Performance Optimization

Techniques to make your PySpark applications faster

1. Partitioning

Proper partitioning is crucial for parallel processing. Repartition your data when needed:

# Repartition to 200 partitions
df = df.repartition(200)

# Partition by a column
df = df.repartition("date")

2. Caching

Cache frequently accessed DataFrames to avoid recomputation:

# Cache a DataFrame in memory
df.cache()

# Persist with storage level
from pyspark.storagelevel import StorageLevel
df.persist(StorageLevel.MEMORY_AND_DISK)

3. Broadcast Variables

Use broadcast variables for small lookup tables:

lookup_data = {"a": 1, "b": 2, "c": 3}
broadcast_var = sc.broadcast(lookup_data)

# Use in transformations
rdd.map(lambda x: broadcast_var.value.get(x, 0))

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