tPigLoad Standard properties

Pig

author
Talend Documentation Team
EnrichVersion
6.5
EnrichProdName
Talend Big Data Platform
Talend Big Data
Talend Open Studio for Big Data
Talend Data Fabric
Talend Real-Time Big Data Platform
task
Design and Development > Third-party systems > Processing components (Integration) > Pig components
Data Governance > Third-party systems > Processing components (Integration) > Pig components
Data Quality and Preparation > Third-party systems > Processing components (Integration) > Pig components
EnrichPlatform
Talend Studio

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

The Standard tPigLoad component belongs to the Big Data and the Processing families.

The component in this framework is available in all Talend products with Big Data.

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.

The properties are stored centrally under the Hadoop Cluster node of the Repository tree.

The fields that come after are pre-filled in using the fetched data.

For further information about the Hadoop Cluster node, see the Getting Started Guide.

Schema and Edit Schema

A schema is a row description. It defines the number of fields (columns) to Repository. 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. Related topic: see Talend Studio User Guide.

 

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

Local

Click this radio button to run Pig scripts in Local mode. In this mode, all files are installed and run from your local host and file system.

Tez

Click this radio button to run the Pig Job on the Tez framework.

This Tez mode is available only when you are using one of the following distributions:
  • Hortonworks: V2.2 +.

  • 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 Pig on Tez, see Apache's related documentation in https://cwiki.apache.org/confluence/display/PIG/Pig+on+Tez.

Map/Reduce

Click this radio button to run Pig scripts in Map/Reduce mode.

Once selecting this mode, you need to complete the fields in the Configuration area that appears:
  • Distribution and Version:

    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 WebHCat service, the HD Insight service and the Windows Azure Storage service of that cluster in the areas that are displayed. A demonstration video about how to configure this connection is available in the following link: https://www.youtube.com/watch?v=A3QTT6VsNoM.

    • If you select Amazon EMR, see the following article about how to configure the connection: 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.

      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 Connecting to a custom Hadoop distribution.

    Along with the evolution of Hadoop, please note the following changes:
    1. If you use Hortonworks Data Platform V2.2, the configuration files of your cluster might be using environment variables such as ${hdp.version}. If this is your situation, you need to set the mapreduce.application.framework.path property in the Hadoop properties table of this component with the path value explicitly pointing to the MapReduce framework archive of your cluster. For example:
      mapreduce.application.framework.path=/hdp/apps/2.2.0.0-2041/mapreduce/mapreduce.tar.gz#mr-framework
    2. If you use Hortonworks Data Platform V2.0.0, the type of the operating system for running the distribution and a Talend Job must be the same, such as Windows or Linux. Otherwise, you have to use Talend Jobserver to execute the Job in the same type of operating system in which the Hortonworks Data Platform V2.0.0 distribution you are using is run.

  • 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.

    In addition, since this component performs Map/Reduce computations, you also need to authenticate the related services such as the Job history server and the Resource manager or Jobtracker depending on your distribution in the corresponding field. These principals can be found in the configuration files of your distribution. For example, in a CDH4 distribution, the Resource manager principal is set in the yarn-site.xml file and the Job history principal in the mapred-site.xml file.

    This check box is available depending on the Hadoop distribution you are connecting to.

    The HBase related principals are required by the HBaseStorage function only.

  • 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.

  • NameNode URI:

    Type in the location of the NameNode corresponding to the Map/Reduce version to be used. If you are using WebHDFS, the location should be webhdfs://masternode:portnumber; if this WebHDFS is secured with SSL, the scheme should be swebhdfs and you need to use a tLibraryLoad in the Job to load the library required by the secured WebHDFS.

  • JobTracker host:

    Type in the location of the ResourceManager corresponding to the Map/Reduce version to be used.

    In JobHistory, you can easily find the execution status of your Pig Job because the name of the Job is automatically created by concatenating the name of the project that contains the Job, the name and version of the Job itself and the label of the first tPigLoad component used in it. The naming convention of a Pig Job in JobHistory is ProjectName_JobNameVersion_FirstComponentName.

