Pandas_Udod max batch size not working in notebook

[277745]

Hello 

I am trying to set max batch size for pandas-udf in Databricks notebook, but in my tests it doesn’t have any effect on size. spark.conf.set("spark.sql.execution.arrow.enabled", "true")

spark.conf.set('spark.sql.execution.arrow.maxRecordsPerBatch', 1000000)

should I set it in other way like during cluster configuration?

Thanks for clarification in advance!

[Kaniz]

Hi @277745, It seems you’re working with Pandas UDF in a Databricks Notebook and trying to set the maximum batch size.

Let’s address your query:

1. Setting Max Batch Size for Pandas UDF:

   • You’ve already taken the right steps by configuring the following Spark settings:
     • spark.conf.set("spark.sql.execution.arrow.enabled", "true")
     • spark.conf.set('spark.sql.execution.arrow.maxRecordsPerBatch', 1000000)
   • However, it appears that these settings haven’t had the desired effect on the batch size during your tests.

2. Cluster Configuration vs. Notebook Configuration:

   • The settings you’ve applied are specific to the notebook session. If you want to set them globally for the entire cluster, you should consider adjusting the cluster configuration.
   • To set these parameters at the cluster level, follow these steps:
     1. Go to your Databricks workspace.
     2. Navigate to the Clusters tab.
     3. Click on the cluster you’re using.
     4. In the Spark Config section, add the following configurations:
        • spark.sql.execution.arrow.enabled with a value of "true"
        • spark.sql.execution.arrow.maxRecordsPerBatch with a value of 1000000
     5. Click Save to apply the changes to the entire cluster.

3. Additional Considerations:

   • Keep in mind that the maxRecordsPerBatch setting determines the maximum number of records per batch when using Arrow-based Pandas UDFs. However, it doesn’t guarantee that every batch will have exactly that many records. The actual batch size may vary based on data distribution and other factors.
   • If you encounter any issues even after adjusting the cluster configuration, consider checking other factors such as data skew, memory availability, and resource utilization.

In summary, while notebook-level settings are useful for testing and experimentation, adjusting the cluster configuration will ensure consistent behavior across all notebooks running on that cluster. Feel free to explore this approach, and I hope this clarifies things for you! 😊

If you have any further questions, feel free to ask!