Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 7
Analytics at Scale
high = res['high']
low = res['low']
if self.big:
key = self.client.key('multiplier_
all_num', self.domain + "_big")
res = self.client.get(key)
high_big = res['high']
low_big = res['low']
return high, low, high_big, low_big
return high, low
def on_post(self, req, resp):
if True:
input_json = json.loads(req.stream.
read(),encoding='utf-8')
input_json['visible'] = input_json['client_
size'] + "_" + input_json['ad_position']
del input_json['client_size']
del input_json['ad_position']
howbig = 0
pred = 0
prob = 0
pred_big = 0
prob_big = 0
worker = ThreadPoolExecutor(max_workers=1)
thread = worker.submit(self.get_multiplier)
with ThreadPoolExecutor(max_workers=8) as
pool:
future_array = { pool.submit(self.
get_value,f,input_json[f]) : f for f
in input_json}
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