tUniqRow – Docs for ESB 5.x

tUniqRow

1. Drop a tFileInputDelimited, a tSortRow, a tUniqRow, and two tLogRow
   components from the Palette to the design
   workspace, and name the components as shown above.

2. Connect the tFileInputDelimited
   component, the tSortRow component, and the
   tUniqRow component using Row > Main
   connections.

3. Connect the tUniqRow component and the
   first tLogRow component using a Main > Uniques connection.

4. Connect the tUniqRow component and the
   second tLogRow component using a Main > Duplicates connection.

1. Double-click the tFileInputDelimited
   component to display its Basic settings
   view.

   

2. Click the […] button next to the
   File Name field to browse to your input
   file.

3. Define the header and footer rows. In this use case, the first row of the
   input file is the header row.

4. Click Edit schema to define the schema
   for this component. In this use case, the input file has five columns:
   Id, FirstName,
   LastName, Age, and
   City. Then click OK to propagate the schema and close the schema
   editor.

5. Double-click the tSortRow component to
   display its Basic settings view.

   

6. To rearrange the entries in the alphabetic order of the names, add two
   rows in the Criteria table by clicking the
   plus button, select the FirstName and
   LastName columns under Schema
   column, select alpha as the sorting
   type, and select the sorting order.

7. Double-click the tUniqRow component to
   display its Basic settings view.

   

8. In the Unique key area, select the
   columns on which you want deduplication to be carried out. In this use case,
   you will sort out duplicated names.

9. In the Basic settings view of each of the
   tLogRow components, select the
   Table option to view the Job execution
   result in table mode.

1. Press Ctrl+S to save your Job.

2. Run the Job by pressing F6 or clicking
   the Run button on the Run tab.

   The unique names and duplicated names are displayed in different tables on
   the Run console.

   

1. In the Repository tree view of the Integration perspective of Talend Studio, right-click the
   Job you have created in the earlier scenario to open its contextual menu and
   select Edit properties.

   Then the [Edit properties] dialog box is
   displayed. Note that the Job must be closed before you are able to make any
   changes in this dialog box.

   This dialog box looks like the image below:

   

   Note that you can change the Job name as well as the other descriptive
   information about the Job from this dialog box.

2. Click Convert to Map/Reduce Job. Then a
   Map/Reduce Job using the same name appears under the Map/Reduce Jobs sub-node of the Job
   Design node.

1. Double-click this new Map/Reduce Job to open it in the workspace. The Map/Reduce
   components’ Palette is opened accordingly
   and in the workspace, the crossed-out components, if any, indicate that
   those components do not have the Map/Reduce version.

2. Right-click each of those components in question and select Delete to remove them from the workspace.

3. Drop a tHDFSInput component, a tHDFSOutput component and a tJDBCOutput component in the workspace. The tHDFSInput component reads data from the Hadoop
   distribution to be used, the tHDFSOutput
   component writes data in that distribution and the tJDBCOutput component writes data in a given database, for
   example, a MySQL database in this scenario. The two output components
   replace the two tLogRow components to
   output data.

   If from scratch, you have to drop a tSortRow component and a tUniqRow component, too.

4. Connect tHDFSInput to tSortRow using the Row >
   Main link and accept to get the schema of tSortRow.

5. Connect tUniqRow to tHDFSOutput using Row >
   Uniques and to tJDBCOutput
   using Row > Duplicates.

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.

Configuring tHDFSInput

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

   

2. Click the button next to Edit
   schema to verify that the schema received in the earlier
   steps is properly defined.

   

   Note that if you are creating this Job from scratch, you need to click the button to manually add these schema columns; otherwise,
   if the schema has been defined in Repository, you can select the Repository option from the Schema list in the Basic
   settings view to reuse it. For further information about how
   to define a schema in Repository, see the
   chapter describing metadata management in the Talend Studio User Guide or the chapter describing the
   Hadoop cluster node in Repository of the Getting Started Guide.

3. If you make changes in the schema, click OK to validate these changes and accept the propagation
   prompted by the pop-up dialog box.

4. 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.

Reviewing the transformation components

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

   

   This component keeps its configuration used by the original Job. It sorts
   the incoming entries into alphabetical order depending on the FirstName and the LastName columns.

