# tHiveInput Standard properties These properties are used to configure tHiveInput running in the Standard Job framework. The Standard tHiveInput component belongs to the Big Data and the Databases families. The component in this framework is available in all Talend products with Big Data and in Talend Data Fabric. ## Basic settings Connection configuration: * When you use this component with Qubole on AWS: | API Token | Click the
... button
next to the API
Token field to enter the authentication
token generated for the Qubole user account to be used.
For further information about how to obtain this token,
see [Manage Qubole
account](https://api.qubole.com/v2/control-panel#manage-accounts) from the Qubole documentation.

This
token allows you to specify the user account you want to
use to access Qubole. Your Job automatically uses the
rights and permissions granted to this user account in
Qubole. | | --- | --- | | Cluster label | Select the
Cluster label
check box and enter the name of the Qubole cluster to be
used. If leaving this check box clear, the default
cluster is used.

If you need
details about your default cluster, ask the
administrator of your Qubole service. You can also read
[this article](https://docs.qubole.com/en/latest/quick-start-guide/Azure-quick-start-guide/UsingQDS/azure-update-clusters.html?highlight=default%20cluster)
from the Qubole documentation to find more information
about configuring a default Qubole cluster. | | Change API endpoint | Select the
Change API
endpoint check box and select the region
to be used. If leaving this check box clear, the default
region is used.

For further
information about the Qubole Endpoints supported on
QDS-on-AWS, see [Supported Qubole
Endpoints on Different Cloud Providers](https://docs.qubole.com/en/latest/rest-api/qubole-endpoints.html#qubole-endpoints). | * When you use this component with Google Dataproc: | Project identifier | Enter the ID of your Google Cloud Platform project.

If you are not certain about your project ID, check it in the Manage
Resources page of your Google Cloud Platform services. | | --- | --- | | Cluster identifier | Enter the ID of your Dataproc cluster to be used. | | Region | From this drop-down list, select the Google Cloud region to
be used. | | Google Storage staging bucket | As a Talend Job expects its
dependent jar files for execution, specify the Google Storage directory to
which these jar files are transferred so that your Job can access these
files at execution.

The directory to be entered must end with a slash (/). If not existing, the
directory is created on the fly but the bucket to be used must already
exist. | | Database | Fill this field with the name of the database. | | Authentication type | Select the type of authentication to be used:

Use Access Key and Secret: authenticate using the access and secret keys obtained from Google. When selecting this mode, the parameters to be defined in the Basic settings view are Google Storage Access Key and Google Storage Secret Key.
User Service Account: authenticate using Google credentials file that is associated with your Google Service Account. The credentials file can either be specified in tHiveConfiguration or in tHiveInput.

| | Google Storage Access Key
and Google Storage Secret
Key | Enter the authentication information obtained from Google
for tHiveInput to read temporary data
from Google Storage.

These keys can be consulted on the
Interoperable Access tab view under the Google Cloud Storage tab of the
project from the Google APIs Console.

To enter the secret key, click the [...] button next to
the secret key field, and then in the pop-up dialog box enter the password between double
quotes and click OK to save the settings.

For more information about the access key
and secret key, go to [https://developers.google.com/storage/docs/reference/v1/getting-startedv1?hl=en/](https://developers.google.com/storage/docs/reference/v1/getting-startedv1?hl=en)
and see the description about developer keys.

The Access Key and
Secret Key fields are only
available when you select Use Access Key and
Secret from the Authentication type drop-down list. | | Provide Google Credentials in file | Leave this check box clear, when you
launch your Job from a given machine in which Google Cloud SDK has been
installed and authorized to use your user account credentials to access
Google Cloud Platform. In this situation, this machine is often your
local machine.

When you launch your Job from a remote
machine, such as a Jobserver, select this check box and in the
Path to Google Credentials
file field that is displayed, enter the directory in
which this JSON file is stored in the Jobserver machine. You can also
click the [...] button, and then in the pop-up
dialog box, browse for the JSON file.

For further information about this Google
Credentials file, see the administrator of your Google Cloud Platform or
visit [Google Cloud Platform Auth
Guide](https://cloud.google.com/docs/authentication). | * When you use this component with HDInsight: | WebHCat configuration | Enter the address and the authentication information of the
Microsoft HD Insight cluster to be used. For example, the address could be
your\_hdinsight\_cluster\_name.azurehdinsight.net and the
authentication information is your Azure account name: ychen. The Studio uses this service to submit the Job to the HD
Insight cluster.

