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.
Questo esercizio fa parte del corso
Scaling and Optimizing Data Pipelines with Polars
Istruzioni dell'esercizio
- Scan
HIVE_DIRusing the right argument to enable hive partitioning. - Filter the result to checkouts from 2024 onward.
esercizio interattivo pratico
Prova questo esercizio completando questo codice di esempio.
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)