mmafrar

from pyspark.sql import SparkSession

# Initialize a Spark session
spark = SparkSession.builder.appName("sql_dataframe").getOrCreate()

# Read the CSV file into a DataFrame, with header and schema inference options enabled
productSales = spark.read.option("header", "true")\
    .option("inferSchema", "true").csv("file:///Users/mmafrar/DataRetrieval/Salesstore.csv")

# Print the inferred schema of the DataFrame

mmafrar

import collections
from pyspark import SparkConf, SparkContext

# Set up the Spark configuration and context
cfg = SparkConf().setMaster("local").setAppName("ratings_histogram")
ctx = SparkContext(conf=cfg)

# Load the data from the specified file
lines = ctx.textFile("file:///Users/mmafrar/Partition/ml-100k/u.data")
print(type(lines))  # Print the type of 'lines' to verify it's an RDD

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val input_file = sc. textFile("/Users/mmafrar/WordCount/input_file.txt")

input_file.collect

val word = input_file.flatMap(line => line.split(" "))

word.collect

val mapdata = word. map (word => (word, 1))

mmafrar

package com.example.kafka;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.kafka.support.KafkaHeaders;
import org.springframework.messaging.handler.annotation.Header;
import org.springframework.stereotype.Service;

@Service

mmafrar

package com.example.kafka;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.kafka.support.SendResult;
import org.springframework.stereotype.Service;
import org.springframework.util.concurrent.ListenableFuture;
import org.springframework.util.concurrent.ListenableFutureCallback;

mmafrar

package com.example.kafka;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.CommandLineRunner;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.WebApplicationType;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import org.springframework.kafka.config.KafkaListenerEndpointRegistry;
import org.springframework.kafka.listener.MessageListenerContainer;

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spring:
  kafka:
    bootstrap-servers: <BOOTSTRAP_SERVER_URL>
    properties:
      security:
        protocol: SASL_SSL
      sasl:
        jaas:
          config: org.apache.kafka.common.security.plain.PlainLoginModule required username='<CLUSTER_API_KEY>' password='<CLUSTER_API_SECRET>';
        mechanism: PLAIN

mmafrar

package com.example.demo.service;

import com.example.demo.model.Contact;
import org.junit.jupiter.api.AfterAll;
import org.junit.jupiter.api.Assertions;
import org.junit.jupiter.api.BeforeAll;
import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.TestInstance;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;

mmafrar

package com.example.demo;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.boot.builder.SpringApplicationBuilder;
import org.springframework.boot.web.servlet.support.SpringBootServletInitializer;

@SpringBootApplication
public class DemoApplication extends SpringBootServletInitializer {

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# Java Gradle CircleCI 2.0 configuration file
#
# Check https://circleci.com/docs/2.0/language-java/ for more details
#
version: 2
jobs:
  build:
    docker:
      # specify the version you desire here
      - image: circleci/openjdk:8-jdk