Unexpected Memory Increase When Using Dask to Process Large CSV Files in Python 3.8
I'm stuck on something that should probably be simple... I'm experiencing a important increase in memory usage while using Dask to process large CSV files in Python 3.8. I have a dataset that is about 10GB, and I'm trying to read it in chunks and apply some transformations. However, the memory usage seems to spike dramatically, leading to out-of-memory errors. I've tried using the `dask.dataframe.read_csv()` function to load the data, and here's a simplified version of what my code looks like: ```python import dask.dataframe as dd df = dd.read_csv('large_file.csv', blocks='4MB') def transform(row): # Some transformation logic return row # Apply transformation result = df.map_partitions(lambda df: df.apply(transform, axis=1)) # Compute the result df.compute() ``` Despite the chunking, it seems like all the data is being loaded into memory at once during the computation phase. I've also tried using `df.continue()` before calling `compute()`, but it doesn't seem to alleviate the scenario. Additionally, I've set the Dask configuration to increase the number of workers, but that hasn't helped either. Does anyone have insights on minimizing memory usage when processing large datasets with Dask? Any suggestions on optimizing this further would be greatly appreciated! I'm working in a Windows 11 environment. I'd love to hear your thoughts on this.