BigQuery User-Defined Functions (UDFs)

BigQuery User-Defined Functions (UDFs) extend GoogleSQL with custom logic written in SQL, JavaScript, managed Python (

python-3.11

), or hosted externally as Remote Functions on Cloud Run and Cloud Run functions.

Attribution

Tag every BigQuery or Google Cloud command you execute directly in the terminal while using this skill, so usage can be attributed. The tag identifies only the skill and its version; it carries no user data.

Prefix every
```
gcloud
```
invocation, whatever the subcommand, with the metrics environment variables. Set them inline on each command; shell state may not persist between commands:
bash
```
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-udfs)" \
gcloud <command> [flags]
```
Do not use
```
gcloud config set
```
for this: it would persist beyond the current task and mislabel unrelated usage.
On direct HTTP calls to the BigQuery APIs (for example with
```
curl
```
) or HTTP requests to the MCP server, append this exact string to the end of the existing User-Agent header — the collection pipeline parses the
```
agent-skills/<version>
```
and
```
skill:<name>
```
tokens, so any rewording breaks attribution:
```
User-Agent: <existing_user_agent> agent-skills/1.0 (skill:bigquery-udfs)
```
Constraint for Generated Code: The tracking prefix is strictly for commands you execute directly in the terminal. Do NOT include this tracking prefix in generated SQL, scripts, Terraform templates, or code provided to the user for their own repositories.

Choosing the Right UDF Type

Always select the lightest-weight runtime that satisfies the requirement, because BigQuery optimizes native SQL expressions inline while external runtimes incur serialization and container startup overhead:

UDF Type	Connection Required?	When to Use	Key Constraints
SQL UDF (Default)	No	Composable expressions, regex, struct/array manipulation, aggregate functions ( `CREATE AGGREGATE FUNCTION` ), or polymorphic `ANY TYPE` parameters.	Lowest latency; zero external dependencies; supports `TEMP` or persistent functions.
JavaScript UDF	No	Custom string/JSON logic or bundling `.js` libraries from Cloud Storage ( `gs://` ).	Runs in V8 sandbox; `INT64` unsupported as input (pass as `FLOAT64` or `STRING` ). Max 32 KB inline code, 1 MB per external resource. Supports `TEMP` or persistent functions.
Managed Python UDF (Offline)	No	Pure compute using Python 3.11, PyPI wheels ( `packages=[...]` ), Cloud Storage `.py` modules ( `library=[...]` ), or vectorized Pandas/PyArrow batches.	Persistent functions only ( `CREATE TEMP FUNCTION` unsupported). Outbound network blocked. Max container size: 4 vCPU / 16 GiB. `JSON` , `RANGE` , `INTERVAL` , `GEOGRAPHY` unsupported.
Managed Python UDF ( `WITH CONNECTION` )	Yes ( `CLOUD_RESOURCE` )	Invoking Google Cloud APIs (e.g., Cloud Translation, Gemini Enterprise Agent Platform) or online HTTP endpoints directly from Python inside BigQuery containers.	Requires `WITH CONNECTION` . Outbound traffic restricted to `restricted.googleapis.com` in VPC-SC perimeters (VPC networks unsupported). Max container size: 4 vCPU / 16 GiB.
Remote Function ( `REMOTE WITH CONNECTION` )	Yes ( `CLOUD_RESOURCE` )	Calling external APIs or microservices when you need non-Python runtimes (Go, Java, Node.js), private VPC network access, GPUs, or custom Cloud Run scaling.	Requires Cloud Run HTTPS endpoint implementing BigQuery JSON batch contract and `roles/run.invoker` . Max input 5 MB per row; response limit 15 MB (Cloud Run) / 10 MB (1st gen).

