Pyspark week-9

Few Python Fundamentals

========================

#include

import sys

These modules consists of functions, classes, methods and variables.

Functions are not bound to any classname or object.

methods are bound to classes or its objects..

A module is a python file which holds functions, classes, methods and variables.

import time

print(time.time())

import time as t

print(t.time())

#below will import all the things

import datetime

print(datetime.date.today())

#below is to import specific things and not all

from datetime import date

print(date.today())

so when we are importing specific functions then we can call the function directly.

Module

How to import it
How to call the functions within that module

How to import full module, Partial module

__name__ Global Variable

__main__

if we run the python file directly then the global variable __name__ is set to __main__

but if we have it indirectly then the value of __name__ is set to the name of the file

file1
======
print("name of this module is ", __name__)

file2
=======
import module1

print("in the second file the module name is ",__name__)

if __name__ == "__main__":
print("Executed when invoked directly")
else:
print("executed when imported")

# python comment

// scala comment

a=5

In python, unlike statically typed languages like c or java, there is no need to specifically declare
the data type of the variable. In dynamically typed languages like python, the interpreter itself
predicts the datatype.

Named Function
================
def sum(a,b):
return a+b

total = sum(3,4)

print(total)

Anonymous functions are referred to as lambda functions in python.

Few more differences between scala & python

=============================================

1. Case

var totalCount = 10

//camel case in scala

total_count

//snake case

2. in scala single line comment is done using //

and for multiline we use

/*

*/

but in python we use # to comment

# this is a comment

3. in scala we were using foreach

but in python foreach is not allowed.

in scala

import org.apache.spark.SparkContext
object First extends App {

val arr = Array(1,2,3,4,5)

arr.foreach(println)

in python

arr = [1,2,3,4,5]

for a in arr:
print(a)

4. scala

(x => x.split(" ")) //anonymous function

python

(lambda x : x.split(" ")) //lambda function

word count problem that we wrote in scala

==========================================

import org.apache.spark.SparkContext

object First extends App {

//Common lines
val sc = new SparkContext("local[*]","wordcount")
val input = sc.textFile("/Users/trendytech/Desktop/data/input/file.txt")

//one input row will give multiple output rows

val words = input.flatMap(x => x.split(" "))

//one input row will give one output row only

val wordCounts = words.map(x=> (x, 1))

//take two rows , and does aggregation and returns one row
val finalCount = wordCounts.reduceByKey((x,y) => x+y)

//action
finalCount.collect.foreach(println)

Pyspark code
==============

from pyspark import SparkContext

# common lines
sc = SparkContext("local[*]", "wordcount")
input = sc.textFile("/Users/trendytech/Desktop/data/input/file.txt")

# one input row will give multiple output rows

words = input.flatMap(lambda x: x.split(" "))

# one input row will be giving one output row

word_counts= words.map(lambda x: (x, 1))

final_count = word_counts.reduceByKey(lambda x, y: x + y)

result = final_count.collect()

for a in result:
print(a)

=================

1. Change the logging level

sc.setLogLevel("ERROR")
2. __name__

__main__

3. Holding the program

from sys import stdin

stdin.readline()

4. DAG

localhost:4040

pyspark uses api library

scala was connnecting to spark core directly.

hence scala dag matches to our code but pyspark dag does not.

from pyspark import SparkContext

from sys import stdin

if __name__ == "__main__":
# common lines
sc = SparkContext("local[*]", "wordcount")
# sc.setLogLevel("ERROR")
input = sc.textFile("/Users/trendytech/Desktop/data/search_data.txt")

# one input row will give multiple output rows

words = input.flatMap(lambda x: x.split(" "))

# one input row will be giving one output row

word_counts= words.map(lambda x: (x, 1))

final_count = word_counts.reduceByKey(lambda x, y: x + y)

result = final_count.collect()
for a in result:
print(a)

else:
print("Not executed directly")

stdin.readline()

========

1. Lowercase

word_counts= words.map(lambda x: (x.lower(), 1))

2. countByValue

from pyspark import SparkContext

from sys import stdin

if __name__ == "__main__":
# common lines
sc = SparkContext("local[*]", "wordcount")
# sc.setLogLevel("ERROR")
input = sc.textFile("/Users/trendytech/Desktop/data/search_data.txt")

# one input row will give multiple output rows

words = input.flatMap(lambda x: x.split(" "))

# one input row will be giving one output row

word_counts= words.map(lambda x: (x.lower()))

final_count = word_counts.countByValue()

print(final_count)

else:
print("Not executed directly")

3. sortByKey

from pyspark import SparkContext

from sys import stdin

if __name__ == "__main__":
# common lines
sc = SparkContext("local[*]", "wordcount")
# sc.setLogLevel("ERROR")
input = sc.textFile("/Users/trendytech/Desktop/data/search_data.txt")

# one input row will give multiple output rows

words = input.flatMap(lambda x: x.split(" "))

# one input row will be giving one output row

word_counts= words.map(lambda x: (x.lower(), 1))

final_count = word_counts.reduceByKey(lambda x, y: x + y).map(lambda x: (x[1],x[0]))

result = final_count.sortByKey(False).map(lambda x: (x[1],x[0])).collect()

for a in result:
print(a)

else:
print("Not executed directly")

4. sortBy

from pyspark import SparkContext

from sys import stdin

if __name__ == "__main__":
# common lines
sc = SparkContext("local[*]", "wordcount")
# sc.setLogLevel("ERROR")
input = sc.textFile("/Users/trendytech/Desktop/data/search_data.txt")

# one input row will give multiple output rows

words = input.flatMap(lambda x: x.split(" "))

# one input row will be giving one output row

word_counts= words.map(lambda x: (x.lower(), 1))
final_count = word_counts.reduceByKey(lambda x, y: x + y)

result = final_count.sortBy(lambda x: x[1], False).collect()

for a in result:
print(a)

else:
print("Not executed directly")

stdin.readline()

1. Customers who have spent the most

=====================================

from pyspark import SparkContext

sc = SparkContext("local[*]","customer-orders")

rdd1 = sc.textFile("/Users/trendytech/Desktop/data/customer-orders.csv")

rdd2 = rdd1.map(lambda x: (x.split(",")[0],float(x.split(",")[2])))

rdd3 = rdd2.reduceByKey(lambda x,y: x+y)

rdd4 = rdd3.sortBy(lambda x: x[1],False)

result = rdd4.collect()

for a in result:
print(a)

2. Movie Rating
================

from pyspark import SparkContext

sc = SparkContext("local[*]","movie-data")

lines = sc.textFile("/Users/trendytech/Desktop/data/movie-data.data")

ratings = lines.map(lambda x: (x.split("\t")[2],1))

result = ratings.reduceByKey(lambda x,y: x+y).collect()

for a in result:
print(a)

3. Average number of Friends

=============================

def parseLine(line):
fields = line.split(",")
age = int(fields[2])
numFriends = int(fields[3])
return (age,numFriends)

from pyspark import SparkContext

sc = SparkContext("local[*]","FriendsByAge")

lines = sc.textFile("/Users/trendytech/Desktop/data/friends-data.csv")

rdd = lines.map(parseLine)
# (33,385) input

#(33,(385,1)) output

#(33,(3000,5))

#in scala we used to access the elements of tuple using x._1 , x._2

#in python we access the elements of tuple using x[0],x[2]

totalsByAge = rdd.mapValues(lambda x: (x,1)).reduceByKey(lambda x,y: (x[0]+y[0], x[1]+y[1]))

averagesByAge = totalsByAge.mapValues(lambda x:x[0]/x[1])

result = averagesByAge.collect()
Solution 1 :

val rdd1 = spark.sparkContext.textFile(“/Users/trendytech/Desktop/dataset1”)

rdd1.collect().foreach(println)

val rdd2 = rdd1.map(line => {

val fields = line.split(“,”)
if (fields(1).toInt > 18)
(fields(0),fields(1),fields(2),”Y”)
else
(fields(0),fields(1),fields(2),”N”)
})

rdd2.collect().foreach(println)

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Solution 2 :
import org.apache.spark._
import org.apache.spark.SparkContext._
import org.apache.log4j._
import scala.math.min

/** Find the minimum temperature by weather station */

object MinTemperatures {

def parseLine(line:String)= {
val fields = line.split(“,”)
val stationID = fields(0)
val entryType = fields(2)
val temperature = fields(3)
(stationID, entryType, temperature)
}
Cont...

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/** Our main function where the action happens */
def main(args: Array[String]) {

// Set the log level to only print errors

Logger.getLogger(“org”).setLevel(Level.ERROR)

// Create a SparkContext using every core of the local machine

val sc = new SparkContext(“local[*]”, “MinTemperatures”)

// Read each line of input data

val lines = sc.textFile(“/Users/sumitm/Desktop/spark-data/temp-data.csv”)

// Convert to (stationID, entryType, temperature) tuples

val parsedLines = lines.map(parseLine)

// Filter out all but TMIN entries

val minTemps = parsedLines.filter(x => x._2 == “TMIN”) Cont...

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// Convert to (stationID, temperature)
val stationTemps = minTemps.map(x => (x._1, x._3.toFloat))

// Reduce by stationID retaining the minimum temperature found

val minTempsByStation = stationTemps.reduceByKey( (x,y) => min(x,y))

// Collect, format, and print the results

val results = minTempsByStation.collect()

for (result <- results.sorted) {

val station = result._1
val temp = result._2
val formattedTemp = f”$temp%.2f F”
println(s”$station minimum temperature: $formattedTemp”)
}
}
}

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1

Apache Spark Assignment

Finding Out Popular Courses for a Online Training
Company

Problem Statement:

We have been approached by an Online Training Company which

provides training videos.The company provides different online
courses to the users and each course comprises of multiple
chapters.Courses have been assigned courseIds and Chapters in a
course have chapterIDs.Note that chapterIds are unique.No two
courses can have same chapterId.Each Course has a title also.
Also, Users viewing the courses are given UserIds.

The company wants us to prepare an Apache Spark based report

which will enable them to figure out which are the most popular
courses on their training portal.

The popularity of a course will depend on the total score it

receives.The score which a course will get will be based on few
business rules which are explained later in the document in
detail.Please Refer the Business Rules** Section.To give a brief
overview, consider if a course has 20 chapters and a user views 18
chapters out of those, then the user has watched 90% of the course
and the course will get a good score of 10 say.Like that each
course can be watched by multiple users and scores will
accumulate for that course.
Final Ranking Chart Report will look like below, Starting with
most popular course on the top.

Score Title
X1 Y1
X2 Y2
.… .…
.… .…
2

Dataset Details:

Dataset1:

We have three csv files views-1.csv, views-2.csv and views-3.csv

Also we have a viewsHeader.csv file which has the header information:

userId, chapterId, dateAndTime

This will be a log of viewings, showing which user has viewed which
chapter. These chapterIds belong to courses. Also chapterIds are
unique.

Here is the sample raw data:

But as we can see from this raw data ,there is no reference to courses in
this raw data.

Dataset 2: So we have a much smaller dataset, chapters.csv which is a

mapping of chapterIds to courseId.

Sample Raw Data:

3

Dataset 3:

There is another dump file (titles.csv) containing courseIds against their

titles - we will use this at the end to print the results,so that we also get
the title of each course when we get the popular courses result in the
end.

Note: For every exercise ,the expected output of the sample raw data is
provided .But this is not the actual output. This has been provided for
understanding purpose only.
You will work on actual data and output will be based on the csv files
provided to you.

Solve the following Exercises:

Exercise 1:

Find Out how many Chapters are there per course.

Expected Output of Sample Raw Data:

4

Exercise 2: Your job is to produce a ranking chart detailing based on

which are the most popular courses by score.

**Business Rules:

Scores which each courseId gets, will be based on few rules:

Like ,If a user sticks through most of the course, that's more
deserving of "points" than if someone bails out just a quarter
way through the course. Following is the scoring system
algorithm followed:

 If a user watches more than 90% of the course, the course

gets 10 points
 If a user watches > 50% but <90% , it scores 4
 If a user watches > 25% but < 50% it scores 2
 Less than 25% is no score

Expected Output of Sample Raw Data:

Exercise 3: As a final touch, get the titles using a final (small

data) join. You can then sort by total score (descending) to get
the results in a pleasant format with most popular score on top.
5

**Hints

Exercise 1:

Build an RDD containing a key of courseId together with the

number of chapters on that course. You can use a map
transformation here.

courseId count
1 1
1 1
2 1
3 1
3 1

Exercise 2:

Step 1: Removing Duplicate Views .

the same user watching the same chapter doesn't count, so we

can call distinct to remove duplications.

Step 2: Joining to get Course Id in the RDD

6

This isn't very meaningful as it is - we know for example that

user 14 has watched 96 and 97, but are they from the same
course, or are they different courses???? We need to join the
data together. As the common column in both RDDs is the
chapterId, we need both Rdds to be keyed on that.

You need to do a map to switch the key and the value. Now
that you have two RDDs, each keyed on chapterId, you can join
them together.

And now after joining you will have:

7

Step 3: Drop the Chapter Id

As each "row" in the RDD now represents "a chapter from this
course has been viewed", the chapterIds are no longer relevant.
You can get rid of them at this point - this will avoid dealing
with tricky nested tuples later on. At the same time, given
we're counting how many chapters of a course each user
watched, we will be counting shortly, so we can drop a "1" in
for each user/course combination.

