Profiling a lazy query

Before adding a new query to a daily workflow, the team wants to see where time is actually spent. Profiling runs the lazy query like .collect() does, but also returns a timings DataFrame with one row per stage.

Este ejercicio forma parte del curso

Scaling and Optimizing Data Pipelines with Polars

Ver curso

Instrucciones del ejercicio

Filter library to checkouts where use is "Physical".
Keep the 10 longest titles by title_len using .top_k().
Execute the query with profiling so you also get a timings DataFrame.

ejercicio interactivo práctico

Prueba este ejercicio completando este código de ejemplo.

result, timings = (
    library
    # Filter to physical checkouts
    .filter(pl.col("use") == "____")
    .with_columns(pl.col("title").str.len_chars().alias("title_len"))
    # Keep the 10 longest titles
    .top_k(10, by="____")
    # Run with profiling to capture per-stage timings
    .____()
)
print(timings)

Editar y ejecutar código