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How to Work with HCL Domino Data in AWS Glue Jobs Using JDBC


Connect to HCL Domino from AWS Glue jobs using the CData JDBC Driver hosted in Amazon S3.

AWS Glue is an ETL service from Amazon that allows you to easily prepare and load your data for storage and analytics. Using the PySpark module along with AWS Glue, you can create jobs that work with data over JDBC connectivity, loading the data directly into AWS data stores. In this article, we walk through uploading the CData JDBC Driver for HCL Domino into an Amazon S3 bucket and creating and running an AWS Glue job to extract HCL Domino data and store it in S3 as a CSV file.

Upload the CData JDBC Driver for HCL Domino to an Amazon S3 Bucket

In order to work with the CData JDBC Driver for HCL Domino in AWS Glue, you will need to store it (and any relevant license files) in an Amazon S3 bucket.

  1. Open the Amazon S3 Console.
  2. Select an existing bucket (or create a new one).
  3. Click Upload
  4. Select the JAR file (cdata.jdbc.domino.jar) found in the lib directory in the installation location for the driver.

Configure the Amazon Glue Job

  1. Navigate to ETL -> Jobs from the AWS Glue Console.
  2. Click Add Job to create a new Glue job.
  3. Fill in the Job properties:
    • Name: Fill in a name for the job, for example: DominoGlueJob.
    • IAM Role: Select (or create) an IAM role that has the AWSGlueServiceRole and AmazonS3FullAccess permissions policies. The latter policy is necessary to access both the JDBC Driver and the output destination in Amazon S3.
    • Type: Select "Spark".
    • Glue Version: Select "Spark 2.4, Python 3 (Glue Version 1.0)".
    • This job runs: Select "A new script to be authored by you".
      Populate the script properties:
      • Script file name: A name for the script file, for example: GlueDominoJDBC
      • S3 path where the script is stored: Fill in or browse to an S3 bucket.
      • Temporary directory: Fill in or browse to an S3 bucket.
    • Expand Security configuration, script libraries and job parameters (optional). For Dependent jars path, fill in or browse to the S3 bucket where you uploaded the JAR file. Be sure to include the name of the JAR file itself in the path, i.e.: s3://mybucket/cdata.jdbc.domino.jar
  4. Click Next. Here you will have the option to add connection to other AWS endpoints. So, if your Destination is Redshift, MySQL, etc, you can create and use connections to those data sources.
  5. Click "Save job and edit script" to create the job.
  6. In the editor that opens, write a python script for the job. You can use the sample script (see below) as an example.

Sample Glue Script

To connect to HCL Domino using the CData JDBC driver, you will need to create a JDBC URL, populating the necessary connection properties. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Connecting to Domino

To connect to Domino data, set the following properties:

  • URL: The host name or IP of the server hosting the Domino database. Include the port of the server hosting the Domino database. For example: http://sampleserver:1234/
  • DatabaseScope: The name of a scope in the Domino Web UI. The driver exposes forms and views for the schema governed by the specified scope. In the Domino Admin UI, select the Scopes menu in the sidebar. Set this property to the name of an existing scope.

Authenticating with Domino

Domino supports authenticating via login credentials or an Azure Active Directory OAuth application:

Login Credentials

To authenticate with login credentials, set the following properties:

  • AuthScheme: Set this to "OAuthPassword"
  • User: The username of the authenticating Domino user
  • Password: The password associated with the authenticating Domino user

The driver uses the login credentials to automatically perform an OAuth token exchange.

AzureAD

This authentication method uses Azure Active Directory as an IdP to obtain a JWT token. You need to create a custom OAuth application in Azure Active Directory and configure it as an IdP. To do so, follow the instructions in the Help documentation. Then set the following properties:

  • AuthScheme: Set this to "AzureAD"
  • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
  • OAuthClientId: The Client ID obtained when setting up the custom OAuth application.
  • OAuthClientSecret: The Client secret obtained when setting up the custom OAuth application.
  • CallbackURL: The redirect URI defined when you registered your app. For example: https://localhost:33333
  • AzureTenant: The Microsoft Online tenant being used to access data. Supply either a value in the form companyname.microsoft.com or the tenant ID.

    The tenant ID is the same as the directory ID shown in the Azure Portal's Azure Active Directory > Properties page.

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the HCL Domino JDBC Driver. Either double-click the JAR file or execute the JAR file from the command-line.

java -jar cdata.jdbc.domino.jar

Fill in the connection properties and copy the connection string to the clipboard.

To host the JDBC driver in Amazon S3, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

Below is a sample script that uses the CData JDBC driver with the PySpark and AWSGlue modules to extract HCL Domino data and write it to an S3 bucket in CSV format. Make any necessary changes to the script to suit your needs and save the job.

import sys from awsglue.transforms import * from awsglue.utils import getResolvedOptions from pyspark.context import SparkContext from awsglue.context import GlueContext from awsglue.dynamicframe import DynamicFrame from awsglue.job import Job args = getResolvedOptions(sys.argv, ['JOB_NAME']) sparkContext = SparkContext() glueContext = GlueContext(sparkContext) sparkSession = glueContext.spark_session ##Use the CData JDBC driver to read HCL Domino data from the ByName table into a DataFrame ##Note the populated JDBC URL and driver class name source_df = sparkSession.read.format("jdbc").option("url","jdbc:domino:RTK=5246...;Server=https://domino.corp.com;AuthScheme=OAuthPassword;User=my_domino_user;Password=my_domino_password;").option("dbtable","ByName").option("driver","cdata.jdbc.domino.DominoDriver").load() glueJob = Job(glueContext) glueJob.init(args['JOB_NAME'], args) ##Convert DataFrames to AWS Glue's DynamicFrames Object dynamic_dframe = DynamicFrame.fromDF(source_df, glueContext, "dynamic_df") ##Write the DynamicFrame as a file in CSV format to a folder in an S3 bucket. ##It is possible to write to any Amazon data store (SQL Server, Redshift, etc) by using any previously defined connections. retDatasink4 = glueContext.write_dynamic_frame.from_options(frame = dynamic_dframe, connection_type = "s3", connection_options = {"path": "s3://mybucket/outfiles"}, format = "csv", transformation_ctx = "datasink4") glueJob.commit()

Run the Glue Job

With the script written, we are ready to run the Glue job. Click Run Job and wait for the extract/load to complete. You can view the status of the job from the Jobs page in the AWS Glue Console. Once the Job has succeeded, you will have a CSV file in your S3 bucket with data from the HCL Domino ByName table.

Using the CData JDBC Driver for HCL Domino in AWS Glue, you can easily create ETL jobs for HCL Domino data, whether writing the data to an S3 bucket or loading it into any other AWS data store.

Ready to get started?

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