Scenario: Counting words using custom map and reduce code - 6.1

Talend Components Reference Guide

Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Talend Open Studio for Big Data
Talend Open Studio for Data Integration
Talend Open Studio for Data Quality
Talend Open Studio for ESB
Talend Open Studio for MDM
Talend Real-Time Big Data Platform
Data Governance
Data Quality and Preparation
Design and Development
Talend Studio

Inspired by the MapReduce example explained in Apache's documentation on, this scenario demonstrates how to use tJavaMR to create a MapReduce program to count words.

The sample data to be used in this scenario reads as follows:

Hello world goodbye world
Hello hadoop bye Hadoop

Before starting to replicate this scenario, ensure that you have appropriate rights and permissions to access the Hadoop distribution to be used. Then proceed as follows:

Linking components

  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 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.

Setting up Hadoop connection

  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.

    This view looks like the image below:

  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.

    For further information about how to create an Hadoop connection in Repository, see the chapter describing the Hadoop cluster node of Talend Studio User Guide.

  3. In the Version area, select the Hadoop distribution to be used and its version. 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.

    Along with the evolution of Hadoop, please note the following changes:

    • If you use Hortonworks Data Platform V2.2, the configuration files of your cluster might be using environment variables such as ${hdp.version}. If this is your situation, you need to set the mapreduce.application.framework.path property in the Hadoop properties table with the path value explicitly pointing to the MapReduce framework archive of your cluster. For example:

    • If you use Hortonworks Data Platform V2.0.0, the type of the operating system for running the distribution and a Talend Job must be the same, such as Windows or Linux. Otherwise, you have to use Talend Jobserver to execute the Job in the same type of operating system in which the Hortonworks Data Platform V2.0.0 distribution you are using is run. For further information about Talend Jobserver, see Talend Installation Guide.

  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:

  5. In the Job tracker field, enter the location of the JobTracker of your distribution. For example, tal-qa114.talend.lan:8050.

    Note that the notion Job in this term JobTracker designates the MR or the MapReduce jobs described in Apache's documentation on

    If you use YARN in your Hadoop cluster such as Hortonworks Data Platform V2.0.0 or Cloudera CDH4.3 + (YARN mode), you need to specify the location of the Resource Manager instead of the Jobtracker. 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 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.

    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.

    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, or the documentation of the Hadoop distribution you need to use.

  13. If the Hadoop distribution to be used is Hortonworks Data Platform V1.2 or Hortonworks Data Platform V1.3, you need to set proper memory allocations for the map and reduce computations to be performed by the Hadoop system.

    In that situation, you need to enter the values you need in the Mapred job map memory mb and the Mapred job reduce memory mb fields, respectively. By default, the values are both 1000 which are normally appropriate for running the computations.

    If the distribution is YARN, then the memory parameters to be set become Map (in Mb), Reduce (in Mb) and ApplicationMaster (in Mb), accordingly. These fields allow you to dynamically allocate memory to the map and the reduce computations and the ApplicationMaster of YARN.

  14. 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.

For further information about this Hadoop Configuration tab, see the section describing how to configure the Hadoop connection for a Talend Map/Reduce Job of the Talend Big Data Getting Started Guide.

For further information about the Resource Manager, its scheduler and the ApplicationMaster, see YARN's documentation such as

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:

Configuring tHDFSInput

  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.

Creating the MapReduce program

  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);

    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;
      mrValueStruct value =;
      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.

Writing results in HDFS

  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.

Executing the Job

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.