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Summary of Changes

Hello @Megha18jain, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request establishes an automated and robust pipeline for ingesting NOAA storm event data. It covers the entire lifecycle from fetching raw data via FTP, efficiently organizing it into smaller, processable units, to cleaning and transforming it into a structured format. The inclusion of a manifest file ensures that this critical data source can be regularly updated, providing timely and accurate storm information.

Highlights

  • New Data Import Pipeline: Introduces a complete pipeline for importing NOAA storm event data, encompassing download, sharding, and processing stages.
  • Automated Data Download: A new Python script (download_script.py) is added to automatically download and unzip NOAA storm data from an FTP server, featuring retry mechanisms for robust downloads.
  • Data Sharding for Efficiency: A sharding script (shard_noaa_data.py) is included to break down large raw data files into smaller, manageable shards based on STATE_FIPS, optimizing subsequent processing.
  • Data Cleaning and Transformation: The process_noaa_data.py script handles the cleaning, transformation, and geoid resolution of the sharded data, preparing it for import. It also retrieves an API key from GCS for geocoding.
  • Automated Refresh Schedule: A manifest.json file is added, defining the import specifications for the Storm_NOAA data and setting up a weekly cron schedule for automatic data refreshes.
  • Schema Definition: A new template MCF file (storm_noaa.tmcf) is provided to define the structured schema for storm episodes and events, including various properties like injuries, damages, and locations.
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Code Review

This pull request introduces a new data import pipeline for NOAA storm data, including scripts for downloading, sharding, and processing. The implementation is comprehensive and includes good features like parallel processing and download retries. However, I've identified several critical issues related to data correctness and schema mapping, such as typos in the TMCF and CSV header, a bug that would cause crop_damage to be stored as a tuple, and an issue preventing the script from correctly skipping already downloaded files. Addressing these is crucial for the pipeline to function correctly. I've also included some suggestions to improve code robustness and maintainability.

local_gz_path = os.path.join(data_dir, filename)
local_csv_path = local_gz_path[:-3] # Remove .gz extension

if local_csv_path in existing_files:
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high

The check for existing files is incorrect. local_csv_path is a full file path (e.g., noaa_storm_data/some_file.csv), while existing_files contains only file basenames (e.g., {'some_file.csv'}). The in check will therefore always evaluate to false, causing files to be re-downloaded on every run. You should check against the filename without the .gz extension.

Suggested change
if local_csv_path in existing_files:
if filename[:-3] in existing_files:

Node: E:storm_noaa->E0
dcid: C:storm_noaa->stormEpisode
typeOf: dcs:StormEpisode
name: C:storm_noaa->stromEpisodeName
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high

There is a typo in the column name: stromEpisodeName should be stormEpisodeName. This will cause a mismatch with the data and prevent correct schema mapping.

name: C:storm_noaa->stormEpisodeName

Comment on lines +14 to +15
indirectInjuries C:storm_noaa->indirectInjuries
directDeaths C:storm_noaa->directDeaths
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high

These lines are missing a colon (:) between the property and the column mapping. This will cause a parsing error when processing the TMCF file.

indirectInjuries: C:storm_noaa->indirectInjuries
directDeaths: C:storm_noaa->directDeaths

maxClassification: C:storm_noaa->maxClassification
lengthTraveled: C:storm_noaa->lengthTraveled
width: C:storm_noaa->width
lengthTraveled: C:storm_noaa->lengthTraveled
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high

The property lengthTraveled is duplicated. It is already defined on line 28. Please remove this redundant line.

startLocation, endLocation = start_loc_str, end_loc_str

property_damage = cost_to_int(row.get("DAMAGE_PROPERTY", "0"))
crop_damage = cost_to_int(row.get("DAMAGE_CROPS", "0")),
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high

A trailing comma at the end of this line causes crop_damage to be a tuple (e.g., (1000,)) instead of an integer. This will result in incorrectly formatted data in the output CSV. Please remove the comma.

Suggested change
crop_damage = cost_to_int(row.get("DAMAGE_CROPS", "0")),
crop_damage = cost_to_int(row.get("DAMAGE_CROPS", "0"))

property_damage_value = f"[USDollar {property_damage}]" if property_damage else ""
crop_damage_value = f"[USDollar {crop_damage}]" if crop_damage else ""

unit = "MilesPerHour" if row.get("MAGNitude_TYPE") else "Inch"
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high

There is a typo in the dictionary key: MAGNitude_TYPE should be MAGNITUDE_TYPE. This will cause row.get("MAGNitude_TYPE") to always return None, leading to the unit variable being incorrectly assigned in many cases.

Suggested change
unit = "MilesPerHour" if row.get("MAGNitude_TYPE") else "Inch"
unit = "MilesPerHour" if row.get("MAGNITUDE_TYPE") else "Inch"

Comment on lines +302 to +308
try:
# Check if temp dir is empty before trying to remove it
if not os.listdir(FLAGS.temp_dir):
os.rmdir(FLAGS.temp_dir)
logging.info(f"Successfully removed temporary directory {FLAGS.temp_dir}")
except OSError as e:
logging.error(f"Error removing temporary directory {FLAGS.temp_dir}: {e}")
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medium

The logic for cleaning up the temporary directory can be simplified and made more robust. Instead of checking if the directory is empty before removing it (which is prone to race conditions), you can use shutil.rmtree to recursively delete the directory. This is safer and handles cases where some temporary files might not have been deleted due to errors.

Suggested change
try:
# Check if temp dir is empty before trying to remove it
if not os.listdir(FLAGS.temp_dir):
os.rmdir(FLAGS.temp_dir)
logging.info(f"Successfully removed temporary directory {FLAGS.temp_dir}")
except OSError as e:
logging.error(f"Error removing temporary directory {FLAGS.temp_dir}: {e}")
try:
shutil.rmtree(FLAGS.temp_dir)
logging.info(f"Successfully removed temporary directory {FLAGS.temp_dir}")
except OSError as e:
logging.error(f"Error removing temporary directory {FLAGS.temp_dir}: {e}")

Comment on lines +65 to +72
if shard_key not in shard_files:
shard_files[shard_key] = open(out_path, 'w', newline='')
writer = csv.writer(shard_files[shard_key])
writer.writerow(header)
shard_files[shard_key].writer = writer # Attach writer to file handle
logging.info(f"Created shard file: {out_path}")

shard_files[shard_key].writer.writerow(row)
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medium

Attaching a writer attribute to a file object is an unconventional pattern that can be confusing for future maintenance. A more standard and clearer approach would be to store a tuple of (file_handle, writer) in your dictionary. This makes the code easier to understand and maintain.

You would also need to update how you close the files later on line 78 to something like for f, _ in shard_files.values(): f.close().

Suggested change
if shard_key not in shard_files:
shard_files[shard_key] = open(out_path, 'w', newline='')
writer = csv.writer(shard_files[shard_key])
writer.writerow(header)
shard_files[shard_key].writer = writer # Attach writer to file handle
logging.info(f"Created shard file: {out_path}")
shard_files[shard_key].writer.writerow(row)
if shard_key not in shard_files:
outfile = open(out_path, 'w', newline='')
writer = csv.writer(outfile)
writer.writerow(header)
shard_files[shard_key] = (outfile, writer)
logging.info(f"Created shard file: {out_path}")
shard_files[shard_key][1].writerow(row)

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2 participants