Build comments dataset similarly to submissions and improve partitioning scheme

[cdsc_reddit.git] / comments_2_parquet_part1.py

diff --git a/comments_2_parquet_part1.py b/comments_2_parquet_part1.py
new file mode 100755 (executable)
index 0000000..faea040
--- /dev/null
+++ b/comments_2_parquet_part1.py
@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+import json
+from datetime import datetime
+from multiprocessing import Pool
+from itertools import islice
+from helper import find_dumps, open_fileset
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+globstr_base = "/gscratch/comdata/reddit_dumps/comments/RC_20*"
+
+def parse_comment(comment, names= None):
+    if names is None:
+        names = ["id","subreddit","link_id","parent_id","created_utc","author","ups","downs","score","edited","subreddit_type","subreddit_id","stickied","is_submitter","body","error"]
+
+    try:
+        comment = json.loads(comment)
+    except json.decoder.JSONDecodeError as e:
+        print(e)
+        print(comment)
+        row = [None for _ in names]
+        row[-1] = "json.decoder.JSONDecodeError|{0}|{1}".format(e,comment)
+        return tuple(row)
+
+    row = []
+    for name in names:
+        if name == 'created_utc':
+            row.append(datetime.fromtimestamp(int(comment['created_utc']),tz=None))
+        elif name == 'edited':
+            val = comment[name]
+            if type(val) == bool:
+                row.append(val)
+                row.append(None)
+            else:
+                row.append(True)
+                row.append(datetime.fromtimestamp(int(val),tz=None))
+        elif name == "time_edited":
+            continue
+        elif name not in comment:
+            row.append(None)
+
+        else:
+            row.append(comment[name])
+
+    return tuple(row)
+
+
+#    conf = sc._conf.setAll([('spark.executor.memory', '20g'), ('spark.app.name', 'extract_reddit_timeline'), ('spark.executor.cores', '26'), ('spark.cores.max', '26'), ('spark.driver.memory','84g'),('spark.driver.maxResultSize','0'),('spark.local.dir','/gscratch/comdata/spark_tmp')])
+
+dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments"
+
+files = list(find_dumps(dumpdir, base_pattern="RC_20*.*"))
+
+pool = Pool(28)
+
+stream = open_fileset(files)
+
+N = 100000
+
+rows = pool.imap_unordered(parse_comment, stream, chunksize=int(N/28))
+
+schema = pa.schema([
+    pa.field('id', pa.string(), nullable=True),
+    pa.field('subreddit', pa.string(), nullable=True),
+    pa.field('link_id', pa.string(), nullable=True),
+    pa.field('parent_id', pa.string(), nullable=True),
+    pa.field('created_utc', pa.timestamp('ms'), nullable=True),
+    pa.field('author', pa.string(), nullable=True),
+    pa.field('ups', pa.int64(), nullable=True),
+    pa.field('downs', pa.int64(), nullable=True),
+    pa.field('score', pa.int64(), nullable=True),
+    pa.field('edited', pa.bool_(), nullable=True),
+    pa.field('time_edited', pa.timestamp('ms'), nullable=True),
+    pa.field('subreddit_type', pa.string(), nullable=True),
+    pa.field('subreddit_id', pa.string(), nullable=True),
+    pa.field('stickied', pa.bool_(), nullable=True),
+    pa.field('is_submitter', pa.bool_(), nullable=True),
+    pa.field('body', pa.string(), nullable=True),
+    pa.field('error', pa.string(), nullable=True),
+])
+
+with pq.ParquetWriter("/gscratch/comdata/output/reddit_comments.parquet_temp",schema=schema,compression='snappy',flavor='spark') as writer:
+    while True:
+        chunk = islice(rows,N)
+        pddf = pd.DataFrame(chunk, columns=schema.names)
+        table = pa.Table.from_pandas(pddf,schema=schema)
+        if table.shape[0] == 0:
+            break
+        writer.write_table(table)
+
+    writer.close()