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.
Built-in: You create and store the schema locally for this component only. For more information about a component schema in its Basic settings tab, see Basic settings tab.
Repository: You have already created and stored the schema in the Repository. You can reuse it in other projects and Job designs. For more information about a component schema in its Basic settings tab, see Basic settings tab.
Complete this table to define the output columns to be replaced and the corresponding substitute lookup columns. The column to be complete are:
- Output Column: select the column of interest from the drop-down list. On this list, two columns MATCHING_WEIGHT and MATCHING_DISTANCES, are defined by default; the other columns are available only when you have defined them in the schema editor of this component. For further information about these two default columns, see Matching entries using the Q-grams and Levenshtein algorithms;
- Lookup Column: select the column of interest from the drop-down list. This list is available when a lookup link has been defined to provide the corresponding lookup columns to this component.
Click the import icon to select a match rule from the Talend Studio repository.
When you click the import icon, a Match Rule Selector wizard is opened to help you import match rules from the Talend Studio repository and use them in your Job.
You can import rules created with the VSR algorithm. The T-Swoosh algorithm does not work with this component. For further information about importing VSR rules, see Importing match rules from the repository
Type in 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) which you can import by using the tLibraryLoad component.
For further information, see Creating a custom matching algorithm.
Tokenized measure
Tokenization is the concept of splitting a string into words. Select the method to use to compute a tokenized measure for the selected algorithm:
NO: no tokenization method is used on the string. With this option, "John Doe" and "Jon Doe" should match.
Same place: Splits the two strings by words to two lists, list1, and list2. Associates each element from list1 with the element which has the same position in list2. Using this method, "She is red and he is pink" and "Catherine is red and he is pink" should match.
Same order: Splits the two strings by words to two lists, list1, and list2 and assumes that list1 is shorter than list2. Tries to associates the elements from list1 with the elements in list2 taken in the same order. Using this method, "John Doe" and "John B. Doe" match.
This method should be used only with strings which has a few words, otherwise the number of possible combinations can be large.
Any order: Splits the two strings by words to two lists, list1 and list2, and assumes that list1 is shorter than list2. Tries to assign each word of list1 to a word of list2, in order to have the highest global similarity (with respect to the used similarity).
Using this method, "John Doe" and "Doe John" match.
All matches: this option means to output all of the matching and the possibly matching records.
Best match: this option means to output only the record with the highest matching score.
First match: this option means to output the first matching record; if no one is matching, it outputs the first possibly matching record.
Last match: this option means to output the last matching record; if no one is matching, it outputs the last possibly matching record.
When you arrange the matching strategy, the matching scores are critical. You need to define them in the Possible match interval fields of the Advanced settings view.