This tutorial explains how to use AWS in order to run MapReduce jobs written in Java end encoded into a JAR file, either using the Web-based console or through a command line utility.

A basic knowledge of the Hadoop environment (release 2.4.0) and of the Hadoop Java library is requested, as well as a fair proficiency in the use of a terminal shell.

Preliminary operations

Sign up to AWS

AWS provides access to registered users, thus if you haven’t already you need to sign up at The process is relatively simple, but you can refer to several tutorials, such as the one provided by Sebastien Robaszkiewicz.

Be aware that AWS charges users for the provided services, and the sign up procedure requires to specify a valid credit card number. Although charges are low and per effective use, they can sum up to a considerable amount if some services are not esplicitly shut down. That said, there are several options in order to get free access: new AWS users can benefit of a 12-months no-charge period called AWS Free Tier for some services (unfortunately excluding Elastic MapReduce); moreover, you may benefit of promo codes (just keep on reading).

Redeem promo codes (optional)

If you have one or more promo codes (for instance, each year AWS grants the students of my Big data analytics course credit of US$ 100; if you are a teacher I strongly advice to refer to the AWS in Education Grants page), you can redeem them from your AWS account page: log in to AWS and select the drop-down menu showing your account name on the top of the page, then select “My account” and “Credits”, and finally type in your credit code and click the “Redeem” button.

Create a dedicated user

It is best not to use the so-called root account (the one automatically created when signing up to AWS) for all activities. Specific tasks should be run by dedicated users to be created and managed through AWS IAM (Identity and Access Management). To do so, select “IAM” from the Services menu, then “Users” from the left menu and click on the “Create New Users” button: type in emr-develper in the first textbox and click the “Create” button at the bottom-right of the page.

You will be prompted with the possibility to download the new user’s security credentials as a CSV file. Store them in a safe place (changing the default name to emr-developer-credentials.csv) and click on the “Close” link at the bottom of the page.

The contents of this file should not be emailed or otherwise shared, for they will be used in order to secure your user’s access to AWS. Moreover, once the file has been created it will not be possible to wiew the credentials through the IAM service anymore. Thus make sure to save this file in a safe and accessible place. In case you are using a shared computer, it is adivsable to chmod 400 the file to ensure that other users cannot view its contents and the owner cannot accidentally delete or overwrite it.

You’ll be prompted by a list of all users: click on the one just created and click on the “Attach User Policy” button in order to give it permissions: in the list under the “Select Policy Template” radio button, click the “Select” button corresponding to “Amazon Elastic MapReduce Full Access” (you’ll need to scroll a bit to find it). Finally, click on the “Apply Policy” button.

Some fundamental concepts in AWS

The AWS Console

When signing in to AWS, the user is directed to a main page called the AWS Console. This page shows all the available services, gathered into groups focused, for instance, to computing, using databases and so on. The upper part of the page, which is common to all the AWS services, there are several drop-down menu buttons allowing the user to select a particular service, to view and modify the user’s account and to select specific AWS regions (see the next section).

AWS Regions

The AWS servers are located in different places worldwide, and grouped in independent regions. Creation of a resource, say the computing nodes in a cloud or some storage space in a distributed file system, should be attached to a specific region, meaning that the resource will be hosted in one or more servers in that specific region. Currently AWS provides the following regions:

  • Asia Pacific, Tokyo (ap-northeast-1)
  • Asia Pacific, Singapore (ap-southeast-1)
  • Asia Pacific, Sydney (ap-southeast-2)
  • EU, Frankfurt (eu-central-1)
  • EU, Ireland (eu-west-1)
  • South America, Sao Paulo (sa-east-1)
  • US East, N. Virginia (us-east-1)
  • US West, N. California (us-west-1)
  • US West, Oregon (us-west-2)

Choosing a region close to your specific location results in a lower latency time for data transfer, and can also meet specific legal regulations. Keep in mind that transferring resources between different regions is charged on your account, thus it is advisable to refer to the same region when using different services (unless backing up stuff).

The upper right part of the AWS console always show the current region, meanwhile allowing to select a new one. For the purposes of this tutorial, we will keep the default region: us-east-1.

The Simple Storage Service (S3)

The Simple Storage Service (S3, that is “S cubed”, in the AWS terminology) gives access to a distributed file system in order (among other things) to store data to be used as input of our MapReduce jobs as well as to collect their outputs.

