README.md

AWS Glue Streaming ETL Job with Delta Lake CDK Python project!

In this project, we create a streaming ETL job in AWS Glue to integrate Delta Lake with a streaming use case and create an in-place updatable data lake on Amazon S3.

After ingested to Amazon S3, you can query the data with Amazon Glue Studio or Amazon Athena.

This project can be deployed with AWS CDK Python.

The cdk.json file tells the CDK Toolkit how to execute your app.

This project is set up like a standard Python project. The initialization process also creates a virtualenv within this project, stored under the .venv directory. To create the virtualenv it assumes that there is a python3 (or python for Windows) executable in your path with access to the venv package. If for any reason the automatic creation of the virtualenv fails, you can create the virtualenv manually.

To manually create a virtualenv on MacOS and Linux:

$ python3 -m venv .venv

After the init process completes and the virtualenv is created, you can use the following step to activate your virtualenv.

$ source .venv/bin/activate

If you are a Windows platform, you would activate the virtualenv like this:

% .venv\Scripts\activate.bat

Once the virtualenv is activated, you can install the required dependencies.

(.venv) $ pip install -r requirements.txt

In case of AWS Glue 3.0, before synthesizing the CloudFormation, you first set up Delta Lake connector for AWS Glue to use Delta Lake with AWS Glue jobs. (For more information, see References (2))

Then you should set approperly the cdk context configuration file, cdk.context.json.

For example:

{
  "kinesis_stream_name": "deltalake-demo-stream",
  "glue_assets_s3_bucket_name": "aws-glue-assets-123456789012-atq4q5u",
  "glue_job_script_file_name": "spark_deltalake_writes_with_sql_merge_into.py",
  "glue_job_name": "streaming_data_from_kds_into_deltalake_table",
  "glue_job_input_arguments": {
    "--catalog": "spark_catalog",
    "--database_name": "deltalake_db",
    "--table_name": "products",
    "--primary_key": "product_id",
    "--partition_key": "category",
    "--kinesis_database_name": "deltalake_stream_db",
    "--kinesis_table_name": "kinesis_stream_table",
    "--starting_position_of_kinesis_iterator": "LATEST",
    "--delta_s3_path": "s3://glue-deltalake-demo-us-east-1/deltalake_db/products",
    "--aws_region": "us-east-1",
    "--window_size": "100 seconds",
    "--extra-jars": "s3://aws-glue-assets-123456789012-atq4q5u/extra-jars/aws-sdk-java-2.17.224.jar",
    "--user-jars-first": "true"
  },
  "glue_connections_name": "deltalake-connector-1_0_0",
  "glue_kinesis_table": {
    "database_name": "deltalake_stream_db",
    "table_name": "kinesis_stream_table",
    "columns": [
      {
        "name": "product_id",
        "type": "string"
      },
      {
        "name": "product_name",
        "type": "string"
      },
      {
        "name": "price",
        "type": "int"
      },
      {
        "name": "category",
        "type": "string"
      },
      {
        "name": "updated_at",
        "type": "string"
      }
    ]
  }
}

ℹ️ --primary_key option should be set by Delta Lake table's primary column name.

ℹ️ --partition_key option should be set by Delta Lake table's column name for partitioning.

⚠️ You should create a S3 bucket for a glue job script and upload the glue job script file into the s3 bucket.

At this point you can now synthesize the CloudFormation template for this code.

(.venv) $ export CDK_DEFAULT_ACCOUNT=$(aws sts get-caller-identity --query Account --output text)
(.venv) $ export CDK_DEFAULT_REGION=$(aws configure get region)
(.venv) $ cdk synth --all

To add additional dependencies, for example other CDK libraries, just add them to your setup.py file and rerun the pip install -r requirements.txt command.

