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Asked: 2024-09-23T19:33:30+05:30 In:

I am encountering a problem with AWS Glue when processing CSV files that contain commas and double quotes. The data fields in my CSV often include both of these characters, and I am unsure how to configure Glue to properly parse the file without causing errors or misalignment in the data. Can someone provide insights or solutions for handling this issue effectively?

I’ve been wrestling with this annoying issue while working with AWS Glue and processing some CSV files. The problem arises from the fact that my CSV files often contain commas and double quotes within the data fields themselves. It feels like every time I run my job, I encounter errors, misalignment, or some jumbled output, and it’s driving me a bit nuts!

For context, I’m working with a dataset where fields are enclosed in double quotes because they can contain commas, but sometimes there are instances where there’s a double quote within the field itself. I’ve tried a few settings in Glue, but I still feel like I’m missing something crucial to make it work seamlessly.

I’m sure I’m not the only one dealing with CSV parsing issues, especially with Glue. It seems that handling special characters like commas or quotes can get tricky. I’d love to hear if anyone has faced a similar problem and what solutions they found.

Have you configured the Glue job with a custom delimiter? I’ve read that you can specify certain options in the Glue crawler or in the job itself, but I’m unsure what the best approach is. Should I be converting the CSV files beforehand, or is there a way to adjust the Glue settings to accommodate the fields correctly?

Also, I’ve seen people mention using AWS Lambda as a preprocessing step. Is that really necessary, or can Glue handle it on its own if set up right? Honestly, I’m looking for any tips, pointers, or even code snippets you might have that can help me properly handle these characters.

Every time I think I have it figured out, I encounter some new error. It’s like a never-ending puzzle! Any insights you can share would be super helpful. Let’s figure out how to deal with this frustrating parsing issue together! Thanks a ton in advance for any help!

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    2 Answers


    1. I totally get where you’re coming from! Working with CSV files can be such a headache, especially when they have their own quirks with commas and quotes!

      So, about your issue with AWS Glue – a lot of people run into these parsing problems. When your fields already use double quotes, and then you have double quotes inside those fields, it really complicates things. Here are a few ideas you can try:

      • CSV Format: Make sure your CSV files use the correct escaping for quotes. For instance, if you have double quotes inside your fields, they should be escaped with another double quote (i.e., `””` instead of just `”`).
      • Glue Job Config: In AWS Glue, you can set the quoteChar and escapeChar options in your job configuration. This can help Glue understand how to handle quotes and commas within your data. Try setting quoteChar to `”` and see if that helps.
      • Custom Delimiters: Yes, you can define custom delimiters in Glue! If you think that might help, go for it, but remember that you’ll also need to ensure the parser understands how to interpret the format.
      • Preprocessing: You could use AWS Lambda to preprocess your CSV files, like cleaning up the quotes and ensuring that they’re formatted correctly. It’s not necessary, though; Glue can handle a lot. But if preprocessing makes your files less error-prone, it might be worth it!
      • Testing in Small Batches: If you keep getting errors, try testing your Glue job with just a few rows of your CSV file. This way, you can isolate the problem without dealing with massive datasets.

      And if all else fails, you might want to consider converting your CSV files to a different format (like Parquet) if you’re working with large datasets, as this format handles complex data types better.

      Stick with it! It might take a bit of tweaking, but you’ll crack this CSV puzzle eventually. Good luck!


    2. Dealing with CSV files that contain commas and double quotes within fields can indeed be frustrating, especially in AWS Glue. To resolve the parsing issues, it’s essential to ensure that your CSV files are formatted correctly. Fields that contain commas should be enclosed in double quotes, and if a double quote appears within a field, it should be escaped by using another double quote. For example, the string `He said, “Hello,” and I replied, “Hi!”` should be correctly formatted as `”He said, “”Hello,”” and I replied, “”Hi!”””`. Once your CSV is properly formatted, you can configure your Glue job to handle these cases better by setting the `quoteChar` and `escapeChar` options appropriately in your job scripts or crawler. This will help you avoid misalignment and jumbled outputs.

      The question of whether to preprocess your CSV files with AWS Lambda before feeding them to Glue can depend on your specific needs and existing pipeline. If the formatting issues are consistent and you find yourself having to fix them repetitively, a preprocessing step could automate corrections and lead to cleaner data, which Glue can then process seamlessly. Alternatively, if you configure Glue correctly and ensure the escaping and quoting are accurate, it should handle the files without issue. To illustrate, if you’re using a Spark job, make sure to specify the options in the DataFrame read method, such as applying the right schema. Snippets of code for setting delimiters or customizing your DataFrame read operations can enhance your chances of success, so don’t hesitate to share configurations that worked for you.

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