Using Python libraries with Amazon Glue

You can install additional Python modules and libraries for use with Amazon Glue ETL. For Amazon Glue 2.0 and above, Amazon Glue uses the Python Package Installer (pip3) to install additional modules used by Amazon Glue ETL. Amazon Glue provides multiple options to bring the additional Python modules to your Amazon Glue job environment. You can use the “—additional-python-modules" parameter to bring in modules using Python wheel files, Requirements file (requirement.txt, Amazon Glue 5.0 and above), or a list of comma-separated Python modules.

Installing additional Python modules with pip in Amazon Glue 2.0 or later

Amazon Glue uses the Python Package Installer (pip3) to install additional modules to be used by Amazon Glue ETL. You can use the --additional-python-modules parameter with a list of comma-separated Python modules to add a new module or change the version of an existing module. You can install custom distributions of a library by uploading the distribution to Amazon S3, then include the path to the Amazon S3 object in your list of modules.

You can pass additional options to pip3 with the --python-modules-installer-option parameter. For example, you could pass "--upgrade" to upgrade the packages specified by "--additional-python-modules". For more examples, see Building Python modules from a wheel for Spark ETL workloads using Amazon Glue 2.0 .

--additional-python-modules s3://amzn-s3-demo-bucket/path/to/package-1.0.0-py3-none-any.whl,s3://your-bucket/path/to/another-package-2.1.0-cp311-cp311-linux_x86_64.whl

awswrangler==3.9.1
elasticsearch==8.15.1
PyAthena==3.9.0
PyMySQL==1.1.1
PyYAML==6.0.2
pyodbc==5.2.0
pyorc==0.9.0
redshift-connector==2.1.3
scipy==1.14.1
scikit-learn==1.5.2
SQLAlchemy==2.0.36

Including Python files with PySpark native features

Amazon Glue uses PySpark to include Python files in Amazon Glue ETL jobs. You will want to use --additional-python-modules to manage your dependencies when available. You can use the --extra-py-files job parameter to include Python files. Dependencies must be hosted in Amazon S3 and the argument value should be a comma delimited list of Amazon S3 paths with no spaces. This functionality behaves like the Python dependency management you would use with Spark. For more information on Python dependency management in Spark, see Using PySpark Native Features page in Apache Spark documentation. --extra-py-files is useful in cases where your additional code is not packaged, or when you are migrating a Spark program with an existing toolchain for managing dependencies. For your dependency tooling to be maintainable, you will have to bundle your dependencies before submitting.

Programming scripts that use visual transforms

When you create a Amazon Glue job using the Amazon Glue Studio visual interface, you can transform your data with managed data transform nodes and custom visual transforms. For more information about managed data transform nodes, see Transform data with Amazon Glue managed transforms. For more information about custom visual transforms, see Transform data with custom visual transforms . Scripts using visual transforms can only be generated when your job Language is set to use Python.

When generating a Amazon Glue job using visual transforms, Amazon Glue Studio will include these transforms in the runtime environment using the --extra-py-files parameter in the job configuration. For more information about job parameters, see Using job parameters in Amazon Glue jobs. When making changes to a generated script or runtime environment, you will need to preserve this job configuration for your script to run successfully.

Zipping libraries for inclusion

Unless a library is contained in a single .py file, it should be packaged in a .zip archive. The package directory should be at the root of the archive, and must contain an __init__.py file for the package. Python will then be able to import the package in the normal way.

If your library only consists of a single Python module in one .py file, you do not need to place it in a .zip file.

Loading Python libraries in Amazon Glue Studio notebooks

To specify Python libraries in Amazon Glue Studio notebooks, see Installing additional Python modules.

Loading Python libraries in a development endpoint in Amazon Glue 0.9/1.0

If you are using different library sets for different ETL scripts, you can either set up a separate development endpoint for each set, or you can overwrite the library .zip file(s) that your development endpoint loads every time you switch scripts.

You can use the console to specify one or more library .zip files for a development endpoint when you create it. After assigning a name and an IAM role, choose Script Libraries and job parameters (optional) and enter the full Amazon S3 path to your library .zip file in the Python library path box. For example:

s3://bucket/prefix/site-packages.zip

If you want, you can specify multiple full paths to files, separating them with commas but no spaces, like this:

s3://bucket/prefix/lib_A.zip,s3://bucket_B/prefix/lib_X.zip

If you update these .zip files later, you can use the console to re-import them into your development endpoint. Navigate to the developer endpoint in question, check the box beside it, and choose Update ETL libraries from the Action menu.

