tHDFSInput

Extracts the data in a HDFS file for other components to process it.

tHDFSInput reads a file located on a given Hadoop distributed
file system (HDFS) and puts the data of interest from this file into a
Talend schema. Then it passes the data to the component that follows.

Depending on the Talend
product you are using, this component can be used in one, some or all of the following
Job frameworks:

Standard: see tHDFSInput Standard properties.

The component in this framework is available in all Talend products with Big Data
and in Talend Data Fabric.
MapReduce: see tHDFSInput MapReduce properties (deprecated).

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

tHDFSInput Standard properties

These properties are used to configure tHDFSInput running in the Standard Job framework.

The Standard
tHDFSInput component belongs to the Big Data and the File families.

The component in this framework is available in all Talend products with Big Data
and in 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.
Schema and Edit Schema	A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. When you create a Spark Job, avoid the reserved word `line` when naming the fields. Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available: View schema: choose this option to view the schema only. Change to built-in property: choose this option to change the schema to Built-in for local changes. Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion. If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the Repository Content window.
	Built-In: You create and store the schema locally for this component only.
	Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.
Use an existing connection	Select this check box and in the Component List click the HDFS connection component from which you want to reuse the connection details already defined. Note that when a Job contains the parent Job and the child Job, Component List presents only the connection components in the same Job level.
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 Insightcluster and the Windows Azure Storage service of that cluster in the areas that are displayed. For detailed explanation about these parameters, search for configuring the connection manually on Talend Help Center (https://help.talend.com). If you select Amazon EMR, find more details about Amazon EMR getting started in Talend Help Center (https://help.talend.com). 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 . 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. 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. In Talend Exchange, members of Talend community have shared some ready-for-use configuration zip files which you can download from this Hadoop configuration list and directly use them in your connection accordingly. However, because of the ongoing evolution of the different Hadoop-related projects, you might not be able to find the configuration zip corresponding to your distribution from this list; then it is recommended to use the Import from existing version option to take an existing distribution as base to add the jars required by your distribution. 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 the 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. Note: 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.
Version	Select the version of the Hadoop distribution you are using. The available options vary depending on the component you are using.
Scheme	Select the URI scheme of the file system to be used from the Scheme drop-down list. This scheme could be HDFS WebHDFS. WebHDFS with SSL is not supported yet. ADLS. Only Azure Data Lake Storage Gen1 is supported. The schemes present on this list vary depending on the distribution you are using and only the scheme that appears on this list with a given distribution is officially supported by Talend. Once a scheme is selected, the corresponding syntax such as `webhdfs://localhost:50070/` for WebHDFS is displayed in the field for the NameNode location of your cluster. If you have selected ADLS, the connection parameters to be defined become: In the Client ID and the Client key fields, enter, respectively, the authentication ID and the authentication key generated upon the registration of the application that the current Job you are developing uses to access Azure Data Lake Storage. Ensure that the application to be used has appropriate permissions to access Azure Data Lake. You can check this on the Required permissions view of this application on Azure. For further information, see Azure documentation Assign the Azure AD application to the Azure Data Lake Storage account file or folder. In the Token endpoint field, copy-paste the OAuth 2.0 token endpoint that you can obtain from the Endpoints list accessible on the App registrations page on your Azure portal. For a video demonstration, see Configure and use Azure in a Job.
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.
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 user name to authenticate against the credentials stored in Kerberos. If this cluster is a MapR cluster of the version 5.0.0 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. 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 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.
User name	The User name field is available when you are not using Kerberos to authenticate. 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.
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.
File Name	Browse to, or enter the path pointing to the data to be used in the file system. If the path you set points to a folder, this component will read all of the files stored in that folder. Furthermore, if sub-folders exist in that folder and you need to read files in the sub-folders, select the Include sub-directories if path is directory check box in the Advanced settings view.
Type	Select the type of the file to be processed. The type of the file may be: Text file. Sequence file: a Hadoop sequence file consists of binary key/value pairs and is suitable for the Map/Reduce framework. For further information, see http://wiki.apache.org/hadoop/SequenceFile. Once you select the Sequence file format, the Key column list and the Value column list appear to allow you to select the keys and the values of that Sequence file to be processed.
Row separator	The separator used to identify the end of a row. This field is not available for a Sequence file.
Field separator	Enter character, string or regular expression to separate fields for the transferred data. This field is not available for a Sequence file.
Header	Set values to ignore the header of the transferred data. For example, enter 0 to ignore no rows for the data without header and set 1 for the data with header at the first row. This field is not available for a Sequence file.
Custom encoding	You may encounter encoding issues when you process the stored data. In that situation, select this check box to display the Encoding list. Select the encoding from the list or select Custom and define it manually. This field is compulsory for database data handling. The supported encodings depend on the JVM that you are using. For more information, see https://docs.oracle.com. This option is not available for a Sequence file.
Compression	Select the Uncompress the data check box to uncompress the input data. Hadoop provides different compression formats that help reduce the space needed for storing files and speed up data transfer. When reading a compressed file, the Studio needs to uncompress it before being able to feed it to the input flow. This option is not available for a Sequence file.

