Cloud Composer 1 is in the post-maintenance mode. Google does not release any further updates to Cloud Composer 1, including new versions of Airflow, bugfixes, and security updates. We recommend planning migration to Cloud Composer 2.

Launch Dataflow pipelines with Cloud Composer

Cloud Composer 3 | Cloud Composer 2 | Cloud Composer 1

This page describes how to use the DataflowTemplateOperator to launch Dataflow pipelines from Cloud Composer. The Cloud Storage Text to BigQuery pipeline is a batch pipeline that allows you to upload text files stored in Cloud Storage, transform them using a JavaScript User Defined Function (UDF) that you provide, and output the results to BigQuery.

a user defined function, input file, and json schema will get uploaded
to a Cloud Storage bucket. A DAG that references these files will launch a Dataflow batch pipeline, that will apply the user-defined function and json schema file to the input file. Afterwards, this content will get uploaded to a BigQuery table

Overview

Before kicking off the workflow, you will create the following entities:
- An empty BigQuery table from an empty dataset that will hold the following columns of information: location, average_temperature,month and, optionally, inches_of_rain, is_current, and latest_measurement.
- A JSON file that will normalize the data from the .txt file into the correct format for the BigQuery table's schema. The JSON object will have an array of BigQuery Schema, where each object will contain a column name, type of input, and whether or not it is a required field.
- An input .txt file that will hold the data that will be batch uploaded to the BigQuery table.
- A User Defined Function written in JavaScript that will transform each line of the .txt file into the relevant variables for our table.
- An Airflow DAG file that will point to the location of these files.
Next, you will upload the .txt file, .js UDF file, and .json schema file to a Cloud Storage bucket. You'll also upload the DAG to your Cloud Composer environment.
After the DAG is uploaded, Airflow will run a task from it. This task will launch a Dataflow pipeline that will apply the User-Defined Function to the .txt file and format it according to the JSON schema.
Finally, the data will get uploaded to the BigQuery table that you created earlier.

Before you begin

This guide requires familiarity with JavaScript to write the User Defined Function.
This guide assumes that you already have a Cloud Composer environment. See Create environment to create one. You can use any version of Cloud Composer with this guide.
Enable the Cloud Composer, Dataflow, Cloud Storage, BigQuery APIs.
Enable the APIs

Create an empty BigQuery table with a schema definition

Create a BigQuery table with a schema definition. You will use this schema definition later in this guide. This BigQuery table will hold the results of the batch upload.

To create an empty table with a schema definition:

Console

In the Google Cloud console, go to the BigQuery page:

Go to BigQuery
In the navigation panel, in the Resources section, expand your project.
In the details panel, click Create dataset.
In the Create dataset page, in the Dataset ID section, name your Dataset average_weather. Leave all other fields in their default state.
Click Create dataset.
Go back to the navigation panel, in the Resources section, expand your project. Then, click on the average_weather dataset.
In the details panel, click Create table.
On the Create table page, in the Source section, select Empty table.
On the Create table page, in the Destination section:
- For Dataset name, choose the average_weather dataset.
- In the Table name field, enter the name average_weather.
- Verify that Table type is set to Native table.

In the Schema section, enter the schema definition. You can use one of the following approaches:

Enter schema information manually by enabling Edit as text and entering the table schema as a JSON array. Type in the following fields:

[
    {
        "name": "location",
        "type": "GEOGRAPHY",
        "mode": "REQUIRED"
    },
    {
        "name": "average_temperature",
        "type": "INTEGER",
        "mode": "REQUIRED"
    },
    {
        "name": "month",
        "type": "STRING",
        "mode": "REQUIRED"
    },
    {
        "name": "inches_of_rain",
        "type": "NUMERIC"
    },
    {
        "name": "is_current",
        "type": "BOOLEAN"
    },
    {
        "name": "latest_measurement",
        "type": "DATE"
    }
]

Use Add field to manually input the schema:

For Partition and cluster settings leave the default value, No partitioning.
In the Advanced options section, for Encryption leave the default value, Google-owned and managed key.
Click Create table.

bq

Use the bq mk command to create an empty dataset and a table in this dataset.

Run the following command to create a dataset of average global weather:

bq --location=LOCATION mk \
    --dataset PROJECT_ID:average_weather

Replace the following:

LOCATION: the region where the environment is located.
PROJECT_ID: the Project ID.