Run Test

1. Set up Delta Lake connector for AWS Glue to use Delta Lake with AWS Glue jobs.

   (.venv) $ cdk deploy GlueDeltaLakeConnection
   

2. Create a S3 bucket for Delta Lake table

   (.venv) $ cdk deploy DeltaLakeS3Path
   

3. Create a Kinesis data stream

   (.venv) $ cdk deploy KinesisStreamAsGlueStreamingJobDataSource
   

4. Define a schema for the streaming data

   (.venv) $ cdk deploy GlueSchemaOnKinesisStream
   

   Running cdk deploy GlueSchemaOnKinesisStream command is like that we create a schema manually using the AWS Glue Data Catalog as the following steps:

   (1) On the AWS Glue console, choose Data Catalog.
   (2) Choose Databases, and click Add database.
   (3) Create a database with the name deltalake_stream_db.
   (4) On the Data Catalog menu, Choose Tables, and click Add Table.
   (5) For the table name, enter kinesis_stream_table.
   (6) Select deltalake_stream_db as a database.
   (7) Choose Kinesis as the type of source.
   (8) Enter the name of the stream.
   (9) For the classification, choose JSON.
   (10) Define the schema according to the following table.
   

   Column name	Data type	Example
   product_id	string	"00001"
   product_name	string	"Volkswagen Golf"
   price	int	10370
   category	string	"Volkswagen"
   updated_at	string	"2023-06-13 07:24:26"

   (11) Choose Finish

5. Create Database in Glue Data Catalog for Delta Lake table

   (.venv) $ cdk deploy GlueSchemaOnDeltaLake
   

6. Upload AWS SDK for Java 2.x jar file into S3

   (.venv) $ wget https://repo1.maven.org/maven2/software/amazon/awssdk/aws-sdk-java/2.17.224/aws-sdk-java-2.17.224.jar
   (.venv) $ aws s3 cp aws-sdk-java-2.17.224.jar s3://aws-glue-assets-123456789012-atq4q5u/extra-jars/aws-sdk-java-2.17.224.jar
   

   A Glue Streaming Job might fail because of the following error:

   py4j.protocol.Py4JJavaError: An error occurred while calling o135.start.
   : java.lang.NoSuchMethodError: software.amazon.awssdk.utils.SystemSetting.getStringValueFromEnvironmentVariable(Ljava/lang/String;)Ljava/util/Optional
   

   We can work around the problem by starting the Glue Job with the additional parameters:

   --extra-jars s3://path/to/aws-sdk-for-java-v2.jar
   --user-jars-first true
   

   In order to do this, we might need to upload AWS SDK for Java 2.x jar file into S3.

7. Create Glue Streaming Job

   • (step 1) Select one of Glue Job Scripts and upload into S3

     List of Glue Job Scirpts

     File name	Spark Writes
     spark_deltalake_writes_with_dataframe.py	DataFrame append
     spark_deltalake_writes_with_sql_insert_overwrite.py	SQL insert overwrite
     spark_deltalake_writes_with_sql_merge_into.py	SQL merge into

     (.venv) $ ls src/main/python/
      spark_deltalake_writes_with_dataframe.py
      spark_deltalake_writes_with_sql_insert_overwrite.py
      spark_deltalake_writes_with_sql_merge_into.py
     (.venv) $ aws s3 mb s3://aws-glue-assets-123456789012-atq4q5u --region us-east-1
     (.venv) $ aws s3 cp src/main/python/spark_deltalake_writes_with_sql_merge_into.py s3://aws-glue-assets-123456789012-atq4q5u/scripts/
     

   • (step 2) Provision the Glue Streaming Job

     (.venv) $ cdk deploy GlueStreamingSinkToDeltaLakeJobRole \
                          GrantLFPermissionsOnGlueJobRole \
                          GlueStreamingSinkToDeltaLake
     

8. Make sure the glue job to access the Kinesis Data Streams table in the Glue Catalog database, otherwise grant the glue job to permissions

   We can get permissions by running the following command:

