cdsc_reddit/datasets/comments_2_parquet_part1.py

#!/usr/bin/env python3
import os
import json
from datetime import datetime
from multiprocessing import Pool
from itertools import islice
from helper import open_input_file, find_dumps
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from pathlib import Path
import fire

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','../../data/spark_tmp')])

def parse_dump(partition):

    dumpdir = f"../../data/reddit_dumps/comments/{partition}"

    stream = open_input_file(dumpdir)
    rows = map(parse_comment, stream)

    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),
    ])

    p = Path("../../data/temp/reddit_comments.parquet")
    p.mkdir(exist_ok=True,parents=True)

    N=10000
    with pq.ParquetWriter(f"../../data/temp/reddit_comments.parquet/{partition}.parquet",
                          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()


def gen_task_list(dumpdir="../../data/raw_data/reddit_dumps/comments", overwrite=True):
    files = list(find_dumps(dumpdir,base_pattern="RC_20*.*"))
    with open("comments_task_list.sh",'w') as of:
        for fpath in files:
            partition = os.path.split(fpath)[1]
            if (not Path(f"../../data/temp/reddit_comments.parquet/{partition}.parquet").exists()) or (overwrite is True):
                of.write(f'python3 comments_2_parquet_part1.py parse_dump {partition}\n')


if __name__ == '__main__':
    fire.Fire({'parse_dump':parse_dump,
              'gen_task_list':gen_task_list})