A generalised approach is:
1. Dynamically read all Feature Classes to a single Generic Output Port, and expose fme_feature_type
2. Send the output of this to Counter: Set Counter Name to @Value(fme_feature_type). This will count how many features are in each feature class
3. Send the output of this to Sampler. Group By: fme_feature_type. Set Sampler to read the last 1 feature . This will output 1 feature per feature class, with the Counter Attribute on this Feature being the Total Count of the features in the feature class
FME kinda takes some mental adjustment in that sometimes our old SQL, Grouping/Pivot methods are sometimes unnecessary for getting Stats!
Statistics calculator for stats
Read all feature classes and expose fme_feature_type
Statistics calculator with group by on fme_feature_type
A generalised approach is:
1. Dynamically read all Feature Classes to a single Generic Output Port, and expose fme_feature_type
2. Send the output of this to Counter: Set Counter Name to @Value(fme_feature_type). This will count how many features are in each feature class
3. Send the output of this to Sampler. Group By: fme_feature_type. Set Sampler to read the last 1 feature . This will output 1 feature per feature class, with the Counter Attribute on this Feature being the Total Count of the features in the feature class
FME kinda takes some mental adjustment in that sometimes our old SQL, Grouping/Pivot methods are sometimes unnecessary for getting Stats!
This worked great. Thank you!
Statistics calculator for stats
Read all feature classes and expose fme_feature_type
Statistics calculator with group by on fme_feature_type
Awesome. Just what I needed and super simple to use. Thank You!