Expected RDD after this step:

Step 4 - Count Views for User/Course

We can now run a reduce as usual..

8

We can read these results as "user 14 watched 2 chapters of 1;

user 13 watched 1 chapter of course 1; user 14 watched 2
chapters of course 2. etc".

Step 5 - Drop the userId

The user id is no longer relevant. We've needed it up until now

to ensure that for example, 10 users watching 1 chapter
doesn't count the same as 1 user watching 10 chapters!
Another map transformation should get you...

To rephrase the previous step, we now read this as "someone

watched 2 chapters of course 1. Somebody different watched 1
chapter of course 1; someone watched 1 chapter of course 2,
etc".

Step 6: of how many chapters?

9

The scoring is based on what percentage of the total course

they watched. So we will need to get the total number of
chapters for each course into our RDD:

"Someone watched 2 chapters of 3. Somebody different

watched 1 chapter of 3".

Step 7: Convert to percentages

This should be an easy mapping.

Step 8: Convert to scores

As described earlier, percentages are converted to scores:

10

Step 9: Add up the total scores

Exercise 3:

You have to associate titles with the course.Use Inner Join.Then

later as a finishing job you will not require courseIds any more
as the titles are available.You can sort the data by most popular
course to display data in format - Score, Title .

***********************************************************************************

.
Pyspark week-10
================

BigData campaign data in the spark with scala

from pyspark import SparkContext

sc = SparkContext("local[*]","KeywordAmount")

initial_rdd = sc.textFile("/Users/trendytech/Desktop/data/bigdata-campaign-data.csv")

mapped_input = initial_rdd.map(lambda x: (float(x.split(",")[10]),x.split(",")[0]))

words = mapped_input.flatMapValues(lambda x: x.split(" "))

final_mapped = words.map(lambda x: (x[1].lower(),x[0]))

total = final_mapped.reduceByKey(lambda x,y: x+y)

sorted = total.sortBy(lambda x: x[1],False)

result = sorted.take(20)

for x in result:
print(x)

=========

from pyspark import SparkContext

def loadBoringWords():
boring_words = set(line.strip() for line in
open("/Users/trendytech/Desktop/data/boringwords.txt"))
return boring_words

sc = SparkContext("local[*]","KeywordAmount")

name_set = sc.broadcast(loadBoringWords())

initial_rdd = sc.textFile("/Users/trendytech/Desktop/data/bigdata-campaign-data.csv")

mapped_input = initial_rdd.map(lambda x: (float(x.split(",")[10]),x.split(",")[0]))

words = mapped_input.flatMapValues(lambda x: x.split(" "))

final_mapped = words.map(lambda x: (x[1].lower(),x[0]))

filtered_rdd = final_mapped.filter(lambda x: x[0] not in name_set.value)

total = filtered_rdd.reduceByKey(lambda x,y: x+y)

sorted = total.sortBy(lambda x: x[1],False)

result = sorted.take(20)

for x in result:
print(x)

Accumulator example
=====================

from pyspark import SparkContext

def blankLineChecker(line):
if(len(line) == 0):
myaccum.add(1)

sc = SparkContext("local[*]","AccumulatorExample")

myrdd = sc.textFile("/Users/trendytech/Desktop/data/samplefile.txt")

myaccum = sc.accumulator(0.0)

myrdd.foreach(blankLineChecker)

print(myaccum.value)

=======

you can use foreach on a rdd but not on a local variable example list

a = rdd.collect

======
from pyspark import SparkContext

sc = SparkContext("local[*]", "logLevelCount")

sc.setLogLevel("INFO")

if __name__ == "__main__":
my_list = ["WARN: Tuesday 4 September 0405",
"ERROR: Tuesday 4 September 0408",
"ERROR: Tuesday 4 September 0408",
"ERROR: Tuesday 4 September 0408",
"ERROR: Tuesday 4 September 0408",
"ERROR: Tuesday 4 September 0408"]

original_logs_rdd = sc.parallelize(my_list)

else:
original_logs_rdd = sc.textFile("/Users/trendytech/Desktop/data/logsample.txt")
print("inside the else part")

new_pair_rdd = original_logs_rdd.map(lambda x:(x.split(":")[0],1))

resultant_rdd = new_pair_rdd.reduceByKey(lambda x,y: x+y)

result = resultant_rdd.collect()

for x in result:
print(x)

=========

bigLog.txt 10 million log level entries

groupByKey

reduceByKey

from pyspark import SparkContext

# Set the log level to only print errors

sc = SparkContext("local[*]", "LogLevelCount")

sc.setLogLevel("INFO")

# Create a SparkContext using every core of the local machine

base_rdd = sc.textFile("/Users/trendytech/Desktop/data/bigLog.txt")

mapped_rdd = base_rdd.map(lambda x: (x.split(":")[0], x.split(":")[1]))

grouped_rdd = mapped_rdd.groupByKey()

final_rdd = grouped_rdd.map(lambda x: (x[0], len(x[1])))

result = final_rdd.collect()

for x in result:
print(x)

============

from pyspark import SparkContext

sc = SparkContext("local[*]", "LogLevelCount")

sc.setLogLevel("INFO")

base_rdd = sc.textFile("/Users/trendytech/Desktop/data/bigLog.txt")

mapped_rdd = base_rdd.map(lambda x: (x.split(":")[0], 1))

reduced_rdd = mapped_rdd.reduceByKey(lambda x,y: x+y)

result = reduced_rdd.collect()

for x in result:
print(x)

==========
Miscellaneous things
=====================

1)

scala
=======
val a = 1 to 100
val base = sc.parallelize(a)
base.reduce((x,y) => x+y)

pyspark
=========
a = range(1,101)
base = sc. parallelize(a)
base.reduce(lambda x,y: x+y)

2)

input = sc.textFile("/Users/trendytech/Desktop/data/customer-orders.csv")

input.saveAsTextFile("/Users/trendytech/Desktop/data/output10")

3. Count - this is an action and works the same way as we saw in scala codes.

4. sc.defaultParallelism

5. get the num of partitions in an rdd

rdd.getNumPartitions()

6.my_list = ("WARN: Tuesday 4 September 0405",

"ERROR: Tuesday 4 September 0408",
"ERROR: Tuesday 4 September 0408",
"ERROR: Tuesday 4 September 0408",
"ERROR: Tuesday 4 September 0408",
"ERROR: Tuesday 4 September 0408")

original_logs_rdd = sc.parallelize(my_list)
original_logs_rdd.getNumPartitions()
7) sc.defaultMinPartitions - 2

8) repartition

9) coalesce
Solution 1:
//Assignemnet-Problem 1
import org.apache.log4j.Level
import org.apache.log4j.Logger
import org.apache.spark.SparkConf
import org.apache.spark.sql.SaveMode
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.DoubleType
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.types.StructType

object Spark_Assignment_windowdata extends App

{ //creating sparkConf object

val sparkConf = new SparkConf()

sparkConf.set(“spark.app.name”, “Assignment windowdata”)

sparkConf.set(“spark.master”,”local[2]”) 9108179578
//Step1 -creating a spark session
val spark = SparkSession.builder()
.config(sparkConf)
.getOrCreate()
//Step 2 - Setting the logging level to Error
Logger.getLogger(“org”).setLevel(Level.ERROR)

// Step 3 Explicit schema definition programmatically using

StructType val windowdataSchema = StructType(List(
StructField(“Country”,StringType),
StructField(“weeknum”,IntegerType),
StructField(“numinvoices”,IntegerType),
StructField(“totalquantity”,IntegerType),
StructField(“invoicevalue”,DoubleType)

)) 9108179578
// Step 3 contd.. Loading the file and creation of dataframe using dataframe reader API, using
explicitly specified schema
val windowdataDF = spark.read
.format(“csv”)
.schema(windowdataSchema)
.option(“path”, “C:/xyz/TrendyTech/Spark_data/structuredAPI/windowdata.csv”)
.load()

//print first 20 records of the dataframe

windowdataDF.show()

//Step 4: Saving the data in Parquet format using Dataframe Writer API
//Data is two-level partitioned on Country and weeknum column , these columns have low
cardinality //Default output format is parquet
/* windowdataDF.write
.partitionBy(“Country”, “weeknum”)
.mode(SaveMode.Overwrite)
.option(“path”,”C:/xyz/TrendyTech/Spark_data/structuredAPI/Output/windowdata_output”)
.save()

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//Step 5: Save the Dataframe to Avro Format and also partitioning data by Country column
windowdataDF.write
.format(“avro”)
.partitionBy(“Country”)
.mode(SaveMode.Overwrite)
.option(“path”,”C:/xyz/TrendyTech/Spark_data/structuredAPI/Output/windowdata_avrooutput”)
.save()

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Solution 2:
import org.apache.log4j.Level
import org.apache.log4j.Logger
import org.apache.spark.SparkConf
import org.apache.spark.sql.SaveMode
import org.apache.spark.sql.SparkSession
object WEEK11_SOLUTION_2_WINDOWDATA extends App {
// Setting the Logging~Level To ERROR
Logger.getLogger("org").setLevel(Level.ERROR)

// define a schema for employee record data using a case class

case class windowData(Country: String, Weeknum: Int, NumInvoices: Int, TotalQuantity: Int,
InvoiceValue: String)
//Create Spark Config Object
val sparkConf = new SparkConf()
sparkConf.set("spark.app.name", "WEEK11_SOLUTION_2_WINDOWDATA")
sparkConf.set("spark.master", "local[2]")
//Create Spark Session
val spark = SparkSession.builder()
.config(sparkConf)
.getOrCreate()
import spark.implicits._

val windowDataDF = spark.sparkContext.textFile("G:/TRENDY~TECH/WEEK-

11/Assignment_Dataset/windowdata-201021-002706.csv") //.toDF
.map(_.split(","))
.map(e => windowData(e(0), e(1).trim.toInt, e(2).trim.toInt, e(3).trim.toInt, e(4)))
.toDF()
.repartition(8)

windowDataDF.write
.format("json")
.mode(SaveMode.Overwrite)
.option("path", "G:/TRENDY~TECH/WEEK-
11/Assignment_Dataset/OutPut_Prb2/windowData_jsonoutput")
.save()
//windowDataDF.show()
spark.stop()
scala.io.StdIn.readLine()
}

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Pyspark week 11
================

1. cache & persist

2. spark-submit

from sys import stdin

sc = SparkContext("local[*]","PremiumCustomers")

base_rdd = sc.textFile("/Users/trendytech/Desktop/data/customer-orders.csv")

mapped_input = base_rdd.map(lambda x: (x.split(",")[0],float(x.split(",")[2])))

total_by_customer = mapped_input.reduceByKey(lambda x,y: x+y)

premium_customers = total_by_customer.filter(lambda x: x[1] > 5000)

doubled_amount = premium_customers.map(lambda
x:(x[0],x[1]*2)).persist(StorageLevel.MEMORY_ONLY)

result = doubled_amount.collect()

for x in result:
print(x)

print(doubled_amount.count())

stdin.readline()

spark-submit /Users/trendytech/PycharmProjects/pysparklearning/module1.py

Ratings.dat
Movies.dat

find the top rated movies

1. atleast 1000 people have rated

2. rating should be > 4.5
finding top movies (scala code)
==================================

import org.apache.log4j.Logger
import org.apache.log4j.Level
import org.apache.spark.SparkContext

object JoinDemo extends App {

Logger.getLogger("org").setLevel(Level.ERROR)
val sc = new SparkContext("local[*]","joindemo")

val ratingsRdd= sc.textFile("/Users/trendytech/Desktop/data/ratings.dat")

val mappedRdd = ratingsRdd.map(x => {

val fields = x.split("::")
(fields(1),fields(2))
})

val newmappedRdd= mappedRdd.mapValues(x=>(x.toFloat,1.0))

val reduceRdd = newmappedRdd.reduceByKey((x,y) => (x._1+y._1, x._2+y._2))

val filteredRdd = reduceRdd.filter(x=>x._2._1 > 1000)

val finalRdd = filteredRdd.mapValues(x => x._1/x._2).filter(x => x._2 > 4.5)

val moviesRdd= sc.textFile("/Users/trendytech/Desktop/data/movies.dat")

val moviesmappedRdd = moviesRdd.map(x => {

val fields = x.split("::")
(fields(0),(fields(1),fields(2)))
})

val joinedRdd = moviesmappedRdd.join(finalRdd)

val topMoviesRdd = joinedRdd.map(x=>x._2._1)

topMoviesRdd.collect.foreach(println)
}

=========================
equivalent pyspark code
=========================

from pyspark import SparkContext

sc = SparkContext("local[*]","joindemo")

ratings_rdd = sc.textFile("/Users/trendytech/Desktop/data/ratings.dat")

mapped_rdd = ratings_rdd.map(lambda x: (x.split("::")[1], x.split("::")[2]))

new_mapped_rdd = mapped_rdd.mapValues(lambda x: (float(x),1.0))

reduce_rdd = new_mapped_rdd.reduceByKey(lambda x,y: (x[0]+y[0], x[1]+y[1]))

filtered_rdd = reduce_rdd.filter(lambda x: x[1][0] > 1000)

final_rdd = filtered_rdd.mapValues(lambda x: x[0]/x[1]).filter(lambda x: x[1] > 4.5)

movies_rdd= sc.textFile("/Users/trendytech/Desktop/data/movies.dat")

movies_mapped_rdd = movies_rdd.map(lambda x: (x.split("::")[0],(x.split("::")[1],x.split("::")[2])))

joined_rdd = movies_mapped_rdd.join(final_rdd)

top_movies_rdd = joined_rdd.map(lambda x: x[1][0])

result = top_movies_rdd.collect()

for x in result:
print(x)

Structured API's
==================

DataFrame, DataSets, Spark SQL

DataSets are not supported.

