AWS Big Data Blog

New Amazon CloudWatch log class to cost-effectively scale your AWS Glue workloads

AWS Glue is a serverless data integration service that makes it easier to discover, prepare, and combine data for analytics, machine learning (ML), and application development. You can use AWS Glue to create, run, and monitor data integration and ETL (extract, transform, and load) pipelines and catalog your assets across multiple data stores.

One of the most common questions we get from customers is how to effectively optimize costs on AWS Glue. Over the years, we have built multiple features and tools to help customers manage their AWS Glue costs. For example, AWS Glue Auto Scaling and AWS Glue Flex can help you reduce the compute cost associated with processing your data. AWS Glue interactive sessions and notebooks can help you reduce the cost of developing your ETL jobs. For more information about cost-saving best practices, refer to Monitor and optimize cost on AWS Glue for Apache Spark. Additionally, to understand data transfer costs, refer to the Cost Optimization Pillar defined in AWS Well-Architected Framework. For data storage, you can apply general best practices defined for each data source. For a cost optimization strategy using Amazon Simple Storage Service (Amazon S3), refer to Optimizing storage costs using Amazon S3.

In this post, we tackle the remaining piece—the cost of logs written by AWS Glue.

Before we get into the cost analysis of logs, let’s understand the reasons to enable logging for your AWS Glue job and the current options available. When you start an AWS Glue job, it sends the real-time logging information to Amazon CloudWatch (every 5 seconds and before each executor stops) during the Spark application starts running. You can view the logs on the AWS Glue console or the CloudWatch console dashboard. These logs provide you with insights into your job runs and help you optimize and troubleshoot your AWS Glue jobs. AWS Glue offers a variety of filters and settings to reduce the verbosity of your logs. As the number of job runs increases, so does the volume of logs generated.

To optimize CloudWatch Logs costs, AWS recently announced a new log class for infrequently accessed logs called Amazon CloudWatch Logs Infrequent Access (Logs IA). This new log class offers a tailored set of capabilities at a lower cost for infrequently accessed logs, enabling you to consolidate all your logs in one place in a cost-effective manner. This class provides a more cost-effective option for ingesting logs that only need to be accessed occasionally for auditing or debugging purposes.

In this post, we explain what the Logs IA class is, how it can help reduce costs compared to the standard log class, and how to configure your AWS Glue resources to use this new log class. By routing logs to Logs IA, you can achieve significant savings in your CloudWatch Logs spend without sacrificing access to important debugging information when you need it.

CloudWatch log groups used by AWS Glue job continuous logging

When continuous logging is enabled, AWS Glue for Apache Spark writes Spark driver/executor logs and progress bar information into the following log group:

/aws-glue/jobs/logs-v2

If a security configuration is enabled for CloudWatch logs, AWS Glue for Apache Spark will create a log group named as follows for continuous logs:

<Log-Group-Name>-<Security-Configuration-Name>

The default and custom log groups will be as follows:

  • The default continuous log group will be /aws-glue/jobs/logs-v2-<Security-Configuration-Name>
  • The custom continuous log group will be <custom-log-group-name>-<Security-Configuration-Name>

You can provide a custom log group name through the job parameter –continuous-log-logGroup.

Getting started with the new Infrequent Access log class for AWS Glue workload

To gain the benefits from Logs IA for your AWS Glue workloads, you need to complete the following two steps:

  1. Create a new log group using the new Log IA class.
  2. Configure your AWS Glue job to point to the new log group

Complete the following steps to create a new log group using the new Infrequent Access log class:

  1. On the CloudWatch console, choose Log groups under Logs in the navigation pane.
  2. Choose Create log group.
  3. For Log group name, enter /aws-glue/jobs/logs-v2-infrequent-access.
  4. For Log class, choose Infrequent Access.
  5. Choose Create.

Complete the following steps to configure your AWS Glue job to point to the new log group:

  1. On the AWS Glue console, choose ETL jobs in the navigation pane.
  2. Choose your job.
  3. On the Job details tab, choose Add new parameter under Job parameters.
  4. For Key, enter --continuous-log-logGroup.
  5. For Value, enter /aws-glue/jobs/logs-v2-infrequent-access.
  6. Choose Save.
  7. Choose Run to trigger the job.

New log events are written into the new log group.

View the logs with the Infrequent Access log class

Now you’re ready to view the logs with the Infrequent Access log class. Open the log group /aws-glue/jobs/logs-v2-infrequent-access on the CloudWatch console.

When you choose one of the log streams, you will notice that it redirects you to the CloudWatch console Logs Insight page with a pre-configured default command and your log stream selected by default. By choosing Run query, you can view the actual log events on the Logs Insights page.

Considerations

Keep in mind the following considerations:

  • You cannot change the log class of a log group after it’s created. You need to create a new log group to configure the Infrequent Access class.
  • The Logs IA class offers a subset of CloudWatch Logs capabilities, including managed ingestion, storage, cross-account log analytics, and encryption with a lower ingestion price per GB. For example, you can’t view log events through the standard CloudWatch Logs console. To learn more about the features offered across both log classes, refer to Log Classes.

Conclusion

This post provided step-by-step instructions to guide you through enabling Logs IA for your AWS Glue job logs. If your AWS Glue ETL jobs generate large volumes of log data that makes it a challenge as you scale your applications, the best practices demonstrated in this post can help you cost-effectively scale while centralizing all your logs in CloudWatch Logs. Start using the Infrequent Access class with your AWS Glue workloads today and enjoy the cost benefits.

About the Authors

Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team. He works based in Tokyo, Japan. He is responsible for building software artifacts to help customers. In his spare time, he enjoys cycling on his road bike.

Abeetha Bala is a Senior Product Manager for Amazon CloudWatch, primarily focused on logs. Being customer obsessed, she solves observability challenges through innovative and cost-effective ways.

Kinshuk Pahare is a leader in AWS Glue’s product management team. He drives efforts on the platform, developer experience, and big data processing frameworks like Apache Spark, Ray, and Python Shell.