    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 ):
    1. Select the Set resourcemanager scheduler address check box and enter the Scheduler address in the field that appears.

    2. 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.

    3. 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.

    4. 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.

    5. 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.

    6. 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.
  • User name:

    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. Note that this field is available depending on the distribution you are using.

WebHCat configuration

Enter the address and the authentication information of the WebHCat service of the Microsoft HD Insight cluster to be used. 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.

HDInsight configuration

Enter the authentication information of the HD Insight cluster to be used.

Windows Azure Storage configuration

Enter the address and the authentication information of the Azure Storage account to be used.

In the Container field, enter the name of the container 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.

Inspect the classpath for configurations

Select this check box to allow the component to check the configuration files in the directory you have set with the $HADOOP_CONF_DIR variable and directly read parameters from these files in this directory. This feature allows you to easily change the Hadoop configuration for the component to switch between different environments, for example, from a test environment to a production environment.

In this situation, the fields or options used to configure Hadoop connection and/or Kerberos security are hidden.

If you want to use certain parameters such as the Kerberos parameters but these parameters are not included in these Hadoop configuration files, you need to create a file called talend-site.xml and put this file into the same directory defined with $HADOOP_CONF_DIR. This talend-site.xml file should read as follows:
<!-- Put site-specific property overrides in this file. --> 
<configuration> 
    <property> 
        <name>talend.kerberos.authentication </name> 
        <value>kinit </value>
         <description> Set the Kerberos authentication method to use. Valid values are: kinit or keytab.  </description> 
    </property> 
    <property> 
        <name>talend.kerberos.keytab.principal </name>
        <value>user@BIGDATA.COM </value>
        <description> Set the keytab's principal name.  </description>
    </property> 
    <property>   
        <name>talend.kerberos.keytab.path </name> 
        <value>/kdc/user.keytab </value> 
        <description> Set the keytab's path.  </description> 
    </property> 
    <property> 
        <name>talend.encryption </name> 
        <value>none </value> 
        <description> Set the encryption method to use. Valid values are: none or ssl.  </description> 
    </property> 
    <property> 
        <name>talend.ssl.trustStore.path </name> 
        <value>ssl </value> 
        <description> Set SSL trust store path.  </description> 
    </property> 
    <property> 
        <name>talend.ssl.trustStore.password </name> 
        <value>ssl </value> 
        <description> Set SSL trust store password.  </description> 
    </property> 
</configuration>

The parameters read from these configuration files override the default ones used by the Studio. When a parameter does not exist in these configuration files, the default one is used.

Load function

Select a load function for data to be loaded:
  • PigStorage: Loads data in UTF-8 format.

  • BinStorage: Loads data in machine-readable format.

  • TextLoader: Loads unstructured data in UTF-8 format.

  • HCatLoader: Loads data from HCataLog managed tables using Pig scripts.

    This function is available only when you have selected HortonWorks as the Hadoop distribution to be used from the Distribution and the Version fields displayed in the Map/Reduce mode. For further information about HCatLoader, see https://hive.apache.org/javadocs/r2.1.1/api/org/apache/hive/hcatalog/pig/HCatLoader.html.

  • HBaseStorage: Loads data from HBase. Then you need to complete the HBase configuration in the HBase configuration area displayed.

  • SequenceFileLoader: Loads data of the SequenceFile formats. Then you need to complete the configuration of the file to be loaded in the Sequence Loader Configuration area that appears. This function is for the Map/Reduce mode only.

  • RCFilePigStorage: Loads data of the RCFile format. This function is for the Map/Reduce mode only.

  • AvroStorage: Loads Avro files. For further information about AvroStorage, see Apache's documentation on https://cwiki.apache.org/confluence/display/PIG/AvroStorage. This function is for the Map/Reduce mode only.

  • ParquetLoader: Loads Parquet file. This function is for the Map/Reduce mode only.

