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Introducing generative AI troubleshooting for Apache Spark in AWS Glue (preview)
- AWS has announced the preview of generative AI troubleshooting for Apache Spark in its Glue service.
- The tool uses machine learning and AI tech to provide root cause analysis for failed Spark apps, along with remediation advice.
- It works by analysing job metadata, metrics and logs to create detailed root cause analyses.
- Users can initiate the process by clicking one button in the AWS Glue console.
- The tool aims to reduce mean time to resolution from days to minutes, optimise Spark applications for cost and performance, and allow users to focus on deriving value from data.
- Manually debugging Spark apps is challenging because of the distributed nature of the platform and the multiple configuration issues that often arise.
- Common Spark issues, such as resource setup and access problems, memory and disk exceptions, are supported in the preview.
- The preview is currently available in all commercial regions and on AWS Glue version 4.0.
- Validation runs, used to test proposed solutions, will be charged according to standard AWS Glue pricing.
- Generative AI Spark troubleshooting aims to simplify the process of debugging Spark applications by automatically identifying the root cause of failures and providing actionable recommendations to resolve the issue.
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