33 lines
1.7 KiB
Python
33 lines
1.7 KiB
Python
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import pyarrow.dataset as ds
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from itertools import chain, groupby, islice
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# A pyarrow dataset abstracts reading, writing, or filtering a parquet file. It does not read dataa into memory.
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#dataset = ds.dataset(pathlib.Path('/gscratch/comdata/output/reddit_submissions_by_subreddit.parquet/'), format='parquet', partitioning='hive')
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dataset = ds.dataset('/gscratch/comdata/output/reddit_submissions_by_author.parquet', format='parquet', partitioning='hive')
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# let's get all the comments to two subreddits:
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subreddits_to_pull = ['seattlewa','seattle']
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# instead of loading the data into a pandas dataframe all at once we can stream it. This lets us start working with it while it is read.
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scan_tasks = dataset.scan(filter = ds.field('subreddit').isin(subreddits_to_pull), columns=['id','subreddit','CreatedAt','author','ups','downs','score','subreddit_id','stickied','title','url','is_self','selftext'])
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# simple function to execute scantasks and create a stream of pydict rows
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def execute_scan_task(st):
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# an executed scan task yields an iterator of record_batches
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def unroll_record_batch(rb):
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df = rb.to_pandas()
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return df.itertuples()
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for rb in st.execute():
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yield unroll_record_batch(rb)
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# now we just need to flatten and we have our iterator
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row_iter = chain.from_iterable(chain.from_iterable(map(lambda st: execute_scan_task(st), scan_tasks)))
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# now we can use python's groupby function to read one author at a time
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# note that the same author can appear more than once since the record batches may not be in the correct order.
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author_submissions = groupby(row_iter, lambda row: row.author)
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for auth, posts in author_submissions:
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print(f"{auth} has {len(list(posts))} posts")
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