Installing Python Packages in Serverless Dataproc GCP

Are you struggling with installing Python packages in Serverless Dataproc on Google Cloud Platform (GCP)? Look no further! In this guide, we will explore two effective methods to simplify the installation process and enhance your Serverless Dataproc environment. Whether you are a beginner or an experienced developer, these step-by-step instructions will help you seamlessly integrate the required dependencies. Let’s dive in!

Option 1: Using the gcloud Command in Terminal

If you prefer a command-line approach, you can utilize the powerful gcloud command to create a custom container image with your desired Python packages. Follow these steps:

1. Create a custom image with dependencies: Build a custom image using the Google Container Registry (GCR) and specify the necessary Python packages in the image. You can define the image’s URI as a parameter in the command.
   • code$ gcloud beta dataproc batches submit --container-image=gcr.io/my-project-id/my-image:1.0.1 --project=my-project-id --region=us-central1 --jars=file:///usr/lib/spark/external/spark-avro.jar --subnet=projects/my-project-id/regions/us-central1/subnetworks/my-subnet-name Customize the command according to your project details and required packages.
2. Create a custom container image for Dataproc Serverless: This option allows you to leverage the power of custom Docker images. By following the guidelines in the provided link, you can create a custom container image specifically tailored for Dataproc Serverless with your desired Python packages.

Option 2: Using the DataprocCreateBatchOperator in Airflow

If you prefer an orchestration approach using Apache Airflow, you can utilize the DataprocCreateBatchOperator to install the required Python packages. Follow these steps:

1. Create a Python script: Begin by creating a Python script that installs the desired packages and loads them into the container path for Dataproc Serverless. In the script, you can use the pip package manager to install the required dependencies.
   • codeimport pip import importlib from warnings import warn from dataclasses import dataclass def load_package(package, path): warn("Update path order. Watch out for importing errors!") if path not in sys.path: sys.path.insert(0, path) module = importlib.import_module(package) return importlib.reload(module) @dataclass class PackageInfo: import_path: str pip_id: str packages = [ PackageInfo("google.cloud.secretmanager", "google-cloud-secret-manager==2.4.0") ] path = '/tmp/python_packages' pip.main(['install', '-t', path, *[package.pip_id for package in packages] ]) for package in packages: load_package(package.import_path, path=path)Customize the script by adding or modifying the packages according to your requirements.
2. Upload the Python script to a bucket: Save the Python script in a bucket within your GCP project. This script will be referenced in the DataprocCreateBatchOperator to install the packages.
3. Use the DataprocCreateBatchOperator: In your Airflow workflow, create an instance of the DataprocCreateBatchOperator and provide the necessary parameters, including the URI of the Python script in the bucket.
   • codecreate_batch = DataprocCreateBatchOperator( task_id="batch_create", batch={ "pyspark_batch": { "main_python_file_uri": "gs://bucket-name/python-file.py", "args": ["value1", "value2"] , "jar_file_uris": "gs://bucket-name/jar-file.jar", }, "environment_config": { "execution_config": { "subnetwork_uri": "projects/my-project-id/regions/us-central1/subnetworks/my-subnet-name", }, }, }, batch_id="batch-create", )Customize the operator according to your project details, Python script URI, and other parameters.

By following either of these options, you can successfully install Python packages in Serverless Dataproc on GCP without any hassle.

We hope this guide has provided you with the insights and solutions you were seeking. Now, you can seamlessly integrate the necessary Python packages into your Serverless Dataproc environment and enhance your data processing and analytics workflows. Enjoy the power and efficiency of Serverless Dataproc!

Remember, if you have any questions or need further assistance, don’t hesitate to reach out to the Google Cloud Collective community.