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msk-serverless-to-iceberg

README.md

AWS Glue Streaming ETL Job with Amazon MSK Serverless and Apace Iceberg

glue-streaming-data-from-msk-serverless-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 streaming data are ingested from Amazon MSK Serverles 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:

{
  "vpc_name": "default",
  "msk_cluster_name": "iceberg-demo-stream",
  "glue_assets_s3_bucket_name": "aws-glue-assets-123456789012-atq4q5u",
  "glue_job_script_file_name": "spark_sql_merge_into_iceberg_from_msk_serverless.py",
  "glue_job_name": "streaming_data_from_msk_serverless_into_iceberg_table",
  "glue_job_input_arguments": {
    "--catalog": "job_catalog",
    "--database_name": "iceberg_demo_db",
    "--table_name": "iceberg_demo_table",
    "--primary_key": "name",
    "--kafka_topic_name": "ev_stream_data",
    "--starting_offsets_of_kafka_topic": "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"
}

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=$(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 Apache Iceberg connector for AWS Glue to use Apache Iceberg with AWS Glue jobs.

  2. Create a MSK

    (.venv) $ cdk deploy MSKServerlessToIcebergStackVpc MSKServerlessAsGlueStreamingJobDataSource

  3. Create a MSK connector for Glue Streaming Job

    (.venv) $ cdk deploy GlueMSKServerlessConnection

    For more information, see References (8)

  4. Create a IAM Role for Glue Streaming Job

    (.venv) $ cdk deploy GlueStreamingMSKServerlessToIcebergJobRole

  5. Set up a Kafka Client Machine

    (.venv) $ cdk deploy MSKServerlessClientEC2Instance

  6. Create a Glue Database for an Apache Iceberg table

    (.venv) $ cdk deploy GlueIcebergeDatabase

  7. 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.

  8. Create a 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_dataframe_insert_iceberg_from_msk_serverless.py DataFrame append
      spark_sql_insert_overwrite_iceberg_from_msk_serverless.py SQL insert overwrite
      spark_sql_merge_into_iceberg_from_msk_serverless.py SQL merge into 
      (.venv) $ ls src/main/python/
 spark_dataframe_insert_iceberg_from_msk_serverless.py
 spark_sql_insert_overwrite_iceberg_from_msk_serverless.py
 spark_sql_merge_into_iceberg_from_msk_serverless.py
(.venv) $ aws s3 mb s3://aws-glue-assets-123456789012-atq4q5u --region us-east-1
(.venv) $ aws s3 cp src/main/python/spark_sql_merge_into_iceberg_from_msk_serverless.py s3://aws-glue-assets-123456789012-atq4q5u/scripts/

    • (step 2) Provision the Glue Streaming Job

      (.venv) $ cdk deploy GrantLFPermissionsOnGlueJobRole \ GlueStreamingJobMSKServerlessToIceberg

  9. 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 SELECT DESCRIBE ALTER INSERT DELETE DROP \
         --resource '{ "Table": {"DatabaseName": "iceberg_demo_db", "TableWildcard": {}} }'

  10. Make sure the glue job to access the Iceberg table in the Glue Catalog database

    We can get permissions by running the following command:

    (.venv) $ aws lakeformation list-permissions | jq -r '.PrincipalResourcePermissions[] | select(.Principal.DataLakePrincipalIdentifier | endswith(":role/GlueJobRole-MSKServerless2Iceberg"))'

    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}: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}:role/GlueJobRole-MSKServerless2Iceberg \
            --permissions SELECT DESCRIBE ALTER INSERT DELETE \
            --resource '{ "Table": {"DatabaseName": "iceberg_demo_db", "TableWildcard": {}} }'

  11. Run glue job to load data from MSK into S3

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

  12. Generate streaming data

    1. Connect the MSK client EC2 Host.

      You can connect to an EC2 instance using the EC2 Instance Connect CLI.
      Install ec2instanceconnectcli python package and Use the mssh command with the instance ID as follows.

      $ sudo pip install ec2instanceconnectcli
$ mssh ec2-user@i-001234a4bf70dec41EXAMPLE

    2. Create an Apache Kafka topic After connect your EC2 Host, you use the client machine to create a topic on the cluster. Run the following command to create a topic called ev_stream_data.

      [ec2-user@ip-172-31-0-180 ~]$ export PATH=$HOME/opt/kafka/bin:$PATH
[ec2-user@ip-172-31-0-180 ~]$ export BS={BootstrapBrokerString}
[ec2-user@ip-172-31-0-180 ~]$ kafka-topics.sh --bootstrap-server $BS \
   --command-config client.properties \
   --create \
   --topic ev_stream_data \
   --partitions 3 \
   --replication-factor 2

    3. Produce and consume data

      (1) To produce messages

      Run the following command to generate messages into the topic on the cluster.

      [ec2-user@ip-172-31-0-180 ~]$ python3 gen_fake_data.py | kafka-console-producer.sh \
   --bootstrap-server $BS \
   --producer.config client.properties \
   --topic ev_stream_data \
   --property parse.key=true \
   --property key.seperator='\t'

      (2) To consume messages

      Keep the connection to the client machine open, and then open a second, separate connection to that machine in a new window.

      [ec2-user@ip-172-31-0-180 ~]$ kafka-console-consumer.sh --bootstrap-server $BS \
   --consumer.config client.properties \
   --topic ev_stream_data \
   --from-beginning

      You start seeing the messages you entered earlier when you used the console producer command. Enter more messages in the producer window, and watch them appear in the consumer window.

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

    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"}

  13. Check streaming data in S3

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

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

  14. 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 streaming job. 
    (.venv) $ JOB_RUN_IDS=$(aws glue get-job-runs --job-name streaming_data_from_msk_serverless_into_iceberg_table | jq -r '.JobRuns[] | select(.JobRunState == "RUNNING") | .Id')
(.venv) $ aws glue batch-stop-job-run --job-name streaming_data_from_msk_serverless_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-123456789012-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