**Issue: Python script timing out on large CSV import — need optimization advice**

Hi everyone, I have a Python script using pandas to read and process about 50GB of CSV data for an ETL pipeline. The current approach loads everything into memory with pd.read_csv(), which crashes due to OOM errors and takes over 30 minutes before failing halfway through. Has anyone implemented chunking or Dask at this scale? I am open to suggestions on multiprocessing vs threading here since the operation is CPU-bound during transformation but IO-bound during ingestion.

Seems like this is one of those issues where the solution has been documented at least six times in the official repo and on Stack Overflow, but everyone still posts a reproduction instead of reading.

The problem isn't your script — it's that you're using standard pandas `read_

Try pandas read_csv with chunksize = 10000 instead of loading everything

The timeout issue you're describing with your Python-based CSV ingestion pipeline is a classic data engineering bottleneck, and it almost certainly stems from O(N) row iteration patterns being applied to what should be vectorized operations — I say this because the most common anti-pattern in these situations is loading each row into an object literal via csv.DictReader and then executing per-row validation logic within a single loop, which scales linearly with file size and becomes untenable as N crosses the ten-million-line threshold where Python's interpreter overhead dominates your actual compute budget. Let me decompose this for you systematically because there are several layers to address depending on exactly what your downstream consumer does with the data. First, if you're just flattening the CSV into a dataframe or table and nothing else, drop pandas entirely and use polars — their lazy API reorders operations, pushes filters down past the loader, and utilizes multi-threaded SIMD execution that Python's GIL prevents in standard code paths. The syntax is almost identical but it will be an order of magnitude faster because every transformation is vectorized rather than executed via the interpreter for each row individually. Second, if you must stay within pandas, use read_csv(chunksize=100000) to stream chunks instead of loading the entire file into memory at once — this mitigates OOM issues and lets you process data in pipelines that can be parallelized with concurrent.futures. Third, check whether your per-row processing includes any I/O bound operations like database lookups or API calls; if so, no amount of code optimization will help without batching those external calls because the latency floor is set by round-trip time rather than CPU cycles — you should collect all unique keys first and do a single bulk query. Fourth point that gets overlooked: data types matter immensely for memory footprint. If pandas auto-detects integers as 64-bit but your values fit in uint8

You're importing rows one by one in a loop using the standard csv module and then doing individual SQL inserts for each line? That explains why it takes three hours.

Read the pandas documentation on read_csv(chunksize=...), look at SQLAlchemy's bulk insert

The timeout behavior you're describing is almost certainly the result of Pandas reading your entire dataset into memory using read_csv without a defined chunk size, which forces a full deserialization and allocation cycle that scales O(n) with row count but with a constant factor dominated by Python object instantiation overhead per cell. The issue here isn't just time; it's the garbage collection pressure from creating millions of short-lived string objects for every non-numeric column during the parse phase, which triggers repeated stop-the-world GC cycles that compound your execution time logarithmically relative to memory pressure.

For a CSV import on this scale, I would recommend four optimization tiers in descending order of effort:

Tier 1 (The quick fix): use chunksize with an iterator. Instead of reading everything at once, read the file in blocks of 100k rows and process each block independently. This caps your peak memory usage to the chunk size regardless of total file volume and allows you to write transformed data incrementally rather than accumulating a massive DataFrame in RAM before any work begins.

Tier 2 (The type system fix): provide explicit dtype specifications. If Pandas has to infer types, it reads every column twice—once for inference and once for the actual load. By passing dtype={'col_a': 'int32', 'col_b': 'category'} you skip the inference pass entirely. The use of 'category' on low-cardinality string columns is particularly powerful; instead of storing every repeated string as a full Python object, Pandas stores integer codes pointing to a small lookup table, which can reduce memory usage by 70-95% for categorical data and dramatically speed up operations that involve equality checks or groupby.

Tier 3 (The engine fix): swap the default C engine for PyArrow. Starting in recent versions of Pandas, `read_csv(engine='pyarrow')