# tHiveLoad Writes data of different formats into a given Hive table or to export data from a Hive table to a directory. tHiveLoad connects to a given Hive database and copies or moves data into an existing Hive table or a directory you specify. The tHiveLoad component first prepares the lines to be written to Hive before eventually writing them to Hive. This approach is more efficient with regard to Hive than the line-bye-line approach typically employed by an output component. For this reason, tHiveOutput does not exist in a Job designed in the Standard framework. This component is not shipped with your Talend Studio by default. You need to install it using the Feature Manager. For more information, see [Installing features using the Feature Manager](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/install-features-to-talend-studio). ## tHiveLoad Standard properties These properties are used to configure tHiveLoad running in the Standard Job framework. The Standard tHiveLoad 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 Google Dataproc: | Project ID | Enter the ID of your Google Cloud Platform project.

If you are not certain about your project ID, confirm it in the Manage
Resources page of your Google Cloud Platform services. | | --- | --- | | Cluster ID | 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. | | 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 Talend 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 Talend 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 Microsoft HDInsight distribution: | Livy
configuration | Enter the address and the authentication information of the Microsoft HD
Insight cluster to be used. For example, the address could be
https://your\_hdinsight\_cluster\_name.azurehdinsight.net and the
authentication information is your Azure account name: yourname.
Talend 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 Talend 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 Talend Studio should
retry to get a status when there is no status response. | | HDInsight
configuration |

The Username is the one defined when creating your cluster. You can find it in the SSH + Cluster login blade of your cluster.
The Password is defined when creating your HDInsight cluster for authentication to this cluster.

| | 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
version | Select the Hive version you are using. | | Host | Enter the database server IP address. | | Port | Enter the listening port number of DB
server. | | Database | Fill this field with the name of the
database. | | Username
and Password | DB user authentication data.

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

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, 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
checkbox 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 checkbox 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, enter the password in double quotes in the pop-up dialog box, 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](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](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: | Properties | Description | | --- | --- | | 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, do the following if you
want to share an existing connection between the parent Job and the child Job (for example,
to share the connection created by the parent Job with the child Job).

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 [Sharing a database connection](https://help.qlik.com/talend/r/en-US/7.3/studio-user-guide/sharing-database-connection). | | 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 .

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 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.

Information noteNote: From Talend Studio 8.0 R2024-04 onwards, the
Distribution parameter has been removed. | | Hive version | Select the HBase version of the distribution you are using.

Information noteNote: From Talend Studio 8.0 R2024-04 onwards, the
Hive version parameter has been removed. | | Hiver Server | Select the Hive version you are using. | | Hadoop version | Select the Hadoop version you are using. | | Load action | Select the action you need to carry for writing data into the
specified destination.

When you select LOAD, you are moving or copying data from a directory you specify.
When you select INSERT, you are moving or copying data based on queries.

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

This property is available with the following connection modes:

Dataproc
HDInsight
Standalone

Before using Tez or MapReduce, ensure that the Hadoop cluster you are using
supports Tez by checking the hive.execution.engine parameter in the
hive-site.xml file. 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](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. | | Target type | This drop-down list appears only when you have selected
INSERT from the Load list.

Select from this list the type of the location you need to write
data in.

If you select Table as destination, you can still choose to append data to or overwrite the contents in the specified table.
If you select Directory as destination, you are overwriting the contents in the specified directory

| | Table name | Enter the name of the Hive table you need to write data in.

Note that with the INSERT action, this field is available only when you have
selected Table from the Target type list. | | File path | Enter the directory you need to read data from or write data
in, depending on the action you have selected from the Load list.

If you have selected LOAD: this is the path to the data you want to copy or move into the specified Hive table.
If you have selected INSERT: this is the directory to which you want to export data from a Hive table. With this action, the File path field is available only when you have selected Directory from the Target type list.

| | The target table uses the Parquet
format | If the table in which you need to write data is a
PARQUET
table, select this check box.

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 Talend Studio.

When the connection mode to Hive is Embedded, the Job is run in your local machine and calls this JAR installed in Talend 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 correctly 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 [Installing external modules](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/installing-external-modules).

Then from the Compression list that appears,
select the compression mode you need to use to handle the
PARQUET
file. The default mode is Uncompressed. | | Action on file | Select the action to be carried out for writing data from the
drop-down list:

Overwrite: overwrites the existing data.
Append: append data to the existing data.

This list is available only when the target is a Hive table;
if the target is a directory, the action to be used is automatically
Overwrite. | | Query | This field appears when you have selected INSERT from the Load
list.

Enter the appropriate query for selecting the data to be exported
to the specified Hive table or directory. | | Local | Select this check box to use the Hive LOCAL statement
for accessing a local directory. Note that this local directory is
actually in the machine in which the Job is run. Therefore, when the
connection mode to Hive is Standalone, the Job is run in the machine where the
Hive application is installed and thus this local directory is in
that machine.

This statement is used along with the directory you have defined
in the File path field. Therefore,
this Local check box is available
only when the File path field is
available.

If you are using the LOAD action, tHiveLoad copies the local data to the target table.
If you are using the INSERT action, tHiveLoad copies data to a local directory.
If you leave this Local check box clear, the directory defined in the File path field is assumed to be in the HDFS system to be used and data will be moved to the target location.

For further information about this LOCAL statement,
see Apache's documentation about Hive's Language. | | Set partitions | Select this check box to use the Hive Partition
clause in loading or inserting data in a Hive table. You need to
enter the partition keys and their values to be used in the field
that appears.

For example, enter *contry='US',
state='CA'*. This makes a partition clause reading
Partition (contry='US', state='CA'), that is to
say, a *US and CA*
partition.

Also, it is recommended to select the Create
partition if not exist check box that appears to
ensure that you will not create a duplicate partition. | | Die on error | Select this check box to kill the Job when an error occurs. | ## Advanced settings | Properties | Description | | --- | --- | | Tez lib | Select how the Tez libraries are accessed:

Auto install: at runtime, the Job uploads and deploys the Tez libraries provided by Talend 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. | | 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](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.

| | 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, see [Hive dedicated properties](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 | Variables | Description | | --- | --- | | Global Variables | 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 checkbox is
cleared, if the component has this checkbox.

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 more information about variables, see [Using contexts and variables](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/contexts-and-variables). | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component works standalone and supports writing a wide range
of data formats such as RC, ORC or AVRO.

If Talend 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 Talend Studio
is installed. | | 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/8.0/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/8.0/components/context/reading-data-from-different-mysql-databases-using-dynamically-loaded-connection-parameters). For more information on
Dynamic settings and context variables, see
[Dynamic schema](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/dynamic-schema) and [Creating a context group and define context
variables in it](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/creating-context-group-and-define-context-variables-in-it). | | 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. | **Related concepts** * [Creating a JDBC Connection to Azure HDInsight Hive](https://help.qlik.com/talend/en-US/components/8.0/hive/thiveconnection-thiveload-thivecreatetable-thdfsoutput-creating-jdbc-connection-to-azure-hdinsight-hive-standard-component-this) * [Creating a partitioned Hive table](https://help.qlik.com/talend/en-US/components/8.0/hive/thiveconnection-thiveload-thivecreatetable-thdfsoutput-creating-partitioned-hive-table-standard-component-this)