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AWS Glue Streaming ETL Job with Apace Iceberg CDK Python project!

glue-streaming-data-to-iceberg-table

In this project, we create a streaming ETL job in AWS Glue to integrate Iceberg 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 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 Apache Iceberg connector for AWS Glue to use Apache Iceber 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": "iceberg-demo-stream",
  "glue_assets_s3_bucket_name": "aws-glue-assets-123456789012-atq4q5u",
  "glue_job_script_file_name": "spark_iceberg_writes_with_dataframe.py",
  "glue_job_name": "streaming_data_from_kds_into_iceberg_table",
  "glue_job_input_arguments": {
    "--catalog": "job_catalog",
    "--database_name": "iceberg_demo_db",
    "--table_name": "iceberg_demo_table",
    "--primary_key": "name",
    "--kinesis_table_name": "iceberg_demo_kinesis_stream_table",
    "--starting_position_of_kinesis_iterator": "LATEST",
    "--iceberg_s3_path": "s3://glue-iceberg-demo-atq4q5u/iceberg_demo_db",
    "--lock_table_name": "iceberg_lock",
    "--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": "iceberg-connection",
  "glue_kinesis_table": {
    "database_name": "iceberg_demo_db",
    "table_name": "iceberg_demo_kinesis_stream_table",
    "columns": [
      {
        "name": "name",
        "type": "string"
      },
      {
        "name": "age",
        "type": "int"
      },
      {
        "name": "m_time",
        "type": "string"
      }
    ]
  }
}

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

⚠️ 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=$(curl -s 169.254.169.254/latest/dynamic/instance-identity/document | jq -r .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 Apache Iceberg connector for AWS Glue to use Apache Iceberg with AWS Glue jobs.

  2. Create a S3 bucket for Apache Iceberg table

    (.venv) $ cdk deploy IcebergS3Path

  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 iceberg_demo_db.
    (4) On the Data Catalog menu, Choose Tables, and click Add Table.
    (5) For the table name, enter iceberg_demo_kinesis_stream_table.
    (6) Select iceberg_demo_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
    name string "Ricky"
    age int 23
    m_time string "2023-06-13 07:24:26"

    (11) Choose Finish

  5. 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.

  6. 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_iceberg_writes_with_dataframe.py DataFrame append
      spark_iceberg_writes_with_sql_insert_overwrite.py SQL insert overwrite
      spark_iceberg_writes_with_sql_merge_into.py SQL merge into 
      (.venv) $ ls src/main/python/
 spark_iceberg_writes_with_dataframe.py
 spark_iceberg_writes_with_sql_insert_overwrite.py
 spark_iceberg_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_iceberg_writes_with_dataframe.py s3://aws-glue-assets-123456789012-atq4q5u/scripts/

    • (step 2) Provision the Glue Streaming Job

      (.venv) $ cdk deploy GlueStreamingSinkToIcebergJobRole \
                     GrantLFPermissionsOnGlueJobRole \
                     GlueStreamingSinkToIceberg

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

    Wec can get permissions by running the following command:

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

    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-Iceberg \
            --permissions SELECT DESCRIBE ALTER INSERT DELETE \
            --resource '{ "Table": {"DatabaseName": "iceberg_demo_db", "TableWildcard": {}} }'

  8. Create a table with partitioned data in Amazon Athena

    Go to Athena on the AWS Management console.

    • (step 1) Create a database

      In order to create a new database called iceberg_demo_db, enter the following statement in the Athena query editor and click the Run button to execute the query.

      CREATE DATABASE IF NOT EXISTS iceberg_demo_db

    • (step 2) Create a table

      Copy the following query into the Athena query editor, replace the xxxxxxx in the last line under LOCATION with the string of your S3 bucket, and execute the query to create a new table.

      CREATE TABLE iceberg_demo_db.iceberg_demo_table (
  name string,
  age int,
  m_time timestamp
)
PARTITIONED BY (`name`)
LOCATION 's3://glue-iceberg-demo-atq4q5u/iceberg_demo_db/iceberg_demo_table'
TBLPROPERTIES (
  'table_type'='iceberg'
);

      If the query is successful, a table named iceberg_demo_table is created and displayed on the left panel under the Tables section.

      If you get an error, check if (a) you have updated the LOCATION to the correct S3 bucket name, (b) you have mydatabase selected under the Database dropdown, and (c) you have AwsDataCatalog selected as the Data source.