SparkSession

from pyspark import SparkConf

from pyspark.sql import SparkSession

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

orderDf = spark.read.csv("/Users/trendytech/Desktop/data/orders.csv")

orderDf.show()

spark.stop()

=======

find the total orders placed by each customer where customer id > 10000

from pyspark import SparkConf

from pyspark.sql import SparkSession

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

orderDf =
spark.read.option("header",True).option("inferSchema",True).csv("/Users/trendytech/Desktop/da
ta/orders.csv")

groupedDf = orderDf.repartition(4) \
.where("order_customer_id > 10000") \
.select("order_id","order_customer_id") \
.groupBy("order_customer_id") \
.count()

groupedDf.show()
===============

1. wrong column name

so when we give a column name which does not exist then the error is shown at runtime and
not at compile time.

2. standard way

orderDf = spark.read.format("csv")\
.option("header",True)\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/orders.csv")\
.load()

orderDf = spark.read.format("json")\
.option("path","/Users/trendytech/Desktop/data/orders.json")\
.load()
1
Spark StructuredAPIs -Assignment Solutions

Assignment 1 :

Given 2 Datasets employee.json and dept.json

We need to calculate the count of employees against each department. Use Structured
APIs.

Code:

//Find the count of employees against each department

import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.log4j.Level
import org.apache.log4j.Logger
import org.apache.spark.sql.functions._

object Assignment1_Week12 extends App{

//Setting the Log Level

Logger.getLogger("org").setLevel(Level.ERROR)

//Setting the spark conf

val sparkConf = new SparkConf()
sparkConf.set("spark.app.name","Assignment1_Week12")
sparkConf.set("spark.master","local[2]")

//Creating Spark Session

val spark = SparkSession.builder()
.config(sparkConf)
.getOrCreate()

//Load the department data into a Dataframe using dataframe reader API

val deptDf = spark.read

.format("json")
.option("path","C:/TrendyTech/SparkExamples/dept.json")
.load()

// deptDf.show()
// deptDf.printSchema()
2
//Load the employee data into a Dataframe using dataframe reader API

val employeeDf = spark.read

.format("json")
.option("path","C:/TrendyTech/SparkExamples/employee.json")
.load()

// employeeDf.show()
// employeeDf.printSchema()

//Joining of two dataframes using left outer join, with department dataframe on left
side

val joinCondition = deptDf.col("deptid") === employeeDf.col("deptid")//join

condition

val joinType = "left" //joinType

val joinedDf = deptDf.join(employeeDf, joinCondition, joinType) //Joining of two

dataframes

//drop the ambiguous column deptid of employee dataframe,from the joined

Dataframe

val joinedDfNew = joinedDf.drop(employeeDf.col("deptid"))

//Use first function so as to get other columns also along with aggregated columns

joinedDfNew.groupBy("deptid").agg(count("empname").as("empcount"),first("deptNam
e").as ("deptName")).dropDuplicates("deptName").show()

spark.stop()
}

Output:

Assignment 2
3

Find the top movies as shown in spark practical 18 using broadcast join. Use
Dataframes or Datasets to solve it this time.

Code:

import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.log4j.Level
import org.apache.log4j.Logger
import org.apache.spark.sql.functions._

object Assignment2_Week12 extends App {

//Setting the Log Level

Logger.getLogger("org").setLevel(Level.ERROR)

//Setting the spark conf

val sparkConf = new SparkConf()
sparkConf.set("spark.app.name","Assignment2_Week12")
sparkConf.set("spark.master","local[2]")

//Creating Spark Session

val spark = SparkSession.builder()
.config(sparkConf)
.getOrCreate()

//Creation of a ratings dataframe using a case class approach

case class Ratings(userid:Int,movieid:Int,rating:Int,timestamp:String)//create a

case-class that represents the schema

//Creation of base RDD for ratings data

val ratingsRDD =
spark.sparkContext.textFile("C:/TrendyTech/SparkExamples/ratings.dat")//ratings data
does not have a schema, so first loading to an RDD

// map the RDD elements into instances of the case class

val caseClassSchemaRDD = ratingsRDD.map(x => x.split("::")).map(x =>

Ratings(x(0).toInt,x(1).toInt,x(2).toInt,x(3)) )
4

//Transform to a Dataframe:

import spark.implicits._

val ratingsDf = caseClassSchemaRDD.toDF()

// ratingsDf.show()
// ratingsDf.printSchema()

//Creation of base RDD for movies data

val moviesRDD =
spark.sparkContext.textFile("C:/TrendyTech/SparkExamples/movies.dat")

//defining the schema using case class

case class Movies(movieid:Int,moviename:String,genre:String)

val moviestransformedRDD = moviesRDD.map(line => line.split("::")).map(fields =>

Movies(fields(0).toInt,fields(1),fields(2)) )

val moviesNewDf =
moviestransformedRDD.toDF().select("movieid","moviename")

// moviesNewDf.show()
//moviesNewDf.printSchema()

val transformedmovieDf = ratingsDf.groupBy("movieid")

.agg(count("rating").as("movieViewCount"),avg("rating").as("avgMovieRating"))
.orderBy(desc("movieViewCount"))

//transformedmovieDf.show()

val popularMoviesDf = transformedmovieDf.filter("movieViewCount > 1000 AND

avgMovieRating > 4.5")

// popularMoviesDf.show()
5

//Now we want to associate the Movie names also, so we use a broadcast join

spark.sql("SET spark.sql.autoBroadcastJoinThreshold = -1")

val joinCondition = popularMoviesDf.col("movieid") ===

moviesNewDf.col("movieid") //join condition

val joinType = "inner" //type of

join

val finalPopularMoviesDf =
popularMoviesDf.join(broadcast(moviesNewDf),joinCondition,joinType).drop(popularM
oviesDf.col("movieid")).sort(desc("avgMovieRating")) //joining the 2 dataframes using
broadcast join where movies data is the smaller dataset

finalPopularMoviesDf.drop("movieViewCount","movieid","avgMovieRating").show(false
)

spark.stop()

Output:

Assignment 3

File A is a text file of size 1.2 GB in HDFS at location /loc/x. It contains match by match
statistics of runs scored by all the batsman in the history of cricket.
File B is a text file of size 1.2 MB present in local dir /loc/y. It contains list of batsman
playing in cricket world cup 2019.
6
File A:
1 Rohit_Sharma India 200 100.2
1 Virat_Kohli India 100 98.02
1 Steven_Smith Aus 77 79.23
35 Clive_Lloyd WI 29 37.00
243 Rohit_Sharma India 23 150.00
243 Faf_du_Plesis SA 17 35.06
File B:
Rohit_Sharma India
Steven_Smith Aus
Virat_Kohli India

Find the batsman participating in 2019 who has the best average of scoring runs in his
career. Solve this using Dataframes or Datasets.

** File is tab separated.Headers not part of file

Code:

import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.log4j.Level
import org.apache.log4j.Logger
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.Row

object Assignment3_Week12 extends App {

//Setting the Log Level

Logger.getLogger("org").setLevel(Level.ERROR)

//Setting the spark conf

val sparkConf = new SparkConf()
sparkConf.set("spark.app.name","Assignment3_Week12")
sparkConf.set("spark.master","local[2]")

//Creating Spark Session

val spark = SparkSession.builder()
.config(sparkConf)
.getOrCreate()
7
//Case class creation

case class BatsmenHistory(MatchNumber:Int,Batsman:String,Team:String,

RunsScored:Int,StrikeRate:Double)

//Creation of base RDD for historical data

val batsmenHistoryRDD =
spark.sparkContext.textFile("C:/TrendyTech/SparkExamples/FileA_BatsmenDetails_Histo
ry.txt")

val batsmenHistorySchemaRDD = batsmenHistoryRDD.map(line =>

line.split("\t")).map(fields =>
BatsmenHistory(fields(0).toInt,fields(1),fields(2),fields(3).toInt,fields(4).toDouble) )

// Dataframe creation

import spark.implicits._

val batsmenHistoryDf = batsmenHistorySchemaRDD.toDF()

//batsmenHistoryDf.show()

//batsmenHistoryDf.printSchema()

//Calculating Average runs scored by a batsman in history, with highest average at top

val batsmenBestRunsAvgHistoryDf =
batsmenHistoryDf.groupBy("Batsman").agg(avg("RunsScored").as("AverageRunsScored"))
.select("Batsman","AverageRunsScored")

//batsmenBestRunsAvgHistoryDf.sort(col("AverageRunsScored").desc).show()

//create a base RDD from input data of worldcup

val batsmenWorldCupRDD =
spark.sparkContext.textFile("C:/TrendyTech/SparkExamples/FileB_BatsemenDetails_Wor
ldcup2019.txt")

//Alternative Approach instead of using case class ,though case class can also be used
instead-
8
//Programmatically create an explicit schema of the worldcup 2019 file:

val batsmenworldcupSchema = StructType(List(

StructField("batsman",StringType,false),
StructField("team",StringType)
))

//Convert RDD[Array(String)] to RDD[Row].

val batsmenWorldCupRowRDD = batsmenWorldCupRDD.map(line =>

line.split("\t")).map( fields => Row(fields(0),fields(1)))

//Apply the explicitly defined Struct Type schema to the RDD[Row]

val batsmenWorldCupDf = spark.createDataFrame(batsmenWorldCupRowRDD,

batsmenworldcupSchema)

batsmenWorldCupDf.show()
batsmenWorldCupDf.printSchema()

//autoBroadcast Join is turned off

spark.sql("SET spark.sql.autoBroadcastJoinThreshold = -1")

val joinCondition = batsmenBestRunsAvgHistoryDf.col("Batsman") ===

batsmenWorldCupDf.col("batsman")

val joinType = "inner"

//Using broadcast join

val finalBestBatsmenPlayingWorldCupDf =
batsmenBestRunsAvgHistoryDf.join(broadcast(batsmenWorldCupDf),joinCondition,joinT
ype).drop (batsmenBestRunsAvgHistoryDf.col("Batsman"))

finalBestBatsmenPlayingWorldCupDf.orderBy(desc("AverageRunsScored")).show()

spark.stop()

Output:
+-----------------+------------+
|AverageRunsScored| batsman|
9

+-----------------+------------+
| 111.5|Rohit_Sharma|
| 100.0| Virat_Kohli|
| 77.0|Steven_Smith|
+-----------------+------------+

**********************************************************************
Problem 1:

Given 2 Datasets employee.json and dept.json

We need to calculate the count of employees against each department. Use

Structured API’s.

Sample output
depName,deptid,empcount
IT,11,1
HR,21,1
Marketing,31,1
Fin,41,2
Admin,51,0

TRENDYTECH 9108179578
Problem 2:

Find the top movies as shown in spark practical 18 using broadcast join. Use
Dataframes or Datasets to solve it this time.

Problem 3:

File A is a text file of size 1.2 GB in HDFS at location /loc/x. It contains match by
match statistics of runs scored by all the batsman in the history of cricket.

File B is a text file of size 1.2 MB present in local dir /loc/y. It contains list of
batsman playing in cricket world cup 2019.

TRENDYTECH 9108179578
File A:
MatchNumber Batsman Team RunsScored StrikeRate
1 Rohit Sharma India 200 100.2
1 Virat Kohli India 100 98.02
1 Steven Smith Aus 77 79.23
35 Clive Lloyd WI 29 37.00
243 Rohit Sharma India 23 150.00
243 Faf du Plesis SA 17 35.06

File B:
Batsman Team
Rohit_Sharma India
Steven_Smith Aus
Virat_Kohli India

Question: Find the batsman participating in 2019 who has the best average of

TRENDYTECH 9108179578
scoring runs in his career. Solve using Dataframes or Datasets.
1. reading the data - Reader API

2. crunching of data - transformations

3. write the data back - Writer API

Scala
======

orderDf.write.format("csv")
.mode(SaveMode.Overwrite)
.option("path","/Users/trendytech/Desktop/newfolder1")
.save()

pyspark
======

orderDf.write.format("csv")\
.mode("overwrite")\
.option("path","/Users/trendytech/Desktop/newfolder1")\
.save()

from pyspark import SparkConf

from pyspark.sql import SparkSession

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

orderDf = spark.read.format("csv")\
.option("header",True)\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/orders.csv")\
.load()

print("number of partitions are ", orderDf.rdd.getNumPartitions())

ordersRep = orderDf.repartition(4)
ordersRep.write.format("csv")\
.mode("overwrite")\
.option("path","/Users/trendytech/Desktop/newfolder1")\
.save()

====

overwrite
append
errorIfExists
ignore

=====

Parquet is the default file format in apache spark when we talk about structured api's

=====

Spark File Layout

Number of files is equal to number of partitions.

