This component will be available in the Palette of Talend Studio on the condition that you have subscribed to one of the Talend Platform products.
tMatchGroup compares columns in both standard input data flows and in M/R input data flows by using matching methods and groups similar encountered duplicates together.
Several tMatchGroup components can be used sequentially to match data against different blocking keys. This will refine the groups received by each of the tMatchGroup components through creating different data partitions that overlap previous data blocks and so on.
In defining a group, the first processed record of each group is the master record of the group. The other records are computed as to their distances from the master records and then are distributed to the due master record accordingly.
This component helps you to create groups of similar data records in any source data including large volumes of data by using one or several match rules.
If you have subscribed to one of the Talend solutions with Big Data, this component is available in the following types of Jobs:
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.
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
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.
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.
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.
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:
q-grams: matches processed entries by
dividing strings into letter blocks of length
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.
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.
Set a numerical weight for each attribute (column) of the key definition. The values can be anything >= 0.
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:
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:
Null Match Null: they match.
Null Match None: they do not match.
Null Match All: they match.
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.
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.
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.
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.
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.
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
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.
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.
This component is an intermediary step. It requires an input flow as well as an output flow.
The configuration wizard enables you to create different production environments, Configurations, and their match rules. You can also use the configuration wizard to import match rules created and tested in the studio and stored in the repository, and use them in your match Jobs. For further information, see Importing match rules from the studio repository.
You can not open the configuration wizard unless you link the input component to the tMatchGroup component.
To open the configuration wizard:
In the studio workspace, design your job and link the components together, for example as below:
Double-click tMatchGroup, or
Right-click it and from the contextual menu select Configuration Wizard, or
Click Preview in the basic settings view of tMatchGroup.
The configuration wizard is composed of three areas:
the Configuration view, where you can set the match rules and the blocking column(s).
the matching chart, which presents the graphic matching result,
the matching table, which presents the details of the matching result.
From this view, you can edit the configuration of the tMatchGroup component or define different configurations in which to execute the Job. You can use these different configurations for testing purposes for example, but you can only save one configuration from the wizard, the open configuration.
In each configuration, you can define blocking key(s) and multiple conditions using several match rules. You can also set different match intervals for each rule. The match results on multiple conditions will list data records that meet any of the defined rules. When a configuration has multiple conditions, 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 other rules.
The parameters required to edit or create a match rule are:
The Limit field indicates the maximum number of rows to be processed by the match rule(s) in the wizard. The by-default maximum row number is 1000.
The Key definition parameters.
The Match Threshold field.
To create a new configuration and new match rules from the configuration wizard, do the following:
Click the [+] button on the top right corner of the Configuration view.
This creates, in a new tab, an exact copy of the last configuration.
Edit or set the parameters for the new configuration in the Key definition and Blocking Selection tables.
If needed, define several match rules for the open configuration as the following:
Click the [+] button on the match rule bar.
This creates, in a new tab, an exact copy of the last rule.
Set the parameters for the new rule in the Key definition table and define its match interval.
Follow the steps above to create as many match rules for a configuration as needed. You can define a different match interval for each rule.
When a configuration has multiple conditions, 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 and so on.
Click the Chart button at the top right corner of the wizard to execute the Job in the open configuration.
The matching results are displayed in the matching chart and table.
Follow the steps above to create as many new configuration in the wizard as needed.
To execute the Job in a specific configuration, open the configuration in the wizard and click the Chart button.
The matching results are displayed in the matching chart and table.
At the bottom right corner of the wizard, click either:
OK to save the open configuration.
You can save only one configuration in the wizard.
Cancel to close the wizard and keep the configuration saved initially in the wizard.
From the matching chart, you can have a global picture about the duplicates in the analyzed data.
The Hide groups less than parameter, which is set to 2 by default, enables you to decide what groups to show in the result chart. Usually you want to hide groups of small group size.
For example, the above matching chart indicates that:
48 items are analyzed and classified into 18 groups according to a given match rule and after excluding items that are unique, by setting the Hide groups less than parameter to 2.
11 groups have 2 items each. In each group, the 2 items are duplicates of each other.
3 groups have 3 items each. In each group, these items are duplicates of one another.
3 groups have 4 items each. In each group, these items are duplicates of one another.
One single group has 5 duplicate items.
From the matching table, you can read details about the different duplicates.
This table indicates the matching details of items in each group and colors the groups in accordance with their color in the matching chart.
You can decide what groups to show in this table by setting the Hide groups of less than parameter. This parameter enables you to hide groups of small group size. It is set to 2 by default.
The buttons under the table helps you to navigate back and forth through pages.
From the tMatchGroup configuration wizard, you can import match keys from the match rules created and tested in the Profiling perspective of Talend Studio and stored in the repository. You can then use these imported matching keys in your match Jobs.
The tMatchGroup component is based on the VSR algorithm. You can not import match rules configured with the T-Swoosh algorithm. A warning message displays in the wizard when you try to import rules with T-Swoosh.
The VSR algorithm takes a set of records as input and groups similar encountered duplicates together according to defined match rules. It compares pairs of records and assigns them to groups. The first processed record of each group is the master record of the group. The VSR algorithm compares each record with the master of each group and uses the computed distances, from master records, to decide to what group the record should go.
To import match rules from the studio repository:
From the configuration wizard, click the icon on the top right corner.
The [Match Rule Selector] wizard opens listing all match rules created in the studio and saved in the repository.