These properties are used to configure tJavaMR running in the MapReduce Job framework.

The MapReduce
tJavaMR component belongs to the Custom Code family.

The component in this framework is available in all Talend Platform products with Big Data and in Talend Data Fabric.

The MapReduce framework is deprecated from Talend 7.3 onwards. Use Talend Jobs for Apache Spark to accomplish your integration tasks.

Basic settings

Advanced settings

Global Variables

Usage

1. In the
   Integration
   perspective
   of the Studio, create an empty Map/Reduce Job from the
   Job Designs node in the Repository tree view.
   For further information about how to create a Map/Reduce Job, see
   
   Talend Open Studio for Big Data Getting Started Guide
   .
2. Drop a tHDFSInput component, a tJavaMR component, and a tHDFSOutput component in the workspace.
   The tHDFSInput component reads data from
   the Hadoop distribution to be used and the tHDFSOutput component writes processed data into a that
   distribution.
3. Connect these components using the Row >
   Main link.

1. Click Run to open its view and then click the
   Hadoop Configuration tab to display its
   view for configuring the Hadoop connection for this Job.
2. From the Property type list,
   select Built-in. If you have created the
   connection to be used in Repository, then
   select Repository and thus the Studio will
   reuse that set of connection information for this Job.
3. In the Version area, select the
   Hadoop distribution to be used and its version.

   • If you use Google Cloud Dataproc, see Google Cloud Dataproc.

   • If you cannot
     find the Cloudera version to be used from this drop-down list, you can add your distribution
     via some dynamic distribution settings in the Studio.

   • If you cannot find from the list the distribution corresponding to
     yours, select Custom so as to connect to a
     Hadoop distribution not officially supported in the Studio. For a
     step-by-step example about how to use this
     Custom option, see Connecting to a custom Hadoop distribution.

4. In the Name node field, enter the location of
   the master node, the NameNode, of the distribution to be used. For example,
   hdfs://tal-qa113.talend.lan:8020.

   • If you are using a MapR distribution, you can simply leave maprfs:/// as it is in this field; then the MapR
     client will take care of the rest on the fly for creating the connection. The
     MapR client must be properly installed. For further information about how to set
     up a MapR client, see the following link in MapR’s documentation: http://doc.mapr.com/display/MapR/Setting+Up+the+Client

   • If you are using WebHDFS, the location should be
     webhdfs://masternode:portnumber; WebHDFS with SSL is not
     supported yet.

5. In the Resource Manager field,
   enter the location of the ResourceManager of your distribution. For example,
   tal-qa114.talend.lan:8050.

   • Then you can continue to set the following parameters depending on the
     configuration of the Hadoop cluster to be used (if you leave the check
     box of a parameter clear, then at runtime, the configuration about this
     parameter in the Hadoop cluster to be used will be ignored):

     • Select the Set resourcemanager
       scheduler address check box and enter the Scheduler address in
       the field that appears.

     • Select the Set jobhistory
       address check box and enter the location of the JobHistory
       server of the Hadoop cluster to be used. This allows the metrics information of
       the current Job to be stored in that JobHistory server.

     • Select the Set staging
       directory check box and enter this directory defined in your
       Hadoop cluster for temporary files created by running programs. Typically, this
       directory can be found under the yarn.app.mapreduce.am.staging-dir property in the configuration files
       such as yarn-site.xml or mapred-site.xml of your distribution.

     • Select the Use datanode hostname check box to allow the
       Job to access datanodes via their hostnames. This actually sets the dfs.client.use.datanode.hostname
       property to true. When connecting to a
       S3N filesystem, you must select this check box.

6. 
   If you are accessing the Hadoop cluster running
   with Kerberos security, select this check box, then, enter the Kerberos
   principal name for the NameNode in the field displayed. This enables you to use
   your user name to authenticate against the credentials stored in Kerberos.
   

   • If this cluster is a MapR cluster of the version 5.0.0 or later, you can set the
     MapR ticket authentication configuration in addition or as an alternative by following
     the explanation in Connecting to a security-enabled MapR.

     Keep in mind that this configuration generates a new MapR security ticket for the username
     defined in the Job in each execution. If you need to reuse an existing ticket issued for the
     same username, leave both the Force MapR ticket
     authentication check box and the Use Kerberos
     authentication check box clear, and then MapR should be able to automatically
     find that ticket on the fly.

   In addition, since this component performs Map/Reduce computations, you
   also need to authenticate the related services such as the Job history server and
   the Resource manager or Jobtracker depending on your distribution in the
   corresponding field. These principals can be found in the configuration files of
   your distribution. For example, in a CDH4 distribution, the Resource manager
   principal is set in the yarn-site.xml file and the Job history
   principal in the mapred-site.xml file.

