August 16, 2023

tPigJoin – Docs for ESB 6.x

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tPigJoin

Performs inner joins and outer joins of two files based on join keys to create data
that will be used by Pig.

The tPigJoin performs join of two
files based on join keys.

tPigJoin Standard properties

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

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

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

Basic settings

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. The schema is either Built-In or stored remotely in the Repository.

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.

Note:

To make this component work, two schemas must be set: the
schema of the main flow and the schema of the lookup flow. In
the output part of the main schema, the columns of the main
input file must be manually concatenated with those of the
lookup file.

 

Built-in: The schema will be
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 in
various projects and Job designs. Related topic: see


Talend Studio
 User Guide.

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. The schema is either Built-In or stored remotely in the Repository.

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.

Note:

To make this component work, two schemas must be set: the
schema of the main flow and the schema of the lookup flow. In
the output part of the main schema, the columns of the main
input file must be manually concatenated with those of the
lookup file.

 

Built-in: The schema will be
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 in
various projects and Job designs. Related topic: see


Talend Studio
 User Guide.

Filename

Fill in the path of the Lookup
file.

Field Separator

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

Join key

Click the plus button to add lines to set the Join key for Input
file and Lookup
file.

Join mode

Select a join mode from the list:

inner-join: Select this mode to
perform an inner join of two or more relations based on Join
keys.

left-outer-join: Select this mode
to performs a left outer join of two or more relations based on Join
keys.

right-outer-join: Select this mode
to performs a right outer join of two or more relations based on
Join keys.

full-outer-join: Select this mode
to combine the effect of applying both left and right outer
joins.

For further information about inner join and outer join,
see:

http://en.wikipedia.org/wiki/Join_%28SQL%29

Advanced settings

Optimize the join

Select this check box to optimize the performance of joins using
REPLICATED, SKEWED, or MERGE joins. For further information about optimized
joins, see:

http://pig.apache.org/docs/r0.8.1/piglatin_ref1.html#Specialized+Joins

Use partitioner

Select this check box to specify the Hadoop Partitioner that
controls the partitioning of the keys of the intermediate
map-outputs. For further information about the usage of Hadoop
Partitioner, see:

http://hadoop.apache.org/docs/r2.2.0/api/org/apache/hadoop/mapred/Partitioner.html

Increase parallelism

Select this check box to set the number of reduce tasks for the
MapReduce Jobs

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 commonly used as intermediate step together with
input component and output 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
    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.

Limitation

Knowledge of Pig scripts is required.

Scenario: Joining two files based on an exact match and saving the result to a local
file

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

This scenario describes a four-component Job that combines data of an input file and a
reference file that matches a given join key, removes unwanted columns, and then saves
the final result to a local file.

The main input file contains the information about people’s IDs, first names, last
names, group IDs, and salaries, as shown below:

The reference file contains only the information of group IDs and group names:

Dropping and linking the components

  1. Drop the following components from the Palette to the design workspace: tPigLoad, tPigJoin,
    tPigFilterColumns, and tPigStoreResult.
  2. Connect these components in a series using Row >
    Pig Combine     connections.

    use_case_tpigjoin-1.png

Configuring the components

Loading the main input file

  1. Double-click tPigLoad to open its
    Basic settings view.

    use_case_tpigjoin-2.png

  2. Click the […] button next to Edit schema to open the [Schema] dialog box.

    use_case_tpigjoin-3.png

  3. Click the [+] button to add columns, name
    them and define the column types according to the structure of the input
    file. In this example, the input schema has five columns: id (integer), firstName (string), lastName (string), groupId (integer), and salary (double).

    Then click OK to validate the setting and
    close the dialog box.
  4. Click Local in the Mode area.
  5. Select PigStorage from the Load function list.
  6. Fill in the Input file URI field with the
    full path to the input file, and leave the rest of the setting as they
    are.

Loading the reference file and setting up an inner join

  1. Double-click tPigJoin to open its
    Basic settings view.

    use_case_tpigjoin-4.png

  2. Click the […] for the main schema to
    open the [Schema] dialog box.

    use_case_tpigjoin-5.png

  3. Check that input schema is correctly retrieved from the preceding
    component. If needed, click the [->>]
    button to copy all the columns of the input schema to the output schema.
  4. Click the [+] button under the output
    panel to add new columns according to the data structure of the reference
    file, groupId_ref (integer) and groupName (string) in this example. Then click
    OK to close the dialog box.
  5. Click the […] for the schema lookup
    flow to open the [Schema] dialog
    box.

    use_case_tpigjoin-6.png

  6. Click the [+] button under the output
    panel to add two columns: groupId_ref
    (integer) and groupName (string), and
    then click OK to close the dialog
    box.
  7. In the Filename field, specify the full
    path to the reference file.
  8. Click the [+] button under the Join key table to add a new line, and select
    groupId and groupId_ref
    respectively from the Input and Lookup lists to match data from the main input
    flow with data from the lookup flow based on the group ID.
  9. From the Join Mode list, select inner-join.

Defining the final output schema and the output file

  1. Double-click tPigFilterColumns to open
    its Basic settings view.

    use_case_tpigjoin-7.png

  2. Click the […] button next to Edit schema to open the [Schema] dialog box.

    use_case_tpigjoin-8.png

  3. From the input schema, select the columns you want to include in your
    result file by clicking them one after another while pressing the Shift key, and click the
    [->]     button to copy them to the output schema. Then, click
    OK to validate the schema setting and
    close the dialog box.

    In this example, we want the result file to include all the information
    except the group IDs.
  4. Double-click tPigStoreResult to open its
    Basic settings view.

    use_case_tpigjoin-9.png

  5. Click Sync columns to retrieve the schema
    structure from the preceding component.
  6. Fill in the Result file field with the
    full path to the result file, and select the Remove
    result file directory if exists     check box.
  7. Select PigStorage from the Store function list, and leave rest of the
    settings as they are.

Saving and executing the Job

  1. Press Ctrl+S to save your Job.
  2. Press F6 or click Run on the Run tab to run
    the Job.

    The result file includes all the information related to people of group A
    and group B, except their group IDs.
    use_case_tpigjoin-10.png

Document get from Talend https://help.talend.com
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Docs 6.x
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