Run the following command to create an empty table in this dataset with the schema definition:

bq mk --table \
PROJECT_ID:average_weather.average_weather \
location:GEOGRAPHY,average_temperature:INTEGER,month:STRING,inches_of_rain:NUMERIC,is_current:BOOLEAN,latest_measurement:DATE

After the table is created, you can update the table's expiration, description, and labels. You can also modify the schema definition.

Python

Save this code as dataflowtemplateoperator_create_dataset_and_table_helper.py and update the variables in it to reflect your project and location, then run it with the following command:

python dataflowtemplateoperator_create_dataset_and_table_helper.py

Python

To authenticate to Cloud Composer, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


# Make sure to follow the quickstart setup instructions beforehand.
# See instructions here:
# https://cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries

# Before running the sample, be sure to install the bigquery library
# in your local environment by running pip install google.cloud.bigquery

from google.cloud import bigquery

# TODO(developer): Replace with your values
project = "your-project"  # Your GCP Project
location = "US"  # the location where you want your BigQuery data to reside. For more info on possible locations see https://cloud.google.com/bigquery/docs/locations
dataset_name = "average_weather"


def create_dataset_and_table(project, location, dataset_name):
    # Construct a BigQuery client object.
    client = bigquery.Client(project)

    dataset_id = f"{project}.{dataset_name}"

    # Construct a full Dataset object to send to the API.
    dataset = bigquery.Dataset(dataset_id)

    # Set the location to your desired location for the dataset.
    # For more information, see this link:
    # https://cloud.google.com/bigquery/docs/locations
    dataset.location = location

    # Send the dataset to the API for creation.
    # Raises google.api_core.exceptions.Conflict if the Dataset already
    # exists within the project.
    dataset = client.create_dataset(dataset)  # Make an API request.

    print(f"Created dataset {client.project}.{dataset.dataset_id}")

    # Create a table from this dataset.

    table_id = f"{client.project}.{dataset_name}.average_weather"

    schema = [
        bigquery.SchemaField("location", "GEOGRAPHY", mode="REQUIRED"),
        bigquery.SchemaField("average_temperature", "INTEGER", mode="REQUIRED"),
        bigquery.SchemaField("month", "STRING", mode="REQUIRED"),
        bigquery.SchemaField("inches_of_rain", "NUMERIC", mode="NULLABLE"),
        bigquery.SchemaField("is_current", "BOOLEAN", mode="NULLABLE"),
        bigquery.SchemaField("latest_measurement", "DATE", mode="NULLABLE"),
    ]

    table = bigquery.Table(table_id, schema=schema)
    table = client.create_table(table)  # Make an API request.
    print(f"Created table {table.project}.{table.dataset_id}.{table.table_id}")

Create a Cloud Storage bucket

Create a bucket to hold all of the files needed for the workflow. The DAG you create later in this guide will reference the files that you upload to this storage bucket. To create a new storage bucket:

Console

Open the Cloud Storage in the Google Cloud console.

Go to Cloud Storage
Click Create Bucket to open the bucket creation form.
1. Enter your bucket information and click Continue to complete each step:
  - Specify a globally unique Name for your bucket. This guide uses bucketName as an example.
  - Select Region for the location type. Next, select a Location where the bucket data will be stored.
  - Select Standard as your default storage class for your data.
  - Select Uniform access control to access your objects.
2. Click Done.

gcloud

Use the gcloud storage buckets create command:

gcloud storage buckets create gs://bucketName/

Replace the following:

bucketName: the name of the bucket that you created earlier in this guide.

Code samples

C#

To authenticate to Cloud Composer, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


using Google.Apis.Storage.v1.Data;
using Google.Cloud.Storage.V1;
using System;

public class CreateBucketSample
{
    public Bucket CreateBucket(
        string projectId = "your-project-id",
        string bucketName = "your-unique-bucket-name")
    {
        var storage = StorageClient.Create();
        var bucket = storage.CreateBucket(projectId, bucketName);
        Console.WriteLine($"Created {bucketName}.");
        return bucket;
    }
}

Go

To authenticate to Cloud Composer, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import (
	"context"
	"fmt"
	"io"
	"time"

	"cloud.google.com/go/storage"
)

// createBucket creates a new bucket in the project.
func createBucket(w io.Writer, projectID, bucketName string) error {
	// projectID := "my-project-id"
	// bucketName := "bucket-name"
	ctx := context.Background()
	client, err := storage.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("storage.NewClient: %w", err)
	}
	defer client.Close()

	ctx, cancel := context.WithTimeout(ctx, time.Second*30)
	defer cancel()

	bucket := client.Bucket(bucketName)
	if err := bucket.Create(ctx, projectID, nil); err != nil {
		return fmt.Errorf("Bucket(%q).Create: %w", bucketName, err)
	}
	fmt.Fprintf(w, "Bucket %v created\n", bucketName)
	return nil
}

