August 15, 2023

tELTHiveMap – Docs for ESB 6.x

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tELTHiveMap

Builds graphically the Hive QL statement in order to transform data.

The three ELT Hive components are closely related, in terms of their
operating conditions. These components should be used to handle Hive DB schemas to
generate Insert statements, including clauses, which are to be executed in the DB output
table defined.

This component uses the tables provided as input, to feed the
parameter in the built statement. The statement can include inner or
outer joins to be implemented between tables or between one table
and its aliases.

tELTHiveMap Standard properties

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

The Standard
tELTHiveMap component belongs to the ELT family.

The component in this framework is available when you are using one of the Talend solutions 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.

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.

ELT Hive Map editor

The ELT Map editor helps you to define the output schema as
well as build graphically the Hive QL statement to be executed. The column
names of schema can be different from the column names in the database.

Style link

Select the way in which links are displayed.

Auto: By default, the links between the
input and output schemas and the Web service parameters are in the form of
curves.

Bezier curve: Links between the schema
and the Web service parameters are in the form of curve.

Line: Links between the schema and the
Web service parameters are in the form of straight lines.

This option slightly optimizes performance.

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 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    , you can find more details about how
    to configure an Amazon EMR cluster 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
    .

  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. Along with the evolution of
Hadoop, please note the following changes:

  • 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:

  • 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.

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.

Connection mode

Select a connection mode from the list. The options vary
depending on the distribution you are using.

Host

Database server IP address.

Port

Listening port number of DB server.

Database

Name of the database. According to the documentation of Hive,
the only database name supported is default.

For further information, see https://cwiki.apache.org/confluence/display/Hive/HiveClient.

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 4.0.1 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.

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.

When you need to enable Hive components to access HBase:

These parameters are available only when the Use an existing
connection check box is clear.

Store by HBase

Select this check box to display the parameters to be set to allow the Hive components to
access HBase tables:

  • Once this access is configured, you will be able to use, in tHiveRow and tHiveInput, the Hive QL statements to read and write data in
    HBase.

  • If you are using the Kerberos authentication, you need to define the HBase
    related principals in the corresponding fields that are displayed.

For further information about this access involving Hive and HBase, see Apache’s Hive
documentation about Hive/HBase integration.

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.

Define the jars to register for
HBase

Select this check box to display the Register jar for
HBase table, in which you can register any missing jar file required by
HBase, for example, the Hive Storage Handler, by default, registered along with your Hive
installation.
Register jar for HBase

Click the [+] button to add rows to this table, then, in the Jar name column, select the jar file(s) to be registered and in the
Jar path column, enter the path(s) pointing to that or
those jar file(s).

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.

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

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.

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.

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.

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

tELTHiveMap is used along with a
tELTHiveInput and tELTHiveOutput. Note that the Output link
to be used with these components must correspond strictly to the
syntax of the table name.

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.

Note:

The ELT components do not handle actual data flow but only
schema information.

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 Scenario: Reading data from databases through context-based dynamic connections and Scenario: Reading data from different MySQL databases using dynamically loaded connection parameters. For more information on Dynamic
settings and context variables, see
Talend Studio User Guide.

Prerequisites

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

  • Ensure that you have installed the MapR client in the machine where the Studio is,
    and added the MapR client library to the PATH variable of that machine. According
    to MapR’s documentation, the library or libraries of a MapR client corresponding to
    each OS version can be found under MAPR_INSTALL
    hadoophadoop-VERSIONlib
    ative    . For example, the library for
    Windows is lib
    ativeMapRClient.dll     in the MapR
    client jar file. For further information, see the following link from MapR: http://www.mapr.com/blog/basic-notes-on-configuring-eclipse-as-a-hadoop-development-environment-for-mapr.

    Without adding the specified library or libraries, you may encounter the following
    error: no MapRClient in java.library.path.

  • Set the -Djava.library.path argument, for example, in the Job Run VM arguments area
    of the Run/Debug view in the [Preferences] dialog box in the Window menu. This argument provides to the Studio the path to the
    native library of that MapR client. This allows the subscription-based users to make
    full use of the Data viewer to view locally in the
    Studio the data stored in MapR.

For further information about how to install a Hadoop distribution, see the manuals
corresponding to the Hadoop distribution you are using.

Scenario: Joining table columns and writing them into Hive

This scenario applies only to a Talend solution with Big Data.

This scenario uses a four-component Job to join the columns selected from two Hive
tables and write them into another Hive table.

use_case-telthivemap1.png

Preparing the Hive tables

  1. Create the Hive table you want to write data in. In this scenario, this
    table is named as agg_result, and you can
    create it using the following statement in tHiveRow:
    create table agg_result (id int, name string, address string, sum1 string, postal string, state string, capital string, mostpopulouscity string) partitioned by (type string) row format delimited fields terminated by ';' location '/user/ychen/hive/table/agg_result'

    In this statement,
    ‘/user/ychen/hive/table/agg_result’     is the directory used in
    this scenario to store this created table in HDFS. You need to replace it
    with the directory you want to use in your environment.
    For further information about tHiveRow,
    see tHiveRow.
  2. Create two input Hive tables containing the columns you want to join and
    aggregate these columns into the output Hive table, agg_result. The statements to be used are:
    create table customer (id int, name string, address string, idState int, id2 int, regTime string, registerTime string, sum1 string, sum2 string) row format delimited fields terminated by ';' location '/user/ychen/hive/table/customer'
    and
    create table state_city (id int, postal string, state string, capital int, mostpopulouscity string) row format delimited fields terminated by ';' location '/user/ychen/hive/table/state_city'
  3. Use tHiveRow to load data into the two
    input tables, customer and state_city. The statements to be used are:
    "LOAD DATA LOCAL INPATH 'C:/tmp/customer.csv' OVERWRITE INTO TABLE customer"
    and
    "LOAD DATA LOCAL INPATH 'C:/tmp/State_City.csv' OVERWRITE INTO TABLE state_city"

    The two files, customer.csv and
    State_City.csv, are two local files
    we created for this scenario. You need to create your own files to provide
    data to the input Hive tables. The data schema of each file should be
    identical with their corresponding table.
    You can use tRowGenerator and tFileOutputDelimited to create these two files
    easily. For further information about these two components, see tRowGenerator and tFileOutputDelimited.

