Field Mapping

A Field Mapping stage is used to do customized mapping of the fields in an Index Pipeline document to fields in the Solr schema. For examples of how to use this stage in the Managed Fusion UI, see Get Data In of the Getting Started with Fusion Server tutorial.

Get Data In

Fusion’s Index Workbench provides the tools to configure datasources, parsers, and index pipelines. It lets you preview the results of indexing before you load your data into the actual index.Index Workbench first sets up the necessary data extraction configuration, and then retrieves a small number of documents as sample data. You can use the sample documents to test and refine your index pipeline. All processing is simulated processing of the test data. No actual data ingestion takes place.After you have a complete configuration, Index Workbench saves this as a Fusion datasource. To load your data into Fusion, use the Fusion Datasource tool to run the resulting configuration.Part 2 takes you through configuring a datasource using Index Workbench. In Part 3, you will load the data into Fusion and view it using Query Workbench.

Before you begin

To proceed with this part of the tutorial, you must first complete Part 1, which gives you a running instance of Fusion and a Fusion app.

Download the MovieLens dataset

Download the dataset. This is a MovieLens dataset created by the Grouplens research lab.
Unpack the ml-latest-small.zip file. Fusion can parse .zip files, but for simplicity we will index just one file from the archive (movies.csv).

The movies.csv file contains a list of 9,125 movie titles, plus a header row. Here is a truncated listing:

movieId,title,genres
1,Toy Story (1995),Adventure|Animation|Children|Comedy|Fantasy
2,Jumanji (1995),Adventure|Children|Fantasy
3,Grumpier Old Men (1995),Comedy|Romance
4,Waiting to Exhale (1995),Comedy|Drama|Romance
5,Father of the Bride Part II (1995),Comedy
6,Heat (1995),Action|Crime|Thriller
7,Sabrina (1995),Comedy|Romance
8,Tom and Huck (1995),Adventure|Children
9,Sudden Death (1995),Action
10,GoldenEye (1995),Action|Adventure|Thriller

Open the Movie Search app

If the Fusion UI is not already open, then open it.
Enter the password for the user admin, and then click Log in. The Fusion launcher appears. You see the Movie Search app you created in Part 1:
In the Fusion launcher, click the Movie Search app. The Fusion workspace appears. It has controls along the left and top sides.
In the upper left, hover over Apps . You can see that Movie Search is the currently selected app. Also, the user collection Movie_Search is selected in the collection picker. This is the default collection for the Movie Search app, and where Fusion will place index data.

Configure the datasource

A collection includes one or more datasources. A datasource is a configuration that manages the import, parsing, and indexing of data into a collection. You will use Index Workbench to configure a datasource for the movie data.

In the collection picker, verify that the collection Movie_Search is selected.
Open Index Workbench. Navigate to Indexing > Index Workbench. Initially, no data preview appears because no datasource has been configured. When you configure a datasource, Fusion samples the data and displays a preview of how it would be formatted in the index using the default parsing and index pipeline configurations.
In the upper right, click New.
Select Or, upload a file.
Click Choose File.
Navigate to the movies.csv file, select it, and then click Open.
Click Add New Datasource. The Datasource (File Upload) configuration panel appears, with the default datasource ID movies_csv-Movie_Search and the default file ID movies.csv. These default values are fine.
Enter the Description Movies CSV file.
Click Apply. Index Workbench reads up to 20 documents into memory from the movies.csv file, and then displays a preview of how they would be indexed. You have finished configuring the datasource. At the bottom of the page, click Cancel. In the lower right, you can select the number of documents to preview.

Analyze the default output

Notice that Fusion made some assumptions about your original fields:
- genres became genres_t (the text_general field type) and genres_s (the string field type). String fields are useful for faceting and sorting, while text fields are for full-text search. At this point, Fusion does not know whether you intend to use this field for faceting and sorting, for full-text search, or for both.
- title became title_t and title_s for the same reason.
- movieId became movieId_t and movieId_s for the same reason. This might seem odd, because the original field contains numbers. But, at this stage, Fusion creates text_general and string fields. To use the contents of this field as an integer, you would map the field to an integer field.
You also see fields that begin with _lw. These fields contain data that Fusion creates for its own housekeeping. You can ignore them. These fields are created by the Solr Dynamic Field Name Mapping stage in the default index pipeline. This stage attempts to automatically detect field types, and renames fields accordingly. For this tutorial, you will manually configure the fields instead.
Turn off the Solr Dynamic Field Name Mapping stage by clicking the green circle next to it. Your data’s original fields reappear: genres, movieId, and title.