1. simple repartition - repartition

2. partitioning - partitionBy (allows partitioning pruning)

3. bucketBy

4. maxRecordsPerFile

from pyspark import SparkConf

from pyspark.sql import SparkSession

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

orderDf = spark.read.format("csv")\
.option("header",True)\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/orders.csv")\
.load()

orderDf.write.format("csv").partitionBy("order_status")\
.mode("overwrite")\
.option("path","/Users/trendytech/Desktop/newfolder4")\
.save()

=======

Avro

3.1.2 pyspark

from pyspark import SparkConf

from pyspark.sql import SparkSession

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")
my_conf.set("spark.jars","/Users/trendytech/Downloads/spark-avro_2.12-3.1.2.jar")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

orderDf = spark.read.format("csv")\
.option("header",True)\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/orders.csv")\
.load()

orderDf.write.format("avro")\
.mode("overwrite")\
.option("path","/Users/trendytech/Desktop/newfolder4")\
.save()

====

Spark SQL

from pyspark import SparkConf

from pyspark.sql import SparkSession
my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

orderDf = spark.read.format("csv")\
.option("header",True)\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/orders.csv")\
.load()

orderDf.createOrReplaceTempView("orders")

resultDf = spark.sql("select order_status, count(*) as total_orders from orders group by

order_status")

resultDf.show()

====

from pyspark import SparkConf

from pyspark.sql import SparkSession

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

orderDf = spark.read.format("csv")\
.option("header",True)\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/orders.csv")\
.load()

orderDf.createOrReplaceTempView("orders")

resultDf = spark.sql("select order_customer_id, count(*) as total_orders from orders where

order_status = 'CLOSED' group by order_customer_id order by total_orders desc")
resultDf.show()

====
Table has 2 parts

1. data - warehouse - spark.sql.warehouse.dir

2. metadata - catalog metastore - memory

from pyspark import SparkConf

from pyspark.sql import SparkSession

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

orderDf = spark.read.format("csv")\
.option("header",True)\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/orders.csv")\
.load()

orderDf.write.format("csv")\
.mode("overwrite")\
.saveAsTable("orders1")

========

from pyspark import SparkConf

from pyspark.sql import SparkSession

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

orderDf = spark.read.format("csv")\
.option("header",True)\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/orders.csv")\
.load()

spark.sql("create database if not exists retail")

orderDf.write.format("csv")\
.mode("overwrite")\
.saveAsTable("retail.orders2")

==========

from pyspark import SparkConf

from pyspark.sql import SparkSession

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).enableHiveSupport().getOrCreate()

orderDf = spark.read.format("csv")\
.option("header",True)\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/orders.csv")\
.load()

spark.sql("create database if not exists retail")

orderDf.write.format("csv")\
.mode("overwrite")\
.saveAsTable("retail.orders3")

============

from pyspark import SparkConf

from pyspark.sql import SparkSession

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).enableHiveSupport().getOrCreate()

orderDf = spark.read.format("csv")\
.option("header",True)\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/orders.csv")\
.load()

spark.sql("create database if not exists retail")

orderDf.write.format("csv")\
.mode("overwrite")\
.bucketBy(4,"order_customer_id")\
.sortBy("order_customer_id")\
.saveAsTable("retail.orders4")

============

Spark DF session 12
===================

from pyspark import SparkConf

from pyspark.sql import SparkSession
from pyspark.sql.functions import regexp_extract

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

myregex = r'^(\S+) (\S+)\t(\S+)\,(\S+)'

lines_df = spark.read.text("/Users/trendytech/Desktop/data/orders_new.csv")

#lines_df.printSchema()

#lines_df.show()

final_df =
lines_df.select(regexp_extract('value',myregex,1).alias("order_id"),regexp_extract('value',myreg
ex,2).alias("date"),regexp_extract('value',myregex,3).alias("customer_id"),regexp_extract('value'
,myregex,4).alias("status"))

final_df.printSchema()

final_df.show()

final_df.select("order_id").show()
final_df.groupby("status").count().show()

===============

spark df session 13

==============

Column String

Column object

from pyspark import SparkConf

from pyspark.sql import SparkSession
from pyspark.sql.functions import *

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

orderDf = spark.read.format("csv")\
.option("header",True)\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/orders.csv")\
.load()

orderDf.select("order_id","order_date").show()

orderDf.select(col("order_id")).show()

==========

Spark DF Session 14
=====================

Creating our own user defined function is spark.

1. Column object expression -- the function won't be registered in catalog

2. SQL expression -- the function will be registered in catalog.
So in this case we can even use it with spark SQL.

if the age is greater than 18 we have to populate the 4th column named Adult with "Y"

else we need to populated the column with "N"

from pyspark import SparkConf

from pyspark.sql import SparkSession
from pyspark.sql.functions import *

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

df = spark.read.format("csv")\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/dataset1")\
.load()

df1 = df.toDF("name","age","city")

def ageCheck(age):
if(age > 18):
return "Y"
else:
return "N"

parseAgeFunction = udf(ageCheck,StringType())

df2 = df1.withColumn("adult",parseAgeFunction("age"))

df2.printSchema()

df2.show()

===========

from pyspark import SparkConf

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

df = spark.read.format("csv")\
.option("inferSchema",True)\
.option("path","/Users/trendytech/Desktop/data/dataset1")\
.load()

df1 = df.toDF("name","age","city")

def ageCheck(age):
if(age > 18):
return "Y"
else:
return "N"

spark.udf.register("parseAgeFunction",ageCheck,StringType())

for x in spark.catalog.listFunctions():
print(x)

df2 = df1.withColumn("adult",expr("parseAgeFunction(age)"))

df2.show()

============

Spark DF session 15
=====================

create the spark session

create a local list

create a dataframe from this local list and give column names

add a new column date1 with unix timestamp

add one more column with monotonically increasing id

drop the duplicates based on combination of 2 columns

drop the orderid column

sort based on order date

from pyspark import SparkConf

from pyspark.sql import SparkSession
from pyspark.sql.functions import *

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

myList = [(1,"2013-07-25",11599,"CLOSED"),
(2,"2014-07-25",256,"PENDING_PAYMENT"),
(3,"2013-07-25",11599,"COMPLETE"),
(4,"2019-07-25",8827,"CLOSED")]

ordersDf = spark.createDataFrame(myList)\
.toDF("orderid","orderdate","customerid","status")

newDf = ordersDf\
.withColumn("date1",unix_timestamp(col("orderdate"))) \
.withColumn("newid", monotonically_increasing_id()) \
.dropDuplicates(["orderdate","customerid"])\
.drop("orderid")\
.sort("orderdate")

ordersDf.printSchema()
ordersDf.show()
newDf.show()

===========

Spark DF session 16
=====================

Aggregate transformations

1. Simple aggregations
2. Grouping aggregations
3. window aggregates

//simple aggregates

from pyspark import SparkConf

from pyspark.sql import SparkSession
from pyspark.sql.functions import *

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

invoiceDF = spark.read
.format("csv")
.option("header",true)
.option("inferSchema",true)
.option("path","/Users/trendytech/Desktop/order_data.csv")
.load()

//column object expression

invoiceDF.select(
count("*").as("RowCount"),
sum("Quantity").as("TotalQuantity"),
avg("UnitPrice").as("AvgPrice"),
countDistinct("InvoiceNo").as("CountDistinct")).show()

//column string expression

invoiceDF.selectExpr(
"count(*) as RowCount",
"sum(Quantity) as TotalQuantity",
"avg(UnitPrice) as AvgPrice",
"count(Distinct(InvoiceNo)) as CountDistinct").show()

//spark SQL
invoiceDF.createOrReplaceTempView("sales")
spark.sql("select count(*),sum(Quantity),avg(UnitPrice),count(distinct(InvoiceNo)) from
sales").show
spark.stop()
}

from pyspark import SparkConf

from pyspark.sql import SparkSession
from pyspark.sql.functions import *

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

invoiceDF = spark.read\
.format("csv")\
.option("header",True)\
.option("inferSchema",True) \
.option("path","/Users/trendytech/Desktop/data/order_data.csv") \
.load()

invoiceDF.select(
count("*").alias("RowCount"),
sum("Quantity").alias("TotalQuantity"),
avg("UnitPrice").alias("AvgPrice"),
countDistinct("InvoiceNo").alias("CountDistinct")).show()

invoiceDF.selectExpr(
"count(*) as RowCount",
"sum(Quantity) as TotalQuantity",
"avg(UnitPrice) as AvgPrice",
"count(Distinct(InvoiceNo)) as CountDistinct").show()

invoiceDF.createOrReplaceTempView("sales")
spark.sql("select count(*),sum(Quantity),avg(UnitPrice),count(distinct(InvoiceNo)) from
sales").show()

=============

from pyspark import SparkConf

from pyspark.sql import SparkSession
from pyspark.sql.functions import *

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

invoiceDF = spark.read\
.format("csv")\
.option("header",True)\
.option("inferSchema",True) \
.option("path","/Users/trendytech/Desktop/data/order_data.csv") \
.load()

#column object expression

val summaryDF = invoiceDF.groupBy("Country","InvoiceNo")
.agg(sum("Quantity").alias("TotalQuantity"),
sum(expr("Quantity * UnitPrice")).alias("InvoiceValue"))

summaryDF.show()

#string expression
val summaryDf1 = invoiceDF.groupBy("Country","InvoiceNo")
.agg(expr("sum(Quantity) as TotalQunatity"),
expr("sum(Quantity * UnitPrice) as InvoiceValue"))

summaryDf1.show()

#spark SQL
invoiceDF.createOrReplaceTempView("sales")

spark.sql("""select country,InvoiceNo,sum(Quantity) as totQty,sum(Quantity * UnitPrice) as

InvoiceValue from sales group by country,InvoiceNo""").show()

from pyspark import SparkConf

from pyspark.sql import SparkSession
from pyspark.sql.functions import *

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master","local[*]")
spark = SparkSession.builder.config(conf=my_conf).getOrCreate()

invoiceDF = spark.read\
.format("csv")\
.option("header",True)\
.option("inferSchema",True) \
.option("path","/Users/trendytech/Desktop/data/order_data.csv") \
.load()

#column object expression

summaryDF = invoiceDF.groupBy("Country","InvoiceNo")\
.agg(sum("Quantity").alias("TotalQuantity"),
sum(expr("Quantity * UnitPrice")).alias("InvoiceValue"))

summaryDF.show()

#string expression
summaryDf1 = invoiceDF.groupBy("Country","InvoiceNo")\
.agg(expr("sum(Quantity) as TotalQunatity"),
expr("sum(Quantity * UnitPrice) as InvoiceValue"))

summaryDf1.show()

#spark SQL
invoiceDF.createOrReplaceTempView("sales")

spark.sql("""select country,InvoiceNo,sum(Quantity) as totQty,sum(Quantity * UnitPrice) as

InvoiceValue from sales group by country,InvoiceNo""").show()

==============

from pyspark import SparkConf

from pyspark.sql import SparkSession, Window
from pyspark.sql.functions import *

my_conf = SparkConf()
my_conf.set("spark.app.name", "my first application")
my_conf.set("spark.master", "local[*]")

spark = SparkSession.builder.config(conf=my_conf).getOrCreate()
invoiceDF = spark.read \
.format("csv") \
.option("header", True) \
.option("inferSchema", True) \
.option("path", "/Users/trendytech/Desktop/data/windowdata.csv") \
.load()

myWindow = Window.partitionBy("country")\
.orderBy("weeknum")\
.rowsBetween(Window.unboundedPreceding, Window.currentRow)

mydf = invoiceDF.withColumn("RunningTotal",sum("invoicevalue").over(myWindow))

mydf.show()

==========
spark2-shell --master yarn

rdd1 = sc.textFile("bigLogNew.txt")

rdd2 = rdd1.map(lambda x: (x.split(":")[0], x.split(":")[1]))

rdd3 = rdd2.groupByKey()

rdd4 = rdd3.map(lambda x: (x[0], len(x[1])))

rdd4.collect()

=========

import random

def randomGenerator():
return random.randint(1,60)

def myFunction(x):
if(x[0][0:4]=="WARN"):
return("WARN",x[1])
else:
return ("ERROR",x[1])

rdd1 = sc.textFile("bigLogNew.txt")

rdd2 = rdd1.map(lambda x:(x.split(":")[0] + str(randomGenerator()),x.split(":")[1]))

rdd3 = rdd2.groupByKey()

rdd4 = rdd3.map(lambda x: (x[0] , len(x[1])))

rdd4.cache()

rdd5 = rdd4.map(lambda x: myFunction(x))

rdd6 = rdd5.reduceByKey(lambda x,y: x+y)

rdd6.collect()
1

Week14: Apache Spark - Optimization Part2

Spark Optimization Session - 12

================================

Broadcast join can be used when we have 1 large table and 1 small table.

and we want to join these.

RDD (lower level API's)

=========================

spark2-shell --conf spark.dynamicAllocation.enabled=false --master yarn --num-executors 6

--executor-cores 2 --executor-memory 3G --conf spark.ui.port=4063

we will have 2 rdd's

one of them will be large

one of them will be smaller.