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

Linking the components

  1. In the
    Integration
     perspective
    of
    Talend Studio
    ,
    create an empty Job from the Job Designs
    node in the Repository tree view.

    For further information about how to create a Job, see
    Talend Studio User Guide    .
  2. Drop two tELTHiveInput components and
    tELTHiveMap and tELTHiveOutput onto the workspace.
  3. Connect them using the Row > Main
    link.

    Each time when you connect two components, a wizard pops up to prompt you
    to name the link you are creating. This name must be the same as that of the
    Hive table you want the active component to process. In this scenario, the
    input tables the two tELTHiveInput
    components will handle are customer and
    state_city and the output table
    tELTHiveOutput will handle is agg_result.

Configuring the input schemas

  1. Double-click the tELTHiveInput component
    using the customer link to open its
    Component view.

    use_case-telthivemap3.png

  2. Click the […] button next to Edit schema to open the schema editor.
  3. Click the

    Button_Plus.png

    button as many times as required to add columns and
    rename them to replicate the schema of the customer table we created earlier in Hive.

    use_case-telthivemap4.png

    You can as well use the customer schema you retrieve and store in the Repository to set up this schema. For further
    information about how to set up a connection to Hive and retrieve and store the
    schema in Repository, see
    Talend Studio User Guide    .
  4. In the Default table name field, enter
    the name of the input table, customer, to
    be processed by this component.
  5. Double-click the other tELTHiveInput
    component using the state_city link to
    open its Component view.

    use_case-telthivemap10.png

  6. Click the […] button next to Edit schema to open the schema editor.
  7. Click the

    Button_Plus.png

    button as many times as required to add columns and
    rename them to replicate the schema of the state_city table we created earlier in Hive.

    use_case-telthivemap5.png

  8. In the Default table name field, enter
    the name of the input table, state_city,
    to be processed by this component.

Mapping the input and the output schemas

Configuring the connection to Hive

  1. Click tELTHiveMap, then, click Component to open its Component view.

    use_case-telthivemap6.png

  2. In the Version area, select the Hadoop
    distribution you are using and the Hive version.
  3. In the Connection mode list, select the
    connection mode you want to use. If your distribution is HortonWorks, this
    mode is Embedded only.
  4. In the Host field and the Port field, enter the authentication information
    for the component to connect to Hive. For example, the host is talend-hdp-all and the port is 9083.
  5. Select the Set Jobtracker URI check box
    and enter the location of the Jobtracker. For example, talend-hdp-all:50300.
  6. Select the Set NameNode URI
    check box and enter the location of the NameNode. For example, hdfs://talend-hdp-all:8020. 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.

Mapping the schemas

  1. Click ELT Hive Map Editor to map the
    schemas

    use_case-telthivemap7.png

  2. On the input side (left in the figure), click the Add alias button to add the table to be used.

    add_alias.png

  3. In the pop-up window, select the customer table, then click OK.
  4. Repeat the operations to select the state_city table.
  5. Drag and drop the idstate column from
    the customer table onto the id column of the state_city table. Thus an inner join is created
    automatically.
  6. On the output side (the right side in the figure), the agg_result table is empty at first. Click

    Button_Plus.png

    at the bottom of this side to add as many columns as
    required and rename them to replicate the schema of the agg_result table you created earlier in Hive.

    use_case-telthivemap8.png

    Note:

    The type column is the partition
    column of the agg_result table and
    should not be replicated in this schema. For further information about
    the partition column of the Hive table, see the Hive manual.

  7. From the customer table, drop id, name,
    address, and sum1 to the corresponding columns in the agg_result table.
  8. From the state_city table, drop
    postal, state, capital and
    mostpopulouscity to the corresponding
    columns in the agg_result table.
  9. Click OK to validate these
    changes.

Configuring the output schema

  1. Double-click tELTHiveOutput to open its
    Component view.

    use_case-telthivemap9.png

  2. If this component does not have the same schema of the preceding
    component, a warning icon appears. In this case, click the Sync columns button to retrieve the schema from
    the preceding one and once done, the warning icon disappears.
  3. In the Default table name field, enter
    the output table you want to write data in. In this example, it is agg_result.
  4. In the Field partition table, click

    Button_Plus.png

    to add one row. This allows you to write data in the
    partition column of the agg_result
    table.

    This partition column was defined the moment we created the agg_result table using partitioned by
    (type string)     in the Create statement presented earlier. This
    partition column is type, which describes
    the type of a customer.
  5. In Partition column, enter type without any quotation marks and in
    Partition value, enter prospective in single quotation marks.

Executing the Job

Press F6 to run this Job.
Once done, verify agg_result in Hive using, for
example,
use_case-telthivemap11.png

This figure present only a part of the table. You can find that the selected input
columns are aggregated and written into the
agg_result table and the partition column is filled with the value
prospective.

Document get from Talend https://help.talend.com
Thank you for watching.
Docs 6.x
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