tMatchGroup in Talend Map/Reduce Jobs - 6.1

Talend Components Reference Guide

EnrichVersion
6.1
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Talend Big Data
Talend Big Data Platform
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Talend Data Integration
Talend Data Management Platform
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task
Data Governance
Data Quality and Preparation
Design and Development
EnrichPlatform
Talend Studio

Warning

The information in this section is only for users that have subscribed to one of the Talend solutions with Big Data and is not applicable to Talend Open Studio for Big Data users.

In a Talend Map/Reduce Job, tMatchGroup, as well as the other Map/Reduce components preceding it, generates native Map/Reduce code. This section presents the specific properties of tMatchGroup when it is used in that situation. For further information about a Talend Map/Reduce Job, see Talend Big Data Getting Started Guide.

Component family

Data Quality

 

Basic settings

Schema and Edit schema

A schema is a row description, it defines the number of fields to be processed and passed on to the next component. The schema is either Built-in or stored remotely in the Repository.

Click Sync columns to retrieve the schema from the previous component in the Job.

The output schema of this component contains the following read-only fields:

GID: provides a group identifier of the data type String.

Note

All Jobs with tMatchGroup that are migrated from older releases into your current studio may provide a group identifier of the data type Long. If you want to have a group identifier of the data type String, you must replace the tMatchGroup component in these Jobs with tMatchGroup from the studio Palette.

GRP_SIZE: counts the number of records in the group, computed only on the master record.

MASTER: identifies, by true or false, if the record used in the matching comparisons is a master record. There is only one master record per group.

Each input record will be compared to the master record, if they match, the input record will be in the group.

SCORE: measures the distance between the input record and the master record according to the matching algorithm used.

In case the tMatchGroup component is used to have multiple output flows, the score in this column decides to what output group the record should go.

GRP_QUALITY: provides the quality of similarities in the group by taking the minimal matching value. Only the master record has a quality score.

 

 

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 and stored the schema in the Repository. You can reuse it in other projects and job designs. Related topic: see Talend Studio User Guide.

PREVIEW

This button opens a configuration wizard that enables you to define production environments and their match rules or to import match rules from the studio repository. For further information, see Configuration wizard

Key Definition

Input Key Attribute

Select the column(s) from the input flow on which you want to apply a matching algorithm.

Note

When you select a date column on which to apply an algorithm or a matching algorithm, you can decide what to compare in the date format.

For example, if you want to only compare the year in the date, in the component schema set the type of the date column to Date and then enter "yyyy" in the Date Pattern field. The component then converts the date format to a string according to the pattern defined in the schema before starting a string comparison.

 

 

Matching Function

Select a matching algorithm from the list:

Exact: matches each processed entry to all possible reference entries with exactly the same value. It returns 1 when the two strings exactly match, otherwise it returns 0.

Exact - ignore case: matches each processed entry to all possible reference entries with exactly the same value while ignoring the value case.

Soundex: matches processed entries according to a standard English phonetic algorithm. It indexes strings by sound, as pronounced in English, for example "Hello": "H400".

Levenshtein (edit distance): calculates the minimum number of edits (insertion, deletion or substitution) required to transform one string into another. Using this algorithm in the tMatchGroup component, you do not need to specify a maximum distance. The component automatically calculates a matching percentage based on the distance. This matching score will be used for the global matching calculation, based on the weight you assign in the Confidence Weight field.

Metaphone: Based on a phonetic algorithm for indexing entries by their pronunciation. It first loads the phonetics of all entries of the lookup reference and checks all entries of the main flow against the entries of the reference flow.

Double Metaphone: a new version of the Metaphone phonetic algorithm, that produces more accurate results than the original algorithm. It can return both a primary and a secondary code for a string. This accounts for some ambiguous cases as well as for multiple variants of surnames with common ancestry.

Soundex FR: matches processed entries according to a standard French phonetic algorithm.

Jaro: matches processed entries according to spelling deviations. It counts the number of matched characters between two strings. The higher the distance is, the more similar the strings are.

Jaro-Winkler: a variant of Jaro, but it gives more importance to the beginning of the string.