S3 gathers data in special containers called buckets, where a given bucket (along with all its contents) is stored in servers belonging to a specific AWS region. A bucket contains zero or more objects, which can be thought of as the analogous of files and directories in a PC file system. Note that the analogy holds only in an abstract way: at low level, the contents of an object could be distributed over several machines; moreover, you can use objects that appear as nested directories when viewed through the AWS console, but actually all objects in a bucket are stored in a flat way, although each object is identified by a key name which can be used to organize objects hyerarchically through a specific namespace convention. Buckets and their contents are univocally identified through a URI-like scheme using s3 as protocol, also accessible through a Web browser. Moreover, buckets can be associated to access control lists in order to define which users can access them and how.

buckets are configured in order to be accessed from Web browsers using the URL like

Connect to the S3 service in the AWS console and click on the “Create Bucket” button. Select us-east-1 region and type in a name for your bucket. Bucket names should be unique across regions; moreover, there are several (both hard and soft) naming conventions to be considered. Briefly put:

  1. just use one or more words composed by characters, numbers, and hyphens, and use one single period to separate words;
  2. use at least three and at most 63 characters.

For the purposes of this tutorial, let us pretend that you type in the name emr-tutorial, but in reality you will have to find up a bucket name not already chosen which is easy for you to remember. Note that this bucket will be accessible through the s3://emr-tutorial univocal name.

Once the “Create” button is clicked, the newly created bucket is listed along with the ones previously created (thus, in case you signed up to follow this tutorial you’ll only see the emr-tutorial bucket). Clicking on the bucket name has the effect of showing its contents and allowing standard file system operations on them, as well as a direct data upload. In our case the emr-tutorial bucket will be empty, and we need to add some folders to be used later on: click the “Create Folder” button, type in the name emr-logs and press return: the new folder will be shown among the contents of the emr-logs bucket. Repeat this procedure in order to create the emr-code and the emr-input folders, all directly contained in the emr-tutorialbucket.

The Elastic Compute Cloud service (EC2)

The Elastic Compute Cloud service (EC2) provides computing facilities organized in a reconfigurable cloud. You can think of such resources as a set of remote virtual machines which you can configure and boot before logging in in order to run computations. Each of these virtual machines can be set up in terms of specific hardware and software, resulting in a specific AMI (Amazon Machine Image) file. Several AMI are already available in order to meet the typical requirements of AWS users.

In the context of MapReduce jobs, the cloud will contain nodes of a Hadoop cluster according to a predefined AMI, and AWS will take care of booting all nodes and executing the job.

Access keys

Access to the AWS services is secured through two security credentials known as access key id and secret access key. You can broadly think them as a login name and a password. They are generated through the IAM service when creating a user, and indeed your emr-developer-credentials.csv file exactly contains such information for the emr-developer user.

Writing the software and setting up input

In order to be able to execute a MapReduce job it is necessary to write and compile the software to be run, and to gather up the data to be fed in input to the job.

Writing and compiling the software

The hello-world program in a MapReduce environment corresponds to a job counting the frequencies of words in a set of textual documents. Such program is composed by three Java classes, whose source is shown hereafter with reference to the 2.4.0 release of Hadoop:

  • the mapper class
import java.util.StringTokenizer;

import org.apache.hadoop.mapreduce.Mapper;

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
    private final IntWritable one = new IntWritable(1);
    private Text word = new Text();

    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();
        StringTokenizer itr = new StringTokenizer(line.toLowerCase(), " .,:;!?{}[]()|_");
        while(itr.hasMoreTokens()) {
            context.write(word, one);
  • the reducer class

import org.apache.hadoop.mapreduce.Reducer;

public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum = 0;
        for(IntWritable v : values)
            sum += v.get();

        context.write(key, result);
  • the driver class

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class WordCount extends Configured implements Tool {

    public static void main(String[] args) throws Exception {
        int res = Configuration(), new WordCount(), args);

    public int run(String args[]) {
        try {
            Configuration conf = new Configuration();

            Job job = Job.getInstance(conf);

            // specify a mapper

            // specify a reducer

            // specify output types

            // specify input and output DIRECTORIES
            FileInputFormat.addInputPath(job, new Path(args[0]));

            FileOutputFormat.setOutputPath(job, new Path(args[1]));

            return(job.waitForCompletion(true) ? 0 : 1);
        } catch (InterruptedException|ClassNotFoundException|IOException e) {
            System.err.println("Error during mapreduce job.");
            return 2;

Once these classes have been compiled including the Hadoop JAR library, the produced bytecode should be exported into a JAR file whose main method is that of the WordCount class. Both tasks are easily accomplished using a IDE such as Eclipse. Save the JAR file on your local computer giving it the name wordcount.jar. This file should be uploaded to S3 in order to be executed in a cloud: log in to the AWS console and then connect to the S3 service, which should list the emr-tutorialbucket. Click on the bucket name, then on the emr-code folder, and finally on the “Upload” button: in the window which will appear, click on “Add Files” and navigate to the wordcount.jar file. Finally, click on the “Start Upload” button and wait until the upload completes.