      ℹ️ If you fail to create the table, give Athena users access permissions on iceberg_demo_db through AWS Lake Formation, or you can grant anyone using Athena to access iceberg_demo_db by running the following command:

      (.venv) $ aws lakeformation grant-permissions \
          --principal DataLakePrincipalIdentifier=arn:aws:iam::{account-id}:user/example-user-id \
          --permissions CREATE_TABLE DESCRIBE ALTER DROP \
          --resource '{ "Database": { "Name": "iceberg_demo_db" } }'
(.venv) $ aws lakeformation grant-permissions \
        --principal DataLakePrincipalIdentifier=arn:aws:iam::{account-id}:user/example-user-id \
        --permissions SELECT DESCRIBE ALTER INSERT DELETE DROP \
        --resource '{ "Table": {"DatabaseName": "iceberg_demo_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_iceberg_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 name and m_time

    {"name": "Arica", "age": 48, "m_time": "2023-04-11 19:13:21"}
{"name": "Arica", "age": 32, "m_time": "2023-10-20 17:24:17"}
{"name": "Arica", "age": 45, "m_time": "2023-12-26 01:20:49"}
{"name": "Fernando", "age": 16, "m_time": "2023-05-22 00:13:55"}
{"name": "Gonzalo", "age": 37, "m_time": "2023-01-11 06:18:26"}
{"name": "Gonzalo", "age": 60, "m_time": "2023-01-25 16:54:26"}
{"name": "Micheal", "age": 45, "m_time": "2023-04-07 06:18:17"}
{"name": "Micheal", "age": 44, "m_time": "2023-12-14 09:02:57"}
{"name": "Takisha", "age": 48, "m_time": "2023-12-20 16:44:13"}
{"name": "Takisha", "age": 24, "m_time": "2023-12-30 12:38:23"}

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

    {"name": "Arica", "age": 48, "m_time": "2023-04-11 19:13:21"}
{"name": "Arica", "age": 32, "m_time": "2023-10-20 17:24:17"}
{"name": "Arica", "age": 45, "m_time": "2023-12-26 01:20:49"}
{"name": "Fernando", "age": 16, "m_time": "2023-05-22 00:13:55"}
{"name": "Gonzalo", "age": 37, "m_time": "2023-01-11 06:18:26"}
{"name": "Gonzalo", "age": 60, "m_time": "2023-01-25 16:54:26"}
{"name": "Micheal", "age": 45, "m_time": "2023-04-07 06:18:17"}
{"name": "Micheal", "age": 44, "m_time": "2023-12-14 09:02:57"}
{"name": "Takisha", "age": 48, "m_time": "2023-12-20 16:44:13"}
{"name": "Takisha", "age": 24, "m_time": "2023-12-30 12:38:23"}

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

    {"name": "Arica", "age": 45, "m_time": "2023-12-26 01:20:49"}
{"name": "Fernando", "age": 16, "m_time": "2023-05-22 00:13:55"}
{"name": "Gonzalo", "age": 60, "m_time": "2023-01-25 16:54:26"}
{"name": "Micheal", "age": 44, "m_time": "2023-12-14 09:02:57"}
{"name": "Takisha", "age": 24, "m_time": "2023-12-30 12:38:23"}

  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.

    iceberg-table iceberg-table iceberg-table iceberg-table

  12. Run test query

    Enter the following SQL statement and execute the query.

    SELECT COUNT(*)
FROM iceberg_demo_db.iceberg_demo_table;

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

References

Troubleshooting

  • Granting database or table permissions error using AWS CDK

    • Error message:

      AWS::LakeFormation::PrincipalPermissions | CfnPrincipalPermissions Resource handler returned message: "Resource does not exist or requester is not authorized to access requested permissions. (Service: LakeFormation, Status Code: 400, Request ID: f4d5e58b-29b6-4889-9666-7e38420c9035)" (RequestToken: 4a4bb1d6-b051-032f-dd12-5951d7b4d2a9, HandlerErrorCode: AccessDenied)

    • Solution:

      The role assumed by cdk is not a data lake administrator. (e.g., cdk-hnb659fds-deploy-role-12345678912-us-east-1)
      So, deploying PrincipalPermissions meets the error such as:

      Resource does not exist or requester is not authorized to access requested permissions.

      In order to solve the error, it is necessary to promote the cdk execution role to the data lake administrator.
      For example, https://github.com/aws-samples/data-lake-as-code/blob/mainline/lib/stacks/datalake-stack.ts#L68

    • Reference:

      https://github.com/aws-samples/data-lake-as-code - Data Lake as Code

Security

See CONTRIBUTING for more information.

License

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

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Streaming ETL job cases in AWS Glue to integrate Iceberg and creating an in-place updatable data lake on Amazon S3

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