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process.py
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process.py
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import sys
import gzip
import math
import time
import datetime
import json
import redis
import logging
import concurrent.futures
import numpy as np
import pandas as pd
import utils
from api import ApiClient
from parser import YouTubeHistoryParser
api_client = ApiClient()
def query_api(queries):
print('to query for {0} videos'.format(len(queries)))
queries = list(queries)
queries = utils.chunks(queries, 50)
with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
result = {}
for response in executor.map(api_client.request_video_list, queries):
if type(response) == str: #An error occured
return response
for item in response['items']:
vid = item['id']
if 'contentDetails' in item and 'duration' in item['contentDetails']:
result[vid] = item['contentDetails']['duration']
return result
def reformat_data(datetimes, durations):
data = {}
df = pd.DataFrame(datetimes, columns=['date'])
month_count = df.groupby([df["date"].dt.year, df["date"].dt.month]).count()
month_count = month_count.rename(columns={'date': 'value'})
month_count['date'] = month_count.index
month_count = month_count.reset_index(drop=True)
month_count = month_count.to_dict(orient='records')
data['month_count'] = json.dumps(month_count, indent=2)
day_count = df.groupby([df["date"].dt.year, df["date"].dt.month, df['date'].dt.day]).count()
day_count = day_count.rename(columns={'date': 'value'})
day_count['date'] = day_count.index
day_count = day_count.reset_index(drop=True)
day_count = day_count.to_dict(orient='records')
data['day_count'] = json.dumps(day_count, indent=2)
df = pd.DataFrame({'date': datetimes, 'duration': durations})
month_sum = df.groupby([df["date"].dt.year, df["date"].dt.month]).sum() / 60.0
month_sum = month_sum.rename(columns={'duration': 'value'})
month_sum['date'] = month_sum.index
month_sum = month_sum.reset_index(drop=True)
month_sum = month_sum.to_dict(orient='records')
data['month_sum'] = json.dumps(month_sum, indent=2)
day_sum = df.groupby([df["date"].dt.year, df["date"].dt.month, df['date'].dt.day]).sum() / 60.0
day_sum = day_sum.rename(columns={'duration': 'value'})
day_sum['date'] = day_sum.index
day_sum = day_sum.reset_index(drop=True)
day_sum = day_sum.to_dict(orient='records')
data['day_sum'] = json.dumps(day_sum, indent=2)
data['durations'] = json.dumps(durations, indent=2)
return data
def process_watch_history(data):
print('starting watch history job')
data = gzip.decompress(data).decode()
try:
data = json.loads(data)
except json.decoder.JSONDecodeError as e:
return "PARSER"
if type(data) != list:
print('json type ', type(data))
return "PARSER"
parser = YouTubeHistoryParser()
parser.feed(data)
print('{0} video ids and {1} datetimes'.format(len(parser.video_ids), len(parser.datetimes)))
if len(parser.video_ids) == 0 or len(parser.video_ids) != len(parser.datetimes):
return "PARSER"
queries = set(parser.video_ids)
result = query_api(queries)
if type(result) == str: #an error occured
print('an error occured, type:', result)
return result
total_seconds = 0
durations, datetimes = [], []
for i in range(len(parser.video_ids)):
vid = parser.video_ids[i]
if vid in result:
duration = result[vid]
h, m, s = utils.parse_duration(duration)
seconds = 3600 * h + 60 * m + s
durations.append(seconds / 60.0)
datetimes.append(parser.datetimes[i])
total_seconds += seconds
number_of_videos = len(durations)
total_days = total_seconds / 3600.0 / 24.0
data = {'number_of_videos': number_of_videos, 'total_days': total_days}
data = reformat_data(datetimes, durations)
data['number_of_videos'] = number_of_videos
data['total_days'] = total_days
data = json.dumps(data).encode()
file_size = sys.getsizeof(data) * 1E-6
data = gzip.compress(data)
compressed_file_size = sys.getsizeof(data) * 1E-6
savings = file_size - compressed_file_size
print('original result {0:.1f} MB, compressed {1:.1f} MB, savings {2:.1f} MB ({3:.1f} %)'.format(file_size, compressed_file_size, savings, savings / file_size * 100))
print('job complete')
return data