Prerequisites & Setup

Ensure the Google Cloud project has an active billing account attached (see BigQuery Pricing):

Install and Authenticate Google Cloud SDK: Install the Google Cloud SDK and authenticate:
bash
```
gcloud auth login
```
Pass
```
--project={project_id}
```
and
```
--region={region}
```
(or
```
--location
```
for
```
bq
```
) explicitly on each command instead of running
```
gcloud config set
```
, which persists beyond the task. Default to
```
us-central1
```
when the user has not specified a region (see BigQuery Locations).

Enable Required APIs:

For SQL, JavaScript, and offline Python UDFs:
```
bigquery.googleapis.com
```
Additionally for Python UDFs calling APIs (
```
WITH CONNECTION
```
) and Remote Functions:
```
bigqueryconnection.googleapis.com
```
(and
```
run.googleapis.com
```
for Remote Functions)

bash

CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-udfs)" \
gcloud services enable bigquery.googleapis.com \
  bigqueryconnection.googleapis.com \
  run.googleapis.com --quiet

Verify Required IAM Roles:
- All UDFs:
```
roles/bigquery.dataEditor
```
  on the dataset and
```
roles/bigquery.jobUser
```
  on the project.
- When using
  WITH CONNECTION
  (API-calling Python UDFs & Remote Functions):
  - UDF Creator:
    roles/bigquery.connectionAdmin
    on the project (plus
    resourcemanager.projects.setIamPolicy
    , e.g.,
    roles/resourcemanager.projectIamAdmin
    , when granting project-level IAM roles to the connection service account via
    GRANT ... ON PROJECT
    ).
  - UDF Caller:
    roles/bigquery.connectionUser
    on the connection.

Core Implementation Patterns

1. SQL UDFs (Scalar & Templated)

Use

CREATE OR REPLACE FUNCTION

for persistent UDFs or

CREATE TEMP FUNCTION

for query-scoped functions. Use

ANY TYPE

for polymorphic parameters:

sql

CREATE OR REPLACE FUNCTION `{project_id}.{dataset_id}.normalize_phone`(
  raw_phone STRING,
  country_code ANY TYPE
)
RETURNS STRING
AS (
  CONCAT('+', CAST(country_code AS STRING), '-', REGEXP_REPLACE(raw_phone, r'[^0-9]', ''))
);

See SQL and JavaScript UDFs for aggregate UDFs (

CREATE AGGREGATE FUNCTION

) and JavaScript Cloud Storage library loading.

2. JavaScript UDFs

Use raw triple-quoted strings (

r"""..."""

) to avoid escaping backslashes:

sql

CREATE OR REPLACE FUNCTION `{project_id}.{dataset_id}.extract_url_query_keys`(
  url_str STRING
)
RETURNS ARRAY<STRING>
DETERMINISTIC
LANGUAGE js
OPTIONS (
  library = ['gs://{bucket_name}/libs/uri-parser.js']
)
AS r"""
  if (!url_str) return [];
  try {
    return URIParser.getQueryKeys(url_str);
  } catch (err) {
    return [];
  }
""";

3. Managed Python UDFs (With and Without Connection)

Python UDFs must be persistent (do not use

TEMP

), specify

runtime_version = 'python-3.11'

, and define an

entry_point

numpy

pandas

, and

pyarrow

are pre-installed in the

python-3.11

base image.

BigQuery defaults to

container_memory = '512Mi'

container_cpu = 1.0

, and

container_request_concurrency = 80

. Omit these options when the defaults are sufficient, and configure them explicitly only when a workload requires custom container sizing or concurrency (see Python UDFs).

Top-level Python statements (

import

, helper functions, classes, and

def {entry_point}(...)

) inside

AS r'''...'''

must have zero leading indentation (flush to the left margin). Indenting top-level Python definitions to align with surrounding SQL causes an

IndentationError

when BigQuery loads the container module.

A. Offline / Vectorized Python UDF (No Connection Needed)

Omit

WITH CONNECTION

when the UDF only performs computation or uses PyPI packages (

packages=[...]

) / Cloud Storage libraries (

library=['gs://...']