Java

To authenticate to Cloud Composer, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.storage.Bucket;
import com.google.cloud.storage.BucketInfo;
import com.google.cloud.storage.Storage;
import com.google.cloud.storage.StorageOptions;

public class CreateBucket {
  public static void createBucket(String projectId, String bucketName) {
    // The ID of your GCP project
    // String projectId = "your-project-id";

    // The ID to give your GCS bucket
    // String bucketName = "your-unique-bucket-name";

    Storage storage = StorageOptions.newBuilder().setProjectId(projectId).build().getService();

    Bucket bucket = storage.create(BucketInfo.newBuilder(bucketName).build());

    System.out.println("Created bucket " + bucket.getName());
  }
}

Python

To authenticate to Cloud Composer, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google.cloud import storage


def create_bucket(bucket_name):
    """Creates a new bucket."""
    # bucket_name = "your-new-bucket-name"

    storage_client = storage.Client()

    bucket = storage_client.create_bucket(bucket_name)

    print(f"Bucket {bucket.name} created")

Ruby

To authenticate to Cloud Composer, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

def create_bucket bucket_name:
  # The ID to give your GCS bucket
  # bucket_name = "your-unique-bucket-name"

  require "google/cloud/storage"

  storage = Google::Cloud::Storage.new
  bucket  = storage.create_bucket bucket_name

  puts "Created bucket: #{bucket.name}"
end

Create a JSON-formatted BigQuery schema for your output table

Create a JSON formatted BigQuery schema file that matches the output table you created earlier. Note that the field names, types, and modes must match the ones defined earlier in your BigQuery table schema. This file will normalize the data from your .txt file into a format compatible with your BigQuery schema. Name this file jsonSchema.json.

{
    "BigQuery Schema": [
    {
        "name": "location",
        "type": "GEOGRAPHY",
        "mode": "REQUIRED"
    },
    {
        "name": "average_temperature",
        "type": "INTEGER",
        "mode": "REQUIRED"
    },
    {
        "name": "month",
        "type": "STRING",
        "mode": "REQUIRED"
    },
    {
        "name": "inches_of_rain",
        "type": "NUMERIC"
    },
    {
        "name": "is_current",
        "type": "BOOLEAN"
    },
    {
        "name": "latest_measurement",
        "type": "DATE"
    }]
}

Create a JavaScript file to format your data

In this file, you will define your UDF (User Defined Function) that supplies the logic to transform the lines of text in your input file. Note that this function takes each line of text in your input file as its own argument, so the function will run once for each line of your input file. Name this file transformCSVtoJSON.js.


function transformCSVtoJSON(line) {
  var values = line.split(',');
  var properties = [
    'location',
    'average_temperature',
    'month',
    'inches_of_rain',
    'is_current',
    'latest_measurement',
  ];
  var weatherInCity = {};

  for (var count = 0; count < values.length; count++) {
    if (values[count] !== 'null') {
      weatherInCity[properties[count]] = values[count];
    }
  }

  return JSON.stringify(weatherInCity);
}

Create your input file

This file will hold the information you want to upload to your BigQuery table. Copy this file locally and name it inputFile.txt.

POINT(40.7128 74.006),45,'July',null,true,2020-02-16
POINT(41.8781 87.6298),23,'October',13,false,2015-02-13
POINT(48.8566 2.3522),80,'December',null,true,null
POINT(6.5244 3.3792),15,'March',14,true,null

Upload your files to your bucket

Upload the following files to the Cloud Storage bucket that you created earlier:

JSON-formatted BigQuery schema (.json)
JavaScript User Defined Function (transformCSVtoJSON.js)
The input file of the text you'd like to process (.txt)

Console

In the Google Cloud console, go to the Cloud Storage Buckets page.

Go to Buckets
In the list of buckets, click on your bucket.
In the Objects tab for the bucket, do one of the following:
- Drag and drop the desired files from your desktop or file manager to the main pane in the Google Cloud console.
- Click the Upload Files button, select the files you want to upload in the dialog that appears, and click Open.

gcloud

Run the gcloud storage cp command:

gcloud storage cp OBJECT_LOCATION gs://bucketName

Replace the following:

bucketName: the name of the bucket that you created earlier in this guide.
OBJECT_LOCATION: the local path to your object. For example, Desktop/transformCSVtoJSON.js.