ERROR: Thu Jun 04 10:37:51 BST 2015

WARN: Sun Nov 06 10:37:51 GMT 2016
WARN: Mon Aug 29 10:37:51 BST 2016
ERROR: Thu Dec 10 10:37:51 GMT 2015
ERROR: Fri Dec 26 10:37:51 GMT 2014
ERROR: Thu Feb 02 10:37:51 GMT 2017
WARN: Fri Oct 17 10:37:51 BST 2014
ERROR: Wed Jul 01 10:37:51 BST 2015
WARN: Thu Jul 27 10:37:51 BST 2017
WARN: Thu Oct 19 10:37:51 BST 2017

the size of this data is 1.4 GB (Large dataset)

val rdd1 = sc.textFile("bigLogNew.txt")

Input:
ERROR: Thu Jun 04 10:37:51 BST 2015
WARN: Sun Nov 06 10:37:51 GMT 2016
WARN: Mon Aug 29 10:37:51 BST 2016
2
ERROR: Thu Dec 10 10:37:51 GMT 2015
ERROR: Fri Dec 26 10:37:51 GMT 2014
ERROR: Thu Feb 02 10:37:51 GMT 2017
WARN: Fri Oct 17 10:37:51 BST 2014
ERROR: Wed Jul 01 10:37:51 BST 2015
WARN: Thu Jul 27 10:37:51 BST 2017
WARN: Thu Oct 19 10:37:51 BST 2017

output:
(ERROR,Thu Jun 04 10:37:51 BST 2015)
(WARN, Sun Nov 06 10:37:51 GMT 2016)

MAP TRANFORMATION

val rdd2 = rdd1.map(x => (x.split(":")(0),x.split(":")(1))

rdd2 will have something like the below:

RDD2 is Large
(ERROR,Thu Jun 04 10:37:51 BST 2015)
(WARN, Sun Nov 06 10:37:51 GMT 2016)

RDD3 IS SMALL
("ERROR",0)
("WARN",1)

val rdd4 = rdd2.join(rdd3)

(ERROR,(Thu Jun 04 10:37:51 BST 2015,0))

(WARN, (Sun Nov 06 10:37:51 GMT 2016,1))

val a = Array(
("ERROR",0),
("WARN",1)
)

val rdd3 = sc.parallelize(a)

val a = Array(
3
("ERROR",0),
("WARN",1)
)

val keyMap = a.toMap

val bcast = sc.broadcast(keyMap)

val rdd1 = sc.textFile("bigLogNew.txt")

val rdd2 = rdd1.map(x => (x.split(":")(0),x.split(":")(1)))

val rdd3 = rdd2.map(x => (x._1,x._2,bcast.value(x._1)))

rdd3.saveAsTextFile("joinresults2")

Dataframes (Structured API's)

Spark Optimization Session - 13

================================

we will have 2 dataframes.

one of them will be large and other will be small.

orders is 2.6 GB
customers is around 900 kb

customers - customer_id (small)

orders - order_customer_id (large)

we will try to create dataframe for each of these.

spark2-shell --conf spark.dynamicAllocation.enabled=false --master yarn --num-executors 21

val customerDF =
spark.read.format("csv").option("header",true).option("inferSchema",true).option("path","custom
ers.csv").load
4

val orderDF =
spark.read.format("csv").option("header",true).option("inferSchema",true).option("path","orders.c
sv").load

spark.conf.set("spark.sql.autoBroadcastJoinThreshold",-1)

val joinedDF = customerDF.join(orderDF,customerDF("customer_id") ===

orderDF("order_customer_id"))

how many partitions are there in this big DF - 21

and for the small dataframe we have just a single partition.

whenever we do shuffling in case of structured API's we ge 200 partitions by default.

if I do groupBy on a Dataframe then we will get 200 partitions after the shuffling is done.

500 mb file..

rdd - groupBy then

before groupBy we had 4 partitions..

after groupBy the partitions remain the same that means 4 partitions.

Spark Optimization Session - 14

================================

2 things..

1. lets not infer the schema and save time.

2. use a broadcast join.

import org.apache.spark.sql.types._

val ordersSchema = StructType(

List(
StructField("order_id",IntegerType,true),
StructField("order_date",TimestampType,true),
StructField("order_customer_id",IntegerType,true),
StructField("order_status",StringType,true)
5
)
)

val customerDF =
spark.read.format("csv").option("header",true).option("inferSchema",true).option("path","custom
ers.csv").load

val orderDF =
spark.read.format("csv").schema(ordersSchema).option("header",true).option("path","orders.csv
").load

val joinedDF = customerDF.join(orderDF,customerDF("customer_id") ===

orderDF("order_customer_id"))

joinedDF.take(1000000)

increase the --driver-memory when you are collecting more data on driver machine.

otherwise you will get out of memory error.

--num-executors

--driver-memory

--executor-memory

--executor-cores

===============================
Spark Optimization Session - 15
===============================

1. demo on repartition vs coalesce when we want to decrease the number of partitions. -

coalesce

2. using spark-submit we will try to submit a job.

while submitting the jar we will see

a) client
6
b) cluster

you have a 1 gb file

and you create a rdd on top of it.

8 partitions..

a cluster of 4 nodes..

on each machine there might be 2 partitions..

rdd.coalesce(4)

when we are using coalesce then the resultant partitions can be of unequal sizes..

when we are using repartition then full shuffling is involved which is time consuming but we
know that the resultant partitions will be of similar size..

1. write your code in IDE like eclipse/intelliJ

2. bundle your code as a java jar file and export the jar.

3. move this jar file to your edge node/gateway machine.

scp wordcount.jar bigdatabysumit@gw02.itversity.com:/home/bigdatabysumit

wordcount.jar

I want to run LogLevelGrouping

bigLogNew.txt - 1.4 GB (11 partitions)

spark2-submit \
--class LogLevelGrouping \
--master yarn \
--deploy-mode cluster \
7
--executor-memory 3G \
--num-executors 4 \
wordcount.jar bigLogNew.txt

since the deploy mode is cluster mode that means our driver is running on one of the executors
residing in the cluster.

​ n02.itversity.com:37973​ (cluster) => recommended

driver w
driver g​ w02.itversity.com:52754​ (client)

driver is running on one of the worker node..

spark2-submit \
--class LogLevelGrouping \
--master yarn \
--executor-memory 3G \
--num-executors 4 \
wordcount.jar bigLogNew.txt

Spark Optimization Session - 16

===============================

Join opimizations
==================

we have 2 things which we work for:

1. we always want to avoid or minimize the shuffling.
2. we want to increase the parallelism.

1 large table and 1 small table - broadcast join

no shuffling required.

2 large tables - there is no way to completely avoid the shuffling.

but we can think of minimizing it.

To make sure we shuffle less data

===================================
8
try to filter the data as early as possible before the shuffle. cut down the size before the shuffle
phase. and do aggregation before.

filter & aggregation try to do before the shuffle.

Increase the Parallelism

=========================

100 node cluster each worker node having 64 GB RAM & 16 CPU Cores.

1. how many cpu cores we are grabbing.

50 executors with 5 cores each - 250 total cpu cores (at the max 250 tasks)

2. whenever shuffling happens in case of structured API's

we get 200 partitions after the shuffle.

spark.sql.shuffle.partitions

200 partitions - (at the max 200 tasks running in parallel)

3. in your data you have 100 distinct keys..

after the shuffle only at max 100 partitions will be full and other will be empty.

whenever the cardinality of data is low then some of the partitions will be empty.

(at the max 100 tasks will be running in parallel)

we grabbed 250 cpu cores (50 executors X 5)

shuffle partitions - 200

we have just 100 distinct keys..

min(Total CPU Cores , Number of Shuffle partitions, Number of Distinct Keys)

min(250, 200, 100)

100
9

you can try increasing the cardinality - salting.

we can increase the Number of shuffle partitions if required.

we can try grabbing more resources if feasible

Skew Partitions
================

customers table and orders table..

customers table - customer_id

orders table - order_customer_id

one of the Customer (APPLE) has placed a lot of orders..

10000000 - 10 million total orders

9000000 - 9 million orders are just for one customer(APPLE)

same key always goes to same partition..

200 shuffle partitions..

1 of the partition will be holding majority of data..

which ever task is working on this heavily loaded partition will be very slow.

and other tasks will complete quickly..

your job is dependent on the slowest performing task..

there should not be partition skew, else the job will be delayed.

orders - order_customer_id
customers - customer_id

bucketing & sorting on the datasets on join column

10

orders into 32 buckets.. - 32 files

customers into 32 buckets.. - 32 files

15 minutes to get the bucketing and sorting done.

when you try to join these 2 tables it will be very quick.

SMB join - shuffling

if you are not doing bucketing and sorting

and you are doing a plain join

30 minutes * 1000 = 30000 minutes

bucketed both the tables on the join column and sorted it.

60 minutes to do bucketing and sorting.

join - 5 minutes.. * 1000 = 5000 minutes

SMB join.

Connecting to External Data Source

===================================

in mysql we will have a table and we will try to create a dataframe by directly connecting from
that.

mysql-connector-java.jar
11
spark-shell --driver-class-path /usr/share/java/mysql-connector-java.jar

val connection_url ="jdbc:mysql://cxln2.c.thelab-240901.internal/retail_db"

val mysql_props = new java.util.Properties

mysql_props.setProperty("user","sqoopuser")

mysql_props.setProperty("password","NHkkP876rp")

val orderDF = spark.read.jdbc(connection_url,"orders",mysql_props)

orderDF.show()

Spark Optimization Session - 17

================================

Sort Aggregate vs Hash Aggregate

orders.csv - 2.6 GB

order_id,order_date,order_customer_id,order_status
1,2013-07-25 00:00:00.0,11599,CLOSED
2,2013-07-25 00:00:00.0,256,PENDING_PAYMENT
3,2013-07-25 00:00:00.0,12111,COMPLETE
4,2013-07-25 00:00:00.0,8827,CLOSED
5,2013-07-25 00:00:00.0,11318,COMPLETE
6,2013-07-25 00:00:00.0,7130,COMPLETE
7,2013-07-25 00:00:00.0,4530,COMPLETE
8,2013-07-25 00:00:00.0,2911,PROCESSING
9,2013-07-25 00:00:00.0,5657,PENDING_PAYMENT

we want to find out the number of orders which are placed by each customer in each month.

grouping based on order_customer_id and Month - count of number of orders.

spark2-shell --conf spark.dynamicAllocation.enabled=false --master yarn --num-executors 11

--conf spark.ui.port=4063
12
val orderDF =
spark.read.format("csv").option("inferSchema",true).option("header",true).option("path","orders.c
sv").load

orderDF.createOrReplaceTempView("orders")

spark.sql("select * from orders").show

spark.sql("select order_customer_id, date_format(order_date, 'MMMM') orderdt, count(1) cnt,

first(date_format(order_date,'M')) monthnum from orders group by order_customer_id, orderdt
order by cast(monthnum as int)").show

It took 3.9 minutes to complete this query

spark.sql("select order_customer_id, date_format(order_date, 'MMMM') orderdt, count(1) cnt,

first(cast(date_format(order_date,'M') as int)) monthnum from orders group by
order_customer_id, orderdt order by monthnum").show

It took 1.2 minutes to complete this query

Spark Optimization Session - 18

================================

spark.sql("select order_customer_id, date_format(order_date, 'MMMM') orderdt, count(1) cnt,

first(date_format(order_date,'M')) monthnum from orders group by order_customer_id, orderdt
order by cast(monthnum as int)").explain

It took 3.9 minutes to complete this query - sort aggregate

spark.sql("select order_customer_id, date_format(order_date, 'MMMM') orderdt, count(1) cnt,

first(cast(date_format(order_date,'M') as int)) monthnum from orders group by
order_customer_id, orderdt order by monthnum").explain

It took 1.2 minutes to complete this query - hash aggregate

2 questions
============

1. difference between sort aggregate and hash aggregate

13

2. why in query1 it used sort aggregate

and why in query2 it used hash aggregate

sort aggregate
===============

customer_id:month value
1024:january 1 "1"
1024:january 1 "1"
1024:january 1 "1"
1024:january 1 "1"
1025:january 1 "1"
1025:january 1 "!"

first the data is sorted based on the grouping columns.

1024:january ,{1,1,1,1,1}

sorting of data takes time..

2000
O(nlogn)
1000 * log(1000)
1000 * 10 = 10000

2000 * 11 = 22000

sort aggregate takes lot of time when the data grows..

1024:january 1 "1"
1024:january 1 "1"
1024:january 1 "1"
1025:january 1 "1"
1025:january 1 "!"

Hash Aggregate
===============

hash table
14
============

customer_id:month value
1024:january 3 "1"
1025:january 2 "1"

no sorting is required..
additional memory is required to have the hashtable kind of structure.

1000 rows...

sort aggregate = O(nlogn) = 1000 * 10 = 10000 operations

hash aggregate = O(n) = 1000

this hash table kind of structure is not a part of container..

rather this additional memory is grabbed as part of off heap memory..

this memory is not the part of your jvm..

question 2: why in the first query it used sort aggregate and why in second query it used hash
aggregate..

slower - sort aggregate

======
spark.sql("select order_customer_id, date_format(order_date, 'MMMM') orderdt, count(1) cnt,
first(date_format(order_date,'M')) monthnum from orders group by order_customer_id,
orderdt").explain

faster - hash aggregate

=======
spark.sql("select order_customer_id, date_format(order_date, 'MMMM') orderdt, count(1) cnt,
first(cast(date_format(order_date,'M') as int)) monthnum from orders group by
order_customer_id, orderdt").explain
15

customer_id:month value
1024:january 3 1

month number was string

string is immutable

when we are using hash aggregate we should have mutable types in the values

Spark Optimization Session - 19

================================

Catalyst optimizer

Structured API's (DF, DS, Spark SQL) perform better than Raw RDD's

catalyst optimizer will optimize the execution plan for Structured API's

Rule Based Optimization.

Many rules are already available. Also if we want we can add our own optimization rules.

