CSV Reading with Dask: MemoryError when working with large files containing nested structures
I'm dealing with I'm stuck on something that should probably be simple. After trying multiple solutions online, I still can't figure this out. I'm trying to read a large CSV file (around 5GB) that contains nested JSON-like structures within some of the fields using Dask. The file is structured with columns that sometimes include lists represented as strings, which are causing issues during the read operation. When I attempt to load the data using the following code: ```python import dask.dataframe as dd # Attempting to read the CSV file with Dask file_path = 'large_file.csv' df = dd.read_csv(file_path, dtype={'nested_column': 'object'}) ``` I get a `MemoryError` even though I have 16GB of RAM. I've tried increasing the `blocksize` parameter, but that didn't seem to help. Additionally, when inspecting the file, it seems that some rows have varying amounts of data in the nested columns, which could be contributing to the performance optimization. I've also explored using `pd.read_csv` from pandas, but it runs into similar memory constraints. Here’s an example of a problematic entry in the `nested_column`: ```json {"items": ["item1", "item2"], "count": 2} ``` I tried pre-processing the file with a script that replaces the nested structures with simpler representations, but it still seems to load inefficiently. Is there a recommended strategy or Dask configuration to handle such large CSV files with nested data that might optimize memory usage? Any insights would be greatly appreciated. My development environment is Linux. Any ideas what could be causing this? My team is using Python for this service. I appreciate any insights! Is there a simpler solution I'm overlooking?