Script for example of streaming pyarrow.

examples/pyarrow_streaming.py

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+import pyarrow.dataset as ds
+from itertools import chain, groupby, islice
+
+# A pyarrow dataset abstracts reading, writing, or filtering a parquet file. It does not read dataa into memory. 
+#dataset = ds.dataset(pathlib.Path('/gscratch/comdata/output/reddit_submissions_by_subreddit.parquet/'), format='parquet', partitioning='hive')
+dataset = ds.dataset('/gscratch/comdata/output/reddit_submissions_by_author.parquet', format='parquet', partitioning='hive')
+
+# let's get all the comments to two subreddits:
+subreddits_to_pull = ['seattlewa','seattle']
+
+# 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.
+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'])
+
+# simple function to execute scantasks and create a stream of pydict rows 
+def execute_scan_task(st):
+    # an executed scan task yields an iterator of record_batches
+    def unroll_record_batch(rb):
+        df = rb.to_pandas()
+        return df.itertuples()
+
+    for rb in st.execute():
+        yield unroll_record_batch(rb)
+
+
+# now we just need to flatten and we have our iterator
+row_iter = chain.from_iterable(chain.from_iterable(map(lambda st: execute_scan_task(st), scan_tasks)))
+
+# now we can use python's groupby function to read one author at a time
+# note that the same author can appear more than once since the record batches may not be in the correct order.
+author_submissions = groupby(row_iter, lambda row: row.author)
+for auth, posts in author_submissions:
+    print(f"{auth} has {len(list(posts))} posts")