  • Custom: Loads data using any user-defined load function. To do this, you need to register, in the Advanced settings tab view, the jar file containing the function to be used, and then, in the field displayed next to this Load function field, specify that function.

    For example, after registering a jar file called piggybank.jar, you can enter org.apache.pig.piggybank.storage.XMLLoader('attr') as (xml:chararray) to use the custom function, XMLLoader contained in that jar file. For further information about this piggybank.jar file, see https://cwiki.apache.org/confluence/display/PIG/PiggyBank.

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 .

Input file URI

Fill in this field with the full local path to the input file.
Note:

This field is not available when you select HCatLoader from the Load function list or when you are using an S3 endpoint.

Use S3 endpoint

Select this check box to read data from a given Amazon S3 bucket folder.

Once this Use S3 endpoint check box is selected, you need to enter the following parameters in the fields that appear:
  • S3 bucket name and folder: enter the bucket name and its folder from which you need to read data. You need to separate the bucket name and the folder name using a slash (/).

  • Access key and Secret key: enter the authentication information required to connect to the Amazon S3 bucket to be used.

    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.

Note that the format of the S3 file is S3N (S3 Native Filesystem).

HCataLog Configuration

Fill the following fields to configure HCataLog managed tables on HDFS (Hadoop distributed file system):

Distribution and Version:

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 WebHCat service, the HD Insight service and the Windows Azure Storage service of that cluster in the areas that are displayed. A demonstration video about how to configure this connection is available in the following link: https://www.youtube.com/watch?v=A3QTT6VsNoM.

  • If you select Amazon EMR, see the following article about how to configure the connection: 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 .

Along with the evolution of Hadoop, please note the following changes:
  1. If you use Hortonworks Data Platform V2.2, the configuration files of your cluster might be using environment variables such as ${hdp.version}. If this is your situation, you need to set the mapreduce.application.framework.path property in the Hadoop properties table of this component with the path value explicitly pointing to the MapReduce framework archive of your cluster. For example:
    mapreduce.application.framework.path=/hdp/apps/2.2.0.0-2041/mapreduce/mapreduce.tar.gz#mr-framework
  2. If you use Hortonworks Data Platform V2.0.0, the type of the operating system for running the distribution and a Talend Job must be the same, such as Windows or Linux. Otherwise, you have to use Talend Jobserver to execute the Job in the same type of operating system in which the Hortonworks Data Platform V2.0.0 distribution you are using is run.

HCat metastore: Enter the location of the HCatalog's metastore, which is actually Hive's metastore, a system catalog. For further information about Hive and HCatalog, see http://hive.apache.org/.

Database: The database in which tables are placed.

Table: The table in which data is stored.

Partition filter: Fill this field with the partition keys to list partitions by filter.

Note:

HCataLog Configuration area is enabled only when you select HCatLoader from the Load function list. For further information about the usage of HCataLog, see https://cwiki.apache.org/confluence/display/Hive/HCatalog. For further information about the usage of Partition filter, see https://cwiki.apache.org/confluence/display/HCATALOG/Design+Document+-+Java+APIs+for+HCatalog+DDL+Commands.

Field separator

Enter character, string or regular expression to separate fields for the transferred data.

Note:

This field is enabled only when you select PigStorage from the Load function list.

Compression

Select the Force to compress the output data check box to compress the data when the data is outputted by tPigStoreResult at the end of a Pig process.

Hadoop provides different compression formats that help reduce the space needed for storing files and speed up data transfer. When you need to write and compress data using the Pig program, by default you have to add a compression format as a suffix to the path pointing to the folder in which you want to write data, for example, /user/ychen/out.bz2. However, if you select this check box, the output data will be compressed even if you do not add any compression format to that path, such as /user/ychen/out.

