Partition-Aware Data Pruning

How can we ensure Spark reads only the required partition directories on storage rather than scanning terabytes of raw data?

Expert⏱ 3 Minutes⭐ 880 XP

🤔 THE QUESTION

How can we ensure Spark reads only the required partition directories on storage rather than scanning terabytes of raw data?

💡 WHAT IS IT?

Filtering on partitioned directory columns (e.g. year, month, date) allows the catalog reader to prune unneeded files completely.

🎯 WHAT IS IT USED FOR?

Cutting lakehouse query latency and cloud read I/O costs by 95% on multi-terabyte datasets.

💻 EXAMPLE

df_pruned = spark.read.parquet("s3://bucket/events/").filter((col("year") == 2026) & (col("month") == 8) & (col("day") == 30))

Practice typing production-grade PySpark code for Partition-Aware Data Pruning.