tDuplicateRow
Creates duplicates with meaningful data for data quality functional testing
purposes.
tDuplicateRow generates duplicate
data from an input flow. It groups similar duplicates together and
identifies the original record of each group by
true
.
This component can be used in combination with the tRowGenerator component to generate
duplicate data.
Date functions in tDuplicateRow
The Function list for a Date
column is date-specific. It allows you to decide the type of modification you want to do
on date values.
There are three different ways to generate the date value for duplicated
records:
-
Modify date value: randomly selects the day,
month or year to modify and puts a random value in place. -
Switch day month value: switches the day and
month values. If the original day value is greater than 12, then the new month
value will be ((N-1) mod 12) +1. For example: If the
original day is equal to 13, then the new month is equal to 1. -
Replace by random date: generates a random
date with day, month and year values.
tDuplicateRow Standard properties
These properties are used to configure tDuplicateRow running
in the Standard Job framework.
The Standard
tDuplicateRow component belongs to the Data Quality family.
This component is available in Talend Data Management Platform, Talend Big Data Platform, Talend Real Time Big Data Platform, Talend Data Services Platform, Talend MDM Platform and Talend Data Fabric.
Basic settings
Schema and Edit schema |
A schema is a row description. It defines the number of fields Click Sync columns The output schema of this component contains one read-only |
 |
Built-In: You create and store the schema locally for this component |
 |
Repository: You have already created the schema and stored it in the |
Percentage of duplicated records |
Enter the percentage of the duplicate rows you want to |
Distribution of duplicates |
Name: Select the probability
Average group size: Set the |
Modifications |
Define in the table what fields to change in a row and –Input Column: –Modification These modifications are based on the function you select –Function: Select The Function list –Max Modification –Synonym Index This field is available if you select the Synonym replace function which means that the You must use the tSynonymOutput component to create a Lucene index and |
Advanced settings
Seed for random generator |
Set a random number if you want to generate the same Repeating the execution with a different value for the Keep this field empty if you want to generate a different |
tStat |
Select this check box to collect log data at the |
Usage
Usage rule |
This component helps you to generate duplicate data of an |
Generating duplicate data from an input flow
This scenario applies only to Talend Data Management Platform, Talend Big Data Platform, Talend Real Time Big Data Platform, Talend Data Services Platform, Talend MDM Platform and Talend Data Fabric.
This scenario describes a basic Job that generates a sample of duplicate data from an
input flow by using probability theories and specific criteria on three columns:
Name, City and DOB (date
of birth).
Below is a capture of a sample data of the input flow:
Setting up the Job
-
Drop the following components from the Palette onto the design workspace: tFileInputDelimited, tDuplicateRow and tFileOutputDelimited.
-
Connect all the components together using the Row
> Main link.
Configuring the input data
-
Double-click tFileInputDelimited to display
the Basic settings view and define the
component properties. -
In the File name/Stream field, browse to the
file to be used as the main input.This file provides some information about customers. -
Define the row and field separators the header and footer in the corresponding
fields, if any. -
Click the […] button next to Edit schema to open a dialog box and define the input
schema. According to the input file structure, the schema is made of ten
columns. -
Click the [+] button and define the input
columns in the dialog box as in the above figure. Click OK to close the dialog box. -
If needed, right-click tFileInputDelimited
and select Data Viewer to display a view of the
input data.
Configuring the duplicate data
-
Double-click tDuplicateRow to display the
Basic settings view and define the
component properties. -
Click the Edit schema button to view the
input and output columns and do any modifications in the output schema, if
needed.The output schema of this component contains one read-only column,
ORIGINAL_MARK. This column identifies, by
true
orfalse
, if the record is an original or a
duplicate record. There is only one original record per group of
duplicates. -
In the Percentage of duplicated records
field, enter the percentage of the duplicate rows you want to have in the output
flow. -
In the Distribution of duplicates area,
select the Bernoulli distribution (probability
theory) you want to use to generate duplicates. Set an average of how many
duplicate records to have in each group. -
Click the plus button below the Modifications
table and add four lines in the table.This table enables you to define what values to change in a given column and
how to change them in order to generate duplicates:-
In the Input Column, select the
column from the input flow from which you want to generate duplicates,
Name, City and
DOB in this example.When you add a column twice in the table and select different
functions, you generate duplicates from the same field with different
values. For example, in this scenario you modify the duplicate names
with the Soundex replace function with probability
of 50%, and then you modify it again with the Exchange
characters function with the probability of 50%. This
means, the Name field of the duplicate record may
still not be modified after the second function with the following
probability: (1-0.5)*(1-0.5) = 0.25.If you want to make sure that all the duplicated records are modified,
you must set the Modification Rate to
1. -
In the Modification Rate column,
enter a rate of the duplicate records you want to generate from the
input column. -
From the Function list, select the
function that will decide what modification to do on a value to
duplicate it.In this example, there will be duplicate names with different sounds
and characters and duplicate city names with different sounds. Date
values in the date of birth column will be randomly changed here. -
In the Max Modification Count column,
enter a maximum number of the values to be modified in each
field.
