How to ensure pyspark udf execution is distributed across worker nodes

pjv
New Contributor III

‎10-09-2024 03:05 AM

Hi,

I have the following databricks notebook code defined:

 
pyspark_dataframe = create_pyspark_dataframe(some input data)
MyUDF = udf(myfunc, StringType())
pyspark_dataframe = pyspark_dataframe.withColumn('UDFOutput', DownloadUDF(input data columns))
output_strings = [x["UDFOutput"] for x in pyspark_dataframe.select("UDFOutput").collect()]
 
Im running this notebook on a cluster with multiple worker nodes. How can I ensure that the udf execution is distributed equally across the worker nodes?
 
Kind regards,
 
Pim
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VZLA
Databricks Employee
Databricks Employee

‎11-01-2024 06:59 AM

@pjv Can you please try the following, you'll basically want to have more than a single partition:

from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType

# Initialize Spark session (if not already done)
spark = SparkSession.builder.appName("AppName").getOrCreate()

# Create a PySpark DataFrame from your input data
pyspark_dataframe = create_pyspark_dataframe(some_input_data)

# Repartition the DataFrame to ensure even distribution across worker nodes
num_partitions = 4  # Adjust based on your cluster size
pyspark_dataframe = pyspark_dataframe.repartition(num_partitions)

# Define your UDF
MyUDF = udf(myfunc, StringType())

# Apply the UDF to the DataFrame
pyspark_dataframe = pyspark_dataframe.withColumn('UDFOutput', MyUDF(input_data_columns))

# Collect the results
output_strings = [x["UDFOutput"] for x in pyspark_dataframe.select("UDFOutput").collect()]

# Confirm the distribution UDF execution.

 

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