Students.csv - 60 mb

student_id,exam_center_id,subject,year,quarter,score,grade
1,1,Math,2005,1,41,D
1,1,Spanish,2005,1,51,C
1,1,German,2005,1,39,D
1,1,Physics,2005,1,35,D
1,1,Biology,2005,1,53,C
1,1,Philosophy,2005,1,73,B
1,1,Modern Art,2005,1,32,E
1,1,History,2005,1,43,D
1,1,Geography,2005,1,54,C
16
val df1 =
spark.read.format("csv").option("header",true).option("inferSchema",true).option("path","/Users/t
rendytech/Desktop/students.csv").load

df1.createOrReplaceTempView("students")

spark.sql("select * from students").show

1. Parsed Logical Plan - un-resolved

our query is parsed and we get a parsed logical plan - unresolved

it checks for any of the syntax errors.

syntax is correct

2. Resolved/Analysed Logical plan..

it will try to resolve the table name the column names etc..

if the columnname or table name is not available then we will get analysis exception.

if we have referred to the correct columnnames and table name

3. Optimized Logical Plan - catalyst optimizer.

filter push down

combining of filters..

combining of projections

There are many such rules which are already in place.

If we want we can add our own rules in the catalyst optimizer.

consider you are doing a Aggregate.

as per the logical plan lets say it says we have to do Aggregate.

in physical plan..
17
physical plan1
===============
sortAggregate

physical plan2
===============
HashAggregate

It will select the physical plan which is the most optimized one with minimum cost.

This selected physical plan is converted to Lower Level API's

RDD code.

Spark Optimization Session - 20

================================

student_id,exam_center_id,subject,year,quarter,score,grade
1,1,Math,2005,1,41,D
1,1,Spanish,2005,1,51,C
1,1,German,2005,1,39,D
1,1,Physics,2005,1,35,D
1,1,Biology,2005,1,53,C
1,1,Philosophy,2005,1,73,B
1,1,Modern Art,2005,1,32,E
1,1,History,2005,1,43,D
1,1,Geography,2005,1,54,C

a*b

if b is 1 then return a

Catalyst optimizer

RDD vs Structured API's

Parsed Logical Plan - Unresolved

Analysed/Resolved Logical Plan

18
Optimized Logical Plan

Physical Plan

val df1 = spark.read.format("csv").option("header",

true).option("inferSchema",true).option("path","/Users/trendytech/Desktop/students.csv").load

df1.createOrReplaceTempView("students")

spark.sql("select student_id from (select student_id, exam_center_id from students) where

student_id <5").explain(true)

spark.sql("select student_id,sum(score) from (select student_id, exam_center_id,score from

students where exam_center_id=5) where student_id < 5 group by student_id ").explain(true)

http://localhost:4040/

import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.catalyst.expressions.Multiply
import org.apache.spark.sql.catalyst.expressions.Literal

object MultiplyOptimizationRule extends Rule[LogicalPlan] {

def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
case Multiply(left,right) if right.isInstanceOf[Literal] &&
right.asInstanceOf[Literal].value.asInstanceOf[Integer] == 1 =>
println("optimization of one applied")
left
}
}

spark.experimental.extraOptimizations = Seq(MultiplyOptimizationRule)
TRENDY TECH

Week 14 Scala – PySpark equivalent programs

Week 14 is based on optimization where we need to allocate resources and hence, we
are using shell prompt on cluster to write programs.

Generalized changes that are required in every program

1. To start cmd prompt for PySpark. We PySpark instead of scala-shell.
2. Remove all val, var keyword as python does not have val and var types.
3. Anonymous functions are replaced with lambda in python.
4. Comment is given using # in python instead of // in scala
Note
1. Best practice is to use your own itversity hdfs location in the program for input and
output files. You can also use Linux root as shown in video.
2. There are be many ways to get the output for particular problem, we are showcasing
one way.
3. Changes are highlighted in yellow.

WEEK 14 – Spark Optimization Part 2 TRENDY TECH

TRENDY TECH

Problem Statement: We are creating array as one source and second source is a file. We are going to do join on both the
sources.
Solution:
Scala Spark Program PySpark Program
spark2-shell --conf spark.dynamicAllocation.enabled=false --master PySpark --conf spark.dynamicAllocation.enabled=false --master yarn --
yarn --num-executors 6 --executor-cores 2 --executor-memory 3G -- num-executors 6 --executor-cores 2 --executor-memory 3G --conf
conf spark.ui.port=4063 spark.ui.port=4063

val rdd1 = sc.textFile("bigLogNew.txt") rdd1=sc.textFile("/user/itv000001/bigLog.txt")

rdd1.getNumPartitions rdd1.getNumPartitions
val rdd2 = rdd1.map(x => (x.split(":")(0),x.split(":")(1)) rdd2 = rdd1.map(lambda x : (x.split(":")[0],x.split(":")[1]))
val a = Array(("ERROR",0),("WARN",1)) a = {"ERROR":0, "WARN":1}
val rdd3 = sc.parallelize(a) rdd3 = sc.parallelize(a)
val rdd4 = rdd2.join(rdd3) rdd4 = rdd2.join(rdd3)
rdd4.saveAsTextFile("joinResults1") rdd4.saveAsTextFile("/user/itv000001/joinResult1")

Specific changes that are required in above program

1. Replace () with [] in python for accessing array
2. Array in scala is changed to dict (Dictionary) in python. Variations are possible, we have showed one way.

WEEK 14 – Spark Optimization Part 2 TRENDY TECH

TRENDY TECH

Problem Statement: We are creating array as one source and second source is a file. We are going to do join on both the
sources. In above program we did normal join and, in this program, we will use broadcast join.
Solution:
Scala Spark Program PySpark Program
spark2-shell --conf spark.dynamicAllocation.enabled=false --master PySpark --conf spark.dynamicAllocation.enabled=false --master yarn --
yarn --num-executors 6 --executor-cores 2 --executor-memory 3G -- num-executors 6 --executor-cores 2 --executor-memory 3G --conf
conf spark.ui.port=4063 spark.ui.port=4063

val a = Array(("ERROR",0),("WARN",1)) a = {"ERROR":0, "WARN":1}

val rdd3 = sc.parallelize(a) # not required
val keyMap = a.toMap # not required
val bcast = sc.broadcast(keyMap) bcast = sc.broadcast(a)
val rdd1 = sc.textFile("bigLogNew.txt") rdd1=sc.textFile("/user/itv000001/bigLog.txt")
val rdd2 = rdd1.map(x => (x.split(":")(0),x.split(":")(1))) rdd2 = rdd1.map(lambda x : (x.split(":")[0],x.split(":")[1]))
val rdd4 = rdd2.map(x => (x._1,x._2,bcast.value(x._1))) rdd4 = rdd2.map(lambda x : (x[0], x[1], bcast.value[x[0]]))
rdd4.saveAsTextFile("joinresults2") rdd4.saveAsTextFile("/user/itv000001/joinResult2")

Specific changes that are required in above program

1. Array in scala is changed to dict (Dictionary) in python. Variations are possible, we have showed one way.
2. Second and third line of scala program is not required, hence fourth line broadcast method accepts a as input
3. Replace () with [] in python for accessing array
4. Tuples in python is 0 index and accessed with []

WEEK 14 – Spark Optimization Part 2 TRENDY TECH

TRENDY TECH

Problem Statement: Join using two data frames

Solution:
Scala Spark Program PySpark Program
spark2-shell --conf spark.dynamicAllocation.enabled=false --master yarn -- PySpark --conf spark.dynamicAllocation.enabled=false --master
num-executors 21 yarn --num-executors 21

val customerDF = customerDF = spark.read.format("csv").option("header",True)\

spark.read.format("csv").option("header",true).option("inferSchema",true) .option("inferSchema",True)
.option("path","customers.csv").load .option("path","/user/itv000173/customers.csv").load()
val orderDF = orderDF = spark.read.format("csv").option("header",True).\
spark.read.format("csv").option("header",true).option("inferSchema",true) option("inferSchema",True)
.option("path","orders.csv").load .option("path","/user/itv000173/orders.csv").load()

spark.conf.set("spark.sql.autoBroadcastJoinThreshold",-1) spark.conf.set("spark.sql.autoBroadcastJoinThreshold",-1)
val joinedDF = customerDF.join(orderDF,customerDF("customer_id") === joinedDF = customerDF.join(orderDF,customerDF["customer_id"]
orderDF("order_customer_id")) == orderDF["order_customer_id"])
joinedDF.write.csv("output1") joinedDF.write.csv("/user/itv000173/output1")

Specific changes that are required in above program

1. Replace true with True
2. Replace load with load()
3. Replace === with ==

WEEK 14 – Spark Optimization Part 2 TRENDY TECH

TRENDY TECH

Problem Statement: Join using two data frames and providing orders schema
Solution:
Scala Spark Program PySpark Program
spark2-shell --conf spark.dynamicAllocation.enabled=false -- PySpark --conf spark.dynamicAllocation.enabled=false --master yarn --num-
master yarn --num-executors 21 executors 21

import org.apache.spark.sql.types._ from PySpark.sql.types import StructType, StructField, IntegerType,

TimestampType, StringType
val ordersSchema = StructType(
List( ordersSchema = StructType([StructField("order_id",IntegerType(),True),
StructField("order_id",IntegerType,true), StructField("order_date",TimestampType(),True),
StructField("order_date",TimestampType,true), StructField("order_customer_id",IntegerType(),True),
StructField("order_customer_id",IntegerType,true), StructField("order_status",StringType(),True)
StructField("order_status",StringType,true)) ]
) )

val customerDF = customerDF =

spark.read.format("csv").option("header",true) spark.read.format("csv").option("header",True).option("inferSchema",True).\
.option("inferSchema",true).option("path","custom option("path","/user/itv000173/customers.csv").load()
ers.csv").load

val orderDF = orderDF =

spark.read.format("csv").schema(ordersSchema) spark.read.format("csv").schema(ordersSchema).option("header",True).\
.option("header",true).option("path","orders.csv ").load option("path","/user/itv000173/orders.csv").load()

val joinedDF = joinedDF = customerDF.join(orderDF,customerDF.customer_id ==

customerDF.join(orderDF,customerDF("customer_id") === orderDF.order_customer_id)
orderDF("order_customer_id"))

joined.write.csv("output21") joinedDF.write.csv("/user/itv000173/output21")

WEEK 14 – Spark Optimization Part 2 TRENDY TECH

TRENDY TECH

//----------call take so that it will bring data to driver and may

raise OOM #----------call take so that it will bring data to driver and may raise OOM
joinedDF.take(1000000) joinedDF.take(1000000)

//----------increase driver memory and call take again , now no

OOM error #----------increase driver memory and call take again , now no OOM error
spark2-shell --conf spark.dynamicAllocation.enabled=false -- PySpark --conf spark.dynamicAllocation.enabled=false --master yarn --num-
master yarn --num-executors 21 --driver-memory 4G executors 21 --driver-memory 4G
joinedDF.take(1000000)
joinedDF.take(1000000)

Specific changes that are required in above program

1. Replace appropriate imports in python.
2. Replace List() with []
3. Replace true with True
4. Replace load with load(), same for IntegerType(), TimestampType(), StringType()
5. Replace === with ==
6. Replace customerDF("customer_id") === orderDF("order_customer_id") with customerDF.customer_id ==
orderDF.order_customer_id)

WEEK 14 – Spark Optimization Part 2 TRENDY TECH

TRENDY TECH

Problem Statement: try repartition and coalesce

Solution:
Scala Spark Program PySpark Program
val rdd1 = sc.textFile("bigLogFinal.txt") rdd1 = sc.textFile("bigLogFinal.txt")
rdd1.getNumPartitions rdd1.getNumPartitions
val rdd2 = rdd1.repartition(6) rdd2 = rdd1.repartition(6)
rdd2.count rdd2.count
val rdd2 = rdd1.coalesce(6) rdd2 = rdd1.coalesce(6)
rdd2.count rdd2.count

Specific changes that are required in above program

1. Remove all val keyword

WEEK 14 – Spark Optimization Part 2 TRENDY TECH

TRENDY TECH

Problem Statement: Execute Code in production. Create jar and execute using spark-submit in cluster mode. Program is same
as week13 except few changes mentioned in video
Solution:
Scala Spark Program PySpark Program
spark2-submit \ spark2-submit \
--class LogLevelGrouping \ --class LogLevelGrouping \
--master yarn \ --master yarn \
--deploy-mode cluster \ --deploy-mode cluster \
--executor-memory 3G \ --executor-memory 3G \
--num-executors 4 \ --num-executors 4 \
wordcount.jar bigLogNew.txt LogLevelGrouping.py bigLogNew.txt

Specific changes that are required in above program

1. –class is not required, remove it
2. In python we execute python file directly

WEEK 14 – Spark Optimization Part 2 TRENDY TECH

TRENDY TECH

Problem Statement: Execute Code in production. Create jar and execute using spark-submit in local mode. Program is same as
week13 except few changes mentioned in video
Solution:
Scala Spark Program PySpark Program
spark2-submit \ spark2-submit \
--class LogLevelGrouping \ --class LogLevelGrouping \
--master yarn \ --master yarn \
--executor-memory 3G \ --executor-memory 3G \
--num-executors 4 \ --num-executors 4 \
wordcount.jar bigLogNew.txt LogLevelGrouping.py bigLogNew.txt

Specific changes that are required in above program

1. –class is not required, remove it
2. In python we execute python file directly

