The CRCCalculator creates a unique ID for a feature based on its geometry and/or attributes. CRC stands for Cyclic Redundancy Check. This is particularly useful if the dataset has a "natural key" - http://en.wikipedia.org/wiki/Natural_key. Natural keys are created from a list of attribute names and their values for a particular data record or feature. These can be cumbersome to work with in an FME workflow (i.e. FeatureMerger) so combining the natural key into a single CRC unique ID can simplify the workspace.
Another use case is to use the CRC value for change detection - to make sure it hasn't been changed, corrupted, deleted etc. It can also be used as a form of ChangeDetection (instead of using the ChangeDetector to compare geometry, use the Matcher to compare CRC values).
The value could also be used as a guaranteed unique ID number within a dataset.
The attached workspace shows an example use of the CRCCalculator transformer.
The CRCCalculator is a great tool for carrying out change detection.
In this example, address records are checked against an updated dataset using CRC values as identification whether a record has been changed. This workspace generates CRC values on-the-fly; an alternative is to save CRC values for each record, to save having to generate them for each run of the workspace. This is the big advantage of using the CRCCalculator over the ChangeDetector.
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