Configuring the connection to the file system to be used by Spark - 7.0

MongoDB

author
Talend Documentation Team
EnrichVersion
7.0
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Open Studio for Big Data
Talend Real-Time Big Data Platform
task
Data Governance > Third-party systems > Database components > MongoDB components
Data Quality and Preparation > Third-party systems > Database components > MongoDB components
Design and Development > Third-party systems > Database components > MongoDB components
EnrichPlatform
Talend Studio

Skip this section if you are using Google Dataproc or HDInsight, as for these two distributions, this connection is configured in the Spark configuration tab.

Procedure

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

    Spark uses this component to connect to the HDFS system to which the jar files dependent on the Job are transferred.

  2. If you have defined the HDFS connection metadata under the Hadoop cluster node in Repository, select Repository from the Property type drop-down list and then click the [...] button to select the HDFS connection you have defined from the Repository content wizard.

    For further information about setting up a reusable HDFS connection, see Centralizing HDFS metadata

    If you complete this step, you can skip the following steps about configuring tHDFSConfiguration because all the required fields should have been filled automatically.

  3. In the Version area, select the Hadoop distribution you need to connect to and its version.
  4. In the NameNode URI field, enter the location of the machine hosting the NameNode service of the cluster. 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 Username field, enter the authentication information used to connect to the HDFS system to be used. Note that the user name must be the same as you have put in the Spark configuration tab.