tAvroInput – Docs for ESB 5.x

tAvroInput

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 tAvroInput, tMap, tHDFSOutput and
   tAvroOutput onto the workspace.

3. Connect tAvroInput to tMap using the Row >
   Main link.

4. Do the same to connect tMap to tHDFSOutput and tAvroOutput, respectively. In doing it, you are prompted to
   name each link. In this example, name the link to tHDFSOutput to out1 and
   the link to tAvroOutput to reject.

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 tAvroInput

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

   

2. Click the button next to Edit
   schemato open the schema editor.

3. Click the button four times to add four rows and in the Column column, rename them to Id, FirstName, LastName and
   Reg_date, respectively.

   

4. In the Type column, select Integer for Id
   and Date for Reg_date. The date pattern used in this scenario is
   dd-MM-yyyy.

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

6. In the Folder/File field, enter the path,
   or browse to the source file you need the Job to read.

Configuring tMap

1. Double-click tMap to open the Map Editor.

   

2. Drop the four columns of the input schema from the input side (left) into
   each of the output flows of the output side (right), that is to say,
   out1 and reject. This way the input flow and the output flows are
   mapped.

3. In the table representing the out1
   flow, click to display the filter editing area and enter the
   following expression to select the employee records that were registered
   before January 1^st, 2008 (01-01-2008).

   row1.Reg_date.before( new Date(108,0,1))

   1	row1.Reg_date.before( new Date(108,0,1))

4. In the table representing the reject
   flow, click to display the property settings panel.

5. In the Value field of the Catch output reject row, click the and select true in the
   pop-up dialog box. This allows you to output the records rejected by the
   out1 flow.

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

Configuring the selected employee data

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 employee records registered
   before January 1^st, 2008.

3. From the Type list, select the data
   format for the records 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 the file for the merged data exists, select the Override target file check box to overwrite that
   file.

Configuring the rejected employee data

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

   

2. In the Folder field, enter the path, or
   browse to the folder you want to write the employee records registered after
   January 1^st, 2008.

3. 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.