Advanced settings

Include sub-directories if path is directory	Select this check box to read not only the folder you have specified in the File name field but also the sub-folders in that folder.
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 on http://hadoop.apache.org/docs and then select the version of the documentation you want. For demonstration purposes, the links to some properties are listed below: Typically, the HDFS-related properties can be found in the hdfs-default.xml file of your distribution, such as http://hadoop.apache.org/docs/r2.6.0/hadoop-project-dist/hadoop-hdfs/hdfs-default.xml. Apache also provides a page to list the Hive-related properties: https://cwiki.apache.org/confluence/display/Hive/Configuration+Properties.
tStatCatcher Statistics	Select this check box to collect log data at the component level. Note that this check box is not available in the Map/Reduce version of the component.

Global Variables

Global Variables	ERROR_MESSAGE: the error message generated by the component when an error occurs. This is an After variable and it returns a string. This variable functions only if the Die on error check box is cleared, if the component has this check box. A Flow variable functions during the execution of a component while an After variable functions after the execution of the component. To fill up a field or expression with a variable, press Ctrl + Space to access the variable list and choose the variable to use from it. For further information about variables, see Talend Studio User Guide.

ERROR_MESSAGE: the error message generated by the
component when an error occurs. This is an After variable and it returns a string. This
variable functions only if the Die on error check box is
cleared, if the component has this check box.

A Flow variable functions during the execution of a component while an After variable
functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl +
Space to access the variable list and choose the variable to use from it.

For further information about variables, see
Talend Studio

User Guide.

Usage

Usage rule	This component needs an output link.
Dynamic settings	Click the [+] button to add a row in the table and fill the Code field with a context variable to choose your HDFS connection dynamically from multiple connections planned in your Job. This feature is useful when you need to access files in different HDFS systems or different distributions, especially when you are working in an environment where you cannot change your Job settings, for example, when your Job has to be deployed and executed independent of Talend Studio . The Dynamic settings table is available only when the Use an existing connection check box is selected in the Basic settings view. Once a dynamic parameter is defined, the Component List box in the Basic settings view becomes unusable. For examples on using dynamic parameters, see Reading data from databases through context-based dynamic connections and Reading data from different MySQL databases using dynamically loaded connection parameters. For more information on Dynamic settings and context variables, see Talend Studio User Guide.
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 the 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 hadoophadoop-VERSIONlib ative. For example, the library for Windows is lib ativeMapRClient.dll in the MapR client jar file. For further information, see the following link from MapR: http://www.mapr.com/blog/basic-notes-on-configuring-eclipse-as-a-hadoop-development-environment-for-mapr. 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 the 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 the 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.
Limitations	JRE 1.6+ is required.

Using HDFS components to work with Azure Data Lake Storage (ADLS)

This scenario describes how to use the HDFS components to read data from and write data to Azure Data Lake Storage.

This scenario applies only to Talend products with Big Data.

tFixedFlowInput: it provides sample data to the Job.
tHDFSOutput: it writes sample data to Azure Data Lake
Store.
tHDFSInput: it reads sample data from Azure Data Lake
Store.
tLogRow: it displays the output of the Job on the console of
the Run view of the Job.

Grant your application the access to your ADLS Gen2

An Azure subscription is required.