). To vectorize execution over batches of rows, annotate the parameter with

pd.DataFrame

(or

pyarrow.RecordBatch

) and return a

pd.Series

[!WARNING] Vectorized operations must be row-independent: BigQuery calls vectorized UDFs on arbitrary row batches (up to
max_batching_rows
). Do NOT compute global column statistics (such as z-scores, dataset mean/std, or rank) across
df
, because each batch only sees an arbitrary slice of rows. Use SQL window functions (
(val - AVG(val) OVER()) / NULLIF(STDDEV(val) OVER(), 0)
) for dataset-wide statistics. Vectorized Python UDFs should perform row-independent calculations (e.g. geometric distances or multi-column math).

sql

CREATE OR REPLACE FUNCTION `{project_id}.{dataset_id}.vectorized_distance`(
  x1 FLOAT64, y1 FLOAT64, x2 FLOAT64, y2 FLOAT64
)
RETURNS FLOAT64
LANGUAGE python
OPTIONS (
  runtime_version = 'python-3.11',
  entry_point = 'compute_distance'
)
AS r'''
import numpy as np
import pandas as pd

def compute_distance(df: pd.DataFrame) -> pd.Series:
    return pd.Series(
        np.hypot(df['x2'] - df['x1'], df['y2'] - df['y1']),
        index=df.index,
    )
''';

B. Online Python UDF Calling an External / Cloud API (

WITH CONNECTION

)

Include

WITH CONNECTION

only when the Python code makes outbound network or API calls at runtime.

Always configure explicit timeouts, logging, and prescriptive exception handling in the

try...except

block:

Transient errors only →
NULL
: Catch retryable exceptions (

google.api_core.exceptions.TooManyRequests

ServiceUnavailable

DeadlineExceeded

InternalServerError

, and

RetryError

), log them with

logging.warning

, and return

None

so a transient blip does not abort the query. Do not catch the base class

GoogleAPICallError

here: it also matches

PermissionDenied

Unauthenticated

, and

InvalidArgument

Fatal errors → raise: Let auth, permission, and invalid-argument errors propagate (or log and re-raise as
```
RuntimeError
```
) so they surface as a user error on the query instead of silently producing
```
NULL
```
s.
Never log row values: Anyone with Cloud Logging access can read UDF logs regardless of BigQuery table permissions. Log exception types and messages, not input or output data. Logs are only emitted when the session sets
```
SET @@udf.properties = JSON '{"enableDebugOutput": true}';
```
.
Set a per-request
timeout
:
```
Retry(timeout=...)
```
only caps the total retry duration; also pass
```
timeout=
```
on each client call.

Create a Cloud Resource Connection and Retrieve its Service Account:

bash

bq mk --connection --connection_type=CLOUD_RESOURCE \
  --location={region} --project_id={project_id} {connection_id}

bq show --connection {project_id}.{region}.{connection_id}

Grant Permissions to the Connection Service Account: Grant

roles/serviceusage.serviceUsageConsumer

{project_id}

plus the required API role (e.g.,

roles/cloudtranslate.user

) on the target project. Running

GRANT ... ON PROJECT

modifies the project's IAM policy and requires

resourcemanager.projects.setIamPolicy

(for example,

roles/resourcemanager.projectIamAdmin

); confirm with the user before running project-level IAM grants. The DCL

connection:

grantee takes the connection path (

[project_id.]location.connection_id

), not the service account email; BigQuery resolves the connection's service account automatically (use

"serviceAccount:{connection_sa_email}"

to grant by email instead):

sql

GRANT `roles/serviceusage.serviceUsageConsumer`
ON PROJECT `{project_id}`
TO "connection:{project_id}.{region}.{connection_id}";

GRANT `roles/cloudtranslate.user`
ON PROJECT `{project_id}`
TO "connection:{project_id}.{region}.{connection_id}";

