Step Snap 1: [Optimizing Spark Partitions: Balancing Performance and Resource Cost]

Current Scenario

The code below loads Parquet data and processes it through partitions, showing significant partition size imbalance:

df_green = spark.read.parquet('/home/paradx/data/pq/green/*/*')

duration_rdd = df_green \\\\
    .select(columns) \\\\
    .rdd

def apply_model_in_batch(partition):
    cnt = 0

    for row in partition:
        cnt = cnt + 1

    return [cnt]

duration_rdd.mapPartitions(apply_model_in_batch).collect()

Current partition sizes:

[335827, 311259, 311019, 183043, 110517, 110331, 109373, 107836, 107864, 104436, 103052, 97921, 91386, 95395, 89646, 35612]

The data clearly shows partition skew - some partitions contain up to 10x more records than others, which can lead to inefficient processing as some executors work much harder than others.

The Repartitioning Challenge

While repartition() can redistribute data evenly, it has significant costs:

1. Full shuffle operation: All data must be moved across the network
2. Memory overhead: Requires additional memory for the shuffle
3. Disk I/O: May involve writing intermediate shuffle files
4. Network traffic: Data transfer between nodes

Optimization Strategies

1. Coalesce Instead of Repartition

For reducing partition count without a full shuffle:

# Instead of: df.repartition(8)
balanced_df = df_green.coalesce(8)

Benefits:

• Minimizes data movement by combining existing partitions