How to Work with IBM Cloud Object Storage Data in AWS Glue Jobs Using JDBC



Connect to IBM Cloud Object Storage 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 IBM Cloud Object Storage into an Amazon S3 bucket and creating and running an AWS Glue job to extract IBM Cloud Object Storage data and store it in S3 as a CSV file.

Upload the CData JDBC Driver for IBM Cloud Object Storage to an Amazon S3 Bucket

In order to work with the CData JDBC Driver for IBM Cloud Object Storage 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.ibmcloudobjectstorage.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: IBMCloudObjectStorageGlueJob.
    • 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: GlueIBMCloudObjectStorageJDBC
      • 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.ibmcloudobjectstorage.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 IBM Cloud Object Storage 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.

Register a New Instance of Cloud Object Storage

If you do not already have Cloud Object Storage in your IBM Cloud account, follow the procedure below to install an instance of SQL Query in your account:

  1. Log in to your IBM Cloud account.
  2. Navigate to the page, choose a name for your instance and click Create. You will be redirected to the instance of Cloud Object Storage you just created.

Connecting using OAuth Authentication

There are certain connection properties you need to set before you can connect. You can obtain these as follows:

API Key

To connect with IBM Cloud Object Storage, you need an API Key. You can obtain this as follows:

  1. Log in to your IBM Cloud account.
  2. Navigate to the Platform API Keys page.
  3. On the middle-right corner click "Create an IBM Cloud API Key" to create a new API Key.
  4. In the pop-up window, specify the API Key name and click "Create". Note the API Key as you can never access it again from the dashboard.

Cloud Object Storage CRN

If you have multiple accounts, you will need to specify the CloudObjectStorageCRN explicitly. To find the appropriate value, you can:

  • Query the Services view. This will list your IBM Cloud Object Storage instances along with the CRN for each.
  • Locate the CRN directly in IBM Cloud. To do so, navigate to your IBM Cloud Dashboard. In the Resource List, Under Storage, select your Cloud Object Storage resource to get its CRN.

Connecting to Data

You can now set the following to connect to data:

  • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
  • ApiKey: Set this to your API key which was noted during setup.
  • CloudObjectStorageCRN (Optional): Set this to the cloud object storage CRN you want to work with. While the connector attempts to retrieve this automatically, specifying this explicitly is recommended if you have more than Cloud Object Storage account.

When you connect, the connector completes the OAuth process.

  1. Extracts the access token and authenticates requests.
  2. Saves OAuth values in OAuthSettingsLocation to be persisted across connections.

Built-in Connection String Designer

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

java -jar cdata.jdbc.ibmcloudobjectstorage.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 IBM Cloud Object Storage 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 IBM Cloud Object Storage data from the Objects table into a DataFrame ##Note the populated JDBC URL and driver class name source_df = sparkSession.read.format("jdbc").option("url","jdbc:ibmcloudobjectstorage:RTK=5246...;ApiKey=myApiKey;CloudObjectStorageCRN=MyInstanceCRN;Region=myRegion;OAuthClientId=MyOAuthClientId;OAuthClientSecret=myOAuthClientSecret;").option("dbtable","Objects").option("driver","cdata.jdbc.ibmcloudobjectstorage.IBMCloudObjectStorageDriver").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 IBM Cloud Object Storage Objects table.

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

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