Refer this page for Pyspark and Delta table optimization techniques: https://www.databricks.com/discover/pages/optimize-data-workloads-guide
PySpark Optimization Techniques for Data Engineers
Optimizing PySpark performance is essential for efficiently processing large-scale data. Here are some key optimization techniques to enhance the performance of your PySpark applications:
Use Broadcast Variables
When joining smaller DataFrames with larger ones, consider using broadcast variables. This technique helps in distributing smaller DataFrames to all worker nodes, reducing data shuffling during the join operation.
from pyspark.sql import SparkSession
from pyspark.sql.functions import broadcast
spark = SparkSession.builder.appName("example").getOrCreate()
small_df = spark.createDataFrame([...])
large_df = spark.createDataFrame([...])
result_df = large_df.join(broadcast(small_df), "common_column")