In the Job result
folder field, enter the location in which you want to store the
execution result of a Job in the Azure Storage to be used. | | --- | --- | | Job status polling configuration | In the Poll interval
when retrieving Job status (in ms) field, enter the time interval
(in milliseconds) at the end of which you want the Studio to ask Spark for the
status of your Job. For example, this status could be Pending or Running.

In the Maximum number of
consecutive statuses missing field, enter the maximum number of
times the Studio should retry to get a status when there is no status response. | | HDInsight configuration | Enter the address and the authentication information of the
Microsoft HD Insight cluster to be used. For example, the address could be
your\_hdinsight\_cluster\_name.azurehdinsight.net and the
authentication information is your Azure account name: ychen. The Studio uses this service to submit the Job to the HD
Insight cluster.

In the Job result
folder field, enter the location in which you want to store the
execution result of a Job in the Azure Storage to be used. | | Windows Azure Storage
configuration | Enter the address and the authentication information of the
Azure Storage or ADLS Gen2 account to be used. In this configuration, you do not
define where to read or write your business data but define where to deploy your Job
only.

In the Container field, enter the name of the container to be used. You
can find the available containers in the Blob
blade of the Azure Storage account to be used.

In the Deployment
Blob field, enter the location in which you want to store the
current Job and its dependent libraries in this Azure Storage account.

In the Hostname field, enter the Primary Blob Service Endpoint of your
Azure Storage account without the https:// part. You can find this endpoint in the
Properties blade of this storage
account.

In the Username field, enter the name of the Azure Storage account to be
used.

In the Password field, enter the access key of the Azure Storage account
to be used. This key can be found in the Access
keys blade of this storage account. | | Database | Fill this field with the name of the
database. | * When you use the other distributions: | Connection mode | Select a connection mode from the list. The
options vary depending on the distribution you are
using. | | --- | --- | | Hive server | Select the Hive server through which you want the Job using this component
to execute queries on Hive.

This Hive server list is available
only when the Hadoop distribution to be used such as HortonWorks Data Platform V1.2.0 (Bimota) supports HiveServer2. It
allows you to select HiveServer2 (Hive 2), the
server that better support concurrent connections of multiple clients than
HiveServer (Hive 1).

For further information about HiveServer2, see https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2. | | Host | Database server IP address. | | Port | Listening port number of DB server. | | Database | Fill this field with the name of the
database.

Information noteNote:

This field is not available when you
select Embedded
from the Connection
mode list. | | Username and
Password | DB user authentication data.

To enter the password, click the [...] button next to the
password field, and then in the pop-up dialog box enter the password between double quotes
and click OK to save the settings. | | Use kerberos authentication | If you are accessing a Hive Metastore running with Kerberos security,
select this check box and then enter the relevant parameters in the fields that
appear.

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.

The values of the following parameters can be found in the *hive-site.xml* file of the Hive system to be used.

Hive principal uses the value of hive.metastore.kerberos.principal. This is the service principal of the Hive Metastore.
HiveServer2 local user principal uses the value of hive.server2.authentication.kerberos.principal.
HiveServer2 local user keytab uses the value of hive.server2.authentication.kerberos.keytab
Metastore URL uses the value of javax.jdo.option.ConnectionURL. This is the JDBC connection string to the Hive Metastore.
Driver class uses the value of javax.jdo.option.ConnectionDriverName. This is the name of the driver for the JDBC connection.
Username uses the value of javax.jdo.option.ConnectionUserName. This, as well as the Password parameter, is the user credential for connecting to the Hive Metastore.
Password uses the value of javax.jdo.option.ConnectionPassword.

For the other parameters that are displayed, please consult the Hadoop
configuration files they belong to. For example, the Namenode
principal can be found in the *hdfs-site.xml* file
or the *hdfs-default.xml* file of the distribution you are
using.

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. | | Use SSL encryption | Select this check box to enable the SSL or TLS encrypted connection.