-
In the Input Column, select the
-
Click the Advanced settings tab and enter a
random number in the Seed for random generator
field.By setting a number in this field, you will generate the same sample of
duplicate data in each execution of the Job. Change the value if you want to
generate a different sample.
Configuring the output component
-
Double-click tFileOutputDelimited to display
its Basic settings view and define the
component properties. -
In the File Name field, specify the path to
the file to which you want to write the duplicate data,
duplicated_records in this example. -
Define the row and field separators in the corresponding fields, if
any.
Executing the Job
-
Save your Job and press F6 to execute
it.Duplicate data is generated and written to the output file. -
Right-click the output component and select Data
Viewer to display the duplicate data.Duplicate records have been marked as false in the
ORIGINAL_MARK column.Some data has been modified in the Name,
City and DOB fields according to
the criteria you set in the Modifications table
and duplicate records have been generated based on these modifications.For example, if you compare the original name Mrs Morgan
Ross and the duplicate name Mrs M
rganosRiss, you will see that the two functions have been used on
this duplicate record: the letter o has been exchanged with
a space, and also the sound has been replaced in Ross and
Riss. However, the soundex code has not been changed
for the replaced sound. -
In the tDuplicateRow basic settings and in
the Distribution of duplicates area, select a
different distribution, Bernoulli distribution for example,
and run the Job.Different duplicates are generated from the same input flow according to the
selected distribution as shown in the below figure.
Showing chart results of each of the probability distributions
The best way to see how duplicates are generated according to each of the three
probability distributions is to create a match analysis on each of the results and
compare the charts.
Define the match analysis
-
From the
Profiling
perspective,
right-click Metadata and create a file
connection to the duplicated_records output file generated
by the Job.For further information, check the Data Profiling part in the
Talend Studio User Guide. -
Expand the new file connection under Metadata
and select Analyze matches. -
Follow the steps in the wizard to define the analysis metadata and click
Finish to open the analysis editor. -
In the Matching Key table, define a match key
on the Code column to group records by their
identification, records which have the same code are grouped together. -
Click Chart below the table to show the
duplicates generated according to the Bernoulli
distribution selected previously in the Job.
Run the analysis with different probability distributions
-
Switch back to the
Integration
perspective,
select Poisson distribution in the basic
settings of tDuplicateRow and run the
Job. -
In the
Profiling
perspective, click Chart below the Matching Key
table to show the duplicates generated according to the Poisson distribution. -
Run the Job with the Geometric distribution,
then click the Chart in the Profiling to show the duplicates generated according
to the Geometric distribution.The table below shows how results of the generated duplicates differ according
to the probability distribution you select in the tDuplicateRow component.Probability distribution
Duplicate results
Description
Bernoulli distribution
The curve is symmetrical. The groups of duplicates are
distributed evenly on each side of an average value, 4 in
this example. This average value is the average number of
duplicates in a group of duplicates and this value is the
number you set in the Average group
size field in the basic settings of the
tDuplicateRow
component.Poisson distribution
The curve is not symmetrical. The groups of duplicates are
distributed unevenly.Geometric distribution
The form of the curve is decided by the percentage you set
for the duplicated records in the tDuplicateRow basic settings. The higher the
percentage is, the fewer groups with many records you will
have.In this example the percentage for the duplicate records
is set to 80%. This is why many groups
with two-record duplicates are generated
(148 groups), while there is only
one group that has 14, 15 and 16
duplicates.