Configure the index pipeline

First you will configure the field mappings in the index pipeline so each field has the correct data type. Then you will split the genres field into multiple values so each value can be used as a facet in Part 3 of this tutorial.

Configure field mappings

Configure field mappings to control the field types of Fusion documents. Fusion uses field name suffixes to determine field types. When a field name has no suffix, Fusion stores it as a string field and treats it as an unanalyzed whole. For precise analysis and search, most fields need suffixes to indicate their specific types. You will see how this relates to the fields in the dataset.

In the list of index pipeline stages on the left, click Field Mapping to open the Field Mapping stage configuration panel.
In Field Translations, click Add to create a new field mapping rule.
Under Source Field, enter genres.
Under Target Field, enter genres_ss. The field suffix _ss means that this field is a multi-valued string field.
Fusion currently interprets this field as having a single value. You can see that the field actually contains a pipe-delimited array of values. You will fix this after you finish configuring field mappings.
Under Operation, select move. The move operation means that the resulting document no longer has a genres field; it only has genres_ss.
Click Apply. Applying the new configuration re-runs the simulation and updates the contents of the preview panel. Notice the change in the field name from genres to genres_ss:
Before After
Click Add to add more field mapping rules as follows:
- The movieId field is a unique document identifier. It should be copied into the document’s id field.
- The title should be searchable as a text field, so you move it to the field title_txt.
Your field mappings should look like this:
Click Apply. After you have specified these explicit field mapping rules, you can browse the resulting documents in the preview panel to check your work.
Before After
In the upper right, click Save. This saves your modified index pipeline. Get in the habit of saving your work as you work.

Now your document ID is more useful, and your movie titles are full-text searchable.

Because the input documents in this tutorial are simple documents with a fixed number of known fields, it is easy to configure the Field Mapping stage to ensure the correct document structure for Fusion. When documents have large numbers of fields, the Solr Dynamic Field Mapping stage can reduce the work required to configure the index pipeline.

Split a multi-value field

The genres_ss field has been parsed as a single-value field, but you can see that it is really a pipe-delimited array of values. To split this field into its constituent values, you will add a Regex Field Extraction stage to your index pipeline. This stage uses regular expressions to extract data from specific fields. It can append or overwrite existing fields with the extracted data, or use the data to populate new fields.

Click Add a stage.
Scroll down and select Regex Field Extraction (under Field Transformation). The Regex Field Extraction stage configuration panel appears.
Under Regex Rules, click Add .
On the new line, hover over the [...] under Source Fields, and then click Edit . The Source Fields window opens.
Click Add .
Enter genres_ss, and then click Apply.
Under Target Field, enter genres_ss.
In the Write Mode field, select overwrite.
In the Regex Pattern field, enter this expression:
```
[^|\s][^\|]*[^|\s]*
```
You might need to scroll horizontally to see this field.
The first bracketed term in the regex matches any character that is not a vertical bar or a space. The second term matches any character that is not a vertical bar, zero or more times. The last term matches any character that is not a vertical bar, zero or more times.
In the Return If No Match field, select input_string.
Click Apply.

Initially, your data does not change. 12. In the list of index pipeline stages, drag the Regex Field Extraction stage down so that it comes after the Field Mapping stage:

Now the preview shows multiple values for the genres_ss field:


Before	After

If the preview panel does not update automatically, select a different number of documents to view using the dropdown in the bottom right of the screen. This forces the preview to update.

13. To view the values of the genres_ss field, expand it and values under it by clicking the right triangle triangles

These field values are useful for faceting, which you will explore in Part 3 of this tutorial. 14. In the upper right, click Save. This saves your modified index pipeline.

Create a new field from part of an existing one

Notice that the title_txt field also contains the year in which the movie was released. Instead of including the year in your full-text search field, it would be more useful as a separate field that you can use for faceting. This is another job for the Regex Field Extraction stage.

