Hadoop 2.6 Running a MapReduce Job (Single-Node Cluster)

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MapReduce Preparation

Before we jump into the programming our MapReduce, we may need to talk about the preparation steps that are commonly taken. Because MapReduce is usually operating on a huge data, we need to consider those steps before we actually do the MapReduce.

The underlying structure of the HDFS filesystem is very different from our normal file systems. The block sizes are quite a bit larger, and the actual block size for our clusters dependent on the cluster configuration as shown in the picture below: 64, 128, or 256 MB. So, we may need to have blocks with customized partitioned.

Picture source : Hadoop MapReduce Fundamentals.

Another consideration is where we're going to retrieve our data from in order to perform the MapReduce operations or the parallel processing on it. Though we'll work with the core Hadoop filesystem, we may execute MapReduce algorithms against information stored on different locations such as native filesystem, cloud storage such as Amazon S3 buckets, or Windows Azure blobs.

Another considration is the output of the MapReduce job results are immutable. So, our output is a one-time output, and when a new output is generated, we have a new file name for it.

The last consideration in preparing for MapReduce is about the logic that we'll be writing, and it should fit our situation that we're trying to address. We'll be writing logic in some programming language, library, or tools to map our data to, and then reduce it, and then we have some output.

Note also that we'll be working with key-value pairs, so regardless of the format of the data coming in, we want to output key-value pairs.

Hadoop shell commands

Before performing MapReduce jobs, we should be familiar with some of the Hadoop shell commands. Please visit List of Apache Hadoop hdfs commands.

Running a MapReduce Job

Now it's time to run our first Hadoop MapReduce job. We will use one of the examples that come with Hadoop package.

hduser@laptop:~$ cd /usr/local/hadoop

hduser@laptop:/usr/local/hadoop$ ls
bin  include  libexec      logs        README.txt  share
etc  lib      LICENSE.txt  NOTICE.txt  sbin

The following sample "pi" program calculates the value of pi using a quasi-Monte Carlo method with MapReduce. This is a straightforward example we can use to verify that we can run MapReduce jobs.

hduser@laptop:/usr/local/hadoop$ hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.5.jar pi 2 5
Number of Maps  = 2
Samples per Map = 5
Wrote input for Map #0
Wrote input for Map #1
Starting Job
16/11/18 10:03:53 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
16/11/18 10:03:53 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
16/11/18 10:03:54 INFO input.FileInputFormat: Total input paths to process : 2
16/11/18 10:03:54 INFO mapreduce.JobSubmitter: number of splits:2
...
	File Input Format Counters 
		Bytes Read=236
	File Output Format Counters 
		Bytes Written=97
Job Finished in 9.25 seconds
Estimated value of Pi is 3.60000000000000000000

hduser@laptop:/usr/local/hadoop$

We can vary both the number of map tasks we want to create as well as the number of samples to compute:

hduser@laptop:/usr/local/hadoop$ hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.5.jar pi 16 1000
Number of Maps  = 16
Samples per Map = 1000
...
	File Input Format Counters 
		Bytes Read=1888
	File Output Format Counters 
		Bytes Written=97
Job Finished in 9.817 seconds
Estimated value of Pi is 3.14250000000000000000

Hadoop FileSystem (HDFS)

Files are stored in the Hadoop Distributed File System (HDFS). Suppose we're going to store a file called data.txt in HDFS.

This file is 160 megabytes. When a file is loaded into HDFS, it's split into chunks which are called blocks. The default size of each block is 64 megabytes. Each block is given a unique name, which is blk, an underscore, and a large number. In our case, the first block is 64 megabytes. The second block is 64 megabytes. The third block is the remaining 32 megabytes, to make up our 160 megabyte file.

As the file is uploaded to HDFS, each block will get stored on one node in the cluster. There's a Daemon running on each of the machines in the cluster, and it is called the DataNode. Now, we need to know which blocks make up the original file. And that's handled by a separate machine, running the Daemon called the NameNode. The information stored on the NameNode is known as the Metadata.

HDFS Commands

While Hadoop is running, let's create hdfsTest.txt in our home directory (/home/hduser) in local disk:

hduser@laptop:~$ echo "hdfs test" > hdfsTest.txt

Then, we want to create Home Directory in HDFS :

hduser@laptop:~$ hdfs dfs -mkdir -p /user/hduser

Note that, in the command, the dfs is defined as "runs a filesystem command on the file systems supported in Hadoop" in its man page.

We can copy file hdfsTest.txt from local disk to the user's directory in HDFS:

	
hduser@laptop:~$ hdfs dfs -copyFromLocal ~/hdfsTest.txt hdfsTest.txt

We could have used put instead of copyFromLocal:

hduser@laptop:~$ hdfs dfs -put ~/hdfsTest.txt

Get a directory listing of the user's home directory in HDFS:

hduser@laptop:~$ hdfs dfs -ls
Found 1 items
-rw-r--r--   1 hduser supergroup         10 2016-11-18 11:29 hdfsTest.txt

If we want to display the contents of the HDFS file /user/hduser/hdfsTest.txt:

hduser@laptop:~$ hdfs dfs -cat /user/hduser/hdfsTest.txt

Copy that file to the local disk from HDFS, named as hdfsTest2.txt :

hduser@laptop:~$ hdfs dfs -copyToLocal /user/hduser/hdfsTest.txt hdfsTest2.txt

hduser@laptop:~$ ls
hdfsTest2.txt  hdfsTest.txt

To delete the file from Hadoop HDFS:

hduser@laptop:~$ hdfs dfs -rm hdfsTest.txt
16/11/18 11:37:36 INFO fs.TrashPolicyDefault: Namenode trash configuration: Deletion interval = 0 minutes, Emptier interval = 0 minutes.
Deleted hdfsTest.txt

Check if it's been really deleted from Hadoop HDFS:

hduser@laptop:~$ hdfs dfs -ls
hduser@laptop:~$

Hadoop Setup for Development

Picture source : Hadoop MapReduce Fundamentals.

Throughout my tutorials on Hadoop Echo Systems, I used:

Hadoop Binaries - Local (Linux), Cloudera's Demo VM, and AWS for Cloud.
Data Storage - Local (HDFS Pseudo-distributed, single-node) and Cloud.
MapReduce - Both Local and Cloud.

Ways to MapReduce

Java is the most common language to use, but other languages can be used:

Picture source : Hadoop MapReduce Fundamentals.

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