   If you need to use a Kerberos keytab file to log in, select Use a keytab to authenticate. A keytab file contains
   pairs of Kerberos principals and encrypted keys. You need to enter the principal to
   be used in the Principal field and the access
   path to the keytab file itself in the Keytab
   field. This keytab file must be stored in the machine in which your Job actually
   runs, for example, on a Talend
   Jobserver.

   Note that the user that executes a keytab-enabled Job is not necessarily
   the one a principal designates but must have the right to read the keytab file being
   used. For example, the user name you are using to execute a Job is user1 and the principal to be used is guest; in this
   situation, ensure that user1 has the right to read the keytab
   file to be used.

7. In the User name field, enter the login user
   name for your distribution. If you leave it empty, the user name of the machine
   hosting the Studio will be used.
8. In the Temp folder field, enter the path in
   HDFS to the folder where you store the temporary files generated during
   Map/Reduce computations.
9. 
   Leave the default value of the Path separator in
   server as it is, unless you have changed the separator used by your
   Hadoop distribution’s host machine for its PATH variable or in other words, that
   separator is not a colon (:). In that situation, you must change this value to the
   one you are using in that host.
   
10. Leave the Clear temporary folder check box
    selected, unless you want to keep those temporary files.
11. Leave the Compress intermediate map output to reduce
    network traffic check box selected, so as to spend shorter time
    to transfer the mapper task partitions to the multiple reducers.
    However, if the data transfer in the Job is negligible, it is recommended to
    clear this check box to deactivate the compression step, because this
    compression consumes extra CPU resources.
12. If you need to use custom Hadoop properties, complete the Hadoop properties table with the property or
    properties to be customized. Then at runtime, these changes will override the
    corresponding default properties used by the Studio for its Hadoop
    engine.
    For further information about the properties required by Hadoop, see Apache’s
    Hadoop documentation on http://hadoop.apache.org, or
    the documentation of the Hadoop distribution you need to use.
13. 
    If the HDFS transparent encryption has been enabled in your cluster, select
    the Setup HDFS encryption configurations check
    box and in the HDFS encryption key provider field
    that is displayed, enter the location of the KMS proxy.
    

    For further information about the HDFS transparent encryption and its KMS proxy, see Transparent Encryption in HDFS.

14. You can tune the map and reduce computations by
    selecting the Set memory check box to set proper memory allocations
    for the computations to be performed by the Hadoop system.

    The memory parameters to be set are Map (in Mb),
    Reduce (in Mb) and ApplicationMaster (in Mb). These fields allow you to dynamically allocate
    memory to the map and the reduce computations and the ApplicationMaster of YARN.

    For further information about the Resource Manager, its scheduler and the
    ApplicationMaster, see YARN’s documentation such as http://hortonworks.com/blog/apache-hadoop-yarn-concepts-and-applications/.

    For further information about how to determine YARN and MapReduce memory configuration
    settings, see the documentation of the distribution you are using, such as the following
    link provided by Hortonworks: http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.0.6.0/bk_installing_manually_book/content/rpm-chap1-11.html.

15. 
    If you are using Cloudera V5.5+, you can select the Use Cloudera Navigator check box to enable the Cloudera Navigator
    of your distribution to trace your Job lineage to the component level, including the
    schema changes between components.
    

    With this option activated, you need to set the following parameters:

    • Username and Password: this is the credentials you use to connect to your Cloudera
      Navigator.

    • Cloudera Navigator URL : enter the location of the
      Cloudera Navigator to be connected to.

    • Cloudera Navigator Metadata URL: enter the location
      of the Navigator Metadata.

    • Activate the autocommit option: select this check box
      to make Cloudera Navigator generate the lineage of the current Job at the end of the
      execution of this Job.

      Since this option actually forces Cloudera Navigator to generate lineages of
      all its available entities such as HDFS files and directories, Hive queries or Pig
      scripts, it is not recommended for the production environment because it will slow the
      Job.

    • Kill the job if Cloudera Navigator fails: select this check
      box to stop the execution of the Job when the connection to your Cloudera Navigator fails.

      Otherwise, leave it clear to allow your Job to continue to run.

    • Disable SSL validation: select this check box to
      make your Job to connect to Cloudera Navigator without the SSL validation
      process.

      This feature is meant to facilitate the test of your Job but is not
      recommended to be used in a production cluster.