Code samples

Python

To authenticate to Cloud Composer, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google.cloud import storage


def upload_blob(bucket_name, source_file_name, destination_blob_name):
    """Uploads a file to the bucket."""
    # The ID of your GCS bucket
    # bucket_name = "your-bucket-name"
    # The path to your file to upload
    # source_file_name = "local/path/to/file"
    # The ID of your GCS object
    # destination_blob_name = "storage-object-name"

    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)
    blob = bucket.blob(destination_blob_name)

    # Optional: set a generation-match precondition to avoid potential race conditions
    # and data corruptions. The request to upload is aborted if the object's
    # generation number does not match your precondition. For a destination
    # object that does not yet exist, set the if_generation_match precondition to 0.
    # If the destination object already exists in your bucket, set instead a
    # generation-match precondition using its generation number.
    generation_match_precondition = 0

    blob.upload_from_filename(source_file_name, if_generation_match=generation_match_precondition)

    print(
        f"File {source_file_name} uploaded to {destination_blob_name}."
    )

Ruby

To authenticate to Cloud Composer, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

def upload_file bucket_name:, local_file_path:, file_name: nil
  # The ID of your GCS bucket
  # bucket_name = "your-unique-bucket-name"

  # The path to your file to upload
  # local_file_path = "/local/path/to/file.txt"

  # The ID of your GCS object
  # file_name = "your-file-name"

  require "google/cloud/storage"

  storage = Google::Cloud::Storage.new
  bucket  = storage.bucket bucket_name, skip_lookup: true

  file = bucket.create_file local_file_path, file_name

  puts "Uploaded #{local_file_path} as #{file.name} in bucket #{bucket_name}"
end

Configure DataflowTemplateOperator

Before running the DAG, set the following Airflow variables.

Airflow variable	Value
`project_id`	The Project ID
`gce_zone`	Compute Engine zone where the Dataflow cluster must be created
`bucket_path`	The location of the Cloud Storage bucket that you created earlier

Now you will reference the files you created earlier to create a DAG that kicks off the Dataflow workflow. Copy this DAG and save it locally as composer-dataflow-dag.py.

Airflow 2



"""Example Airflow DAG that creates a Cloud Dataflow workflow which takes a
text file and adds the rows to a BigQuery table.

This DAG relies on four Airflow variables
https://airflow.apache.org/docs/apache-airflow/stable/concepts/variables.html
* project_id - Google Cloud Project ID to use for the Cloud Dataflow cluster.
* gce_zone - Google Compute Engine zone where Cloud Dataflow cluster should be
  created.
For more info on zones where Dataflow is available see:
https://cloud.google.com/dataflow/docs/resources/locations
* bucket_path - Google Cloud Storage bucket where you've stored the User Defined
Function (.js), the input file (.txt), and the JSON schema (.json).
"""

import datetime

from airflow import models
from airflow.providers.google.cloud.operators.dataflow import (
    DataflowTemplatedJobStartOperator,
)
from airflow.utils.dates import days_ago

bucket_path = "{{var.value.bucket_path}}"
project_id = "{{var.value.project_id}}"
gce_zone = "{{var.value.gce_zone}}"


default_args = {
    # Tell airflow to start one day ago, so that it runs as soon as you upload it
    "start_date": days_ago(1),
    "dataflow_default_options": {
        "project": project_id,
        # Set to your zone
        "zone": gce_zone,
        # This is a subfolder for storing temporary files, like the staged pipeline job.
        "tempLocation": bucket_path + "/tmp/",
    },
}

# Define a DAG (directed acyclic graph) of tasks.
# Any task you create within the context manager is automatically added to the
# DAG object.
with models.DAG(
    # The id you will see in the DAG airflow page
    "composer_dataflow_dag",
    default_args=default_args,
    # The interval with which to schedule the DAG
    schedule_interval=datetime.timedelta(days=1),  # Override to match your needs
) as dag:
    start_template_job = DataflowTemplatedJobStartOperator(
        # The task id of your job
        task_id="dataflow_operator_transform_csv_to_bq",
        # The name of the template that you're using.
        # Below is a list of all the templates you can use.
        # For versions in non-production environments, use the subfolder 'latest'
        # https://cloud.google.com/dataflow/docs/guides/templates/provided-batch#gcstexttobigquery
        template="gs://dataflow-templates/latest/GCS_Text_to_BigQuery",
        # Use the link above to specify the correct parameters for your template.
        parameters={
            "javascriptTextTransformFunctionName": "transformCSVtoJSON",
            "JSONPath": bucket_path + "/jsonSchema.json",
            "javascriptTextTransformGcsPath": bucket_path + "/transformCSVtoJSON.js",
            "inputFilePattern": bucket_path + "/inputFile.txt",
            "outputTable": project_id + ":average_weather.average_weather",
            "bigQueryLoadingTemporaryDirectory": bucket_path + "/tmp/",
        },
    )