Then in the fields that are displayed, provide the authentication
information:

In the Trust store path field, enter the path, or browse to the TrustStore file to be used. By default, the supported TrustStore types are JKS and PKCS 12.
To enter the password, click the [...] button next to the password field, and then in the pop-up dialog box enter the password between double quotes and click OK to save the settings.

This feature is available only to the HiveServer2 in the Standalone mode of the following distributions:

Hortonworks Data Platform 2.0 +
Cloudera CDH4 +
Pivotal HD 2.0 +
Amazon EMR 4.0.0 +

| | Set Resource Manager | Select this check box and in the displayed 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.
Allocate proper memory volumes to the Map and the Reduce computations and the ApplicationMaster of YARN by selecting the Set memory check box in the Advanced settings view.
Select the Set Hadoop user check box and enter the user name under which you want to execute the Job. Since a file or a directory in Hadoop has its specific owner with appropriate read or write rights, this field allows you to execute the Job directly under the user name that has the appropriate rights to access the file or directory to be processed.
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.

For further information about these parameters, see the documentation or
contact the administrator of the Hadoop cluster to be used.

For further information about the Hadoop Map/Reduce framework, see the
Map/Reduce tutorial in Apache's Hadoop documentation on [http://hadoop.apache.org](http://hadoop.apache.org/). | | Set NameNode URI | Select this check box and in the displayed field, enter the URI of the
Hadoop NameNode, the master node of a Hadoop system. For example, assuming 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.

For further information about the Hadoop Map/Reduce framework, see the
Map/Reduce tutorial in Apache's Hadoop documentation on [http://hadoop.apache.org](http://hadoop.apache.org/). | | Spark catalog | Select the Spark implementation to use.

In-memory: select this value if you set the Hive thrift metastore to a Hive metastore that is not an external metastore.
Hive: select this value if you set the Hive thrift metastore to an external Hive metastore that exists outside of your cluster.

| The other properties: | Property type | Either Built-In or Repository.

Built-In: No property data stored centrally.

Repository: Select the repository file where the
properties are stored. | | --- | --- | | Use an existing connection | Select this check box and in the Component
List drop-down list, select the desired connection component to
reuse the connection details you already defined.

Information noteNote: When a Job contains the parent Job and the child Job, if you
need to share an existing connection between the two levels, for example, to share the
connection created by the parent Job with the child Job, you have to:

In the parent level, register the database connection to be shared in the Basic settings view of the connection component which creates that very database connection.
In the child level, use a dedicated connection component to read that registered database connection.

For an example about how to share a database connection
across Job levels, see *Talend Studio
User Guide*. | | 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, 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.

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.

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.

| | Hive version | Select the version of the Hadoop distribution you are using. The available
options vary depending on the component you are using. | | 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: The schema is created and
stored locally for this component only. Related topic: see *Talend Studio*
*User
Guide*. | | | Repository: The schema already exists
and is stored in the Repository, hence can be reused. Related topic: see
*Talend Studio*
*User
Guide*. | | Table Name | Name of the table to be processed. | | Query type | Either Built-in or Repository. | | | Built-in: Fill in manually the query
statement or build it graphically using SQLBuilder | | | Repository: Select the relevant query
stored in the Repository. The Query field gets accordingly filled
in. | | Guess Query | Click the Guess Query button to
generate the query which corresponds to your table schema in the Query
field. | | Guess schema | Click this button to retrieve the schema from the table. | | This query uses Parquet objects | When available, select this check box to indicate that the table to be handled
uses the
PARQUET
format and thus make the component to call the required jar file.

Note that when the file format to be used is PARQUET, you might be prompted to find the specific
PARQUET jar file and install it into the Studio.

When the connection mode to Hive is Embedded, the Job is run in your local machine and calls this jar installed in the Studio.
When the connection mode to Hive is Standalone, the Job is run in the server hosting Hive and this jar file is sent to the HDFS system of the cluster you are connecting to. Therefore, ensure that you have properly defined the NameNode URI in the corresponding field of the Basic settings view.

This jar file can be downloaded from Apache's site. For details about how to install external modules, see *How to install
external modules in the Talend products*.
You can find more details about how to install
external modules in Talend Help Center ([https://help.talend.com](https://help.talend.com/)). | | Query | Enter your DB query paying particularly attention to properly sequence
the fields in order to match the schema definition.

For further information about the Hive query language, see https://cwiki.apache.org/confluence/display/Hive/LanguageManual.

