When defining a Transform with multiple outlets, how should you write the compute function for optimal performance?

The correct approach is to filter the DataFrame once and assign it to a variable, then use that variable to generate each output. This method enhances performance by avoiding redundant computations. When a DataFrame is filtered multiple times within a compute function, it entails repetitive scanning of the data each time a filter is applied. This can lead to significant inefficiencies, especially with large datasets, as each filtering operation can be resource-intensive.

By filtering the DataFrame once and storing the result in a variable, you create a single, optimized point of computation. This pre-computed DataFrame can then be reused for each distinct output, significantly reducing the computational workload. It minimizes the number of times the original data needs to be accessed and manipulated, thereby saving both time and system resources.

Using the TransformContext to manage DataFrame filtering could be useful, but it may not yield the same level of efficiency as filtering once and utilizing that result. Similarly, employing multiple compute functions, while potentially cleaner in terms of separation of logic, can introduce overhead due to multiple accesses of the same dataset. Thus, efficiently handling multiple outputs by leveraging a single filtered DataFrame is the optimal strategy for performance.