking on your desktop and selecting Open in Terminal. This leaves you at /home/virtuser/Desktop. Type cd and Enter to move to your home directory in Linux, /home/virtuser (designated by “~” in the prompt):

__10. Start with the ls command to list files and directories. In your terminal window, type the following three commands and hit Enter after each. Pause after each to review your results.

hadoop fs -ls hadoop fs -ls . hadoop fs -ls /

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The first of these lists the files in the current directory — there are none. The second is a little more explicit since it asks for files in dot (“,”), a synonym for “here” (again the current directory). The third lists files at the root level within the HDFS (and there are eight directories).

__11. Look at the directory, /user — this is where all “home” directories are kept for HDFS. The equivalent for Linux is /home — and note the spelling “/user” as this distinguishes this directory from the /usr directory in Linux that is used for executable binary programs.

hadoop fs -ls /user

__12. Create a directory called test in your home directory

hadoop fs –mkdir test

Check the contents of your home directory before and after the command to see that is created. You can do this by listing the contents of the home directory simply (hadoop fs –ls) or relative to the root directory of HDFS (hadoop fs -ls /user/virtuser):

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__13. Create a file in your Linux home directory for virtuser (i.e.. /home/virtuser): from a command line, execute the following commands. (Ctrl-c means to press-and-hold the Ctrl key and then the c key.

cd ~ cat > myfile.txt this is some data in my file Ctrl-c

__14. Next upload this newly created file to the test directory that you just created.

hadoop fs -put *.txt test

__15. Now list your text directory in HDFS. You can use either of the following commands:

hadoop fs -ls test hadoop fs –ls –R .

Note the number 3 that follows the permissions. This is the replication factor for that data file. Normally, in a cluster this is 3, but sometimes in a single-node cluster such as the one that you are running with, there might be only one copy of each block (“split”) of the this file.

The value 3 (or 1, or something else) is the result of a configuration setting of HDFS that sets the number of replicants by default.

__16. To view the contents of the uploaded file, execute

hadoop fs -cat test/myfile.txt

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__17. You can pipe (using the “|” character) any HDFS command so that the output can be used by any Linux command with the Linux shell. For example, you can easily use grep with HDFS by doing the following.

hadoop fs -cat test/myfile.txt | grep my

Or,

hadoop fs -ls –R . | grep test

__18. To find the size of a particular file, like myfile.txt, execute the following:

hadoop fs -du /user/virtuser/test/myfile.txt

__19. Or, to get the size of all files in a directory by using a directory name rather than a file name.

hadoop fs -du /user/virtuser

__20. Or get a total file size value for all files in a directory:

hadoop fs -du -s /user/virtuser

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__21. Remember that you can always use the -help parameter to get more help:

hadoop fs -help hadoop fs -help du

You can close the command line window.

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1.5 Summary

Congratulations! You are now familiar with the Hadoop Distributed File System (HDFS). You now know how to manipulate files within HDFS by using the command line.

Remember that the commands that have been illustrated here with hadoop fs can also be executed with hdfs dfs or other combinations of the hadoop | hdfs and fs | dfs.

You may move on to the next unit.

NOTES

NOTES

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