WEEK 14 – Spark Optimization Part 2 TRENDY TECH

TRENDY TECH
Problem Statement: spark sql
Solution:
Scala Spark Program PySpark Program
spark2-shell --conf spark.dynamicAllocation.enabled=false -- PySpark --conf spark.dynamicAllocation.enabled=false --master yarn --num-
master yarn --num-executors 11 --conf spark.ui.port=4063 executors 11 --conf spark.ui.port=4063

val orderDF = orderDF = spark.read.format("csv").option("inferSchema",True)\

spark.read.format("csv").option("inferSchema",true) .option("header",True).option("path","/user/itv000001/orders.csv").load()
.option("header",true).option("path","orders.csv").load

orderDF.createOrReplaceTempView("orders") orderDF.createOrReplaceTempView("orders")
spark.sql("select * from orders").show spark.sql("select * from orders").show()

spark.sql("select order_customer_id, date_format(order_date, spark.sql("select order_customer_id, date_format(order_date, 'MMMM')

'MMMM') orderdt, count(1) cnt, orderdt, count(1) cnt, first(date_format(order_date,'M')) monthnum from
first(date_format(order_date,'M')) monthnum from orders group orders group by order_customer_id, orderdt order by cast(monthnum as
by order_customer_id, orderdt order by cast(monthnum as int)").show()
int)").show

//----------------------------change the cast from order by //----------------------------change the cast from order by
spark.sql("select order_customer_id, date_format(order_date, spark.sql("select order_customer_id, date_format(order_date, 'MMMM')
'MMMM') orderdt, count(1) cnt, orderdt, count(1) cnt, first(date_format(order_date,'M')) monthnum from
first(cast(date_format(order_date,'M') as int)) monthnum from orders group by order_customer_id, orderdt order by cast(monthnum as
orders group by order_customer_id, orderdt order by int)").show()
monthnum").show

Specific changes that are required in above program

1. Replace true with True
2. Replace load with load()
WEEK 14 – Spark Optimization Part 2 TRENDY TECH
TRENDY TECH
3. Replace show with show()
4. Spark sql is same in scala and python

Problem Statement: just add .explain to spark sql from above program
Solution:
Scala Spark Program PySpark Program
spark2-shell --conf spark.dynamicAllocation.enabled=false --master PySpark --conf spark.dynamicAllocation.enabled=false --master yarn --
yarn --num-executors 11 --conf spark.ui.port=4063 num-executors 11 --conf spark.ui.port=4063

spark.sql("select order_customer_id, date_format(order_date, 'MMMM') spark.sql("select order_customer_id, date_format(order_date, 'MMMM')

orderdt, count(1) cnt, first(date_format(order_date,'M')) monthnum from orderdt, count(1) cnt, first(date_format(order_date,'M')) monthnum from
orders group by order_customer_id, orderdt order by cast(monthnum as orders group by order_customer_id, orderdt order by cast(monthnum as
int)").explain int)").explain()
// It took 3.9 minutes to complete this query - sort aggregate // It took 3.9 minutes to complete this query - sort aggregate

spark.sql("select order_customer_id, date_format(order_date, 'MMMM') spark.sql("select order_customer_id, date_format(order_date, 'MMMM')

orderdt, count(1) cnt, first(cast(date_format(order_date,'M') as int)) orderdt, count(1) cnt, first(cast(date_format(order_date,'M') as int))
monthnum from orders group by order_customer_id, orderdt order by monthnum from orders group by order_customer_id, orderdt order by
monthnum").explain monthnum").explain()
// It took 1.2 minutes to complete this query - hash aggregate // It took 1.2 minutes to complete this query - hash aggregate

Specific changes that are required in above program

1. Replace explain with explain()
2. Spark sql is same in scala and python

WEEK 14 – Spark Optimization Part 2 TRENDY TECH

TRENDY TECH

Problem Statement: Connecting to external resources

Solution:
Scala Spark Program PySpark Program
spark-shell --driver-class-path /usr/share/java/mysql-connector-java.jar PySpark -jars /usr/share/java/mysql-connector-java.jar

val connection_url ="jdbc:mysql://cxln2.c.thelab- connection_url ="jdbc:mysql://cxln2.c.thelab-

240901.internal/retail_db" 240901.internal/retail_db"

orderDF = spark.read \
val mysql_props = new java.util.Properties .jdbc(connection_url, "orders",
mysql_props.setProperty("user","sqoopuser") properties={"user": "sqoopuser", "password": "NHkkP876rp"})
mysql_props.setProperty("password","NHkkP876rp")
val orderDF = spark.read.jdbc(connection_url,"orders",mysql_props) orderDF.show()
orderDF.show()

Specific changes that are required in above program

1. Giving properties is different

WEEK 14 – Spark Optimization Part 2 TRENDY TECH

Spark Streaming Session - 1

Real Time Processing

====================

whatever we have done till now is nothing but batch processing.

Batch Processing
=================

doing analysis on top of files.

your processing might take few minutes, few hours or few days.

Real time Processing

======================

we will have continuously flowing data and we have to calculate the results instantly.

credit card fraud detection

finding trending hashtag

track website failures using server logs..

when we talk about hadoop

HDFS + MR + YARN

MR (mapreduce) Processing.

Mapreduce can only handle batch jobs.

mapreduce cannot handle streaming or real time data.

How do we process real time streaming data?

Apache Spark.

Spark is a general purpose compute engine which can handle.

Batch + Streaming Data

IN spark we have a module called as Spark Streaming.

Spark Streaming Session - 2
============================

batch vs real time stream processing

mapreduce can handle only batch processing.

spark can handle streaming data as well.

Spark Streaming.

how is the data stored in spark?

RDD (resilient distributed dataset)

when we load the file

val baseRdd = sc.textFile("file path in hdfs")

if the file is of 500 mb & if the default block size in hdfs is 128 mb.

then our baseRDD will have 4 partitions.

when there is no concept of a static file then how do you visualize your rdd.

consider a water tap which is continuosly flowing.

tap is running for 1 hour

size of my stream is 1 hour

every 2 minutes you are filling one water balloon.

30 water balloons.

a new rdd is framed every 2 minutes.

we will have 30 rdds which are created.

some balloons might have more data than other balloons based on flow of water.

batch interval - 5 seconds

consider whatever 30 balloons that we have filled we are putting them in a tub.
(Dstream)

so basically

Dstream -> Rdd's -> Messages (entities)

1 -> 30 rdd's -> each rdd can have lot of messages.

we need to operate at Dstream level

underneath spark still works in batch style.

the batch size is small like 1 second.

spark's compute engine is a batch engine and not a streaming engine.

whenever batch size is very small example 1 second.

then we get a feeling that it's real time streaming.

Spark Streaming Session - 3

============================

In batch processing in Spark

1. Lower level Constructs (RDD)

2. Higher Level Constructs (Structured API's Dataframes, dataset and spark sql)

Stream Processing in Spark

we have both the lower level as well as higher level constructs.

1. Spark Streaming (RDD's) traditional way

2. Structured Streaming - newer thing - Dataframes

Lets talk about example of normal spark streaming

Producer will be an application which is giving you continuous data.

consumer will be our spark streaming application.

twitter will be producer.

spark streaming will be the consumer.

we will try to simulate a dummy producer.

there will be a terminal and whatever you type will be produced.

producer will write to a socket

and consumer will read from the same socket.

socket
=======

IP Address + Port number (localhost + 9998)

step 1: start a producer

we want to create a dummy producer where we continously type things.

nc -lk 9998

step 2: we have to start a consumer

spark streaming code which reads the data from the socket

spark-shell --master local[2]

sc is already available
we have spark context which is available

when we talk about spark streaming applications we require a spark streaming

context

//creating spark streaming context

val ssc = new StreamingContext(sc, Seconds(5))

//lines is a dstream
val lines = ssc.socketTextStream("localhost",9998)

//words is a transformed dstream

val words = lines.flatMap(x => x.split(" "))
val pairs = words.map(x => (x,1))

val wordCounts = pairs.reduceByKey((x,y) => x+y)

wordCounts.print()

ssc.start()

Spark Streaming Session - 4

============================

what is real time processing.

Dstream -> RDD's -> messages

word count streaming example

when calculating word count it was forgetting the state of previous rdds.

if I write hello 5 times

then (hello,5)

hello 2 times again

(hello,2)

it was giving output for each rdd individually.

when we talk about spark streaming. There are 2 kind of transformations.

1. stateless transformation

is the one which forgets the previous state. we perform operation on a single rdd
always.

2. stateful transformation

the one in which we do aggregation over more than one rdd.

when we talk about batch processing. we load the entire file as one single rdd.
batch processing is always stateless. there is no point of talking about stateful
transformation in case of batch processing.

when we talk about stateful transformations we have 2 choices:

lets consider you have a streaming application which runs for 6 hours.

batch interval size to be 5 minutes - a new rdd will be created every 5 minutes.

during the course of entire streaming application how many rdds will be created?

72 rdd's will be created in 6 hours.

1. consider all of the rdd's within a stream - consider all 72

2. you want to do operations over the last 30 minutes..

we will consider last 6 rdd's always.

Sum() is stateless and work for each rdd individually.

what if we want to get a running total?

in this case we should be using a stateful transformation.

we need to convert his normal rdd's into pair rdd's.

and add a dummy key to all these elements.

157

(k,1)

(k,5)

(k,7)

updateStateByKey() is a stateful transformation.

when we talk about stateless we just talk about 1 single rdd. - stateless

considering the entire stream we talked about including all rdds. - stateful

considering a few rdds (window) - stateful

lets say the batch interval is 10 seconds..

that means a new rdd will be created every 10 seconds.

3 things
=========

1. batch interval - 10 seconds

2. window size - 30 seconds

3. sliding interval - 20 seconds

countByWindow() - stateful transformation

Spark Streaming Session - 5

============================

//creating spark streaming context

val ssc = new StreamingContext(sc, Seconds(5))

//lines is a dstream
val lines = ssc.socketTextStream("localhost",9998)

//words is a transformed dstream

val words = lines.flatMap(x => x.split(" "))

val pairs = words.map(x => (x,1))

val wordCounts = pairs.reduceByKey((x,y) => x+y)

wordCounts.print()

ssc.start()

Spark Streaming Session - 6

============================

I want to calculate the frequency of each word across the entire stream...

stateful transformation..
updateStateByKey is a stateful transformation we can think of using.

this requires 2 steps:

1. Define a state to start with.

2. a function to update the state

big data is interesting big data is fun

(big,1)
(data,1)
(is,1)
(interesting,1)
(big,1)
(data,1)
(is,1)
(fun,1)

(big,1)
(big,1)
(data,1)
(data,1)
(is,1)
(is,1)
(fun,1)

rdd1

(big,{1,1}) newValues = {1,1} 2 previousState = 0 , (big,2)

(data,{1,1}) newValues = {1,1} previousState = 0 , (data,2)
(is,{1,1}) (is,2)
(fun,{1}) (fun,1)

rdd2

big data is vast

(big,1) newValues = {1} previousState = 2 , (big,3)

(data,1) (data,3)
(is,1) (is,3)
(vast,1) (vast,1)

when we talk about stateful transformations then we have to do checkpointing.

=====================================

Spark Streaming Session - 7

=============================

reduceByKey - stateless transformation - one rdd

updateStateByKey - stateful transformation , it considers the entire dstream from the

beginning to the end. - all rdd's

sliding window -

1. batch interval - the time in which each rdd is created.

if we have the batch interval as 2 seconds. that means after every 2 seconds a new
rdd will created.

2. window size - 10 seconds.. we are interested in the last 5 rdd's always.

3. Sliding interval. - 2 seconds..

after every 2 seconds one oldest rdd will go away and one new rdd will come in.

if this sliding interval is 4 seconds.

after every 4 seconds.. 2 oldest rdd's will go away and 2 new rdd will come in.

sliding interval has to be a integral multiple of batch interval.

window size should also be an integral multiple of batch size.

reduceByKeyAndWindow

hello,1
how,1
are,1
you,1
hello,1

reduceByKeyAndWindow transformation takes 4 parameters.

1. the summary function. (x,y) => x+y

2. the inverse function. (x,y) => x-y

3. the window size. Seconds(10)

4. the sliding interval. Seconds(2)

My batch interval is 2 seconds.

our problem statement is find the frequency of each word in the 10 seconds sliding
window.

when will we see the very first result - 10 seconds.

when do you see the second result. - after 12th second

Spark Streaming Session - 8

============================

reduceByKey - stateless

updateStateByKey - stateful (considers entire stream)

reduceByKeyAndWindow - stateful (sliding window) - pair rdd is required.

reduceByWindow - here pair rdd is not required

countByWindow - it will count the number of lines in the window.

Traditional spark streaming - lower level constructs

Dealing at the level of rdd's.

Structured API's are trending these days.