Fingerprint key: matches entries after doing the following sequential process:

  1. remove leading and trailing whitespace,

  2. change all characters to their lowercase representation,

  3. remove all punctuation and control characters,

  4. split the string into whitespace-separated tokens,

  5. sort the tokens and remove duplicates,

  6. join the tokens back together,

    Because the string parts are sorted, the given order of tokens does not matter. So, Cruise, Tom and Tom Cruise both end up with a fingerprint cruise tom and therefore end up in the same cluster.

  7. normalize extended western characters to their ASCII representation, for example gödel to godel.

    This reproduce data entry mistakes performed when entering extended characters with an ASCII-only keyboard. However, this procedure can also lead to false positives, for example gödel and godél would both end up with godel as their fingerprint but they are likely to be different names. So this might work less effectively for datasets where extended characters play substantial differentiation role.

q-grams: matches processed entries by dividing strings into letter blocks of length q in order to create a number of q length grams. The matching result is given as the number of q-gram matches over possible q-grams.

custom...: enables you to load an external matching algorithm from a Java library using the custom matcher class column.

For further information about how to load an external Java library, see tLibraryLoad.

For further information about how to create a custom matching algorithm, see Creating a custom matching algorithm.

For a related scenario about how to use a custom matching algorithm, see Scenario 2: Using a custom matching algorithm to match entries.

 

Custom Matcher

When you select Custom as the matching type, enter the path pointing to the custom class (external matching algorithm) you need to use. This path is defined by yourself in the library file (.jar file).

For example, to use a MyDistance.class class stored in the directory org/talend/mydistance in a user-defined mydistance.jar library, the path to be entered is org.talend.mydistance.MyDistance.

 

 

Weight

Set a numerical weight for each attribute (column) of the key definition. The values can be anything >= 0.

 

Handle Null

To handle null values, select from the list the null operator you want to use on the column:

Null Match Null: a Null attribute only matches another Null attribute.

Null Match None: a Null attribute never matches another attribute.

Null Match All: a Null attribute matches any other value of an attribute.

For example, if we have two columns, name and firstname where the name is never null, but the first name can be null.

If we have two records:

"Doe", "John"

"Doe", ""

Depending on the operator you choose, these two records may or may not match:

Null Match Null: they do not match.

Null Match None: they do not match.

Null Match All: they match.

And for the records:

"Doe", ""

"Doe", ""

Null Match Null: they match.

Null Match None: they do not match.

Null Match All: they match.

Match Threshold

Enter the match probability. Two data records match when the probability is above the set value.

You can enter a different match threshold for each match rule.

Blocking Selection

Input Column

If required, select the column(s) from the input flow according to which you want to partition the processed data in blocks, this is usually referred to as "blocking".

Blocking reduces the number of pairs of records that needs to be examined. In blocking, input data is partitioned into exhaustive blocks designed to increase the proportion of matches observed while decreasing the number of pairs to compare. Comparisons are restricted to record pairs within each block.

Using blocking column(s) is very useful when you are processing very big data.

Advanced settings

Store on disk

Select the Store on disk check box if you want to store processed data blocks on the disk to maximize system performance.

Max buffer size: Type in the size of physical memory you want to allocate to processed data.

Temporary data directory path: Set the location where the temporary file should be stored.

 

Multiple output

Select the Separate output check box to have three different output flows:

-Uniques: when the group score (minimal distance computed in the record) is equal to 1, the record is listed in this flow.

-Matches: when the group score (minimal distance computed in the record) is higher than the threshold you define in the Confidence threshold field, the record is listed in this flow.

-Suspects: when the group score (minimal distance computed in the record) is below the threshold you define in the Confidence threshold field, the record is listed in this flow.

Confident match threshold: set a numerical value between the current Match threshold and 1. Above this threshold, you can be confident in the quality of the group.

 

Multi-pass

Select this check box to enable a tMatchGroup component to receive data sets from another tMatchGroup that precedes it in the Job. This will refine the groups received by each of the tMatchGroup components through creating data partitions based on different blocking keys.

For an example Job, see Scenario 2: Matching customer data through multiple passes

 

Sort the output data by GID

Select this check box to group the output data by the group identifier.

The output is sorted in ascending alphanumeric order by group identifier.