Create your Azure Data Lake Storage Gen2 account if you do not have it
yet.
- For more details, see Create an Azure Data Lake Storage
  Gen2 account from the Azure documentation.
Create an Azure Active Directory application on your Azure portal. For more
details about how to do this, see the “Create an Azure Active Directory
application” section in Azure documentation: Use portal to create an Azure Active Directory
application.
Obtain the application ID, object ID and the client secret of the application
to be used from the portal.
1. On the list of the registered applications, click the application you
  created and registered in the previous step to display its information
  blade.
2. Click Overview to open its blade, and from the
  top section of the blade, copy the Object ID and
  the application ID displayed as Application (client)
  ID. Keep them somewhere safe for later use.
3. Click Certificates & secrets to open its
  blade and then create the authentication key (client secret) to be used
  on this blade in the Client secrets
  section.
Back to the Overview blade of the application to be
used, click Endpoints on the top of this blade, copy the
value of OAuth 2.0 token endpoint (v1) from the endpoint
list that appears and keep it somewhere safe for later use.
Set the read and write permissions to the ADLS Gen2 filesystem to be used for
the service principal of your application.

It is very likely that the administrator of your Azure system has included
your account and your applications in the group that has access to a given ADLS
Gen2 storage account and a given ADLS Gen2 filesystem. In this case, ask your
administrator to ensure that you have the proper access and then ignore this
step.
1. Start your Microsoft Azure Storage Explorer and find your ADLS Gen2
  storage account on the Storage Accounts
  list.
  
  If you have not installed Microsoft Azure Storage Explorer, you can
  download it from the Microsoft Azure official site.
2. Expand this account and the Blob Containers node
  under it; then click the ADLS Gen2 hierarchical filesystem to be used
  under this node.
  
  The filesystem in this image is for demonstration purposes only.
  Create the filesystem to be used under the Blob
  Containers node in your Microsoft Azure Storage
  Explorer, if you do not have one yet.
3. On the blade that is opened, click Manage Access
  to open its wizard.
4. At the bottom of this wizard, add the object ID of your application to
  the Add user or group field and click
  Add.
5. Select the object ID just added from the Users and
  groups list and select all the permission for
  Access and
  Default.
6. Click Save to validate these changes and close
  this wizard.

Creating an HDFS Job in the Studio

On the Integration
perspective, drop the following components from the
Palette onto the design workspace:
tFixedFlowInput, tHDFSOutput,
tHDFSInput and tLogRow.
Connect tFixedFlowInput to
tHDFSOutput using a Row > Main link.
Do the same to connect tHDFSInput to
tLogRow.
Connect tFixedFlowInput to tHDFSInput using a Trigger > OnSubjobOk link.

Configuring the HDFS components to work with Azure Data Lake Storage

Double-click tFixedFlowInput to open its
Component view to provide sample data to the
Job.

The sample data to be used contains only one row with two column:
id and name.
Click the […] button next to Edit
schema to open the schema editor.
Click the [+] button to add the two columns and rename
them to id and name.
Click OK to close the schema editor and validate the
schema.
In the Mode area, select Use single
table.

The id and the name columns
automatically appear in the Value table and you can
enter the values you want within double quotation marks in the
Value column for the two schema values.
Double-click tHDFSOutput to open its
Component view.
In the Version area, select
Hortonworks or Cloudera
depending on the distribution you are using. In the Standard
framework, only these two distributions with ADLS are supported by the HDFS
components.
From the Scheme drop-down list, select
ADLS. The ADLS related parameters appear in the
Component view.
In the URI field, enter the NameNode service of your
application. The location of this service is actually the address of your Data
Lake Store.

For example, if your Data Lake Storage name is
data_lake_store_name, the NameNode URI to be used
is
adl://data_lake_store_name.azuredatalakestore.net.
In the
Client ID and the Client
key fields, enter, respectively, the authentication
ID and the authentication key generated upon the registration of the
application that the current Job you are developing uses to access
Azure Data Lake Storage.

Ensure that the application to be used has appropriate
permissions to access Azure Data Lake. You can check this on the
Required permissions view of this application on Azure. For further
information, see Azure documentation Assign the Azure AD application to
the Azure Data Lake Storage account file or folder.

This application must be the one to
which you assigned permissions to access your Azure Data Lake Storage in
the previous step.
In the
Token endpoint field, copy-paste the
OAuth 2.0 token endpoint that you can obtain from the
Endpoints list accessible on the
App registrations page on your Azure
portal.
In the File name field, enter the directory to be used
to store the sample data on Azure Data Lake Storage.
From the Action drop-down list, select
Create if the directory to be used does not exist yet
on Azure Data Lake Storage; if this folder already exists, select
Overwrite.
Do the same configuration for tHDFSInput.
If you run your Job on Windows, following this procedure to add the winutils.exe program to your Job.
Press F6 to run your Job.