Understanding the BigQuery Python UDF IAM Policy Limit Error

If you are trying to create a Python User-Defined Function (UDF) in Google BigQuery and encountering a bizarre IAM error, you are not alone. The error message typically looks like this:

Error processing job: The number of members in the policy (1,501) is larger than the maximum allowed size 1,500.

What makes this issue highly frustrating is its inconsistency. You might run the exact same SQL query in us-west1 or EU with complete success, but it fails instantly when targeted at the US multi-region or us-central1. Even more confusingly, checking your project-level IAM policy might show only a handful of members (e.g., 20 to 30), which is nowhere near the 1,500 cap.

Why Does This Happen?

BigQuery Python UDFs do not run directly inside the standard BigQuery SQL engine. Instead, they are packaged, built, and deployed securely using Google Cloud's serverless infrastructure behind the scenes. This process involves:

  • Cloud Build to package the Python environment.
  • Artifact Registry to store the container images.
  • Cloud Storage to manage temporary build artifacts.
  • Cloud Run / Cloud Functions to execute the Python code.

Because these services are provisioned automatically by Google-managed service agents, BigQuery must dynamically manage IAM permissions on these backend resources. In highly active regions like US or us-central1, the shared or automated IAM policies applied to these internal resources (often KMS keys or shared Cloud Storage buckets) can reach the hard limit of 1,500 members due to Google's backend service accounts piling up over time.

How to Resolve and Workaround the Issue

1. Change the Dataset and Query Region (Recommended Workaround)

Since this is a regional backend limitation on Google's side, the fastest and most reliable workaround is to deploy your dataset and run your queries in a different, less congested region. For example, instead of using the US multi-region or us-central1, try using:

  • us-west1 (Oregon)
  • us-east4 (Northern Virginia)
  • EU (Europe multi-region)

Simply recreate your dataset in the new region and execute your UDF creation query there.

2. Rewrite the UDF in SQL or JavaScript

If your logic is relatively simple, consider rewriting your UDF using SQL or JavaScript. Unlike Python, JavaScript and SQL UDFs run natively within BigQuery's secure execution environment and do not trigger Cloud Build or external container deployments. This completely bypasses the IAM policy size limit.

-- Example of a simple SQL UDF
CREATE OR REPLACE FUNCTION `your_project.your_dataset.plus_one`(x INT64)
RETURNS INT64
AS (x + 1);

3. Force Re-creation of the BigQuery Connection Service Agent

Sometimes, the issue is tied to a bloated or corrupted IAM state on your project's BigQuery Connection Service Account. You can try to force Google to refresh these permissions by disabling and re-enabling the BigQuery Connection API, or by manually granting the BigQuery Connection User role to your service agent via the GCP Console or CLI:

gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
    --member="serviceAccount:service-YOUR_PROJECT_NUMBER@gcp-sa-bigqueryconnection.iam.gserviceaccount.com" \
    --role="roles/bigquery.connectionUser"

4. Track the Google Issue Tracker

This is a known issue on Google Cloud's side. If you are bound to us-central1 or the US multi-region and cannot change locations, you should star and comment on the official Google Issue Tracker thread to escalate the priority with GCP support.