Reduce throughput to nrel api by combining duplicate requests #118
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ahurli
approved these changes
Oct 16, 2020
| info = _get_info(url) | ||
| tz, elevation = info["Local Time Zone"], info["Elevation"] | ||
| info = pd.read_csv(BytesIO(resp.content), nrows=1) | ||
| data_resource = pd.read_csv(BytesIO(resp.content), dtype=float, skiprows=2) |
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Nice! I haven't used BytesIO before, but this seems like a great solution to this problem.
| @retry(interval=self.interval, allowed_exceptions=(TransientError)) | ||
| def download(url): | ||
| resp = requests.get(url) | ||
| if resp.status_code != 200: |
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Awesome! I was thinking we might want to make the TransientError specific to error code 429 (i.e. if resp.status_code == 429:) so that we don't necessarily retry if the user copied the wrong API key into the notebook and was getting 403s for example.
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Yeah I think that makes more sense :)
Merged
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Purpose
Reduce requests to NREL by only making one call, and parsing the 2 csv files in the response from memory.
What it does
See above, plus updated the retry to handle non successful status codes instead of the error
pandas.read_csvwould throw when given a url. Regenerated the notebook and found a 2x speedup, which makes sense assuming the download time is the bottleneck. Interestingly, this doesn't improve when rate limiting is removed - my guess is that the loop this runs in takes long enough that we don't trigger http 429, but only see this now that we don't have 2 requests grouped together between each iteration.Time to review
10 min