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In this article

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Overview

You can use this Snap to evaluate transform incoming data using the given mappings and produce new output data. This Snap evaluates an expression and write writes the result to the specified target path. If an expression fails to evaluate, use the Views tab to specify error handling. This Snap supports both binary and document data streams. The default input and output is document, but you can select Binary from the Views tab in the Snap's settings.to specify error handling. 

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Structural Transformations

The following structural transformations from the Structure Snap are supported in the Mapper Snap:

  • Move - A move is equivalent to doing a mapping without a pass-through. The source value is read from the input data and placed into the output data. Since pass-through is turned off, the input data is not copied to the output.  Also, the source value is treated as an expression in the Mapper, but it is a JSONPath in the Structure Snap. A jsonPath() function was added to the expression language that can be used to execute a JSONPath on a given value. If pass-through is enabled, then you will probably have to delete the old value.

  • Delete - Write a JSONPath in the source column and leave the target column blank.

  • Update - All of the cases for update can be handled by writing the appropriate JSONPath. For example:

    • Update value: target  target path = $last_name

    • Update map: target  target = $address.first_name

    • Update list: target  target = $names[(value.length)]

      • The '(value.length)' evaluates to the current length of the array, so the new value will be placed there at the end.

    • Update list of maps: target  target = $customers[*].first_name

      • This translates into "write the value into the 'first_name' field in all elements of the 'customers' array".

    • Update list of lists: target  target = $lists_of_lists[*][(value.length)]

For performance reasons, the Mapper does not make a copy of any arrays or objects written to the Target Path. If you write the same array or object to more than one target path and plan to modify the object, make the copy yourself. For example, given the array "$myarray" and the following mappings:

$myarray -> $MyArray
$myarray -> $OtherArray

Any future changes made to either "$MyArray" or "$OtherArray" are in the both arrays. In that case, make a copy of the array as shown below:

$myarray -> $MyArray [].concat($myarray) -> $OtherArray

The same is true for objects, except you can make a copy using the ".extend()" method as shown below:

$myobject -> $MyObject $myobject.extend({}) -> $OtherObject

Passing Binary Data

You would convert binary data to document data by preceding the Mapper Snap with the Binary-to-Document Snap.  Likewise, to convert the document output of the Mapper Snap to binary data, you would add the Document-to-Binary Snap after the Mapper Snap.

Currently, you can do this transformation within the Mapper Snap itself. You set the Mapper Snap to take binary data as its input and output by using the $content expression. 

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Expressions used in this Snap, downstream of any Snowflake Snap, that evaluate to very large values such as EXP(900) are displayed as Infinity in the input/output previews as Infinity . However, you can see the exact evaluated values in the validation previews; hence, ignore this error.

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Field Name

Field Type

Description

Label*

Default Value: Mapper
Example: Mapper

String

The name for the Snap. You can modify this to be more specific, especially if you have more than one of the same Snap in your Pipeline.

Null-safe access

Default Value: Deselected
Example: Selected

Checkbox

Select this checkbox to set the target value to null in case the source path does not exist. For example, $person.phonenumbers.pop() ->$ lastphonenumber may result in an error if person.phonenumbers does not exist in the source data. Enabling Null-safe access Selecting this checkbox allows the Snap to write null to lastphonenumber instead of causing displaying an error. 
If you deselect this checkbox, the Snap fails if the source path does not exist, ignores the record entirely, or writes the record to the error view depending on the setting of the error view field.

Pass through

Default Value:Deselected
Example:Selected


Checkbox

This setting determines if data should be passed through or not.

If you select this checkbox, then all the original input data is passed into the output document together with the data transformation results.

If you deselect this checkbox, then only the data transformation results that are defined in the mapping section appear in the output document and the input data is discarded.

Note

This setting is impacted by Mapping Root. If Mapping Root is set to $ and Pass through is not selected, anything not mapped in the table will not pass through. However, if Mapping Root is set to $customer and Pass through is not selected, it will only apply to the items within the Mapping Root level. That means that anything above the Mapping Root level will pass though and items at the Mapping level that are not mapped in the table will not pass through. 

When to always select Pass through

Always select Pass through if you plan to leave the Target path field blank; else, the Snap displays an error that the field that you want to delete does not exist. This is the expected behavior.

For example, you have an input file that contains a number of attributes; but you need only two of these downstream. So, you connect a Mapper to the downstream Snap supplying the input file, select the two attributes you need by listing them in the Expression fields, leave the Target path field blank, and select Pass through. When you execute the Pipeline, this Snap evaluates the input documents/binary data and picks up the two attributes that you want, and passes the entire document/binary data through to the Target schema. From the list of available attributes in the Target Schema, the Mapper Snap picks up the two attributes you listed in the Expression fields, and passes them as output. However, if you had not selected the Pass through checkbox, the Target Schema would be empty, and the Mapper would display a No schema available error.

Transformations*


Use this fieldset to configure the settings for data transformations.

Mapping Root

Default Value: $
Example: $

String/Suggestion

Specify the sub-section of the input data to be mapped.  Learn More: Understanding the Mapping Root. Default:

Input Schema

Dropdown list

Select the input data (that comes from the upstream Snap) that you want to transform.

Mapping table

Use this field set to specify the source path, expression, and target path columns used to map schema structure. This field set contains the following fields:

  • Expression

  • Target path

Expression

Default Value: N/A
Example: $first.concat(" ", $last) 

String/Expression

Specify the expression to write to the target path. Expressions that are evaluated will remove the source targets at the end of the run.

