tGlobalVarLoad – Docs for ESB 5.x

tGlobalVarLoad

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. In the workspace, enter the name of the component to be used and select this component
   from the list that appears. In this scenario, the components are tAggregateRow, tGlobalVarLoad, tMap,
   tLogRow and two tHDFSInput (labelled customer in this scenario) components.

3. Connect one of the tHDFSInput components
   to tAggregateRow using the Row > Main link and then do the same to link
   tAggregateRow to tGlobalVarLoad.

   This subjob is used to calculate the average salary and set this average into a reusable
   variable.

4. Connect the same tHDFSInput component to
   the other tHDFSInput component using the
   Trigger > On Subjob Ok link.

5. Connect this second tHDFSInput component
   to tMap using the Row
   > Main link, then do the same to connect tMap to tLogRow
   and in the popup dialog box, give this link a name you want to use.

   This subjob is used to select the salaries above the average.

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 the Talend Big Data Getting Started 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:

     mapreduce.application.framework.path=/hdp/apps/2.2.0.0-2041/mapreduce/mapreduce.tar.gz#mr-framework

     1	mapreduce.application.framework.path=/hdp/apps/2.2.0.0-2041/mapreduce/mapreduce.tar.gz#mr-framework

   • 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 and Upgrade 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: http://doc.mapr.com/display/MapR/Setting+Up+the+Client

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 http://hadoop.apache.org/.

   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 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.

   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 http://hadoop.apache.org, 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.

1. Double-click either of the two tHDFSInput
   components to display its Basic settings
   view.

   Since these two tHDFSInput components are used to read
   the same source data and are configured the same way. You need to configure
   both of them using the procedure explained in this section.

   

2. Click the […] button next to Edit schema to open the schema editor.

3. Click the [+] button three times to add
   three rows and in the Column column, rename
   them to id, name and salary,
   respectively.

   

4. In the Type column of the salary row, select Double.

5. Click OK to validate these changes and
   accept the propagation prompted by the pop-up dialog box.

6. In the Folder/File field, browse to the
   sample data to be processed in the HDFS system.

7. In the File type area, select Text file from the Type list.

8. In the Field separator field, enter
   .

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

   

2. Click the […] button next to Edit schema to open the schema editor.

3. In the table of the tAggregateRow schema,
   click the [+] button once to add one row
   and in the Column column, rename it to
   avg.

4. In the Type column of the salary row, select Double.

5. Click OK to validate these changes and
   accept the propagation prompted by the pop-up dialog box.

6. Under the Operations table, click the
   [+] button to add one row and configure
   the following columns of this row to define the calculation of the average salary.

   • Output column: select the
     column of the output schema in which the average salary is
     stored. In this scenario, it is avg.

   • Function: select the
     avg function to calculate
     the average.

   • Input column position: select
     the column of the input schema used to provide the source data
     of the calculation.

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

   

2. Click the Sync columns button to ensure
   that this component retrieves the avg
   column of the tAggregateRow component’s
   schema. This way the tGlobalVarLoad
   component defines the avg variable using
   the calculated average salary.

1. Double-click tMap to open the map
   editor.

   Note that the tHDFSInput component linked
   to this tMap has been configured along with
   the other tHDFSInput component linked to
   tAggregateRow.

   

2. From the table representing the input flow (on the left side), select all
   the three columns and drop them to the table representing the output flow
   (on the right side).

3. On the table of the input flow, click the
   button to display the filter
   expression panel.

4. In this filter expression panel, enter

   row5.salary > Double.valueOf(String.valueOf(globalMap.get("avg")))

   1	row5.salary > Double.valueOf(String.valueOf(globalMap.get("avg")))

   This expression allows the tMap component
   to select only the salaries above the average calculated by tAggregateRow.

   Note that the row5 in this expression
   is the ID of the input row to the tMap
   component and therefore, it might be another value in your scenario.

5. Click Apply and then OK to validate these changes.

1. If you want to configure the presentation mode on its Component view, double-click the tLogRow component to open the Component view and then in the Mode area, select the Table (print
   values in cells of a table) option.

2. Press F6 to run this Job.