tHiveInput Standard properties - 7.0

Hive

EnrichVersion
7.0
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Talend Open Studio for Big Data
Talend Open Studio for Data Integration
Talend Open Studio for ESB
Talend Open Studio for MDM
Talend Real-Time Big Data Platform
EnrichPlatform
Talend Studio
task
Data Governance > Third-party systems > Database components > Hive components
Data Quality and Preparation > Third-party systems > Database components > Hive components
Design and Development > Third-party systems > Database components > Hive components

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 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

    Leave the default value global as is, since this is the only region which the Studio supports for Google Cloud Platform.

    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.

    Access Key and 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/ and see the description about developer keys.

    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.

    For further information about this Google Credentials file, see the administrator of your Google Cloud Platform or visit Google Cloud Platform Auth Guide.

  • 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.

    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 this configuration, you do not define where to read or write your business data but define where to deploy your Job only. Therefore always use the Azure Blob Storage system for this configuration.

    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.

    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.

    Note:

    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.
    1. Hive principal uses the value of hive.metastore.kerberos.principal. This is the service principal of the Hive Metastore.

    2. HiveServer2 local user principal uses the value of hive.server2.authentication.kerberos.principal.

    3. HiveServer2 local user keytab uses the value of hive.server2.authentication.kerberos.keytab

    4. Metastore URL uses the value of javax.jdo.option.ConnectionURL. This is the JDBC connection string to the Hive Metastore.

    5. Driver class uses the value of javax.jdo.option.ConnectionDriverName. This is the name of the driver for the JDBC connection.

    6. 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.

    7. 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 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.

    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 ):
    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.

    For further information about the Hadoop Map/Reduce framework, see the Map/Reduce tutorial in Apache's Hadoop documentation on 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; 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.

    For further information about the Hadoop Map/Reduce framework, see the Map/Reduce tutorial in Apache's Hadoop documentation on http://hadoop.apache.org.

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 click the relevant connection component to reuse the connection details you already defined.

Note: 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:
  1. 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.

  2. 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 Insightcluster 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, 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.

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 .

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.

Note: 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 among the following ones:
  • Hortonworks: V2.1 and V2.2.

  • MapR: V4.0.1.

  • 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.

Note:

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:

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

Note:

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 and Reading data from different MySQL databases using dynamically loaded connection parameters. For more information on Dynamic settings and context variables, see Talend Studio User Guide.

Prerequisites

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

  • Ensure that you have installed the MapR client in the machine where the Studio is, and added the MapR client library to the PATH variable of that machine. According to MapR's documentation, the library or libraries of a MapR client corresponding to each OS version can be found under MAPR_INSTALL\ 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.