16. 
    If you are using Hortonworks Data Platform V2.4.0 onwards and you have
    installed Atlas in your cluster, you can select the Use
    Atlas check box to enable Job lineage to the component level, including the
    schema changes between components.
    

    With this option activated, you need to set the following parameters:

    • Atlas URL: enter the location of the Atlas to be
      connected to. It is often http://name_of_your_atlas_node:port

    • Die on error: select this check box to stop the Job
      execution when Atlas-related issues occur, such as connection issues to Atlas.

      Otherwise, leave it clear to allow your Job to continue to run.

    In the Username and Password fields, enter the authentication information for access to
    Atlas.

1. Double-click tHDFSInput to open its
   Component view.
   

2. Click the

   

   button next to Edit
   schema to open the schema editor.

3. Click the

   

   button once to add one row and in the Column column, rename it, for example, to
   record.

   

4. Click OK to validate these changes and
   accept the propagation prompted by the pop-up dialog box.
5. In the Folder/File field, enter the path,
   or browse to the source file you need the Job to read.
   If this file is not in the HDFS system to be used, you have to place it in
   that HDFS, for example, using tFileInputDelimited and tHDFSOutput in a Standard
   Job. For further information about these components, see tFileInputDelimited and tHDFSOutput.

1. Double-click tJavaMR to open its Component view.
   

2. Under the mrKeyStruct table, click the button once to add
   one row.
3. Rename that row to word_mr. This is the key part of the key/value pair to be used
   by the Map/Reduce program being created. In the map method, you need to write mrKey.word_mr to represent the keys to be
   outputted to a reducer.
4. Under the mrValueStruct table, click the button once to add
   one row.
5. Rename that row to count_mr. This is the value part of the above-mentioned
   key/value pair. In the map method, you need to write mrValue.count_mr to represent the values to be outputted to a
   reducer.
6. Click the button next to Edit schema to open the schema
   editor.
7. On the side of the schema of tJavaMR, click the
   button to add two columns and name them to word_output and count_output,
   respectively. This defines the structure of the data to be outputted.
   

8. In the Type column, select Integer for count_output.
9. In the Map code editing field, edit the body of the map method. In this
   example, the code is as follows:
   String line = value.record; java.util.StringTokenizer tokenizer = new java.util.StringTokenizer(line); while(tokenizer.hasMoreTokens()) { mrKey.word_mr = tokenizer.nextToken().toUpperCase(); mrValue.count_mr = 1; output.collect(mrKey, mrValue); }

   1 2 3 4 5 6 7 8 9

   String line = value.record; java.util.StringTokenizer tokenizer = new java.util.StringTokenizer(line); while(tokenizer.hasMoreTokens()) {    mrKey.word_mr = tokenizer.nextToken().toUpperCase();    mrValue.count_mr = 1;    output.collect(mrKey, mrValue); }

   This method is used to split the input data into
   words, change each word to upper case and create and output key/value pairs such as
   (HELLO, 1) and (WORLD, 1) to the reducer.
   Note that at runtime, these pairs are
   automatically shuffled and sorted to take the form of (key, list of values) before being process by the reduce
   method.
10. In the Reduce code editing field, edit the body of the reduce method. In
    this example, the code is as follows:
    int count = 0; while(values.hasNext()){ mrValueStruct value = values.next(); count += value.count_mr; } outputRow.word_output = key.word_mr; outputRow.count_output = count; output.collect(NULL, outputRow);

    1 2 3 4 5 6 7 8 9 10

    int count = 0; while(values.hasNext()){   mrValueStruct value = values.next();   count += value.count_mr; } outputRow.word_output = key.word_mr; outputRow.count_output = count; output.collect(NULL, outputRow);

    This reduce method is used to make the sum of the
    values of the list in each (key, list of
values) pair and map the results to the columns of the output
    schema.

1. Double-click tHDFSOutput to open its
   Component view.
   

2. In the Folder field, enter the path, or
   browse to the folder you want to write the results in.
3. From the Type list, select the data format for the
   results to be written. In this example, select Text
   file.
4. From the Action list, select the
   operation you need to perform on the file in question. If the file already
   exists, select Overwrite; otherwise, select
   Create.
5. Select the Merge result to single file
   check box and enter the path, or browse to the file you need to write the
   merged output data in.
6. If you need to remove the source data of the merge, select Remove source dir. In this scenario, select
   it.
7. If the file for the merged data exists, select the Override target file check box to overwrite that
   file.

Then you can press F6 to run this Job.

Once done, view the merged result in the web console of the HDFS system being
used.

If you need to obtain more execution information of this Job, see the web console
of the Jobtracker of that HDFS system.