AWS Glue Job scenarios with 'InvalidArgumentException' When Using Spark with Large Dataset
I'm having a hard time understanding I'm optimizing some code but I'm refactoring my project and I'm stuck on something that should probably be simple. This might be a silly question, but I'm working with an scenario with my AWS Glue job when trying to process a large dataset stored in S3. The job frequently fails with the behavior message `InvalidArgumentException: The provided job parameters are invalid. (Service: Glue, Status Code: 400)`. I've configured the job to use a Spark ETL script that reads data from an S3 bucket, transforms it, and then writes the output back to another S3 location. Here’s a snippet of the relevant part of my Glue job script: ```python import sys from pyspark.context import SparkContext from awsglue.context import GlueContext from awsglue.transforms import * from awsglue.utils import getResolvedOptions args = getResolvedOptions(sys.argv, ['JOB_NAME']) sc = SparkContext() glueContext = GlueContext(sc) # Reading large dataset from S3 input_data = glueContext.create_dynamic_frame_from_options( connection_type="s3", connection_options={"paths": ["s3://my-bucket/large-dataset/"]}, format="json" ) # Transformations transformed_data = ApplyMapping.apply(frame=input_data, mappings=[ ("field1", "string", "field1", "string"), ("field2", "int", "field2", "int") ]) # Writing output back to S3 glueContext.write_dynamic_frame.from_options( frame=transformed_data, connection_type="s3", connection_options={"path": "s3://my-bucket/output/"}, format="json" ) ``` I've tried adjusting the `DPU` (Data Processing Unit) settings and increasing the number of DPUs allocated to the job, but the behavior continues. I also checked the IAM role permissions attached to my Glue job, and they seem appropriate for S3 access. Additionally, I’ve verified that the input dataset is not corrupted and can be read by smaller Glue jobs without issues. Could this be related to the size of the dataset I'm processing, or is there something else I should be checking in my Glue job configuration? Any insights would be appreciated! This is part of a larger application I'm building. Is there a better approach? The stack includes Python and several other technologies. Am I approaching this the right way? I'm working on a service that needs to handle this. Any advice would be much appreciated. Thanks for your help in advance! What am I doing wrong? For reference, this is a production desktop app. Any ideas how to fix this?