Overview

In this lab, you:

• Convert a custom pipeline into a custom Dataflow Flex Template.
• Run a Dataflow Flex Template.

Prerequisites:

• Basic familiarity with Python.

A pipeline that accepts command-line parameters is vastly more useful than one with those parameters hard-coded. However, running it requires creating a development environment. An even better option for pipelines that are expected to be rerun by a variety of different users or in a variety of different contexts would be to use a Dataflow template.

There are many Dataflow templates that have already been created as part of Google Cloud Platform, to learn more, explore the Get started with Google-provided templates guide. But none of them perform the same function as the pipeline in this lab. Instead, in this part of the lab, you convert the pipeline into a newer custom Dataflow Flex Template (as opposed to a custom traditional template).

Converting a pipeline into a custom Dataflow Flex Template requires the use of a Docker container to package up your code and the dependencies, a Dockerfile to describe what code to build, Cloud Build to build the underlying container that will be executed at runtime to create the actual job, and a metadata file to describe the job parameters.

Setup and requirements

Lab setup

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Qwiklabs using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Check project permissions

Before you begin your work on Google Cloud, you need to ensure that your project has the correct permissions within Identity and Access Management (IAM).

1. In the Google Cloud console, on the Navigation menu (), select IAM & Admin > IAM.

2. Confirm that the default compute Service Account {project-number}-compute@developer.gserviceaccount.com is present and has the editor role assigned. The account prefix is the project number, which you can find on Navigation menu > Cloud Overview > Dashboard.

Note: If the account is not present in IAM or does not have the editor role, follow the steps below to assign the required role.

1. In the Google Cloud console, on the Navigation menu, click Cloud Overview > Dashboard.
2. Copy the project number (e.g. 729328892908).
3. On the Navigation menu, select IAM & Admin > IAM.
4. At the top of the roles table, below View by Principals, click Grant Access.
5. For New principals, type:

{project-number}-compute@developer.gserviceaccount.com

6. Replace {project-number} with your project number.
7. For Role, select Project (or Basic) > Editor.
8. Click Save.

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI > Dashboard.

2. Click Enable All Recommended APIs.

3. In the Navigation menu, click Workbench.

   At the top of the Workbench page, ensure you are in the Instances view.

4. Click Create New.

5. Configure the Instance:

   • Name: lab-workbench
   • Region: Set the region to
   • Zone: Set the zone to
   • Advanced Options (Optional): If needed, click "Advanced Options" for further customization (e.g., machine type, disk size).

6. Click Create.

This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.

7. Click Open Jupyterlab next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.

8. Next, click Terminal. This will open up a terminal where you can run all the commands in this lab.

Download Code Repository

Next you will download a code repository for use in this lab.

1. In the terminal you just opened, enter the following:

git clone https://github.com/GoogleCloudPlatform/training-data-analyst cd /home/jupyter/training-data-analyst/quests/dataflow_python/

2. On the left panel of your notebook environment, in the file browser, you will notice the training-data-analyst repo added.

3. Navigate into the cloned repo /training-data-analyst/quests/dataflow_python/. You will see a folder for each lab, which is further divided into a lab sub-folder with code to be completed by you, and a solution sub-folder with a fully workable example to reference if you get stuck.

Note: To open a file for editing purposes, simply navigate to the file and click on it. This will open the file, where you can add or modify code.

Click Check my progress to verify the objective. Create notebook instance and clone course repo

Task 1. Set up your pipeline

For this lab, we will leverage the existing pipeline code from the Branching Pipelines lab (solutions folder).

Open the appropriate lab

• In the terminal in your jupyterlab environment, run the following commands:

cd 2_Branching_Pipelines/lab export BASE_DIR=$(pwd)

Set up the virtual environment and dependencies

Before you can begin editing the actual pipeline code, you need to ensure that you have installed the necessary dependencies.

1. Go back to the terminal you previously opened in your IDE environment, then create a virtual environment for your work in this lab:

sudo apt-get update && sudo apt-get install -y python3-venv python3 -m venv df-env source df-env/bin/activate

2. Next, install the packages you will need to execute your pipeline:

python3 -m pip install -q --upgrade pip setuptools wheel python3 -m pip install apache-beam[gcp]

3. Finally, ensure that the Dataflow API is enabled:

gcloud services enable dataflow.googleapis.com

Set up the data environment

• Set up the data environment:

# Create GCS buckets and BQ dataset cd $BASE_DIR/../.. source create_batch_sinks.sh # Generate event dataflow source generate_batch_events.sh # Change to the directory containing the practice version of the code cd $BASE_DIR

Update your pipeline code

• Update the my_pipeline.py file in your IDE by using the solution file, which can be found in training-data-analyst/quests/dataflow_python/2_Branching_Pipelines/solution/:

cp /home/jupyter/training-data-analyst/quests/dataflow_python/2_Branching_Pipelines/solution/my_pipeline.py $BASE_DIR

Click Check my progress to verify the objective. Set up the data environment

Task 2. Create a custom Dataflow Flex Template container image

1. First, enable Kaniko cache use by default. Kaniko caches container build artifacts, so using this option speeds up subsequent builds. We will also use pip3 freeze to record the packages and their versions being used in our environment.