In the list of index pipeline stages, click Regex Field Extraction.
In the Regex Field Extraction configuration panel, under Regex Rules, click Add .
On the new line, hover over the [...] under Source Fields, and then click Edit . The Source Fields window appears.
Click Add .
Enter title_txt, and then click Apply.
Under Target Field, enter year_i. The _i suffix indicates an integer point field (specifically, that the field is a dynamic field with a pint field type). Fusion will create this new field whenever the regular expression matches the contents of the source field.
When you use the Regex Field Extraction stage to create a new field, the value of Write Mode makes no difference.
In the Regex Pattern field, enter this expression to match the digits inside the parentheses at the end of the title_txt value:
```
$([0-9]+)$$
```
In the Regex Capture Group field, enter 1. This lets the index pipeline stage transfer the year into the year_i field.
Scroll all the way to the right to see this field.
Click Apply. Now the preview includes the new year_i field:
Before After
In the upper right, click Save. This saves your modified index pipeline.

Trim a field’s value

The title_txt field still includes the year of the film’s release, which you have extracted into its own field, year_i. Let us trim that information from the title_txt values so that only the title text remains.

In the list of index pipeline stages, click Regex Field Extraction.
In the Regex Field Extraction configuration panel, under Regex Rules, click Add .
On the new line, hover over Source Fields, and then click Edit . The Source Fields window appears.
Click Add .
Enter title_txt, and then click Apply.
Under Target Field, enter title_txt.
In the Write Mode field, select overwrite.
In the Regex Pattern field, enter this expression to match the digits inside the parentheses at the end of the title_txt value:
```
^(.+)\s$([0-9]+)$$
```
In the Regex Capture Group field, enter 1.
Click Apply.

Now the preview pane shows the title_txt field with only the title string:


Before	After

In the upper right, click Save. This saves your modified index pipeline.

Run the datasource job

Now you have a correctly-configured index pipeline appropriate to your data. You are ready to index the data.

In the upper left, click Start job. This launches a datasource job that imports and indexes the complete contents of your movies.csv file, using the configuration you just saved. Your datasource job is finished when the Index Workbench displays Status: success in the upper left. If the status does not change, go back to the launcher and relaunch your app.

Close panels you no longer need open

Fusion opens panels beside already open panels. Close all of the panels that are open by clicking Close

Reindexing

Documents are associated with a collection through the name of the datasource. The datasource name is stored as a value in the _lw_data_source_s field. For various reasons, you may wish to remove all documents associated with a datasource from a collection before using CrawlDB to add relevant documents back to the collection. This process is known as reindexing.To accomplish this, navigate to Indexing

> Datasources, select the datasource name, and then click Clear Datasource. This will remove all documents with the selected datasource name in the _lw_data_source_s field. After the documents are removed from the collection, you can repeat [the steps above”/> to reindex the data.

Use caution when creating or changing a datasource name. If you create a new datasource name that is identical to an existing datasource name, all document associations will be shared between the datasource names. Changing the name to match an existing name will have the same result.

What is next

Now you have 9,125 movie listings from the MovieLens database in Fusion’s index, customized to indicate the data type for each field. You also split a multi-valued field so that its values can be treated individually, created a new field to contain partial contents of a different field, and trimmed that content from the original field.Let us compare the initial indexing of your data with the indexing after field mappings and extractions:

Before	After

In Part 3, you will use Query Workbench to get search results from your collection and configure the query pipeline that customizes those results. You will add faceting using the genres_ss and year_i fields so that users can easily filter their search results.

Additional resources

Getting Started with Fusion Server

Field Mapping Stage Properties

A Field Mapping stage specification consists of three things:

a unique ID
a set of mapping rules that specify operations applied to named fields as a triple: { source, target, operation }.
a set of rules called “unmapped” rules which specify operations applied to fields whose name does not match any of the mapping rules, also a triple { source, target, operation }.

Mapping Rules and Unmapped Rules

Each rule has the following properties:

