How to ensure pyspark udf execution is distributed across worker nodes
Options
- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Permalink
- Report Inappropriate Content
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
Labels:
- Labels:
-
Spark
1 REPLY 1
Options
- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Permalink
- Report Inappropriate Content
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.