Create the Python UDF with
WITH CONNECTION
(keep top-level Python statements flush-left at column 0):

sql

CREATE OR REPLACE FUNCTION `{project_id}.{dataset_id}.translate_to_es`(
  text STRING
)
RETURNS STRING
LANGUAGE python
WITH CONNECTION `{project_id}.{region}.{connection_id}`
OPTIONS (
  entry_point = 'do_translate',
  runtime_version = 'python-3.11',
  packages = ['google-cloud-translate>=3.11', 'google-api-core']
)
AS r'''
import logging
from google.api_core import exceptions
from google.api_core.retry import Retry
from google.cloud import translate

client = translate.TranslationServiceClient()

TRANSIENT_ERRORS = (
    exceptions.TooManyRequests,
    exceptions.ServiceUnavailable,
    exceptions.DeadlineExceeded,
    exceptions.InternalServerError,
    exceptions.RetryError,
)

def do_translate(text: str) -> str:
    if not text:
        return None
    try:
        # Note: Replace {project_id} with your Google Cloud Project ID
        response = client.translate_text(
            request={
                "parent": "projects/{project_id}/locations/us-central1",
                "contents": [text],
                "target_language_code": "es",
                "mime_type": "text/plain",
            },
            retry=Retry(timeout=30.0),  # Total retry budget.
            timeout=10.0,  # Per-attempt deadline.
        )
        return response.translations[0].translated_text
    except TRANSIENT_ERRORS as e:
        # Log the error type only; never log row values.
        logging.warning("Transient Translation API error: %s", type(e).__name__)
        return None
    except exceptions.GoogleAPICallError as e:
        # PermissionDenied, Unauthenticated, InvalidArgument, etc. are fatal.
        logging.error("Fatal Translation API error: %s: %s", type(e).__name__, e)
        raise RuntimeError(f"Translation failed: {type(e).__name__}") from e
''';

See Python UDFs for container sizing (

container_cpu

container_memory

container_request_concurrency

), Cloud Logging (

SET @@udf.properties = JSON '{"enableDebugOutput": true}';

), and checking container build status via

INFORMATION_SCHEMA.ROUTINES

4. Remote Functions (Cloud Run Endpoints)

Use Remote Functions when the endpoint is hosted on Cloud Run or Cloud Run functions (for non-Python languages, VPC networking, or custom hardware):

Grant
roles/run.invoker
to the Connection Service Account (confirm with the user before modifying the Cloud Run service IAM policy):

bash

CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-udfs)" \
gcloud run services add-iam-policy-binding {cloud_run_service} \
  --region={region} \
  --member="serviceAccount:{connection_sa_email}" \
  --role="roles/run.invoker"

Register the Remote Function: The endpoint must accept

{"calls": [[arg1, ...], ...]}

and return

{"replies": [res1, ...]}

with matching element count:

sql

CREATE OR REPLACE FUNCTION `{project_id}.{dataset_id}.enrich_address`(
  street STRING, postal_code STRING
)
RETURNS JSON
REMOTE WITH CONNECTION `{project_id}.{region}.{connection_id}`
OPTIONS (
  endpoint = 'https://{cloud_run_service_url}',
  max_batching_rows = 1000
);

See Remote Functions for Cloud Run handler templates and retry handling.

Validation Checklist

After generating or modifying a UDF, follow this validate→fix→repeat loop:

Step 1 — Validate Python code before BigQuery DDL: For Python UDFs, validate the Python function and
```
packages
```
dependencies before submitting the BigQuery DDL—either locally with
```
python3
```
test inputs (
```
None
```
, edge cases,
```
pd.DataFrame
```
batches) or in a containerized Cloud Run Job (
```
gcloud run jobs deploy {job_name} --source . --region={region}
```
and
```
gcloud run jobs execute {job_name} --region={region} --wait
```
; see Python UDFs Validation).

