AWS Glue job takes very long time to write a spark dataframe on S3

When an AWS Glue job takes a very long time to write a Spark dataframe to S3 or results in an Internal Service Error, there are several potential causes and optimizations to consider:

1. Repartitioning: One common issue is improper repartitioning. If you're using repartition(1), you're forcing a single core to do all the writing, which can significantly slow down the process. Instead, consider repartitioning to match your worker configuration. For example, if you have 6 maximum workers, you might try frame.repartition(6).

2. Resource allocation: Ensure your Glue job has sufficient resources allocated. If the job is running out of memory or CPU, it might cause unexpected terminations or slow performance. Review your worker type, vCPU, RAM, and maximum worker settings to ensure they're appropriate for your data volume.

3. Reading from the source: The bottleneck might actually be in reading from the source, not writing to S3. If you're reading from a database like PostgreSQL, consider using parallel read techniques to improve performance.

4. Spark configuration: Check your Spark configuration settings. For instance, ensure that settings like spark.dynamicAllocation.minExecutors are correctly set. Misconfigurations can lead to suboptimal resource allocation and slower job execution.

5. S3 bucket permissions: Verify that the IAM role associated with your Glue job has the necessary permissions to write to the S3 bucket. Access denied errors can cause job failures.

6. Data volume and format: The size and format of your data can impact write times. Consider using efficient file formats like Parquet and appropriate compression methods.

7. Network connectivity: If your Glue job is running in a VPC, ensure proper network connectivity to S3. You might need to set up an S3 gateway VPC endpoint if you're using private subnets.

8. Logging and monitoring: Enable detailed logging for your Glue job and use Amazon CloudWatch Logs Insights to analyze performance. Look for patterns in error messages or warnings that might indicate configuration issues or resource constraints.

To troubleshoot the Internal Service Error:

1. Review the full Glue job logs in CloudWatch for any additional error messages or stack traces.
2. Try running the job with a smaller subset of data to isolate the issue.
3. Check for any recent changes in your ETL script, data schema, or AWS account settings that might have introduced the error.

If the issue persists after trying these optimizations and troubleshooting steps, you may need to consult AWS support for more detailed analysis specific to your job configuration and environment.
Sources
Optimizing AWS Glue Job for Faster S3 Writes of Large Datasets | AWS re:Post
Optimizing AWS Glue Job for Faster S3 Writes of Large Datasets | AWS re:Post
Troubleshooting AWS Glue ETL Jobs using Amazon CloudWatch Logs Insights enhanced queries | AWS Cloud Operations Blog
Error in AWS Glue ETL pipeline | AWS re:Post
AWS Glue + RDS Postgres | AWS re:Post