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Tutorial: Gmail email sender operator

Preview

This feature is in Public Preview.

This tutorial walks through creating a python-run-function operator for Lakeflow Designer that sends the contents of a DataFrame as a CSV attachment via Gmail. Use this example to learn how to build YAML-based operators that perform side effects, such as sending notifications or writing to external systems. To learn more, see User-defined operators in Lakeflow Designer.

Requirements​

  • A Databricks workspace with access to create secret scopes.
  • A Gmail account with a Google App Password (required when multifactor authentication (MFA) is enabled).
  • The Databricks CLI installed on your local development machine.

Step 1: Set up secrets​

Store your Gmail credentials in a Databricks secret scope so the operator can retrieve them at runtime.

  1. Create a secret scope using the Databricks CLI:

    Bash
    databricks secrets create-scope my_email_scope

  2. Store your Gmail App Password in the scope:

    Bash
    databricks secrets put-secret my_email_scope gmail_app_password

    You're prompted to enter the secret value. Paste your Gmail App Password and save.

Step 2: Write the run() function​

The python-run-function operator type requires a run() function with this signature:

Python
def run(config: Dict[str, Any], inputs: Dict[str, Any], spark) -> Dict[str, Any]:
  • config: Configuration values provided by the user in the Lakeflow Designer UI.
  • inputs: Input DataFrames keyed by port name.
  • spark: The active Spark session.

The function must return a dictionary of output DataFrames keyed by output port name.

Define and test the function in a notebook cell:

Python
from typing import Dict, Any

def run(config: Dict[str, Any], inputs: Dict[str, Any], spark) -> Dict[str, Any]:
    input_df = inputs["data"]

    # Skip side effects during Designer preview
    if config.get("is_preview", False):
        return {"data": input_df}

    import smtplib
    import os
    from email.mime.multipart import MIMEMultipart
    from email.mime.text import MIMEText
    from email.mime.base import MIMEBase
    from email import encoders

    sender_email = config.get("sender_email", "")
    secret_scope = config.get("secret_scope", "")
    secret_key = config.get("secret_key", "")
    recipients_raw = config.get("recipients", "")
    subject = config.get("subject", "")
    body = config.get("body", "")

    if not sender_email:
        raise ValueError("Sender Email is required.")
    if not secret_scope or not secret_key:
        raise ValueError("Secret Scope and Secret Key are required.")
    if not recipients_raw:
        raise ValueError("At least one recipient is required.")

    recipients = [r.strip() for r in recipients_raw.split(",") if r.strip()]
    if not recipients:
        raise ValueError("At least one valid recipient email is required.")

    # Retrieve password from Databricks secrets
    from pyspark.dbutils import DBUtils
    dbutils = DBUtils(spark)
    sender_password = dbutils.secrets.get(scope=secret_scope, key=secret_key)

    # Convert DataFrame to CSV
    pdf = input_df.toPandas()
    file_path = "/tmp/designer_email_attachment.csv"
    pdf.to_csv(file_path, index=False)

    # Send email to each recipient
    for recipient in recipients:
        msg = MIMEMultipart()
        msg["From"] = sender_email
        msg["To"] = recipient
        msg["Subject"] = subject
        msg.attach(MIMEText(body, "plain"))

        with open(file_path, "rb") as attachment:
            part = MIMEBase("application", "octet-stream")
            part.set_payload(attachment.read())
            encoders.encode_base64(part)
            part.add_header(
                "Content-Disposition",
                f"attachment; filename={os.path.basename(file_path)}",
            )
            msg.attach(part)

        with smtplib.SMTP_SSL("smtp.gmail.com", 465) as server:
            server.login(sender_email, sender_password)
            server.send_message(msg)

    # Clean up temp file
    if os.path.exists(file_path):
        os.remove(file_path)

    return {"data": input_df}

Step 3: Test the function​

Test the function with a sample DataFrame:

Python
test_df = spark.createDataFrame(
    [("Alice", 100), ("Bob", 200)],
    ["name", "amount"]
)

# Test in preview mode (no email sent)
result = run(
    config={
        "is_preview": True,
        "sender_email": "you@gmail.com",
        "secret_scope": "my_email_scope",
        "secret_key": "gmail_app_password",
        "recipients": "alice@example.com",
        "subject": "Test",
        "body": "Test body"
    },
    inputs={"data": test_df},
    spark=spark
)

result["data"].show()
# Expected: the original DataFrame, unchanged
note

The secret_scope and secret_key values in the config are the names of the secret scope and key you created in Step 1 -- not the actual password. The operator uses these names to retrieve the password from Databricks secrets at runtime.

important

Test with is_preview set to True first to verify the pass-through behavior without sending any email. When you're ready to test the actual email, set is_preview to False.