2. Double-click tUniqRow to open its
   Component view.

   

   The component keeps as well its configuration from the original Job. It
   separates the incoming entries into a Uniques flow and a Duplicates flow, then sends the unique entries to tHDFSOutput and the duplicate entries to
   tJDBCOutput.

Configuring tHDFSOutput

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

   

2. As explained earlier for verifying the schema of tHDFSInput, do the same to verify the schema of tHDFSOutput. If it is not consistent with that of
   its preceding component, tUniqRow, click
   Sync column to retrieve the schema of
   tUniqRow.

   

3. In the Folder field, enter the path, or
   browse to the folder you want to write the unique entries in.

4. From the Action list, select the
   operation you need to perform on the folder in question. If the folder
   already exists, select Overwrite;
   otherwise, select Create.

Configuring tJDBCOutput

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

   

2. In the JDBC URL field, enter the URL of
   the database in which you need to write the duplicate entries. In this
   example, it is jdbc:mysql://10.42.10.13:3306/Talend, a MySQL database
   called Talend.

3. In the Drive JAR table, add one row to
   the table by clicking the button.

4. Click this new row and then click the button to open the [Select
   Module] dialog box from which to import the jar file required
   by the MySQL database.

   

5. In the Class name field, enter the class
   file to be called. In this example, it is org.gjt.mm.mysql.Driver.

6. In the User name and the Password fields, enter the authentication
   information to that database.

7. In the Table name field, enter the name
   of the table in which you need to write data, for example, Namelist. This table must already exist.

1. Drop these components from the Palette to
   the design workspace: tFileInputDelimited,
   tExtractDynamicFields, tUniqRow, tFileOutputDelimited, and tLogRow, and name the components as shown above to better
   identify their roles in the Job.

2. Connect the component labelled People,
   the component labelled Split_Column, and
   the component labelled Deduplicate using
   Row > Main connections.

3. Connect the component labelled Deduplicate and the component labelled Unique_Families using a Main > Uniques
   connection.

4. Connect the component labelled Deduplicate and the component labelled Duplicated_Families using a Main > Duplicates connection.

1. Double-click the component labelled People to display its Basic
   settings view.

   

   Warning

   The dynamic schema feature is only supported in Built-In mode and requires the input file
   to have a header row.

2. Click the […] button next to the
   File Name/Stream field to browse to
   your input file.

3. Define the header and footer rows. In this use case, the first row of the
   input file is the header row.

4. Click Edit schema to define the schema
   for this component.

   In this use case, the input file has five columns:
   FirstName, LastName,
   HouseNo, Street,
   and City. However, as we can leverage the advantage of
   the dynamic schema feature, we simply define one dynamic column in the
   schema, Dyna in this example.

   To do so :

   1. Add a new line by clicking the [+] button.

   2. Type Dyna in the Column field.

   3. Select Dynamic from the Type list.

      

   4. Then, click OK to propagate the
      schema and close the [Schema]
      dialog box.

5. Double-click the component labelled Split_Column to display its Basic
   settings view.

   We will use this component to split the dynamic column of the input schema
   into two columns, one for the first name and the other for the family
   related information. To do so:

   1. Click Edit schema to open the
      [Schema] dialog box.

      

   2. In the output panel, click the [+] button to add two columns for the output schema,
      and name them FirstName and
      FamilyInfo
      respectively.

   3. Select String from the Type list for the FirstName column to extract this column from the
      input schema to carry the first name of each person on the name
      list.

   4. Select Dynamic from the Type list for the FamilyInfo column so that this column will carry the
      rest information of each person on the name list: the last name,
      house number, street and city, which all together will identify a
      family.

   5. Then, click OK to propagate the
      schema and close the [Schema]
      dialog box.

6. Double-click the component labelled Deduplicate to display its Basic
   settings view.

   

7. In the Unique key area, select the
   Key attribute check box for the
   FamilyInfo column to carry out
   deduplication on the family information.

8. In the Basic settings view of the
   tFileOutputDelimited component, which
   is labelled Deduplicated_Families, define
   the output file path, select the Include
   header check box, and leave the other settings as they
   are.

   

9. In the Basic settings view of the
   tLogRow component, which is labelled
   Duplicated_Families, select the
   Table option to view the Job execution
   result in table mode.

1. Press Ctrl+S to save your Job.

2. Run the Job by pressing F6 or clicking
   the Run button on the Run tab.

   The information of duplicated families is displayed on the Run console, and only one person per family stays
   on the name list in the output file.