Airflow 1



"""Example Airflow DAG that creates a Cloud Dataflow workflow which takes a
text file and adds the rows to a BigQuery table.

This DAG relies on four Airflow variables
https://airflow.apache.org/docs/apache-airflow/stable/concepts/variables.html
* project_id - Google Cloud Project ID to use for the Cloud Dataflow cluster.
* gce_zone - Google Compute Engine zone where Cloud Dataflow cluster should be
  created.
  created.
Learn more about the difference between the two here:
https://cloud.google.com/compute/docs/regions-zones
* bucket_path - Google Cloud Storage bucket where you've stored the User Defined
Function (.js), the input file (.txt), and the JSON schema (.json).
"""

import datetime

from airflow import models
from airflow.contrib.operators.dataflow_operator import DataflowTemplateOperator
from airflow.utils.dates import days_ago

bucket_path = "{{var.value.bucket_path}}"
project_id = "{{var.value.project_id}}"
gce_zone = "{{var.value.gce_zone}}"


default_args = {
    # Tell airflow to start one day ago, so that it runs as soon as you upload it
    "start_date": days_ago(1),
    "dataflow_default_options": {
        "project": project_id,
        # Set to your zone
        "zone": gce_zone,
        # This is a subfolder for storing temporary files, like the staged pipeline job.
        "tempLocation": bucket_path + "/tmp/",
    },
}

# Define a DAG (directed acyclic graph) of tasks.
# Any task you create within the context manager is automatically added to the
# DAG object.
with models.DAG(
    # The id you will see in the DAG airflow page
    "composer_dataflow_dag",
    default_args=default_args,
    # The interval with which to schedule the DAG
    schedule_interval=datetime.timedelta(days=1),  # Override to match your needs
) as dag:
    start_template_job = DataflowTemplateOperator(
        # The task id of your job
        task_id="dataflow_operator_transform_csv_to_bq",
        # The name of the template that you're using.
        # Below is a list of all the templates you can use.
        # For versions in non-production environments, use the subfolder 'latest'
        # https://cloud.google.com/dataflow/docs/guides/templates/provided-batch#gcstexttobigquery
        template="gs://dataflow-templates/latest/GCS_Text_to_BigQuery",
        # Use the link above to specify the correct parameters for your template.
        parameters={
            "javascriptTextTransformFunctionName": "transformCSVtoJSON",
            "JSONPath": bucket_path + "/jsonSchema.json",
            "javascriptTextTransformGcsPath": bucket_path + "/transformCSVtoJSON.js",
            "inputFilePattern": bucket_path + "/inputFile.txt",
            "outputTable": project_id + ":average_weather.average_weather",
            "bigQueryLoadingTemporaryDirectory": bucket_path + "/tmp/",
        },
    )

Upload the DAG to Cloud Storage

Upload your DAG to the /dags folder in your environment's bucket. Once the upload has been completed successfully, you can see it by clicking on the DAGs Folder link on the Cloud Composer Environments page.

DAGs folder in your environment holds your DAG

View the task's status

Go to the Airflow web interface.
On the DAGs page, click the DAG name (such as composerDataflowDAG).
On the DAGs Details page, click Graph View.
Check status:
- Failed: The task has a red box around it. You can also hold the pointer over task and look for State: Failed.
- Success: The task has a green box around it. You can also hold the pointer over the task and check for State: Success.

After a few minutes, you can check the results in Dataflow and BigQuery.

View your job in Dataflow

In the Google Cloud console, go to the Dataflow page.

Go to Dataflow
Your job is named dataflow_operator_transform_csv_to_bq with a unique ID attached to the end of the name with a hyphen, like so:
Click on the name to see the job details.

View your results in BigQuery

In the Google Cloud console, go to the BigQuery page.

Go to BigQuery
You can submit queries using standard SQL. Use the following query to see the rows that were added to your table:
```
SELECT * FROM projectId.average_weather.average_weather
```