Note

Incoming fields from previous Snaps that are not expressly defined in the Mapping Table are passed through the Data Snap to the next Snap. However, when defining output fields in the Target Path, if the field name is the same as a field name that would otherwise Pass through, the field in the mapping table overrides the output. 

Lear More: Understanding Expressions in SnapLogic and Using Expressions for usage guidelines.

Managing Numeric Inputs in Mapper Expressions

While working with upstream numeric data, you may see some unexpected behavior. For example, consider a mapping that reads as follows:

Expression: $num +100
Target path: $numnew

For example, the value being passed from upstream for $num is 20.05. You would expect the value of $numnew to now be 120.05. But, when you execute the Snap, the value of $numnew is shown as 20.05100.

This happens because, as of now, the Mapper Snap reads all incoming data as strings, unless they are expressly listed as integers (INT) or decimals (FLOAT). So, to ensure that the upstream numeric data is appropriately interpreted, parse the data as a float. This will convert the numeric data into a decimal; and all calculations performed on the upstream data in the Mapper Snap will work as expected:

Expression: pareFloat($num1)+100
Target path: $numnew

The value of $numnew is now shown as 120.05.

Target path

Default Value: N/A
Example:

String/Suggestion

Specify the target path at which the expression should be written.

Target Path Recommendation

Info

Iris simplifies configuring the Target path property in this Snap by recommending suggestions for the Expression and Target path fields mapping. To make these suggestions, Iris analyzes Expression and Target path mappings in other Pipelines in your Org and suggests the exact matches for the Expressions in your current Pipeline. The suggestions are displayed upon clicking (blue star) against the Target path. 

For example, you have the Expression $Emp.Emp_Personal.FirstName in one of your Pipelines. And you have set the Target path for this expression as $FirstName. Now, if you use the expression $Emp.Emp_Personal.FirstName in a new Pipeline, then Iris suggests $FirstName as one of the recommended Target paths. This helps you standardize the naming standards within your org.

The following video illustrates how Iris recommends Target path in a Mapper Snap:

Snap Execution

Dropdown list

Select one of the three modes in which the Snap executes. Available options are:

  • Validate & Execute: Performs limited execution of the Snap, and generates a data preview during Pipeline validation. Subsequently, performs full execution of the Snap (unlimited records) during Pipeline runtime.

  • Execute only: Performs full execution of the Snap during Pipeline execution without generating preview data.

  • Disabled: Disables the Snap and all Snaps that are downstream from it.

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Add a File Reader Snap to the Canvas and configure it to read the Excel file from which you want to remove specific columns.

Parse the file using the Excel Parser Snap. You can preview the parsed data by clicking the Image Removed(blue star) icon.

From the preview file, you can see the columns that you want to remove. In this instance, you decide to remove the Discounts and Month Number columns. To do so, you add a Mapper Snap to the Pipeline.

In the Expression field, you enter the criteria (as shown below) that you want to use to remove the Discounts and Month Name columns. 

$.filter((value, key) => !key.match("Discounts|Month Number"))

You enter Enter $ in the Target field to indicate that you want to leave the other column names unchanged. You validate Validate the Snap, and you can see that the Discounts and Month Name columns are skipped.

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You now need to write the updated data back into the SLDB as a JSON file. To do so, you add file. Add a JSON Formatter Snap to the Pipeline to convert the documents coming in from the Mapper Snap into binary data. You then Then add a File Writer Snap and configure it to write the input streaming data to the SLDB.

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You can now view the saved file in the destination project in SnapLogic Manager.

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Download this Pipeline

Data Output Example

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Successful Mapping

If your source data looks like

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And your mapping looks like

Your outgoing data will look like

Code Block
{
  "first_name": "John",
  "last_name": "Smith",
  "phone_num": "123-456-7890"
}

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  • Expression: $first_name.concat(" ", $last_name)
  • Target path: $full_name 
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 Code Block
{
  "full_name": "John Smith",
  "phone_num": "123-456-7890"
} 
Unsuccessful Mapping
If your source data looks like: 
 Code Block
{
  "first_name": "John",
  "last_name": "Smith",
  "phone_num": "123-456-7890"
}  
And your mapping looks like:
  • Expression: $middle_name.concat(" ", $last_name)
  • Target path: $full_name 
An error is displayed.
Escaping Special Characters in Source Data 
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If the Escape Character is 
Use Expression
Sample Output 
Single quote (') 
JSON:
$original.mapValues((value,key)=> value.toString().replaceAll("'","''"))
OR
$original.mapValues((value,key)=> value.toString().replaceAll("'","\''"))
CSV:
$[' Business-Name'].replace ("'","''")
Image RemovedImage Added
Ampersand (&)

JSON:
$original.mapValues((value,key)=> value.toString().replaceAll("'","\&'"))
OR
$original.mapValues((value,key)=> value.toString().replaceAll("'","&'"))
CSV:
$[' Business-Name'].replace ("'","&'")
Image RemovedImage Added
Backslash (\)



JSON:
$original.mapValues((value,key)=> value.toString().replaceAll("'","\\'"))
 Info
Backslash is configured as an escape character in SnapLogic. Therefore, it must itself be escaped to be displayed as text. 
CSV:
$[' Business-Name'].replace ("'","\\'")
Image RemovedImage Added
In this way, you can customize the data to be passed on to downstream Snaps using the Expression field in the Mapper Snap. 
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Transform Snap Pack
Transform Snap Pack
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