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  • 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 As the Pass through is disabled, 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- Pass through is enabled, then you will probably have to should 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 path = $last_name

    • Update map: target = $address.first_name

    • Update list: 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 = $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 = $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

  • can convert binary data to document data by

preceding the Mapper Snap with

  • adding the Binary-to-Document Snap upstream of the Mapper Snap.

  Likewise

  •  Similarly, to convert the document output of the Mapper Snap to binary data,

you would

  • add the Document-to-Binary Snap

after

  • downstream of the Mapper Snap.

Currently, you can do this transformation

  • You can also transform binary data to document data 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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Binary Input and Output

  • If you are only working with a binary stream as both input and output, you must set both source and target fields with $content, then manipulate the binary data using the Expression Builder. If you do not specify this mapping, then the binary stream from the binary input document is passed through

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  • without any change.

Snap Type

Mapper Snap is a TRANSFORM-type Snap that transforms data and writes or passes it to the downstream Snap.

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

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Target path

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. Selecting this checkbox allows the Snap to write null to lastphonenumber instead of 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 field set 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.

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

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.

Info

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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You now need to write the updated data back into the SLDB as a JSON file. Add a JSON Formatter Snap to the Pipeline to convert the documents coming in from the Mapper Snap into binary data. 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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Data Output Example

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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 ("'","''")

Ampersand (&)


JSON:

$original.mapValues((value,key)=> value.toString().replaceAll("'","\&'"))

OR

$original.mapValues((value,key)=> value.toString().replaceAll("'","&'"))

CSV:

$[' Business-Name'].replace ("'","&'")

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 ("'","\\'")

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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