Step 4: Build the YAML definition​

Create a file called gmail_email_sender.yaml with the following content:

YAML
schema: user-defined-operator-v0.1.0
id: gmail_email_sender
type: python-run-function
version: '1.0.0'
name: Gmail Email Sender
description: Sends the input DataFrame as a CSV attachment via Gmail SMTP to one or more recipients.

config:
  type: object
  properties:
    is_preview:
      type: boolean
      format: is_preview
      default: false
    sender_email:
      type: string
      title: Sender Email
      default: ''
      examples:
        - 'you@gmail.com'
      x-ui:
        widget: input
    secret_scope:
      type: string
      title: Secret Scope
      default: ''
      examples:
        - 'my_email_scope'
      x-ui:
        widget: input
    secret_key:
      type: string
      title: Secret Key
      default: ''
      examples:
        - 'gmail_app_password'
      x-ui:
        widget: input
    recipients:
      type: string
      title: Recipients
      default: ''
      examples:
        - 'alice@example.com, bob@example.com'
      x-ui:
        widget: textarea
        rows: 2
    subject:
      type: string
      title: Subject
      default: ''
      examples:
        - 'Designer Output Data'
      x-ui:
        widget: input
    body:
      type: string
      title: Email Body
      default: "Hello,\n\nAttached is the latest data.\n\nBest,\nDatabricks Workflow"
      x-ui:
        widget: textarea
        rows: 6
  required:
    - sender_email
    - secret_scope
    - secret_key
    - recipients
    - subject
  additionalProperties: false

ports:
  input:
    - name: data
      title: Input Data
      mime: application/vnd.databricks.dataframe
  output:
    - name: data
      title: Output Data
      mime: application/vnd.databricks.dataframe

run_function:
  type: inline
  code: |
    from typing import Dict, Any

    def run(config: Dict[str, Any], inputs: Dict[str, Any], spark) -> Dict[str, Any]:
        input_df = inputs["data"]

        if config.get("is_preview", False):
            return {"data": input_df}

        import smtplib
        import os
        from email.mime.multipart import MIMEMultipart
        from email.mime.text import MIMEText
        from email.mime.base import MIMEBase
        from email import encoders

        sender_email = config.get("sender_email", "")
        secret_scope = config.get("secret_scope", "")
        secret_key = config.get("secret_key", "")
        recipients_raw = config.get("recipients", "")
        subject = config.get("subject", "")
        body = config.get("body", "")

        if not sender_email:
            raise ValueError("Sender Email is required.")
        if not secret_scope or not secret_key:
            raise ValueError("Secret Scope and Secret Key are required.")
        if not recipients_raw:
            raise ValueError("At least one recipient is required.")

        recipients = [r.strip() for r in recipients_raw.split(",") if r.strip()]
        if not recipients:
            raise ValueError("At least one valid recipient email is required.")

        from pyspark.dbutils import DBUtils
        dbutils = DBUtils(spark)
        sender_password = dbutils.secrets.get(scope=secret_scope, key=secret_key)

        pdf = input_df.toPandas()
        file_path = "/tmp/designer_email_attachment.csv"
        pdf.to_csv(file_path, index=False)

        for recipient in recipients:
            msg = MIMEMultipart()
            msg["From"] = sender_email
            msg["To"] = recipient
            msg["Subject"] = subject
            msg.attach(MIMEText(body, "plain"))

            with open(file_path, "rb") as attachment:
                part = MIMEBase("application", "octet-stream")
                part.set_payload(attachment.read())
                encoders.encode_base64(part)
                part.add_header(
                    "Content-Disposition",
                    f"attachment; filename={os.path.basename(file_path)}",
                )
                msg.attach(part)

            with smtplib.SMTP_SSL("smtp.gmail.com", 465) as server:
                server.login(sender_email, sender_password)
                server.send_message(msg)

        if os.path.exists(file_path):
            os.remove(file_path)

        return {"data": input_df}

Step 5: Save and register the operator​

  1. Save the YAML file to your Databricks workspace. For example:

    /Workspace/Users/<user-name>/gmail_email_sender.yaml

  2. Add the operator to your .user_defined_operators.yaml file:

    YAML
    operators:
      - /Workspace/Users/<user-name>/gmail_email_sender.yaml

For more information about registration options, see Make your operator discoverable.

Permissions​

Users who run a workflow containing this operator need READ access to the secret scope, or they can provide their own secret scope and key values in the operator configuration. Users also need read access to the YAML file in the workspace.

To grant access to the secret scope:

Bash
databricks secrets put-acl my_email_scope <user-or-group> READ

Using the operator in Lakeflow Designer​

After registration, the operator appears in Lakeflow Designer with an input port for your data source and configuration fields for sender email, secret scope, secret key, recipients, subject, and body.

When the workflow runs, the operator converts the input DataFrame to CSV, attaches it to an email, and sends it to each recipient. The DataFrame passes through unchanged to the output port, so you can chain additional operators downstream. During workflow preview, no email is sent.

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