In a similar way, you can specify library files using the Amazon Glue APIs. When you create a development endpoint by calling CreateDevEndpoint action (Python: create_dev_endpoint), you can specify one or more full paths to libraries in the ExtraPythonLibsS3Path parameter, in a call that looks this:

dep = glue.create_dev_endpoint(
             EndpointName="testDevEndpoint",
             RoleArn="arn:aws:iam::123456789012",
             SecurityGroupIds="sg-7f5ad1ff",
             SubnetId="subnet-c12fdba4",
             PublicKey="ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQCtp04H/y...",
             NumberOfNodes=3,
             ExtraPythonLibsS3Path="s3://bucket/prefix/lib_A.zip,s3://bucket_B/prefix/lib_X.zip")

When you update a development endpoint, you can also update the libraries it loads using a DevEndpointCustomLibraries object and setting the UpdateEtlLibraries parameter to True when calling UpdateDevEndpoint (update_dev_endpoint).

Using Python libraries in a job or JobRun

When you are creating a new Job on the console, you can specify one or more library .zip files by choosing Script Libraries and job parameters (optional) and entering the full Amazon S3 library path(s) in the same way you would when creating a development endpoint:

s3://bucket/prefix/lib_A.zip,s3://bucket_B/prefix/lib_X.zip

If you are calling CreateJob (create_job), you can specify one or more full paths to default libraries using the --extra-py-files default parameter, like this:

job = glue.create_job(Name='sampleJob',
                      Role='Glue_DefaultRole',
                      Command={'Name': 'glueetl',
                               'ScriptLocation': 's3://my_script_bucket/scripts/my_etl_script.py'},
                      DefaultArguments={'--extra-py-files': 's3://bucket/prefix/lib_A.zip,s3://bucket_B/prefix/lib_X.zip'})

Then when you are starting a JobRun, you can override the default library setting with a different one:

runId = glue.start_job_run(JobName='sampleJob',
                           Arguments={'--extra-py-files': 's3://bucket/prefix/lib_B.zip'})

Proactively analyze Python dependencies

To proactively identify potential dependency issues before deploying to Amazon Glue, you can use the dependency analysis tool to validate your Python packages against your target Amazon Glue environment.

Amazon provides an open-source Python dependency analyzer tool specifically designed for Amazon Glue environments. This tool is available in the Amazon Glue samples repository and can be used locally to validate your dependencies before deployment.

This analysis helps ensure your dependencies follow the recommended practice of pinning all library versions for consistent production deployments. For more details, please see the tool's README .

# Analyze a single Glue job
python glue_dependency_analyzer.py -j my-glue-job

# Analyze multiple jobs with specific AWS configuration
python glue_dependency_analyzer.py -j job1 -j job2 --aws-profile production --aws-region us-west-2

I have an AWS Glue job named <Job-Name> that has failed due to Python module installation conflicts. Please assist in diagnosing and resolving this issue using the following systematic approach. Proceed once sufficient information is available.

Objective: Implement a fix that addresses the root cause module while minimizing disruption to the existing working environment.

Step 1: Root Cause Analysis
• Retrieve the most recent failed job run ID for the specified Glue job
• Extract error logs from CloudWatch Logs using the job run ID as a log stream prefix
• Analyze the logs to identify:
  • The recently added or modified Python module that triggered the dependency conflict
  • The specific dependency chain causing the installation failure
  • Version compatibility conflicts between required and existing modules

Step 2: Baseline Configuration Identification
• Locate the last successful job run ID prior to the dependency failure
• Document the Python module versions that were functioning correctly in that baseline run
• Establish the compatible version constraints for conflicting dependencies

Step 3: Targeted Resolution Implementation
• Apply pinning by updating the job's additional_python_modules parameter
• Pin only the root cause module and its directly conflicting dependencies to compatible versions, and do not remove python modules unless necessary
• Preserve flexibility for non-conflicting modules by avoiding unnecessary version constraints
• Deploy the configuration changes with minimal changes to the existing configuration and execute a validation test run. Do not change the Glue versions.

Implementation Example:
Scenario: Recently added pandas==2.0.0 to additional_python_modules
Error: numpy version conflict (pandas 2.0.0 requires numpy>=1.21, but existing job code requires numpy<1.20)
Resolution: Update additional_python_modules to "pandas==1.5.3,numpy==1.19.5"
Rationale: Use pandas 1.5.3 (compatible with numpy 1.19.5) and pin numpy to last known working version

Expected Outcome: Restore job functionality with minimal configuration changes while maintaining system stability.

Python modules already provided in Amazon Glue

To change the version of these provided modules, provide new versions with the --additional-python-modules job parameter.