# Process data with a custom JAR
<a name="UseCase_CustomJar"></a>

A custom JAR runs a compiled Java program that you can upload to Amazon S3. You should compile the program against the version of Hadoop you want to launch, and submit a `CUSTOM_JAR` step to your Amazon EMR cluster. For more information about how to compile a JAR file, see [Build binaries using Amazon EMR](emr-build-binaries.md).

For more information about building a Hadoop MapReduce application, see the [MapReduce Tutorial](http://hadoop.apache.org/docs/stable/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) in the Apache Hadoop documentation.

**Topics**
+ [

# Submit a custom JAR step
](emr-launch-custom-jar-cli.md)

# Submit a custom JAR step
<a name="emr-launch-custom-jar-cli"></a>

A custom JAR runs a compiled Java program that you can upload to Amazon S3. You should compile the program against the version of Hadoop you want to launch, and submit a `CUSTOM_JAR` step to your Amazon EMR cluster. For more information about how to compile a JAR file, see [Build binaries using Amazon EMR](emr-build-binaries.md).

For more information about building a Hadoop MapReduce application, see the [MapReduce Tutorial](http://hadoop.apache.org/docs/stable/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) in the Apache Hadoop documentation.

This section covers the basics of submitting a custom JAR step in Amazon EMR. Submitting a custom JAR step enables you to write a script to process your data with the Java programming language. 

## Submit a custom JAR step with the console
<a name="ConsoleCreatingaCustomJARJob"></a>

This example describes how to use the Amazon EMR console to submit a custom JAR step to a running cluster.

**To submit a custom JAR step with the console**

1. Open the Amazon EMR console at [https://console.aws.amazon.com/emr](https://console.aws.amazon.com/emr/).

1. In the **Cluster List**, select the name of your cluster.

1. Scroll to the **Steps** section and expand it, then choose **Add step**.

1. In the **Add Step** dialog:
   + For **Step type**, choose **Custom JAR**.
   + For **Name**, accept the default name (Custom JAR) or type a new name.
   + For **JAR S3 location**, type or browse to the location of your JAR file. JAR location maybe a path into S3 or a fully qualified java class in the classpath.. 
   + For **Arguments**, type any required arguments as space-separated strings or leave the field blank.
   + For **Action on failure**, accept the default option (**Continue**).

1. Choose **Add**. The step appears in the console with a status of Pending. 

1. The status of the step changes from Pending to Running to Completed as the step runs. To update the status, choose the **Refresh** icon above the Actions column. 

## Launching a cluster and submitting a custom JAR step with the AWS CLI
<a name="emr-dev-create-jar-cli"></a>

**To launch a cluster and submit a custom JAR step with the AWS CLI**

To launch a cluster and submit a custom JAR step with the AWS CLI, type the `create-cluster` subcommand with the `--steps` parameter.
+ To launch a cluster and submit a custom JAR step, type the following command, replace *myKey* with the name of your EC2 key pair, and replace *amzn-s3-demo-bucket* with your bucket name.

  ```
  aws emr create-cluster --name "Test cluster" --release-label emr-7.12.0 \
  --applications Name=Hue Name=Hive Name=Pig --use-default-roles \
  --ec2-attributes KeyName=myKey --instance-type m5.xlarge --instance-count 3 \
  --steps Type=CUSTOM_JAR,Name="Custom JAR Step",ActionOnFailure=CONTINUE,Jar=pathtojarfile,Args=["pathtoinputdata","pathtooutputbucket","arg1","arg2"]
  ```
**Note**  
Linux line continuation characters (\$1) are included for readability. They can be removed or used in Linux commands. For Windows, remove them or replace with a caret (^).

  When you specify the instance count without the `--instance-groups` parameter, a single primary node launches, and the remaining instances launch as core nodes. All nodes use the instance type that you specify in the command.
**Note**  
If you have not previously created the default Amazon EMR service role and EC2 instance profile, type `aws emr create-default-roles` to create them before typing the `create-cluster` subcommand.

  For more information on using Amazon EMR commands in the AWS CLI, see [https://docs.aws.amazon.com/cli/latest/reference/emr](https://docs.aws.amazon.com/cli/latest/reference/emr).

## Third-party dependencies
<a name="emr-custom-jar-dependency"></a>

Sometimes it may be necessary to include in the MapReduce classpath JARs for use with your program. You have two options for doing this:
+ Include the `--libjars s3://URI_to_JAR` in the step options for the procedure in [Launching a cluster and submitting a custom JAR step with the AWS CLI](#emr-dev-create-jar-cli).
+ Launch the cluster with a modified `mapreduce.application.classpath` setting in `mapred-site.xml`. Use the `mapred-site` configuration classification. To create the cluster with the step using AWS CLI, this would look like the following:

  ```
  aws emr create-cluster --release-label emr-7.12.0 \
  --applications Name=Hue Name=Hive Name=Pig --use-default-roles \
  --instance-type m5.xlarge --instance-count 2  --ec2-attributes KeyName=myKey \
  --steps Type=CUSTOM_JAR,Name="Custom JAR Step",ActionOnFailure=CONTINUE,Jar=pathtojarfile,Args=["pathtoinputdata","pathtooutputbucket","arg1","arg2"] \
  --configurations https://s3.amazonaws.com/amzn-s3-demo-bucket/myfolder/myConfig.json
  ```

  `myConfig.json`:

  ```
  [
      {
        "Classification": "mapred-site",
        "Properties": {
          "mapreduce.application.classpath": "path1,path2"
        }
      }
    ]
  ```

  The comma-separated list of paths should be appended to the JVM classpath for each task.