Managing concurrent Glue job executions

Amazon Glue is a fully managed ETL (Extract, Transform, Load) service that helps users prepare and transform data for analytics. As your data workflows grow in complexity, you may find yourself needing to run multiple jobs at the same time. Managing concurrent Glue job executions becomes essential to ensure efficiency, avoid resource contention, and prevent failures.

In this blog, we’ll explore how to manage concurrent executions of AWS Glue jobs effectively, including best practices, configuration settings, and strategies to handle parallel processing and resource optimization.


Understanding Glue Job Execution and Concurrency

By default, AWS Glue allows jobs to run in parallel, but every account has service limits based on the number of Data Processing Units (DPUs) available and the job’s worker type. If multiple jobs are started at the same time, they’ll either run concurrently or be queued depending on the availability of resources.

Each Glue job can also be configured with concurrent run settings, allowing multiple instances of the same job to execute at once with different parameters (such as partitions or dynamic inputs).


Key Concepts for Concurrent Execution

1. Maximum Concurrent Runs

In the AWS Glue job properties, there’s a setting called “Maximum concurrent runs”. This defines how many instances of the job can run at the same time.

Default: 1

Max: Depends on your AWS account limits

To update this:

  • Go to the AWS Glue Console
  • Open your job → Configuration → Edit
  • Set the "Maximum concurrent runs" value

This is especially useful for parameterized jobs processing multiple partitions simultaneously.


2. Job Parameters and Partitioning

To leverage concurrent execution, use job parameters to pass specific inputs such as table partitions. This lets you invoke the same job multiple times in parallel, each working on a different data subset.


Example: Run the job with --region=us-east-1 and another with --region=eu-west-1.


Strategies for Managing Concurrent Glue Jobs

A. Use Job Bookmarks Wisely

Job bookmarks track processed data. If multiple concurrent jobs use the same source and bookmarks, data inconsistencies may occur. Ensure each job run processes independent data or uses its own bookmark configuration.


B. Resource Planning

Monitor your DPU quotas. If the total requested DPUs exceed the quota, some jobs will be queued or fail. You can:


Request a DPU increase via AWS support


Use smaller worker types (e.g., Standard or G.1X instead of G.2X)


Stagger job starts using scheduling logic


C. Trigger-Based Execution

AWS Glue triggers can help manage workflow dependencies and control concurrency. Use event-based or conditional triggers to sequence jobs or execute them only when others complete successfully.


D. Monitor with CloudWatch

Use Amazon CloudWatch to monitor job performance and errors. Set up alerts for long wait times, failed runs, or resource bottlenecks. This gives you insights to scale or adjust concurrency settings as needed.


Best Practices

Use unique identifiers or partitions to isolate data per job run.


Avoid overlapping data reads across concurrent jobs.


Define job-level retry policies to handle transient failures.


Leverage workflows for orchestrated and conditional execution paths.


Regularly review Glue job metrics to fine-tune concurrent configurations.


Conclusion

Managing concurrent Glue job executions is key to achieving scalability in your data pipelines. By understanding Glue’s execution model, setting appropriate concurrency limits, and following best practices, you can optimize job performance, reduce costs, and improve the reliability of your ETL workflows. Whether you're dealing with batch processing or near real-time data loads, a well-managed concurrency strategy ensures your data engineering pipelines run smoothly and efficiently.

Learn AWS Data Engineer Training
Read More: Analyzing IoT data streams using AWS Kinesis

Visit IHUB Training Institute Hyderabad
Get Direction

Comments

Post a Comment

Popular posts from this blog

How to Use Tosca's Test Configuration Parameters

Tosca by Tricentis is a powerful model-based test automation tool widely used in enterprise environments for end-to-end testing. One of its most useful features is Test Configuration Parameters (TCPs), which provide flexibility and control in managing test execution. TCPs enable testers to create reusable test cases by passing dynamic values without hardcoding them. This significantly enhances test maintenance, scalability, and efficiency. In this blog, we’ll explore what Test Configuration Parameters are, how to create and use them, and the best practices to follow when working with TCPs in Tosca. What Are Test Configuration Parameters? Test Configuration Parameters are custom-defined key-value pairs that can be associated with test cases, test case folders, or execution lists in Tosca. They help manage configuration data such as environment URLs, browser types, credentials, or any other variable data that may differ across test runs. By using TCPs, you can avoid duplicating test case...
Read more

Using Hibernate ORM for Fullstack Java Data Management

In the world of fullstack Java development, managing data efficiently and seamlessly across the application is critical. One of the most powerful tools in the Java ecosystem for handling database operations is Hibernate ORM (Object-Relational Mapping). Hibernate simplifies the interaction between Java applications and relational databases, reducing boilerplate code and improving maintainability. This blog explores how Hibernate ORM works, why it’s widely used, and how it fits into the data management strategy of a fullstack Java application. What is Hibernate ORM? Hibernate ORM is an open-source Java framework that provides a powerful and flexible mechanism for mapping Java objects to database tables. It abstracts the low-level JDBC code, allowing developers to work with high-level object-oriented concepts. Instead of writing raw SQL queries, Hibernate allows developers to define database interactions using simple Java classes (also known as POJOs) and annotations or XML configurations...
Read more

Creating a Test Execution Report with Charts in Playwright

Modern test automation isn't just about running tests—it's also about presenting clear, visual feedback on test results. When working with Playwright, one of the most powerful end-to-end testing tools for modern web apps, generating detailed execution reports can help your team track progress, identify issues quickly, and make data-driven decisions. A visual report with charts brings your testing efforts to life. In this blog, we’ll walk through how to create a test execution report with charts using Playwright’s built-in capabilities along with third-party tools for enhanced visualization. Why Visual Reports Matter Plain text logs and console outputs are hard to parse at scale. A good report should answer questions like: How many tests passed or failed? What’s the overall success rate? Which tests are flaky or slow? How have test results changed over time? Visualizing this data with charts—like pie charts, bar graphs, or timelines—helps QA teams and stakeholders better underst...
Read more