---

name: bigquery-udfs metadata: version: "1.0.1" category: BigDataAndAnalytics description: >- Creates, deploys, and troubleshoots BigQuery User-Defined Functions (UDFs) and Remote Functions across SQL, JavaScript, Python, and Cloud Run endpoints. Use when writing custom scalar or aggregate functions in BigQuery, installing PyPI packages or Cloud Storage libraries in a Python or JavaScript UDF, or calling Google Cloud and external APIs from a Python UDF (WITH CONNECTION) or Remote Function. Don't use for standard BigQuery table/dataset CRUD (use bigquery-basics), DataFrames client workflows (use bigquery-bigframes), or BigQuery ML models (use bigquery-ai-ml).

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.

Usage Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read and follow the instructions in Usage Attribution.

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):

1. Install and Authenticate Google Cloud SDK: Install the Google Cloud SDK and authenticate:

   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).

2. 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)

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

3. 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.

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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:

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:

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).

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 NULLs.
• 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.

1. Create a Cloud Resource Connection and Retrieve its Service Account:

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

2. Grant Permissions to the Connection Service Account: Grant roles/serviceusage.serviceUsageConsumer on {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):

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