gcloud config set builds/use_kaniko True

2. Next, we will create our Dockerfile. This will specify the code and the dependencies we need to use.

   a. To complete this task, create a New File in the dataflow_python/2_Branching_Pipelines/lab folder in the file explorer of your IDE.

   b. To create New File, click on File >> New >> Text File.

   c. Rename the file name as Dockerfile, to rename the file name right click on it.

   d. Open Dockerfile file in the editor panel, click on the file to open it.

   e. Copy the below code to the Dockerfile file and save it:

FROM gcr.io/dataflow-templates-base/python3-template-launcher-base ARG WORKDIR=/dataflow/template RUN mkdir -p ${WORKDIR} WORKDIR ${WORKDIR} RUN apt-get update && apt-get install -y libffi-dev && rm -rf /var/lib/apt/lists/* COPY my_pipeline.py . ENV FLEX_TEMPLATE_PYTHON_PY_FILE="${WORKDIR}/my_pipeline.py" RUN python3 -m pip install apache-beam[gcp]==2.60.0

3. Finally, use Cloud Build to build the container image:

export PROJECT_ID=$(gcloud config get-value project) export TEMPLATE_IMAGE="gcr.io/$PROJECT_ID/dataflow/my_pipeline:latest" gcloud builds submit --tag $TEMPLATE_IMAGE .

This will take a few minutes to build and push the container.

Click Check my progress to verify the objective. Create a custom Dataflow Flex Template container image

Task 3. Create and stage the flex template

1. To run a template, you need to create a template spec file in a Cloud Storage containing all of the necessary information to run the job, such as the SDK information and metadata.

   a. Create a New File in the dataflow_python/2_Branching_Pipelines/lab folder in the file explorer of your IDE.

   b. To create New File, click on File >> New >> Text File.

   c. Rename the file name as metadata.json, to rename the file name right click on it.

   d. Open metadata.json file in the editor panel. To open the file right click on the metadata.json file then select open With >> Editor.

   e. To complete this task, we need to create a metadata.json file in the following format that accounts for all of the input parameters your pipeline expects. Refer to the solution if you need. This does require you to write your own parameter regex checking. While not best practice, ".*" will match on any input.

{ "name": "Your pipeline name", "description": "Your pipeline description", "parameters": [ { "name": "inputPath", "label": "Input file path.", "helpText": "Path to events.json file.", "regexes": [ ".*\\.json" ] }, { "name": "outputPath", "label": "Output file location", "helpText": "GCS Coldline Bucket location for raw data", "regexes": [ "gs:\\/\\/[a-zA-z0-9\\-\\_\\/]+" ] }, { "name": "tableName", "label": "BigQuery output table", "helpText": "BigQuery table spec to write to, in the form 'project:dataset.table'.", "is_optional": true, "regexes": [ "[^:]+:[^.]+[.].+" ] } ] }

2. Then build and stage the actual template:

export PROJECT_ID=$(gcloud config get-value project) export TEMPLATE_PATH="gs://${PROJECT_ID}/templates/mytemplate.json" # Will build and upload the template to GCS gcloud dataflow flex-template build $TEMPLATE_PATH \ --image "$TEMPLATE_IMAGE" \ --sdk-language "PYTHON" \ --metadata-file "metadata.json"

3. Verify that the file has been uploaded to the template location in Cloud Storage.

Click Check my progress to verify the objective. Create and stage the Flex Template

Task 4. Execute the template from the UI

To complete this task, follow the instructions below:

1. Go to the Dataflow page in the Google Cloud console.

2. Click CREATE JOB FROM TEMPLATE.

3. Enter a valid job name in the Job name field.

4. Set the Regional endpoint to .

5. Select Custom template from the Dataflow template drop-down menu.

6. Enter the Cloud Storage path to your template file in the template Cloud Storage path field.

7. Input the appropriate items under Required parameter

   a. For Input file path, enter

   b. For Output file location, enter

   c. For BigQuery output table, enter

8. Click RUN JOB.

Note: You don't need to specify a staging bucket; Dataflow will create a private one in your project using your project number, similar to

9. Examine the Compute Engine console and you will see a temporary launcher VM that is created to execute your container and initiate your pipeline with the provided parameters.

Task 5. Execute the template using gcloud

One of the benefits of using Dataflow templates is the ability to execute them from a wider variety of contexts, other than a development environment. To demonstrate this, use gcloud to execute a Dataflow template from the command line.

1. To complete this task, execute the following command in your terminal, modifying the parameters as appropriate:

export PROJECT_ID=$(gcloud config get-value project) export REGION={{{project_0.startup_script.lab_region|Region}}} export JOB_NAME=mytemplate-$(date +%Y%m%H%M$S) export TEMPLATE_LOC=gs://${PROJECT_ID}/templates/mytemplate.json export INPUT_PATH=gs://${PROJECT_ID}/events.json export OUTPUT_PATH=gs://${PROJECT_ID}-coldline/template_output/ export BQ_TABLE=${PROJECT_ID}:logs.logs_filtered gcloud dataflow flex-template run ${JOB_NAME} \ --region=$REGION \ --template-file-gcs-location ${TEMPLATE_LOC} \ --parameters "inputPath=${INPUT_PATH},outputPath=${OUTPUT_PATH},tableName=${BQ_TABLE}"

2. Ensure that your pipeline completes successfully.

Click Check my progress to verify the objective. Execute the template from the UI and using gcloud

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