Information noteNote: Compressed data in the form of Gzip or Bzip2 can be processed through the query
statements. For details, see https://cwiki.apache.org/confluence/display/Hive/CompressedStorage.

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. | | Execution engine | Select this check box and from the drop-down list, select the framework you need to use to
run the Job.

This list is available only when you are using the Embedded mode for the Hive connection and the distribution you are working
with is:

Custom: this option allows you connect to a distribution supporting Tez but not officially supported by Talend .

Before using Tez, ensure that the Hadoop cluster you are using supports Tez. You will need to configure the access to the relevant Tez libraries via the Advanced settings view of this component.

For further information about Hive on Tez, see Apache's related documentation in https://cwiki.apache.org/confluence/display/Hive/Hive+on+Tez. Some examples are presented there to show how Tez can be used to gain performance over MapReduce. | ## Advanced settings | Tez lib | Select how the Tez libraries are accessed:

Auto install: at runtime, the Job uploads and deploys the Tez libraries provided by the Studio into the directory you specified in the Install folder in HDFS field, for example, /tmp/usr/tez.

If you have set the tez.lib.uris property in the properties table, this directory overrides the value of that property at runtime. But the other properties set in the properties table are still effective.
Use exist: the Job accesses the Tez libraries already deployed in the Hadoop cluster to be used. You need to enter the path pointing to those libraries in the Lib path (folder or file) field.
Lib jar: this table appears when you have selected Auto install from the Tez lib list and the distribution you are using is Custom. In this table, you need to add the Tez libraries to be uploaded.

| | --- | --- | | Temporary path | If you do not want to set the Jobtracker and the NameNode when you execute
the query select \* from your\_table\_name, you need to
set this temporary path. For example, */C:/select\_all* in
Windows. | | Trim all the String/Char columns | Select this check box to remove leading and trailing whitespace from
all the String/Char columns. | | Trim column | Remove leading and trailing whitespace from defined columns.

Information noteNote:

Clear the Trim all the String/Char
columns check box to enable Trim
column in this field. | | 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.

| | Hive properties | *Talend Studio* uses a default configuration for its engine to
perform operations in a Hive database. 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. For further information for Hive dedicated properties, see https://cwiki.apache.org/confluence/display/Hive/AdminManual+Configuration.

If you need to use Tez to run your Hive Job, add hive.execution.engine to the Properties column and Tez to the Value column, enclosing both of these strings in double quotation marks.
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.

| | Mapred job map memory mb and
Mapred job reduce memory mb | You can tune the map and reduce computations by
selecting the Set memory check box to set proper memory allocations
for the 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.

The memory parameters to be set are Map (in Mb),
Reduce (in Mb) and ApplicationMaster (in Mb). These fields allow you to dynamically allocate
memory to the map and the reduce computations and the ApplicationMaster of YARN. | | Path separator in server | 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. | | tStatCatcher Statistics | Select this check box to collect log data at the component
level. | ## Global Variables | Global Variables | NB\_LINE: the number
of rows read by an input component or transferred to an output component. This is an
After variable and it returns an integer.

QUERY: the query statement
being processed. This is a Flow variable and it returns a string.

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 offers the benefit of flexible DB queries and covers
all possible Hive QL queries.

If the Studio used to connect to a Hive database is operated on Windows,
you must manually create a folder called *tmp* in the root of the
disk where this Studio is installed. | | --- | --- | | *HBase Configuration*

Information noteNote:

Available only when the Use an existing
connection check box is clear | Store by HBase | | | Zookeeper quorum | | | Zookeeper client port | | | Define the jars to register for
HBase | | | Register jar for HBase | | Dynamic settings | Click the [+] button to add a row in the table
and fill the Code field with a context
variable to choose your database connection dynamically from multiple
connections planned in your Job. This feature is useful when you need to
access database tables having the same data structure but in different
databases, 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](https://help.qlik.com/talend/r/en-US/7.3/components/defining-context-groups/reading-data-from-databases-through-context-based-dynamic-connections) and [Reading data from different MySQL databases using dynamically loaded connection parameters](https://help.qlik.com/talend/r/en-US/7.3/components/context/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\ hadoop\hadoop-VERSION\lib\native. For example, the library for Windows is \lib\native\MapRClient.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. |