# Postmortem: Performance Bottleneck in Repeated Python Function Calls from 



## 

A performance-critical data processing pipeline experienced significant slowdowns when integrating Python-calculated variables into an R dataframe using reticulate and dplyr. The implementation repeatedly called the same Python function 7 times per group to extract individual output elements, leading to unnecessary computation.



## Root 

- The code invoked `cbpm_argo()` seven separate times per profile group to extract each output list element  

- Each redundant function call executed identical computations and I/O operations  

- Lack of pipeline stage consolidation forced re-computation for identical inputs  

- `reticulate` interface overhead compounded with Python interpreter calls  



## Why This Happens in Real 

- Legacy integration patterns between languages often prioritize simplicity over efficiency  

- Multi-output functions force developers to choose between readability and performance  

- Technical debt accumulates when glue-code optimizations aren't prioritized  

- Resource-intensive operations (like scientific models) expose inefficient patterns harshly  



## Real-World 

- **Computation Time**: 700% slowdown (7x redundant calculations)  

- **Resource Waste**: Increased Python interpreter overhead & R/Python serialization costs  

- **Scalability Issues**: Exponential time growth with profile groups (7 calls × N groups)  

- **Maintenance Risks**: Hard-coded indices ([1]/[2]...) create fragile column-position coupling  



## Example or 

Problematic Implementation:

Profile_1_2 <- Profile_1_2 %>%

mutate(.by = Profile_number,

pp_z = cbpm_argo(chl_z, Cphyto_z, 30, 113, 30)[[1]],

mu_z = cbpm_argo(chl_z, Cphyto_z, 30, 113, 30)[[2]],

… 5 more identical function calls …

)

Improved Implementation:

Profile_1_2 <- Profile_1_2 %>%

group_by(Profile_number) %>%

mutate(output = list(cbpm_argo(chl_z, Cphyto_z, 30, 113, 30))) %>%

mutate(

pp_z = map(output, 1),

mu_z = map(output, 2),

… map remaining elements …

) %>%

select(-output)

## How Senior Engineers Fix 

- **Compute Once, Extract Many**: Execute Python function once per group and unpack output  

- **Leverage Vectorization**: Use `purrr::map()` for element extraction  

- **Intermediate Results**: Temporarily store output objects for transformation  

- **Pipeline Optimization**: Consolidate reticulate operations in minimal context switches  

- **Memoization**: Cache results for repeated identical inputs (via `memoise`)  

- **Output Naming**: Modify Python function to return named tuple for safer indexing  



## Why Juniors Miss 

- **Symptom Focus**: Seeing only "successful column addition" without clocking execution time  

- **Language Barrier**: Unfamiliar with reticulate communication overhead costs  

- **Single-Pass Mentality**: Treating each `mutate` column as independent  

- **List Handling Gaps**: Uncomfortable with R list structures and element extraction  

- **API Limitations**: Not modifying Python source to return named outputs  

- **Benchmark Blindspot**: Prioritizing code brevity over computational complexity