Dynamic Workflows: Advanced Example - Modifying the Schema Feature

Liz Sanderson
Liz Sanderson
  • Updated

FME Version

  • FME 2021.2

Introduction

Sometimes in a dynamic workflow, the desired schema only exists within the workspace (e.g. after pivoting data). Building a schema feature works, but often the schema needs to be carefully constructed with attribute transformers, like in the last example. There must be a better way!

In this example, a custom transformer called the SchemaScanner will be used to build a schema feature, then the feature will be manipulated using other transformers. This is a common pattern and should be used whenever you need to build a schema and edit its contents.
Note: As of FME 2022, SchemaSetter is replaced by SchemaScanner. 
  

The Pattern

SchemaScanner > ListExploder > (Manipulate the Data) > ListBuilder

Use the SchemaScanner to create a list{} feature in FME using the current feature’s schema. Next, use the ListExploder to split the list into its individual features, where each feature contains an attribute name from the list. The third step is to manipulate the schema as needed (i.e. sort). Finally, bring everything back together into a new version of the schema feature using the ListBuilder.

Here are a few examples of why you would want to do this:

  • Renaming Attribute Names
  • Sorting (e.g. Dates)
  • Removing or Adding New Attributes

 

The Workspace

In the attached workspace, we are creating a pivot table using a dataset of sales figures. The current data looks like:

Quarter Sales Region
2016Q1 1936 New England
2016Q2 167 New England
2016Q1 1235 Mid-Atlantic
2016Q2 169 Mid-Atlantic


The output we want is the following:

Region 2016Q1 2016Q2
New England 1936 167
Mid-Atlantic 1235 169

 

This is a simple pivot around the Region field and also note that we are not doing any grand totals of the data. How can we do this with FME?

 

Step-by-Step Instructions

1. Pivot the Data

Use an AttributeCreator to build attribute names out of the values of ‘Quarter’. The values of the new attribute will contain the Sales data.

1.png

Next use the Aggregator to summarize each Region.

Settings:

Please set the Aggregator Group By to Region

After this step, you have pivoted your data and the actual data looks like:

2016Q1 2016Q2 Region
1936 167 New England
1235 169 Mid-Atlantic

You'll see something like this in the Feature Information window:
FeatureInformation.png

(Note: to see the data in Table View you'd have to add an AttributeExposer to add the new attributes to the workbench schema)

You should notice that this data is structured in a completely new way as it was created directly inside the workspace. This is important! Dynamic workspaces always need to be able to reference a schema definition in order to work and in this case we do not have one defined just yet (Step 3 below will address this).

 

2. Branching the Data

As we only want to manipulate one schema feature (instead of doing this for every feature). We need to split the data up into two streams. The first stream should connect to a brand new FeatureMerger in the Requestor Port while the second stream should connect to a Sampler. Use the sampler to pass one single feature into the next step. See below:

2.png
 

3. Using the Pattern

At this stage, we need to build the Schema Feature and then manipulate it. We will start with the SchemaScanner transformer:

3.png
 

Note: Filter out any extra FME attributes using the Ignore Attributes Containing parameter

If you were to inspect after using the SchemaScanner, you would see the attribute list stored is now stored on the feature.

4.png
 

At this stage the Schema object has been created but isn’t organized in the sort order that you need. Let’s fix that by using a ListExploder:

5.png

 

The Schema has been divided into each individual attribute and stored inside an FME Feature. Time to manipulate each attribute. In the attached example we used a Sorter transformer but since we are talking about a pattern, this is where your particular needs are addressed. Modify the name, fme_data_type and list order as you see fit.

6.png
 

The final step is to bring the data back together into a list. Use the ListBuilder to accomplish this.

7.png

You now have a completed Schema Feature that has been designed and edited. Fantastic!

 

4. Merge the Data and Write Dynamically

Connect the output from step 3 above to the Supplier port on the FeatureMerger. Do a 1:1 match to ensure the Schema is present on each feature. This ensures that the writer will receive the schema on the first feature it encounters.

 

8.png

 

Finally ensure the writer is setup in dynamic mode and the source is your schema feature.

9.png
 

When you run the workspace, you will get your desired output with your new schema defined.

 

Final Workspace:

10.png

 

Notes

Avoiding Dropped Features - FeatureReader:

If using the FeatureReader to create a schema feature, please note that the transformer adds a special attribute and value:

fme_schema_handling = ‘schema_only’

This attribute tells the writer to use that feature as a schema and then remove it from the output. This can be an issue if you do any merging of data and schema in the workspace as the data merged with this schema feature would be dropped when written. For example, if you merge a schema feature so that it gets added to all the features in the workspace.

To ensure this doesn’t occur, remove the fme_schema_handling attribute entirely so that the first merged data feature is not dropped by the writer.

 

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