Data Partitioning Strategies for Optimal Query Performance

Proper partitioning strategy dramatically impacts query performance. Time-based partitioning for time-series, hash for even distribution and range for sequential data.

Why Partitioning¶

Without partitioning, engine scans entire table. Partitioning enables skipping unnecessary data (partition pruning).

Partitioning Types¶

• Time-based — most common, partitioning by date (day, month)
• Hash — even distribution by hash key
• Range — value ranges (A-M, N-Z)
• List — explicit value list (regions, categories)

# Spark: partitioning during write
df.write.format("delta") \
    .partitionBy("year", "month") \
    .save("/data/orders")

# Query with partition pruning
spark.read.format("delta").load("/data/orders") \
    .filter("year = 2026 AND month = 2")  # reads only 1 partition

Best Practices¶

• 1 GB+ per partition — too small partitions are counterproductive
• Max 10k partitions — too many = slow metadata scan
• Partition by filters — by most common WHERE conditions

Summary¶

Proper partitioning is key for performance. Choose by most common filters and keep partitions sufficiently large.