Setting up input

As input to our word-counting job we will use the text of two books: Joyce’s Ulysses and the first volume of Da Vinci’s notebooks. Download the two texts and subsequently upload them in the emr-input folder in S3, following the same procedure of previous section. Note that the specific names you give to the textual files is ininfluent to the job execution.

Running a job: the AWS Console way

In order to run a job through the AWS Console you’ll need to create a cluster, submit the job, monitor the cluster until processing ends, retrieve results and dispose the cluster.

Creating a cluster and submitting a job

All things are in place in order to submit our job for execution: access the Elastic MapReduce service (EMR) from the AWS console and click on the “Create cluster” button. There are several options available to tune how the job will be executed (and—indirectly—how much you will be charged), which are briefly revised hereafter.

Cluster Configuration

When submitting more jobs simultaneously, use the “Cluster name” textbox to assign name to clusters, so as to be quickly able to distinguish them. For the purposes of this tutorial, the default name can be kept.

Let the “Termination protection” selected (this will require an additional user confirmation to avoid accidental shutdown of the cluster) and keep the “Logging” and “Debugging” facilities. In the “Log folder” textbox type in s3://emr-tutorial/emr-logs, or select the folder interactively through the icon on the right of the textbox.


This section allows to tag clusters, for instance to quickly refer to those related to a particular project. Leave everything unchanged.

Software Configuration

Here you can detail which software will be installed on your cluster nodes. Keep the Amazon Hadoop distribution and the 3.3.0 AMI version. Delete the three rows under “Applications to be installed” by clicking on the cross at their right: as we will not be using either Hive, Pig, or Hue, installing them would only result in longer time to wait (and higher charges).

File System Configuration

This section focuses on specific configurations on the cluster distributed file system. Leave it as it is.

Hardware Configuration

Here you can shape your cluster, deciding about how many nodes it will contain as well as on some hardware configuration of the various nodes. For this tutorial, just use the default values, but keep in mind that in real life applications an accurate tuning of the options in this section will considerably affect the performances of your cluster.

Security and Access

Also in this case, use the default values, although in real life you will likely decide to give specific IAM users permissions to use the cluster. Moreover, in this section you can set up SSH access to nodes using a key pair to be generated under the “Key pair section”

Bootstrap Actions

Here you can trigger the execution of specific script before Hadoop starts. We will not use this facility.


This is the section where you specify how the cluster will execute your job. From the “Add step” combo, select “Custom JAR” and subsequently “Configure and add”. In the dialog which is shown specify a meaningful name for the job or leave the default value, and fill in the remaining field as follows:

  • JAR location: either type in s3://emr-tutorial/emr-code/wordcount.jar or navigate through it interactively using the icon on the right;
  • Arguments: type in s3://emr-tutorial/emr-input s3://emr-tutorial/emr-output.

Finally, click on the “Add” and the “Create cluster” buttons: your cluster will be created and booted in order to run the MapReduce job described in the supplied JAR file.

Monitor the cluster

Once created, the cluster will automatically boot up, install additional software and execute your job. In this process, the cluster status will evolve from starting, to running and finally to waiting. Keep in mind that this will take a fair amount of time, typically some minutes. During this time span, the cluster status can be monitored from the EC2 service page.

Retrieve results

Connect to the S3 service, opening a new browser tab or window, navigate from the emr-tutorial bucket in the emr-output folder and view its contents: the presence of a _SUCCESS file denotes that the MapReduce job terminated correctly, and you’ll find one ore more part-r-nnnnn files, nnnnn starting from 00000 and increasing by one unit. The latter files contain the job output, and they can be viewed locally after they have been downloaded through either double clicking of the file name, selecting it and clicking on the “Actions/Download” menu button in the upper part of the page, or finally right-clicking on the file name and selecting the “Download” menu item. Once the download completes, the files can be inspected locally.

If the output folder does not contain a _SUCCESS file, something went wrong, thus some inspection of the logs created in the emr-logs folders should help in bug fixing. A good starting point are the stdout and stderr files containing all output sent to standard output and standard error. Finding them requires to figure out the cluster ID (which is specified in the EMR page), navigate to the corresponding directory within emr-logs and subsequently in the step directory. Here you will find several directories, each related to one of the steps executed in the cluster lifecycle. Look in the EMR page in order to find out which ID corresponds to the “Custom JAR Step”: the corresponding directory will contain stdout and stderr.

