Scanning a hive-partitioned dataset

The team also stores cleaned-up Parquet checkouts in a hive-partitioned layout, with one directory per year (checkoutyear=2023/, checkoutyear=2024/). Scan the partitioned dataset and filter on the partition column so Polars only reads the years you actually need.

polars is loaded as pl, and the root directory is in HIVE_DIR. The partition directories are printed for you, so you can see the layout.

This exercise is part of the course

Scaling and Optimizing Data Pipelines with Polars

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Exercise instructions

Scan HIVE_DIR using the right argument to enable hive partitioning.
Filter the result to checkouts from 2024 onward.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

requests = pl.scan_parquet(
    HIVE_DIR,
    # Enable hive partitioning
    ____=True,
)

result = (
    requests
    # Filter to the 2024 partition
    .filter(pl.col("checkoutyear") >= ____)
    .group_by("format")
    .agg(pl.col("checkouts").sum().alias("total"))
    .sort("total", descending=True)
    .collect()
)
print(result)

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