Which transformer does not have group-related parameters for improving performance?

• A. StatisticsCalculator
• B. SpikeRemover
• C. LineOnLineOverlayer
• D. FeatureMerger

Answer: (B)

In FME, performance gains often come from processing features in groups that share a common key, allowing transformers to batch work, reuse computations, and manage memory more efficiently. A transformer that exposes group-related parameters gives you control over how features are grouped and processed, which can significantly boost performance on large datasets.

SpikeRemover stands out because it operates on a per-feature or per-attribute basis without any options to define or optimize by groups. It focuses on detecting and removing spikes using its internal logic, not on grouping features for batch processing. The other transformers listed provide grouping-related settings that let you tailor per-group processing, which is why they offer group-related parameters for improving performance.

So, the transformer that does not have group-related parameters for improving performance is SpikeRemover.