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sc0tts
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noaa_metrics/aggregate_logs.py
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| def get_file_size_mb(filepath: Path) -> float: | ||
| """Get file size in MB.""" | ||
| try: | ||
| return os.path.getsize(filepath) / (1024 * 1024) |
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In get_available_memory_gb(), the denominator is 1024^3.
For "similarity", I'd suggest either making this denominator 1024^2 or the other 102410241024.
sc0tts
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Sep 10, 2025
sc0tts
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Sep 10, 2025
sc0tts
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Sep 10, 2025
| """Safely read a JSON file and return DataFrame.""" | ||
| try: | ||
| if os.path.getsize(filepath) > 2: | ||
| return pd.read_json(filepath) |
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Is there a reason not to use read_json_file_safe() here?
sc0tts
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sc0tts
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Sep 10, 2025
| continue | ||
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| try: | ||
| df = pd.read_json(filepath) |
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use safe read method from above?
sc0tts
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Sep 10, 2025
sc0tts
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Sep 10, 2025
sc0tts
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Sep 10, 2025
| print(f" {filepath.name} ({size_mb:.1f}MB, {available_gb:.1f}GB available)") | ||
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| # Skip files that are too large for current memory | ||
| if size_mb > available_gb * 300: # Conservative threshold |
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Maybe define 300 as a constant.
And then in the print statement, include the size_mb and the 300*available_gb to give an indication of the "scale" of the issue that occurred here.
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Made lots of small suggestions, but they are not blocking.
Very nice summary stats!
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This dynamically reads in the ingest files to prevent job failure through running out of memory. Here is an example output from the march month