   (.venv) $ aws lakeformation list-permissions | jq -r '.PrincipalResourcePermissions[] | select(.Principal.DataLakePrincipalIdentifier | endswith(":role/GlueStreamingJobRole-DeltaLake"))'
   

   If not found, we need manually to grant the glue job to required permissions by running the following command:

   (.venv) $ aws lakeformation grant-permissions \
               --principal DataLakePrincipalIdentifier=arn:aws:iam::{account-id}:role/GlueStreamingJobRole-DeltaLake \
               --permissions CREATE_TABLE DESCRIBE ALTER DROP \
               --resource '{ "Database": { "Name": "deltalake_db" } }'
   (.venv) $ aws lakeformation grant-permissions \
               --principal DataLakePrincipalIdentifier=arn:aws:iam::{account-id}:role/GlueStreamingJobRole-DeltaLake \
               --permissions SELECT DESCRIBE ALTER INSERT DELETE \
               --resource '{ "Table": {"DatabaseName": "deltalake_db", "TableWildcard": {}} }'
   

9. Run glue job to load data from Kinesis Data Streams into S3

   (.venv) $ aws glue start-job-run --job-name streaming_data_from_kds_into_deltalake_table
   

10. Generate streaming data

    We can synthetically generate data in JSON format using a simple Python application.

    (.venv) $ python src/utils/gen_fake_kinesis_stream_data.py \
               --region-name us-east-1 \
               --stream-name your-stream-name \
               --console \
               --max-count 10
    

    Synthentic Data Example order by product_id and updated_at

    {"product_id": "00001", "product_name": "Buick LeSabre", "price": 2024, "category": "Mercedes-Benz", "updated_at": "2023-02-14 01:15:00"}
    {"product_id": "00001", "product_name": "Holden Commodore", "price": 3650, "category": "Chevrolet", "updated_at": "2023-02-14 08:22:45"}
    {"product_id": "00001", "product_name": "Chevrolet Impala", "price": 5011, "category": "Volkswagen", "updated_at": "2023-02-14 13:10:12"}
    {"product_id": "00002", "product_name": "Peugeot 206", "price": 5659, "category": "Maybach", "updated_at": "2023-02-14 07:01:09"}
    {"product_id": "00002", "product_name": "Fiat Uno", "price": 8819, "category": "Chevrolet", "updated_at": "2023-02-14 07:09:05"}
    {"product_id": "00002", "product_name": "Bugatti Type 40", "price": 8319, "category": "Mercedes-Benz", "updated_at": "2023-02-14 09:16:01"}
    {"product_id": "00003", "product_name": "Buick LeSabre", "price": 6975, "category": "Maybach", "updated_at": "2023-02-14 10:17:00"}
    {"product_id": "00003", "product_name": "AMC Hornet", "price": 8115, "category": "Daihatsu", "updated_at": "2023-02-14 10:21:07"}
    {"product_id": "00003", "product_name": "Checker Marathon", "price": 8860, "category": "Nissan", "updated_at": "2023-02-14 11:01:36"}
    {"product_id": "00004", "product_name": "Checker Marathon", "price": 7526, "category": "Chevrolet", "updated_at": "2023-02-14 01:29:27"}
    {"product_id": "00004", "product_name": "Autobianchi A112", "price": 10979, "category": "Maybach", "updated_at": "2023-02-14 08:08:13"}
    {"product_id": "00004", "product_name": "Excalibur Series II", "price": 6432, "category": "Nissan", "updated_at": "2023-02-14 11:18:44"}
    {"product_id": "00005", "product_name": "Hindustan Ambassador", "price": 11102, "category": "Fiat", "updated_at": "2023-02-14 00:01:42"}
    {"product_id": "00005", "product_name": "Jeep Cherokee (XJ)", "price": 8284, "category": "Daihatsu", "updated_at": "2023-02-14 04:24:18"}
    {"product_id": "00005", "product_name": "Fiat Uno", "price": 11656, "category": "Fiat", "updated_at": "2023-02-14 06:25:04"}
    

    Spark Writes using DataFrame append insert all records into the Delta Lake table.

