How to ensure pyspark udf execution is distributed across worker nodes

pjv — Wed, 09 Oct 2024 10:05:17 GMT

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

Re: How to ensure pyspark udf execution is distributed across worker nodes

VZLA — Fri, 01 Nov 2024 13:59:28 GMT

@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.

topic How to ensure pyspark udf execution is distributed across worker nodes in Data Engineering

How to ensure pyspark udf execution is distributed across worker nodes

Re: How to ensure pyspark udf execution is distributed across worker nodes