Property	Description
source	The name of the source field. This will be the name of the field in the Pipeline document that should be mapped to another field. Java regular expressions can be used in the source field by surrounding the regular expression with forward slashes (’/’). For example, `/(.)text(.)/` is a valid expression that will find field names in the incoming document that contain the string ‘text’ between any number of preceding or succeeding characters. If a regular expression is not used, the value supplied for the source will be treated as a literal field name and will be matched ignoring the case (for example, “text” will match “tExt” or “Text”, etc.).
target	The name of the target field. If the value for the `source` was a regular expression, then this can also be a regular expression. It can also contain substitutions using references to capture groups (using Java’s Matcher.replaceAll). Otherwise, the source field name will be simply substituted by the value of target according to the operation rules described below.
operation	What to do with the field during mapping. Several options are available: ● copy. Content contained in fields matching `source` will be copied to `target`. ● move. Content contained in fields matching `source` will be moved to `target` (it may also help to think of this as the field name being replaced by the value of `target`). ● delete. Content contained in fields matching `source` will be dropped from the document and not indexed. In this case, the `target` can be null or not defined at all. ● add. The literal value of `target` will be added to the `source` if `source` is a regular expression. If `source` is not a regular expression, `target` will be added as a new field. ● set. The literal value of `target` will be set as the new value of `source` if `source` is a regular expression. If `source` is not a regular expression, `target` will be set as a new field. ● keep. Content contained in fields matching `source` will be retained and unchanged, and the fields will be added to a list of known fields and they will not be affected by however the `renameUnknown` rule has been set.

Order of operations Field mapping rules are applied in the order in which they are defined within each operation type. Operation types are applied in this order:

Field Retention - keep or delete
Field Value Updates - add or set
Field Translations - copy or move

As a result, keep and delete rules are always applied before add, set, copy, or move rules. Likewise, add and set rules are always applied before copy or move rules. In some cases, you may wish to delete fields after they are processed by other operations. To accomplish this, you can add another Field Mapping stage which deletes the fields.

Field Mapping Behavior

The field mapping rules are applied in a specific order.

A copy of the Pipeline document is prepared. All further operations are applied to this copy.
The rules are traversed only once, in the order of their declaration in the mapping property. This means it is possible to do multiple operations on a field. However, note that if fields are moved (renamed), further operations should reference the new field name.
Before each rule is evaluated, the current list of field names is prepared and sorted in alphabetic ascending order.
The current rule is applied to field values for each matching name from the list of names prepared in step 3. New field names resulting from the current rule do not affect the snapshot list of field names; in order for a rule to be applied to a new field name, it will be included in a later round of the evaluation cycle.
The process is repeated for each rule, and a list of matching source fields is noted.
If the document contains any fields that were not affected by any mapping rule, the renameUnknown option is applied if it has been set to true.
Finally, the resulting transformed document is returned to the next stage of the index pipeline.

Examples

Map several fields:

{
    "id": "mapping-text",
    "type": "field-mapping",
    "mappings": [
        {
            "operation": "move",
            "source": "plaintextcontent",
            "target": "body"
        },
        {
            "operation": "add",
            "source": "content-length",
            "target": "fileSize"
        },
        {
            "operation": "move",
            "source": "/file(.*)/",
            "target": "lastModified"
        },
        {
            "operation": "delete",
            "source": "last-printed"
        },
        {
            "operation": "copy",
            "source": "mimetype",
            "target": "content_type"
        }
    ],
    "unmapped": {
        "source": "/(.*)/",
        "target": "$1_ss",
        "operation": "move"
    },
    "skip" : false
}

Set the urlX field based on the value of the employee_id field:

{
    "id": "set-field",
    "type": "field-mapping",
    "mappings": [
        {
            "operation": "set",
            "source": "urlX",
            "target": "https://mydomain.com/<employee_id>"
        }
    ],
    "skip" : false
}

Configuration

When entering configuration values in the UI, use unescaped characters, such as \t for the tab character. When entering configuration values in the API, use escaped characters, such as \\t for the tab character.

UI tour

Index data

Query data

Metrics and analytics

Improve your queries

Administration

Developer documentation

Machine learning

Neural Hybrid Search

Release notes

FAQs

Before you begin

Download the MovieLens dataset

Open the Movie Search app

Configure the datasource

Analyze the default output

Configure the index pipeline

Configure field mappings

Split a multi-value field

Create a new field from part of an existing one

Trim a field’s value

Run the datasource job

Close panels you no longer need open

Reindexing

What is next

Additional resources

Field Mapping Stage Properties

Mapping Rules and Unmapped Rules

Field Mapping Behavior

Examples

Configuration

UI tour

Index data

Query data

Metrics and analytics

Improve your queries

Administration

Developer documentation

Machine learning

Neural Hybrid Search

Release notes

FAQs

​Field Mapping Stage Properties

​Mapping Rules and Unmapped Rules

​Field Mapping Behavior

​Examples

​Configuration

Field Mapping Stage Properties

Mapping Rules and Unmapped Rules

Field Mapping Behavior

Examples

Configuration