Dispose the cluster and delete unused files

Get back to the EMR service and click on the “Terminate” button. If you closed the EMR window or tab when browsing the S3 buckets in order to view the job results, just connect to the EMR service in the AWS console: you’ll be prompted with a list of recent clusters, the first being the one we just launched and which is characterized by a “Waiting” status. Click on its name and then on the “Terminate” button: a dialog will warn about the fact that the termination procetction feature is enabled. To disable it, click on “Change”, then on the “Off” radio button, and finally on the green check mark. Now it will be possible to click on the “Terminate” button in order to shut down the cluster. Its status will turn into “Terminating”, and after some minutes to “Terminated”.

Note that it is extremely important to shut down clusters when the job processing completes, in order to avoid additional charges on your account.

Analogously, remember to clean up unused stuff in your S3 bucket, such as logs and, when processing ends the JAR and the input and output files (unless you want to keep them in S3).

EMR reloaded: the “command line” way

If you are the “avoid the GUI” type (I am!), you can use a tool conceived to access AWS through the command line: the AWS Command Line Interface (AWS CLI for short).

Install the AWS CLI

The AWS command line interface can be installed in several ways, although on a Unix-like system some package manager is advisable: for instance

$ brew install awscli

will install the CLI through homebrew on Mac OS X, although an equivalent procedure can exploit pip in all Unix-like systems. Regardless of the chosen installation procedure, if everithing goes smooth

$ aws help

should display a man-like help page.

Configure the AWS CLI

To configure the AWS CLI, simply run

$ aws configure

and insert the following data

  • access key id: the second value in the emr-developer-credentials.csv file corresponding to the user created at the beginning of the tutorial;
  • secret access key: the third value in the emr-developer-credentials.csv file;
  • default region name: the name of thw AWS region you will use; type in us-east-1
  • output format: type in json.

The latter choice sets how the AWS CLI will format the information received from our cluster. Instead of a pure textual format, the use of JSON will allow a easier parsing of the (typically verbose) structures obtained as output. When interacting programmatically with AWS the parsing will be typically done using specific libraries, but for the purposes of this tutorial I suggest to brew install (or equivalent procedure) the jq processor.

Set up command completion (optional)

If you use the shell command completion (and you should), you’ll be happy to add this feature to subcommands of the AWS CLI:

$ export AWSBIN=$(dirname $(greadlink -f $(which aws)))
$ complete -C $AWSBIN/aws_completer aws

Note the use of greadlink in order to resolve the symbolic link to the AWS binary: this is only necessary in Mac OS, as other Unix-like systems directly allow to invoke readlink. Also note that greadlink is available only when the GNU coreutils have been installed: if they weren’t you can brew install them. In all cases, complete can also be invoked directly, manually resolving the absolute pathname of the AWS binary directory.

Submitting a MapReduce job through the AWS CLI

If you are following this tutorial, input data and JAR are already in place. But let’s pretend they aren’t, just to show how that the whole process can be mastered through the AWS CLI. The first step is that of creating our bucket using the mb command:

$ aws s3 mb s3://emr-tutorial

The next operation concerns copying into S3 the job JAR and the input files:

$ aws s3 cp ulysses.txt s3://emr-tutorial/emr-input
$ aws s3 cp notebooks.txt s3://emr-tutorial/emr-input
$ aws s3 cp wordcount.jar s3://emr-tutorial/emr-code

Remember that, although bucket contents in the AWS console are shown in a hiearchical way, there are no directories: the console suitably interpretes slashes in file names in order to visualize them hierarchically. This is why we did not explicitly create directories (and, in fact, we couldn’t have because there is no AWS CLI command to do that).

Creating a cluster and submitting a JAR-encoded MapReduce job requires the execution of the create-cluster command. As the number of parameters to be specified is considerable, this command is typically invoked specifying several options:

$ aws emr create-cluster --ami-version 3.3.0 \
--instance-groups InstanceGroupType=MASTER, InstanceCount=1, InstanceType=m3.xlarge \
                  InstanceGroupType=CORE, InstanceCount=2, InstanceType=m3.xlarge \
--steps Type=CUSTOM_JAR, \
        Name="Custom JAR Step", \
        ActionOnFailure=CONTINUE, \
        Jar=s3://emr-tutorial/emr-code/wordcount.jar, \
        Args=["s3://emr-tutorial/emr-input", "s3://emr-tutorial/emr-output"] \
--no-auto-terminate \
--log-uri s3://emr-tutorial/emr-log \
    "ClusterId": "j-3JS43WVPKL4WC"

The obtained output is a JSON representation containing an ID to be used in order to query AWS about the created cluster.

