Setting up Hadoop connection

Avro

author
Talend Documentation Team
EnrichVersion
6.4
EnrichProdName
Talend Big Data
Talend Real-Time Big Data Platform
Talend Big Data Platform
Talend Data Fabric
task
Design and Development > Third-party systems > File components (Integration) > Avro components
Data Quality and Preparation > Third-party systems > File components (Integration) > Avro components
Data Governance > Third-party systems > File components (Integration) > Avro components
EnrichPlatform
Talend Studio

Procedure

  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 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
    • 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.

  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; if this WebHDFS is secured with SSL, the scheme should be swebhdfs and you need to use a tLibraryLoad in the Job to load the library required by the secured WebHDFS.

  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 4.0.1 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.

    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. 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.

    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 field and the Password field, enter the authentication information for access to Atlas.