    {"product_id": "00001", "product_name": "Buick LeSabre", "price": 2024, "category": "Mercedes-Benz", "updated_at": "2023-02-14 01:15:00"}
    {"product_id": "00001", "product_name": "Holden Commodore", "price": 3650, "category": "Chevrolet", "updated_at": "2023-02-14 08:22:45"}
    {"product_id": "00001", "product_name": "Chevrolet Impala", "price": 5011, "category": "Volkswagen", "updated_at": "2023-02-14 13:10:12"}
    {"product_id": "00002", "product_name": "Peugeot 206", "price": 5659, "category": "Maybach", "updated_at": "2023-02-14 07:01:09"}
    {"product_id": "00002", "product_name": "Fiat Uno", "price": 8819, "category": "Chevrolet", "updated_at": "2023-02-14 07:09:05"}
    {"product_id": "00002", "product_name": "Bugatti Type 40", "price": 8319, "category": "Mercedes-Benz", "updated_at": "2023-02-14 09:16:01"}
    {"product_id": "00003", "product_name": "Buick LeSabre", "price": 6975, "category": "Maybach", "updated_at": "2023-02-14 10:17:00"}
    {"product_id": "00003", "product_name": "AMC Hornet", "price": 8115, "category": "Daihatsu", "updated_at": "2023-02-14 10:21:07"}
    {"product_id": "00003", "product_name": "Checker Marathon", "price": 8860, "category": "Nissan", "updated_at": "2023-02-14 11:01:36"}
    {"product_id": "00004", "product_name": "Checker Marathon", "price": 7526, "category": "Chevrolet", "updated_at": "2023-02-14 01:29:27"}
    {"product_id": "00004", "product_name": "Autobianchi A112", "price": 10979, "category": "Maybach", "updated_at": "2023-02-14 08:08:13"}
    {"product_id": "00004", "product_name": "Excalibur Series II", "price": 6432, "category": "Nissan", "updated_at": "2023-02-14 11:18:44"}
    {"product_id": "00005", "product_name": "Hindustan Ambassador", "price": 11102, "category": "Fiat", "updated_at": "2023-02-14 00:01:42"}
    {"product_id": "00005", "product_name": "Jeep Cherokee (XJ)", "price": 8284, "category": "Daihatsu", "updated_at": "2023-02-14 04:24:18"}
    {"product_id": "00005", "product_name": "Fiat Uno", "price": 11656, "category": "Fiat", "updated_at": "2023-02-14 06:25:04"}
    

    Spark Writes using SQL insert overwrite or SQL merge into insert the last updated records into the Delta Lake table.

    {"product_id": "00001", "product_name": "Chevrolet Impala", "price": 5011, "category": "Volkswagen", "updated_at": "2023-02-14 13:10:12"}
    {"product_id": "00002", "product_name": "Bugatti Type 40", "price": 8319, "category": "Mercedes-Benz", "updated_at": "2023-02-14 09:16:01"}
    {"product_id": "00003", "product_name": "Checker Marathon", "price": 8860, "category": "Nissan", "updated_at": "2023-02-14 11:01:36"}
    {"product_id": "00004", "product_name": "Excalibur Series II", "price": 6432, "category": "Nissan", "updated_at": "2023-02-14 11:18:44"}
    {"product_id": "00005", "product_name": "Fiat Uno", "price": 11656, "category": "Fiat", "updated_at": "2023-02-14 06:25:04"}
    

11. Check streaming data in S3

    After 3~5 minutes, you can see that the streaming data have been delivered from Kinesis Data Streams to S3.

