Merge pull request #92096 from djpmsft/docUpdates

Data Flow script exists

Author: ktoliver

articles/data-factory/data-flow-aggregate.md

@@ -1,5 +1,5 @@
 ---
-title: Aggregate transformation in mapping data flow - Azure Data Factory | Microsoft Docs
+title: Aggregate transformation in Azure Data Factory mapping data flow | Microsoft Docs
 description: Learn how to aggregate data at scale in Azure Data Factory with the mapping data flow Aggregate transformation.
 author: kromerm
 ms.author: makromer
@@ -63,7 +63,9 @@ Aggregate transformations are similar to SQL aggregate select queries. Columns t
 
 ### Example
 
-The below example takes an incoming stream `MoviesYear` and groups rows by column `year`. The transformation creates an aggregate column `avgrating` that evaluates to the average of column `Rating`. This aggregate transformation is named `AvgComedyRatingsByYear`. In the Data Factory UX, this transformation looks like the below image:
+The below example takes an incoming stream `MoviesYear` and groups rows by column `year`. The transformation creates an aggregate column `avgrating` that evaluates to the average of column `Rating`. This aggregate transformation is named `AvgComedyRatingsByYear`.
+
+In the Data Factory UX, this transformation looks like the below image:
 
 ![Group by example](media/data-flow/agg-script1.png "Group by example")
 

articles/data-factory/data-flow-derived-column.md

@@ -1,5 +1,5 @@
 ---
-title: Derived Column transformation in mapping data flow - Azure Data Factory | Microsoft Docs
+title: Derived column transformation in Azure Data Factory mapping data flow | Microsoft Docs
 description: Learn how to transform data at scale in Azure Data Factory with the mapping data flow Derived Column transformation.
 author: kromerm
 ms.author: makromer
@@ -41,7 +41,9 @@ To add additional derived columns, hover over an existing derived column and cli
 
 ### Example
 
-The below example is a derived column named `CleanData` that takes an incoming stream `MoviesYear` and creates two derived columns. The first derived column replaces column `Rating` with Rating's value as an integer type. The second derived column is a pattern that matches each column whose name starts with 'movies'. For each matched column, it creates a column `movie` that is equal to the value of the matched column prefixed with 'movie_'. In the Data Factory UX, this transformation looks like the below image:
+The below example is a derived column named `CleanData` that takes an incoming stream `MoviesYear` and creates two derived columns. The first derived column replaces column `Rating` with Rating's value as an integer type. The second derived column is a pattern that matches each column whose name starts with 'movies'. For each matched column, it creates a column `movie` that is equal to the value of the matched column prefixed with 'movie_'. 
+
+In the Data Factory UX, this transformation looks like the below image:
 
 ![Derive example](media/data-flow/derive-script1.png "Derive example")
 

articles/data-factory/data-flow-exists.md

@@ -1,34 +1,71 @@
 ---
-title: Azure Data Factory mapping data flow Exists transformation
-description: How to check for existing rows using data factory mapping data flows with Exists transformation
+title: Exists transformation in Azure Data Factory mapping data flow | Microsoft Docs
+description: Check for existing rows using the exists transformation in Azure Data Factory mapping data flow
 author: kromerm
 ms.author: makromer
+ms.reviewer: daperlov
 ms.service: data-factory
 ms.topic: conceptual
-ms.date: 01/30/2019
+ms.date: 10/16/2019
 ---
 
 # Mapping data flow exists transformation
 
+The exists transformation is a row filtering transformation that checks whether your data exists in another source or stream. The output stream includes all rows in the left stream that either exist or don't exist in the right stream. The exists transformation is similar to ```SQL WHERE EXISTS``` and ```SQL WHERE NOT EXISTS```.
 
+## Configuration
 
-The Exists transformation is a row filtering transformation that stops or allows rows in your data to flow through. The Exists Transform is similar to ```SQL WHERE EXISTS``` and ```SQL WHERE NOT EXISTS```. After the Exists Transformation, the resulting rows from your data stream will either include all rows where column values from source 1 exist in source 2 or do not exist in source 2.
+Choose which data stream you're checking for existence in the **Right stream** dropdown.
+
+Specify whether you're looking for the data to exist or not exist in the **Exist type** setting.
+
+Choose which key columns you want to compare as your exists conditions. By default, data flow looks for equality between one column in each stream. To compare via a compute value, hover over the column dropdown and select **Computed column**.
 
 ![Exists settings](media/data-flow/exists.png "exists 1")
 
-Choose the second source for your Exists so that Data Flow can compare values from Stream 1 against Stream 2.
+### Multiple exists conditions
 
-Select the column from Source 1 and from Source 2 whose values you wish to check against for Exists or Not Exists.
+To compare multiple columns from each stream, add a new exists condition by clicking the plus icon next to an existing row. Each additional condition is joined by an "and" statement. Comparing two columns is the same as the following expression:
 
-## Multiple exists conditions
+`source1@column1 == source2@column1 && source1@column2 == source2@column2`
 
-Next to each row in your column conditions for Exists, you'll find a + sign available when you hover over reach row. This will allow you to add multiple rows for Exists conditions. Each additional condition is an "And".
+### Custom expression
 
-## Custom expression
+To create a free-form expression that contains operators other than "and" and "equals to", select the **Custom expression** field. Enter a custom expression via the data flow expression builder by clicking on the blue box.
 
 ![Exists custom settings](media/data-flow/exists1.png "exists custom")
 
-You can click "Custom Expression" to instead create a free-form expression as your exists or not-exists condition. Checking this box will allow you to type in your own expression as a condition.
+## Data flow script
+
+### Syntax
+
+```
+<lefttream>, <rightStream>
+    exists(
+        <conditionalExpression>,
+        negate: true | <false>,
+        broadcast: 'none' | 'left' | 'right' | 'both'
+    ) ~> <existsTransformationName>
+```
+
+### Example
+
+The below example is an exists transformation named `checkForChanges` that takes left stream `NameNorm2` and right stream `TypeConversions`.  The exists condition is the expression `NameNorm2@EmpID == TypeConversions@EmpID && NameNorm2@Region == DimEmployees@Region` that returns true if both the `EMPID` and `Region` columns in each stream matches. As we're checking for existence, `negate` is false. We aren't enabling any broadcasting in the optimize tab so `broadcast` has value `'none'`.
+
+In the Data Factory UX, this transformation looks like the below image:
+
+![Exists example](media/data-flow/exists-script.png "Exists example")
+
+The data flow script for this transformation is in the snippet below:
+
+```
+NameNorm2, TypeConversions
+    exists(
+        NameNorm2@EmpID == TypeConversions@EmpID && NameNorm2@Region == DimEmployees@Region,
+	    negate:false,
+	    broadcast: 'none'
+    ) ~> checkForChanges
+```
 
 ## Next steps