merlin-quix

# Set a basic message counter and define the file path
counter = 0
file = "online_retail_II.csv"

for chunk in pd.read_csv(file,encoding='unicode_escape',chunksize=10):

  # For each chunk, convert the invoice date into the correct time format
  chunk["InvoiceDate"] = pd.to_datetime(chunk["InvoiceDate"])

  # Set the counter as the message key

merlin-quix

# Import packages
import pandas as pd
import json
import datetime as dt
from time import sleep
from kafka import KafkaProducer

# Initialize Kafka Producer Client
producer = KafkaProducer(bootstrap_servers=['localhost:9092'])
print(f'Initialized Kafka producer at {dt.datetime.utcnow()}')

merlin-quix

from kafka import KafkaConsumer
import json
import pandas as pd

# Consume all the messages from the topic but do not mark them as 'read' (enable_auto_commit=False)
# so that we can re-read them as often as we like.
consumer = KafkaConsumer('transactions',
                         group_id='test-consumer-group',
                         bootstrap_servers=['localhost:9092'],
                         value_deserializer=lambda m: json.loads(m.decode('utf-8')),

merlin-quix

for message in consumer:
    mframe = pd.DataFrame(message.value)

    # Multiply the quantity by the price and store in a new "revenue" column
    mframe['revenue'] = mframe['Quantity'] * mframe['Price']
    
    # Aggregate the StockCodes in the individual batch by revenue
    summary = mframe.groupby('StockCode')['revenue'].sum()

    print(summary)

merlin-quix

# importing packages
import pandas as pd
import time, json
import datetime as dt
import requests
from kafka import KafkaProducer

df = pd.read_csv("spam.csv")

# initializing Kafka Producer Client

merlin-quix

for i in df.index:

    # Create a message that includes a message ID and email text
    data = {'MessageID': str(i),
            'MessageBody': df['Message'][i]}
            
    # Print the message so you can see what is being sent
    print(f'Sending message: {data}')
    
    # Send the data with the KafkaProducer client

merlin-quix

from pandas import DataFrame
import time, json
import datetime as dt
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import numpy as np
from kafka import KafkaProducer
from kafka import KafkaConsumer
import tensorflow_text as text

merlin-quix

#1 — Initialize a Consumer for reading the email messages from the emails topic
consumer = KafkaConsumer(group_id="python-consumer",
                         bootstrap_servers=['localhost:9092'],
                         auto_offset_reset='earliest',# Always read from beginning
                         enable_auto_commit=False, # Keep Kafka messages as ‘unread’
                         value_deserializer=lambda x:json.loads(x))

consumer.subscribe("emails")

print(f'Initialized Kafka consumer at {dt.datetime.utcnow()})

merlin-quix

for message in consumer:

    # Get the message body
    m=message.value

    # Turn the message back into a DataFrame
    df=DataFrame(m,index=[0])

    # Extract the Message from the DataFrame
    dff = pd.Series(df['MessageBody'])

merlin-quix

import json
from kafka import KafkaProducer
file = 'access.log'

producer_compression = KafkaProducer(bootstrap_servers=['localhost:9092'],
                         value_serializer=lambda x:json.dumps(x, default=str).encode('utf-8'),
                         key_serializer=lambda y:str(y).encode("utf-8"),
                         compression_type="gzip")