Step 2 — Execute DDL and poll container build status: Run

CREATE OR REPLACE FUNCTION

. Because the DDL statement returns before the Python container image finishes building, poll

INFORMATION_SCHEMA.ROUTINES

until

build_state

transitions from

IN_PROGRESS

SUCCEEDED

(or fix and re-run if

FAILED

sql

SELECT build_status.*
FROM `{project_id}.{dataset_id}`.INFORMATION_SCHEMA.ROUTINES
WHERE routine_name = '{udf_name}';

Step 3 — Test the UDF with SQL
SELECT
: Once
```
build_state = 'SUCCEEDED'
```
(or immediately after DDL for SQL/JS UDFs), run a test
```
SELECT
```
query covering
```
NULL
```
and valid inputs.

Cleanup & Teardown

Delete unused routines, connections, Cloud Run services, and staged Cloud Storage artifacts (note that the size of all Python UDF containers per project and per region is restricted to a sum total of 10 GiB):

bash

bq query --use_legacy_sql=false "DROP FUNCTION IF EXISTS \`{project_id}.{dataset_id}.{udf_name}\`;"
bq rm --connection --location={region} {connection_id}
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-udfs)" \
gcloud run services delete {cloud_run_service} --region={region} --project={project_id}
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-udfs)" \
gcloud storage rm gs://{bucket_name}/udf_libs/bundle.js

Reference Directory

SQL and JavaScript UDFs: Templated SQL parameters (
```
ANY TYPE
```
), aggregate UDFs, JavaScript type mappings, and Cloud Storage library loading (
```
https://docs.cloud.google.com/bigquery/docs/user-defined-functions.md.txt
```
).
Python UDFs: Offline vs.
```
WITH CONNECTION
```
online service access, Pandas/PyArrow vectorization, container resource limits (
```
container_cpu
```
/
```
container_memory
```
), and Cloud Logging debugging (
```
https://docs.cloud.google.com/bigquery/docs/user-defined-functions-python.md.txt
```
).
Remote Functions: Cloud Run HTTP request and response JSON schema, batch sizing, retry semantics, and VPC egress (
```
https://docs.cloud.google.com/bigquery/docs/remote-functions.md.txt
```
).
IAM, Authorized Routines, and Quotas: Sharing routines across datasets (
```
Authorized Routines
```
), container quotas, and troubleshooting errors.

Related Skills

BigQuery Basics (
```
bigquery-basics
```
): Dataset, table, and job management.
BigQuery DataFrames: Creating Python UDFs from Python via
```
@bpd.udf
```
.
BigQuery AI & ML: Built-in
```
ML.GENERATE_TEXT
```
and
```
AI.GENERATE
```
functions.

bigquery-udfs

NPX Install

Tags

SKILL.md Content

BigQuery User-Defined Functions (UDFs)

Attribution

Choosing the Right UDF Type

Prerequisites & Setup

Core Implementation Patterns

1. SQL UDFs (Scalar & Templated)

2. JavaScript UDFs

3. Managed Python UDFs (With and Without Connection)

A. Offline / Vectorized Python UDF (No Connection Needed)

B. Online Python UDF Calling an External / Cloud API (
`WITH CONNECTION`
)

4. Remote Functions (Cloud Run Endpoints)

Validation Checklist

Cleanup & Teardown

Reference Directory

Related Skills

bigquery-udfs

NPX Install

Tags

SKILL.md Content

BigQuery User-Defined Functions (UDFs)

Attribution

Choosing the Right UDF Type

Prerequisites & Setup

Core Implementation Patterns

1. SQL UDFs (Scalar & Templated)

2. JavaScript UDFs

3. Managed Python UDFs (With and Without Connection)

A. Offline / Vectorized Python UDF (No Connection Needed)

B. Online Python UDF Calling an External / Cloud API (WITH CONNECTION)

4. Remote Functions (Cloud Run Endpoints)

Validation Checklist

Cleanup & Teardown

Reference Directory

Related Skills

B. Online Python UDF Calling an External / Cloud API (
`WITH CONNECTION`
)