12. Create IAM Role for Glue Studio Notebook and grant Lake Formation permissions

     (.venv) $ cdk deploy GlueStudioNotebookRoleDeltaLake \
                          GrantLFPermissionsOnGlueStudioRole
     

13. Run test queries with Amazon Glue Studio

    ⚠️ Note

    Check that your browser does not block third-party cookies. Any browser that blocks third party cookies either by default or as a user-enabled setting will prevent notebooks from launching. For more inforemation, see References (7)

• (step 1) Download the Jupyter notebook file.
• (step 2) On the AWS Glue console, choose Jobs in the navigation plane.
• (step 3) Under Create job, select Jupyter Notebook.
• (step 4) Select Upload and edit an existing notebook.
• (step 5) Upload native-deltalake-sql.ipynb through Choose file under File upload.
• (step 6) Choose Create.
• (step 7) For Job name, enter native_deltalake_sql.
• (step 9) For IAM Role, choose your IAM role (AWSGlueServiceRole-StudioNotebook).
• (step 10) Choose Start notebook job.
• (step 11) Run each cells in a row.

Clean Up

1. Stop the glue job by replacing the job name in below command.

   (.venv) $ JOB_RUN_IDS=$(aws glue get-job-runs \
              --job-name streaming_data_from_kds_into_deltalake_table | jq -r '.JobRuns[] | select(.JobRunState=="RUNNING") | .Id' \
              | xargs)
   (.venv) $ aws glue batch-stop-job-run \
              --job-name streaming_data_from_kds_into_deltalake_table \
              --job-run-ids $JOB_RUN_IDS
   

2. Delete the CloudFormation stack by running the below command.

   (.venv) $ cdk destroy --all
   

Useful commands

• cdk ls list all stacks in the app
• cdk synth emits the synthesized CloudFormation template
• cdk deploy deploy this stack to your default AWS account/region
• cdk diff compare deployed stack with current state
• cdk docs open CDK documentation

Enjoy!

References

• (1) AWS Glue versions: The AWS Glue version determines the versions of Apache Spark and Python that AWS Glue supports.

  AWS Glue version	Hudi	Delta Lake	Iceberg
  AWS Glue 3.0	0.10.1	1.0.0	0.13.1
  AWS Glue 4.0	0.12.1	2.1.0	1.0.0

• (2) Process Apache Hudi, Delta Lake, Apache Iceberg datasets at scale, part 1: AWS Glue Studio Notebook(2022-07-18)
• (3) Deltalake with Amazon EMR - This guide helps you quickly explore the main features of Delta Lake. It provides code snippets that show how to read from and write to Delta tables with Amazon EMR.
• (4) AWS Glue Notebook Samples - sample iPython notebook files which show you how to use open data dake formats; Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue Interactive Sessions and AWS Glue Studio Notebook.
• (5) Introducing native support for Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue for Apache Spark, Part 1: Getting Started (2023-01-26)
• (6) Introducing native Delta Lake table support with AWS Glue crawlers
• (7) Getting started with notebooks in AWS Glue Studio
  • ⚠️ Check that your browser does not block third-party cookies. Any browser that blocks third party cookies either by default or as a user-enabled setting will prevent notebooks from launching.
• (8) Amazon Athena - Querying Delta Lake tables

Delta Lake References

• (1) Delta Lake(v1.0.0) documentation
• (2) Delta Lake(v1.0.0) - Table batch reads and writes
• (3) Delta Lake(v1.0.0) - Table streaming reads and writes
• (4) Delta Lake(v1.0.0) - Table deletes, updates, and merges

Troubleshooting

• Creating a table in a database with empty LOCATION

  IllegalArgumentException: Can not create a Path from an empty string
  

• Why does my AWS Glue crawler or ETL job fail with the error "Insufficient Lake Formation permission(s)"?

  AnalysisException: Insufficient Lake Formation permission(s) on deltalake_demo_db
  (Service: AWSGlue; Status Code: 400; Error Code: AccessDeniedException;
   Request ID: 79211aa0-e210-4840-a529-54bf4ac69ca4; Proxy: null)
  

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.