 

Output distance details

Select this check box to add an output column MATCHING_DISTANCES in the schema of the component. This column provides the distance between the input and master records in each group.

Note

When you use two tMatchGroup components in a Job and you want to use the Output distance details option, you must select this check box in both components before you link them together. If the components are linked, select the check box in the second component in the Job flow first then in the first component, otherwise you may have an issue as there are two columns in the output schema with the same name. Selecting this option in only one tMatchGroup is not useful and may bring schema mismatch issues.

 

Display detailed labels

Select this check box to have in the output MATCHING_DISTANCES column not only the matching distance but also the names of the columns used as key attributes in the applied rule.

For example, if you try to match on first name and last name fields, lname and fname, the output would be fname:1.0|lname:0.97 when the check box is selected and 1.0|0.97 when it is not selected.

 

tStatCatcher Statistics

Select this check box to collect log data at the component level. Note that this check box is not available in the Map/Reduce version of the component.

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 in Map/Reduce Jobs

In a Talend Map/Reduce Job, this component is used as an intermediate step and other components used along with it must be Map/Reduce components, too. They generate native Map/Reduce code that can be executed directly in Hadoop.

You need to use the Hadoop Configuration tab in the Run view to define the connection to a given Hadoop distribution for the whole Job.

For further information about a Talend Map/Reduce Job, see the sections describing how to create, convert and configure a Talend Map/Reduce Job of the Talend Big Data Getting Started Guide.

For a scenario demonstrating a Map/Reduce Job using this component, see Scenario: Matching data through multiple passes using Map/Reduce components.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs, and non Map/Reduce Jobs.

Limitation/prerequisite

n/a

Working principle

This component implements the MapReduce model, based on the blocking keys defined in the Blocking definition table of the Basic settings view.

This implementation proceeds as follows:

  1. Splits the input rows in groups of a given size.

  2. Implements a Map Class that creates a map between each key and a list of records.

  3. Shuffles the records to group those with the same key together.

  4. Applies, on each key, the algorithm defined in the Key definition table of the Basic settings view.

    Then accordingly, this component reads the records, compares them with the master records, groups the similar ones, and classes each of the rest as a master record.

  5. Outputs the groups of similar records with their group IDs, group sizes, matching distances and scores.

Configuration wizard in Map/Reduce Jobs

Warning

The information in this section is only for users that have subscribed to one of the Talend solutions with Big Data and is not applicable to Talend Open Studio for Big Data users.

In a Talend Map/Reduce Job, tMatchGroup, as well as the whole Map/Reduce Job using it, generates native Map/Reduce code. This section presents the specific settings in the configuration wizard of tMatchGroup when it is used in that situation. For further information about a Talend Map/Reduce Job, see the Talend Big Data Getting Started Guide.

You can not open the configuration wizard unless you link an input component to the tMatchGroup component.

From the configuration wizard in tMatchGroup, you can:

  • define multiple conditions using several match rules to group data,

  • set different match intervals for each rule,

  • import match rules created and tested in the studio and stored in the repository, and use them in your match Jobs. You can only import rules configured with the VSR algorithm. For further information, see Importing match rules from the studio repository.

  • select a blocking key to partition data.

The match results on multiple conditions will list data records that meet any of the defined rules.

To create match rules from the configuration wizard, do the following:

  1. Click the [+] button on the match rule bar.

  2. Set the parameters for the new rule in the Key definition table and define its match interval.

  3. Repeat the above steps to create as many match rules as needed. You can define a different match interval for each rule.

    When you define multiple rules, the Job conducts an OR match operation. It evaluates data records against the first rule and the records that match are not evaluated against the second rule.

  4. In the Blocking Selection table, select the column(s) from the input flow which you want to use as a blocking key.

    Defining a blocking key is not mandatory but is very useful when you are processing big data sets. A blocking key partitions data in blocks and so reduces the number of records that need to be examined. This key can come from a tGenKey component (and would be called T_GEN_KEY) or directly from the input schema.

  5. At the bottom right corner of the wizard, click either:

    • OK to save the current configuration.

    • Cancel to close the wizard and keep the configuration saved initially in the wizard.