Note: The output path is set in the Basic settings view of tPigStoreResult.
HBase configuration

This area is available to the HBaseStorage function. The parameters to be set are:

Zookeeper quorum:

Type in the name or the URL of the Zookeeper service you use to coordinate the transaction between your Studio and your database. Note that when you configure the Zookeeper, you might need to explicitly set the zookeeper.znode.parent property to define the path to the root znode that contains all the znodes created and used by your database; then select the Set Zookeeper znode parent check box to define this property.

Zookeeper client port:

Type in the number of the client listening port of the Zookeeper service you are using.

Table name:

Enter the name of the HBase table you need to load data from.

Load key:

Select this check box to load the row key as the first column of the result schema. In this situation, you must have created this column in the schema.

Mapping:

Complete this table to map the columns of the table to be used with the schema columns you have defined for the data flow to be processed.

Sequence Loader configuration

This area is available only to the SequenceFileLoader function. Since a SequenceFile record consists of binary key/value pairs, the parameters to be set are:

Key column:

Select the Key column of a key/value record.

Value column

Select the Value column of a key/value record.

Die on subjob error

This check box is cleared by default, meaning to skip the row on subjob error and to complete the process for error-free rows.

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.

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:

Register jar

Click the [+] button to add rows to the table and from these rows, browse to the jar files to be added. For example, in order to register a jar file called piggybank.jar, click the [+] button once to add one row, then click this row to display the [...] browse button, and click this button to browse to the piggybank.jar file following the [Select Module] wizard.

Define functions

Use this table to define UDFs (User-Defined Functions), especially those requiring alias such as Apache DataFu Pig functions, to be executed when loading data.

Click the button to add as many rows as you need and specify an alias and a UDF in the relevant fields for each row.

If your Job includes a tPigMap component, once you have defined UDFs for this component in the tPigMap, this table is automatically filled. Likewise, once you have defined UDFs in this table, the Define functions table in the tPigMap component's Map Editor is automatically filled.

For information on how to define UDFs when mapping Pig flows, see the section on mapping Big Data flows of the Talend Open Studio for Big Data Getting Started Guide .

For more information on Apache DataFu Pig, see http://datafu.incubator.apache.org/.

Pig properties

Talend Studio uses a default configuration for its Pig engine to perform operations. 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 example, the default_parallel key used in Pig could be set as 20.

HBaseStorage configuration

Add and set more HBaseStorage loader options in this table. The options are:

gt: the minimum key value;

lt: the maximum key value;

gte: the minimum key value (included);

lte: the maximum key value (included);

limit: maximum number of rows to retrieve per region;

caching: number of rows to cache;

caster: the converter to use for reading values out of HBase. For example, HBaseBinaryConverter.

Define the jars to register for HCatalog

This check box appears when you are using tHCatLoader, while you can leave it clear as the Studio registers the required jar files automatically. In case any jar file is missing, you can select this check box to display the Register jar for HCatalog table and set the correct path to that missing jar.

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.

Mapred job map memory mb and Mapred job reduce memory mb

If the Hadoop distribution to be used is Hortonworks Data Platform V1.2 or Hortonworks Data Platform V1.3, you need to set proper memory allocations for the map and reduce 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.

If the distribution is YARN, then the memory parameters to be set become Map (in Mb), Reduce (in Mb) and ApplicationMaster (in Mb), accordingly. These fields allow you to dynamically allocate memory to the map and the reduce computations and the ApplicationMaster of YARN.

tStatCatcher Statistics

Select this check box to gather the Job processing metadata at the Job level as well as at each component level.

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.

Usage

Usage rule

This component is always used to start a Pig process and needs tPigStoreResult at the end to output its data.

In the Map/Reduce mode, you need only configure the Hadoop connection for the first tPigLoad component of a Pig process (a subjob), and any other tPigLoad component used in this process reuses automatically that connection created by that first tPigLoad component.

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.

Limitation

Knowledge of Pig scripts is required. If you select HCatLoader as the load function, knowledge of HCataLog DDL(HCataLog Data Definition Language, a subset of Hive Data Definition Language) is required. For further information about HCataLog DDL, see https://cwiki.apache.org/confluence/display/Hive/HCatalog.