datasets/comments_2_parquet_part1.py

   1 #!/usr/bin/env python3
   2 import json
   3 from datetime import datetime
   4 from multiprocessing import Pool
   5 from itertools import islice
   6 from helper import find_dumps, open_fileset
   7 import pandas as pd
   8 import pyarrow as pa
   9 import pyarrow.parquet as pq
  10
  11 def parse_comment(comment, names= None):
  12     if names is None:
  13         names = ["id","subreddit","link_id","parent_id","created_utc","author","ups","downs","score","edited","subreddit_type","subreddit_id","stickied","is_submitter","body","error"]
  14
  15     try:
  16         comment = json.loads(comment)
  17     except json.decoder.JSONDecodeError as e:
  18         print(e)
  19         print(comment)
  20         row = [None for _ in names]
  21         row[-1] = "json.decoder.JSONDecodeError|{0}|{1}".format(e,comment)
  22         return tuple(row)
  23
  24     row = []
  25     for name in names:
  26         if name == 'created_utc':
  27             row.append(datetime.fromtimestamp(int(comment['created_utc']),tz=None))
  28         elif name == 'edited':
  29             val = comment[name]
  30             if type(val) == bool:
  31                 row.append(val)
  32                 row.append(None)
  33             else:
  34                 row.append(True)
  35                 row.append(datetime.fromtimestamp(int(val),tz=None))
  36         elif name == "time_edited":
  37             continue
  38         elif name not in comment:
  39             row.append(None)
  40
  41         else:
  42             row.append(comment[name])
  43
  44     return tuple(row)
  45
  46
  47 #    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')])
  48
  49 dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments/"
  50
  51 files = list(find_dumps(dumpdir, base_pattern="RC_20*"))
  52
  53 pool = Pool(28)
  54
  55 stream = open_fileset(files)
  56
  57 N = int(1e4)
  58
  59 rows = pool.imap_unordered(parse_comment, stream, chunksize=int(N/28))
  60
  61 schema = pa.schema([
  62     pa.field('id', pa.string(), nullable=True),
  63     pa.field('subreddit', pa.string(), nullable=True),
  64     pa.field('link_id', pa.string(), nullable=True),
  65     pa.field('parent_id', pa.string(), nullable=True),
  66     pa.field('created_utc', pa.timestamp('ms'), nullable=True),
  67     pa.field('author', pa.string(), nullable=True),
  68     pa.field('ups', pa.int64(), nullable=True),
  69     pa.field('downs', pa.int64(), nullable=True),
  70     pa.field('score', pa.int64(), nullable=True),
  71     pa.field('edited', pa.bool_(), nullable=True),
  72     pa.field('time_edited', pa.timestamp('ms'), nullable=True),
  73     pa.field('subreddit_type', pa.string(), nullable=True),
  74     pa.field('subreddit_id', pa.string(), nullable=True),
  75     pa.field('stickied', pa.bool_(), nullable=True),
  76     pa.field('is_submitter', pa.bool_(), nullable=True),
  77     pa.field('body', pa.string(), nullable=True),
  78     pa.field('error', pa.string(), nullable=True),
  79 ])
  80
  81 from pathlib import Path
  82 p = Path("/gscratch/comdata/output/reddit_comments.parquet_temp2")
  83
  84 if not p.is_dir():
  85     if p.exists():
  86         p.unlink()
  87     p.mkdir()
  88
  89 else:
  90     list(map(Path.unlink,p.glob('*')))
  91
  92 part_size = int(1e7)
  93 part = 1
  94 n_output = 0
  95 writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
  96
  97 while True:
  98     if n_output > part_size:
  99         if part > 1:
 100             writer.close()
 101
 102         part = part + 1
 103         n_output = 0
 104
 105         writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
 106
 107     n_output += N
 108     chunk = islice(rows,N)
 109     pddf = pd.DataFrame(chunk, columns=schema.names)
 110     table = pa.Table.from_pandas(pddf,schema=schema)
 111     if table.shape[0] == 0:
 112         break
 113     writer.write_table(table)
 114
 115