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tHDFSConfiguration properties for Apache Spark Batch

These properties are used to configure tHDFSConfiguration running in the Spark Batch Job framework.

The Spark Batch tHDFSConfiguration component belongs to the Storage family.

The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric.

Basic settings

Property type

Either Built-In or Repository.

Built-In: No property data stored centrally.

Repository: Select the repository file where the properties are stored.

Distribution

Select the cluster you are using from the drop-down list. The options in the list vary depending on the component you are using. Among these options, the following ones requires specific configuration:

  • If available in this Distribution drop-down list, the Microsoft HD Insight option allows you to use a Microsoft HD Insight cluster. For this purpose, you need to configure the connections to the HD Insight cluster and the Windows Azure Storage service of that cluster in the areas that are displayed. For detailed explanation about these parameters, see Configuring the connection manually.

  • If you select Amazon EMR, find more details in Amazon EMR - Getting Started.

  • The Custom option allows you to connect to a cluster different from any of the distributions given in this list, that is to say, to connect to a cluster not officially supported by Talend .

  1. Select Import from existing version to import an officially supported distribution as base and then add other required jar files which the base distribution does not provide.

  2. Select Import from zip to import the configuration zip for the custom distribution to be used. This zip file should contain the libraries of the different Hadoop elements and the index file of these libraries.

    Note that custom versions are not officially supported by Talend . Talend and its community provide you with the opportunity to connect to custom versions from Talend Studio but cannot guarantee that the configuration of whichever version you choose will be easy, due to the wide range of different Hadoop distributions and versions that are available. As such, you should only attempt to set up such a connection if you have sufficient Hadoop experience to handle any issues on your own.

    Information noteNote:

    In this dialog box, the active check box must be kept selected so as to import the jar files pertinent to the connection to be created between the custom distribution and this component.

    For a step-by-step example about how to connect to a custom distribution and share this connection, see Hortonworks.

Hadoop version

Select the version of the Hadoop distribution you are using. The available options vary depending on the component you are using.

Use kerberos authentication

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 username to authenticate against the credentials stored in Kerberos.

This check box is available depending on the Hadoop distribution you are connecting to.
Use a keytab to authenticate

Select the Use a keytab to authenticate check box to log into a Kerberos-enabled system using a given keytab file. 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. This keytab file must be stored in the machine in which your Job actually runs, for example, on a Talend JobServer.

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 username 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.

NameNode URI

Type in the URI of the Hadoop NameNode, the master node of a Hadoop system. For example, we assume that you have chosen a machine called masternode as the NameNode, then the location is hdfs://masternode:portnumber. If you are using WebHDFS, the location should be webhdfs://masternode:portnumber; WebHDFS with SSL is not supported yet.

User name

The User name field is available when you are not using Kerberos to authenticate. In the User name field, enter the login username for your distribution. If you leave it empty, the username of the machine hosting Talend Studio will be used.
Group

Enter the membership including the authentication user under which the HDFS instances were started. This field is available depending on the distribution you are using.

Use datanode hostname

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.

Hadoop properties

Talend Studio uses a default configuration for its engine to perform operations in a Hadoop distribution. If you need to use a custom configuration in a specific situation, complete this table with the property or properties to be customized. Then at runtime, the customized property or properties will override those default ones.

  • Note that if you are using the centrally stored metadata from the Repository, this table automatically inherits the properties defined in that metadata and becomes uneditable unless you change the Property type from Repository to Built-in.

For further information about the properties required by Hadoop and its related systems such as HDFS and Hive, see the documentation of the Hadoop distribution you are using or see Apache's Hadoop documentation and then select the version of the documentation you want. For demonstration purposes, the links to some properties are listed below:

Setup HDFS encryption configurations

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.

Usage

Usage rule

This component is used with no need to be connected to other components.

You need to drop tHDFSConfiguration along with the file system related subJob to be run in the same Job so that the configuration is used by the whole Job at runtime.

This component, along with the Spark Batch component Palette it belongs to, appears only when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs.

Prerequisites

The Hadoop distribution must be properly installed, so as to guarantee the interaction with Talend Studio . The following list presents MapR related information for example.

  • Ensure that you have installed the MapR client in the machine where Talend Studio is, and added the MapR client library to the PATH variable of that machine. According to MapR's documentation, the library or libraries of a MapR client corresponding to each OS version can be found under MAPR_INSTALL\ hadoop\hadoop-VERSION\lib\native. For example, the library for Windows is \lib\native\MapRClient.dll in the MapR client jar file.

    Without adding the specified library or libraries, you may encounter the following error: no MapRClient in java.library.path.

  • Set the -Djava.library.path argument, for example, in the Job Run VM arguments area of the Run/Debug view in the Preferences dialog box in the Window menu. This argument provides to Talend Studio the path to the native library of that MapR client. This allows the subscription-based users to make full use of the Data viewer to view locally in Talend Studio the data stored in MapR.

For further information about how to install a Hadoop distribution, see the manuals corresponding to the Hadoop distribution you are using.

Spark Connection

In the Spark Configuration tab in the Run view, define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, you must specify the directory in the file system to which these jar files are transferred so that Spark can access these files:
  • Yarn mode (Yarn client or Yarn cluster):

    When using on-premises distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.

    Actually, this component is relevant with the traditional on-premises Hadoop distributions only.

  • Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.

    If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

This connection is effective on a per-Job basis.

Specific Spark timeout

When encountering network issues, Spark by default waits for up to 45 minutes before stopping its attempts to submits Jobs. Then, Spark triggers the automatic stop of your Job.

Add the following properties to the Hadoop properties table of tHDFSConfiguration to reduce this duration.

  • ipc.client.ping: false. This prevents pinging if server does not answer.

  • ipc.client.connect.max.retries: 0. This indicates the number of retries if the demand for connection is answered but refused.

  • yarn.resourcemanager.connect.retry-interval.ms: any number. This indicates how often to try to connect to the ResourceManager service until Spark gives up.

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