Monitoring the cluster

The describe-cluster can be used in order to monitor the job execution, specifying the cluster ID returned by cluster-create:

$ aws emr describe-cluster --cluster-id "j-3JS43WVPKL4WC"
    "Cluster": {
        "Status": {
            "Timeline": {
                "ReadyDateTime": 1415823056.83, 
                "CreationDateTime": 1415822731.177
            "State": "RUNNING", 
            "StateChangeReason": {
                "Message": "Running step"
        "Ec2InstanceAttributes": {
            "Ec2AvailabilityZone": "us-east-1d"
        "Name": "Development Cluster", 
        "Tags": [], 
        "TerminationProtected": false, 
        "RunningAmiVersion": "3.3.0", 
        "Id": "j-3JS43WVPKL4WC", 
        "Applications": [
                "Version": "2.4.0", 
                "Name": "hadoop"
        "MasterPublicDnsName": "", 
        "InstanceGroups": [
                "RequestedInstanceCount": 1, 
                "Status": {
                    "Timeline": {
                        "ReadyDateTime": 1415822996.85, 
                        "CreationDateTime": 1415822731.178
                    "State": "RUNNING", 
                    "StateChangeReason": {
                        "Message": ""
                "Name": "MASTER", 
                "InstanceGroupType": "MASTER", 
                "Id": "ig-3TT82IF5M4XRB", 
                "InstanceType": "m3.xlarge", 
                "Market": "ON_DEMAND", 
                "RunningInstanceCount": 1
                "RequestedInstanceCount": 2, 
                "Status": {
                    "Timeline": {
                        "ReadyDateTime": 1415823056.9, 
                        "CreationDateTime": 1415822731.178
                    "State": "RUNNING", 
                    "StateChangeReason": {
                        "Message": ""
                "Name": "CORE", 
                "InstanceGroupType": "CORE", 
                "Id": "ig-1HWOUDNR20CHV", 
                "InstanceType": "m3.xlarge", 
                "Market": "ON_DEMAND", 
                "RunningInstanceCount": 2
        "VisibleToAllUsers": true, 
        "BootstrapActions": [], 
        "LogUri": "s3n://dmalchiodi/emr-log/", 
        "AutoTerminate": false, 
        "RequestedAmiVersion": "3.3.0"

The command describe-cluster has a quite verbose output; being interested only in querying the cluster status, contained in the State field of the Status field nested within Cluster, we can use the jq utility previously installed in order to filter out unwanted information:

$ aws emr describe-cluster --cluster-id "j-3JS43WVPKL4WC" | jq '.Cluster.Status.State'

When the cluster status turns into “WAITING” you can explore the emr-output folder in order to check whether the job did succeed:

$ aws s3 ls s3://emr-tutorial/emr-output --recursive
2014-11-12 21:12:37          0 emr-output/_SUCCESS
2014-11-12 21:12:29      63145 emr-output/part-r-00000
2014-11-12 21:12:29      63902 emr-output/part-r-00001
2014-11-12 21:12:30      63228 emr-output/part-r-00002
2014-11-12 21:12:30      63653 emr-output/part-r-00003
2014-11-12 21:12:31      62845 emr-output/part-r-00004
2014-11-12 21:12:32      64917 emr-output/part-r-00005
2014-11-12 21:12:36      65097 emr-output/part-r-00006

If you don’t find a _SUCCESS file, the computation did not complete successfully, thus it is advisable to inspect the files in emr-logs. Otherwise the part-r-nnnnn files can be downloaded for inspection.

$ aws s3 cp s3://emr-tutorial/emr-output/part-r-00000 .
download: s3://emr-tutorial/emr-output/part-r-00000 to ./part-r-00000
$ cat part-r-00000 
"alpine-glow"   1
"as-is" 1
"bathers        1
"doctrinal      1

As a last step, the cluster has to be shut down through the terminate-clusters command:

$ aws emr terminate-clusters --cluster-ids "j-3JS43WVPKL4WC"

This step requires at least some minutes in order to complete, and also in this case the describe-cluster command can be used in order to monitor how the operation evolves:

$ aws emr describe-cluster --cluster-id "j-3JS43WVPKL4WC" | jq '.Cluster.Status.State'

When the status changes from "TERMINATING" to "TERMINATED" the cluster can be considered safely shut down. As in previous section about AWS Console, at this time you also should verify whether or not it is better to delete some of the produced S3 content.

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