- HOW TO TROUBLESHOOT UNIT TESTS PYTHON IN VISUAL STUDIO CODE HOW TO
- HOW TO TROUBLESHOOT UNIT TESTS PYTHON IN VISUAL STUDIO CODE SOFTWARE LICENSE
- HOW TO TROUBLESHOOT UNIT TESTS PYTHON IN VISUAL STUDIO CODE CODE
* for AWS Glue 2.0 or below, or for AWS Glue 3.1.
HOW TO TROUBLESHOOT UNIT TESTS PYTHON IN VISUAL STUDIO CODE SOFTWARE LICENSE
The library is released with the Amazon Software license ( ). For information about the versions of Python and Apache Spark that areĪvailable with AWS Glue, see the Glue version job property. Local development is available for all AWS Glue versions, including AWS Glue version 0.9 and AWS Glue Transform, and load (ETL) scripts locally, without the need for a network connection. This enables you to develop and test your Python and Scala extract, The AWS Glue ETL library is available in a public Amazon S3 bucket, and can be consumed by theĪpache Maven build system.
HOW TO TROUBLESHOOT UNIT TESTS PYTHON IN VISUAL STUDIO CODE CODE
Test_sample.py: Sample code for unit test of sample.py.Īrgs = getResolvedOptions(sys.argv, )Ĭontext = GlueContext(SparkContext.getOrCreate())ĭyf = sample.read_json(glue_context, "s3://awsglue-datasets/examples/us-legislators/all/persons.json")ĭeveloping using the AWS Glue ETL library You need to grant the IAM managed policy arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess or an IAM custom policy which allows you to call ListBucket and GetObject for the Amazon S3 path. The above code requires Amazon S3 permissions in AWS IAM. ntext = GlueContext(SparkContext.getOrCreate())ĭyf = read_json(ntext, "s3://awsglue-datasets/examples/us-legislators/all/persons.json")ĭynamicframe = glue_context.create_dynamic_om_options( You can start Jupyter for interactive development and ad-hoc queries on notebooks.įrom awsglue.utils import getResolvedOptionsĪrgs = getResolvedOptions(sys.argv, params) = 1 passed, *1 warning* in 21.07s = Jupyter Lab home/glue_user/spark/python/pyspark/sql/context.py:79: DeprecationWarning: Deprecated in 3.0.0. Starting .history.HistoryServer, logging to /home/glue_user/spark/logs/spark-glue_. $ docker run -it -v ~/.aws:/home/glue_user/.aws -v $WORKSPACE_LOCATION:/home/glue_user/workspace/ -e AWS_PROFILE=$PROFILE_NAME -e DISABLE_SSL=true -rm -p 4040:4040 -p 18080:18080 -name glue_pytest amazon/aws-glue-libs:glue_libs_3.0.0_image_01 -c "python3 -m pytest" Run the following command to execute the PySpark command on the container to start the REPL shell: You can run REPL (read-eval-print loops) shell for interactive development. $ docker run -it -v ~/.aws:/home/glue_user/.aws -v $WORKSPACE_LOCATION:/home/glue_user/workspace/ -e AWS_PROFILE=$PROFILE_NAME -e DISABLE_SSL=true -rm -p 4040:4040 -p 18080:18080 -name glue_spark_submit amazon/aws-glue-libs:glue_libs_3.0.0_image_01 spark-submit /home/glue_user/workspace/src/$SCRIPT_FILE_NAME There are the following Docker images available for AWS Glue on Docker Hub.
HOW TO TROUBLESHOOT UNIT TESTS PYTHON IN VISUAL STUDIO CODE HOW TO
This topic describes how to develop and test AWS Glue version 3.0 jobs on a Docker container using the Docker image. You can use your preferred IDE, notebook, or REPL using AWS Glue ETL library. The Docker images help you set up your development environment with additional utilities. You can achieve that by utilizing AWS Glue Docker images hosted on DockerHub. You can flexibly develop and test AWS Glue jobs such as by using a local laptop, and a Docker container on Amazon EC2. Developing using a Docker imageįor a production-ready data platform, the development process and CI/CD pipeline for AWS Glue jobs is a key topic. For more information, see Using Interactive Sessions with AWS Glue.
Interactive sessions allow you to build and test applications from the environment of your choice. For more information, see the AWS Glue Studio User Guide. You can inspect the schema and data results in each step of the job. You can visually compose data transformation workflows and seamlessly run them on AWS Glue's Apache Spark-based serverless ETL engine. The AWS Glue Studio visual editor is a graphical interface that makes it easy to create, run, and monitor extract, transform, and load (ETL) jobs in AWS Glue. If you prefer local development without Docker, installing the AWS Glue ETL library directory locally is a good choice. This helps you to develop and test Glue job script anywhere you prefer without incurring AWS Glue cost. If you prefer local/remote development experience, the Docker image is a good choice.
If you want to use your own local environment, interactive sessions is a good choice. For more information, see Using Notebooks with AWS Glue Studio and AWS Glue. If you prefer an interactive notebook experience, AWS Glue Studio notebook is a good choice. If you prefer no code or less code experience, the AWS Glue Studio visual editor is a good choice. You can choose any of the above options based on your requirements.
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