Spark Structured Streaming

TRENDY TECH

Week 15 Scala – PySpark equivalent programs

Week 15 is based on Spark streaming where we need real time stream. We will use
socket and file
Generalized changes that are required in every program
1. To start cmd prompt for PySpark. We write PySpark instead of scala-shell.
2. Remove all val, var keyword as python does not have val and var types.
3. Anonymous functions are replaced with lambda in python.
4. Comment is given using # in python instead of // in scala
Note
1. Best practice is to use your own itversity hdfs location in the program for input and
output files. You can also use Linux root as shown in video.
2. There are be many ways to get the output for particular problem, we are showcasing
one way.
3. Changes are highlighted in yellow.
4. Ncat is Linux utility. For windows, follow below steps.
WEEK 15 – Spark Streaming Part 1 TRENDY TECH
TRENDY TECH
Steps for streaming program execution on windows.
1. Download the Free Nmap Security Scanner for Linux/Mac/Windows Download nmap-stable setup and install
2. In code give localhost 9998
3. Run the code... It will give error because no port is listening ... That's ok
4. Open cmd – go to Nmap folder
5. ncat -lvp 9998
6. start typing words
7. Cross check in your program

WEEK 15 – Spark Streaming Part 1 TRENDY TECH

TRENDY TECH

Problem Statement: Write a real time word count program

Solution:
Scala Spark Program PySpark Program
Spark-shell –master local[2] PySpark –master local[2]

import org.apache.spark._ from PySpark import *

import org.apache.spark.streaming._ from PySpark.streaming import *
import org.apache.spark.streaming.StreamingContext._
sc.setLogLevel("ERROR")
//creating spark streaming context #creating spark streaming context
val ssc = new StreamingContext(sc, Seconds(2)) ssc = StreamingContext(sc, 2)
//lines is a dstream #lines is a dstream
val lines = ssc.socketTextStream("localhost",9998) lines = ssc.socketTextStream("localhost", 9998)
//words is a transformed dstream #words is a transformed dstream
val words = lines.flatMap(x => x.split(" ")) words = lines.flatMap(lambda x: x.split())
val pairs = words.map(x => (x,1)) pairs = words.map(lambda x: (x, 1))
val wordCounts = pairs. reduceByKey((x,y) => x+y) wordCounts = pairs.reduceByKey(lambda x, y: x + y)
wordCounts.print() wordCounts.pprint()
ssc.start() ssc.start()

Specific changes that are required in above program

1. In scala we give Seconds(2) whereas in python you can give directly 2
2. We use pprint in python. The pprint module provides a capability to “pretty-print” arbitrary Python data structures in a
well-formatted and more readable way.

WEEK 15 – Spark Streaming Part 1 TRENDY TECH

TRENDY TECH

Problem Statement: Write real time stateless word count program in IDE
Solution:
Scala Spark Program PySpark Program
Create word.scala Create word.py
import org.apache.spark.SparkContext from PySpark import *
import org.apache.spark.streaming.Seconds from PySpark.streaming import *
import org.apache.spark.streaming.StreamingContext sc =SparkContext("local[2]","APP")
sc.setLogLevel("ERROR")
object StreamingWordCount extends App{ #creating spark streaming context
val sc = new SparkContext("local[*]","wordcount") ssc = StreamingContext(sc, 2)
//creating spark streaming context #lines is a dstream
val ssc = new StreamingContext(sc, Seconds(5)) lines = ssc.socketTextStream("localhost", 9998)
//lines is a dstream #words is a transformed dstream
val lines = ssc.socketTextStream("localhost",9998) words = lines.flatMap(lambda x: x.split())
//words is a transformed dstream pairs = words.map(lambda x: (x, 1))
val words = lines.flatMap(x => x.split(" ")) wordCounts = pairs.reduceByKey(lambda x, y: x + y)
val pairs = words.map(x => (x,1)) wordCounts.pprint()
val wordCounts = pairs. reduceByKey((x,y) => x+y) ssc.start()
wordCounts.print() ssc.awaitTermination()
ssc.start()
ssc.awaitTermination()
}

Specific changes that are required in above program

1. In IDE you need to create SparkContext object
2. Last line we need to write code to wait for termination which is same.
3. Green color is the change from PySpark shell to IDE
4. Yellow highlight is change from scala to PySpark in shell

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TRENDY TECH
Problem Statement: Write a real time stateful word count program in IDE
Solution:
Scala Spark Program PySpark Program
Create word1.scala Create word1.py
import org.apache.spark.SparkContext from PySpark import *
import org.apache.spark.streaming.Seconds from PySpark.streaming import *
import org.apache.spark.streaming.StreamingContext

object StreamingWordCount extends App{

val sc = new SparkContext("local[*]","wordcount") sc =SparkContext("local[2]","APP")
//creating spark streaming context sc.setLogLevel("ERROR")
val ssc = new StreamingContext(sc, Seconds(5)) #creating spark streaming context
//lines is a dstream ssc = StreamingContext(sc, 2)
val lines = ssc.socketTextStream("localhost",9998) #lines is a dstream
lines = ssc.socketTextStream("localhost", 9998)
ssc.checkpoint(".")
def updatefunc(newValues:Seq[Int],previousState:Option[Int]): Option[Int]={ ssc.checkpoint(".")
val newCount= previousState.getOrElse(0) + newValues.sum def updatefunc(newValues, previousState):
Some(newCount) if previousState is None :
} previousState = 0
val words = lines.flatMap(x => x.split(" ")) return sum(newValues, previousState)
val pairs = words.map(x => (x,1))
val wordCounts = pairs.updateStateByKey(updatefunc) words = lines.flatMap(lambda x: x.split())
wordCounts.print() pairs = words.map(lambda x: (x, 1))
ssc.start() wordCounts = pairs.updateStateByKey(updatefunc)
ssc.awaitTermination() wordCounts.pprint()
} ssc.start()
ssc.awaitTermination()

Specific changes that are required in above program

1. updateFunc function definition is changed
2. Yellow highlight is change in previous and this scala program
3. Green highlight is change between scala to PySpark.

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TRENDY TECH

Problem Statement: Write a real time stateful word count program using sliding window in IDE
Solution:
Scala Spark Program PySpark Program
Create word1.scala Create word1.py
import org.apache.spark.SparkContext from PySpark import *
import org.apache.spark.streaming.Seconds from PySpark.streaming import *
import org.apache.spark.streaming.StreamingContext sc =SparkContext("local[2]","APP")
sc.setLogLevel("ERROR")
object StreamingWordCount extends App{ #creating spark streaming context
val sc = new SparkContext("local[*]","wordcount") ssc = StreamingContext(sc, 2)
//creating spark streaming context sss.checkpoint(".")
val ssc = new StreamingContext(sc, Seconds(5)) #lines is a dstream
//lines is a dstream lines = ssc.socketTextStream("localhost", 9998)
val lines = ssc.socketTextStream("localhost",9998) #words is a transformed dstream
ssc.checkpoint(".") wordCounts = lines.flatMap(lambda x: x.split()) \
//words is a transformed dstream .words.map(lambda x: (x, 1)) \
val wordCounts = lines.flatMap(x => x.split(" ")) .reduceByKeyAndWindow(lambda x, y: int(x) + int(y), lambda x, y: int(x) -
.map(x => (x,1)) int(y), 10, 2)
.reduceByKeyAndWindow((x,y)=>x+y,(x,y)=>x-y,Seconds(10),Seconds(2))
wordCounts.print() wordCounts.pprint()
ssc.start() ssc.start()
ssc.awaitTermination() ssc.awaitTermination()
}

// show filter #add filter to print count > 2

.filter(x => x._2>0) .filter(lambda x:x[1]>2)

Specific changes that are required in above program

1. We are converting x and y into int and seconds are given directly.

WEEK 15 – Spark Streaming Part 1 TRENDY TECH

TRENDY TECH
Problem Statement: Write a real time stateful word count program using sliding window and named function in IDE
Solution:
Scala Spark Program PySpark Program
import org.apache.spark.SparkContext from PySpark import *
import org.apache.spark.streaming.Seconds from PySpark.streaming import *
import org.apache.spark.streaming.StreamingContext sc =SparkContext("local[2]","APP")
sc.setLogLevel("ERROR")
object StreamingWordCount extends App{ #creating spark streaming context
val sc = new SparkContext("local[*]","wordcount") ssc = StreamingContext(sc, 2)
//creating spark streaming context sss.checkpoint(".")
val ssc = new StreamingContext(sc, Seconds(5)) #lines is a dstream
//lines is a dstream lines = ssc.socketTextStream("localhost", 9998)
val lines = ssc.socketTextStream("localhost",9998)
ssc.checkpoint(".") def summaryFuct(x,y):
def summaryFuct(x:Int, y:Int)={x+y} return x + y

def inverseFuct(x:Int, y:Int)={x-y} def inverseFuct(x, y):

return x - y
//words is a transformed dstream
val wordCounts = lines.flatMap(x => x.split(" ")) #words is a transformed dstream
.map(x => (x,1)) wordCounts = lines.flatMap(lambda x: x.split()) \
.reduceByKeyAndWindow(summaryFuct(_,_),inverseFuct(_,_),Seconds(10),Seconds(2)) .words.map(lambda x: (x, 1)) \
.filter(x => x._2>0) .reduceByKeyAndWindow(summaryFuct, inverseFuct, 10, 2) \
.filter(lambda x:x[1]>0)
wordCounts.print()
ssc.start() wordCounts.pprint()
ssc.awaitTermination() ssc.start()
} ssc.awaitTermination()

Specific changes that are required in above program

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TRENDY TECH
1. Function definition is different in python. We don’t specify datatype for parameters. Also, while calling function, just
give function name.

WEEK 15 – Spark Streaming Part 1 TRENDY TECH

TRENDY TECH
Problem Statement: Write a real time stateful word count program using sliding window and named function in IDE.
Implement reduceByWindow method which does not need pair RDD
Solution:
Scala Spark Program PySpark Program
import org.apache.spark.SparkContext from PySpark import *
import org.apache.spark.streaming.Seconds from PySpark.streaming import *
import org.apache.spark.streaming.StreamingContext sc =SparkContext("local[2]","APP")
sc.setLogLevel("ERROR")
object StreamingWordCount extends App{ #creating spark streaming context
val sc = new SparkContext("local[*]","wordcount") ssc = StreamingContext(sc, 2)
//creating spark streaming context sss.checkpoint(".")
val ssc = new StreamingContext(sc, Seconds(5)) #lines is a dstream
//lines is a dstream lines = ssc.socketTextStream("localhost", 9998)
val lines = ssc.socketTextStream("localhost",9998)
ssc.checkpoint(".")
def summaryFuct(x:String, y: String)={ (x.toInt + y.toInt).toString()} def summaryfuct(x, y):
def inverseFuct(x: String, y: String)= { (x.toInt - y.toInt).toString()} return str((int(x)+int(y)))

//words is a transformed dstream

val wordCounts = lines.flatMap(x => x.split(" ")) def inversefuct(x, y):
.map(x => (x,1)) return str((int(x)-int(y)))
.reduceByWindow(summaryFuct(_,_),inverseFuct(_,_),Seconds(10),Seconds(2))
.filter(x => x._2>0)
wordCounts= lines.reduceByWindow(summaryfuct, inversefuct, 10, 2)
wordCounts.print()
ssc.start()
ssc.awaitTermination() wordCounts.pprint()
} ssc.start()
ssc.awaitTermination()

Specific changes that are required in above program

1. reduceByWindow does not need pair RDD hence we don’t need flapMap and map

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Problem Statement: Write a real time program to count number of lines in window.
Solution:
Scala Spark Program PySpark Program
import org.apache.spark.SparkContext from PySpark import *
import org.apache.spark.streaming.Seconds from PySpark.streaming import *
import org.apache.spark.streaming.StreamingContext sc =SparkContext("local[2]","APP")
sc.setLogLevel("ERROR")
object StreamingWordCount extends App{ #creating spark streaming context
val sc = new SparkContext("local[*]","wordcount") ssc = StreamingContext(sc, 2)
//creating spark streaming context sss.checkpoint(".")
val ssc = new StreamingContext(sc, Seconds(2)) #lines is a dstream
//lines is a dstream lines = ssc.socketTextStream("localhost", 9998)
val lines = ssc.socketTextStream("localhost",9998)
ssc.checkpoint(".")

//words is a transformed dstream

val wordCounts = lines.countByWindow(Seconds(10),Seconds(2))
wordCounts= lines.countByWindow(10, 2)
wordCounts.print()
ssc.start() wordCounts.pprint()
ssc.awaitTermination() ssc.start()
} ssc.awaitTermination()

Specific changes that are required in above program

1. To specify seconds for interval in python, specify directly.

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Week 16 Scala – PySpark equivalent programs

Week 16 is based on Spark structured streaming where we need real time stream. We
will use socket and file as source for streaming
Generalized changes that are required in every program
1. Remove all val, var keyword as python does not have val and var types.
2. Anonymous functions are replaced with lambda in python.
3. Comment is given using # in python instead of // in scala
4. To write multi line code in pyspark, end every line with \ except the last line.
Note
1. There are be many ways to get the output for particular problem, we are showcasing
one way.
2. Changes are highlighted in yellow.
3. Ncat is Linux utility. For windows, follow below steps.

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Steps for streaming program execution on windows.
1. Download the Free Nmap Security Scanner for Linux/Mac/Windows Download nmap-stable setup and install
2. In code give localhost 9998
3. Run the code... It will give error because no port is listening ... That's ok
4. Open cmd – go to Nmap folder
5. ncat -lvp 9998
6. start typing words
7. Cross check in your program

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TRENDY TECH

Problem Statement: Write a real time word count program using spark structured stream
Solution:
Scala Spark Program PySpark Program
import org.apache.spark.sql.SparkSession from pyspark.sql import SparkSession

val spark = SparkSession.builder spark = SparkSession.builder \

.master("local[2]") .master("local[2]") \
.appName("My Streaming Application") .appName("My Streaming Application") \
.getOrCreate() .getOrCreate()

// read from the stream # read from the stream

val linesDf = spark.readStream linesDf = spark.readStream \
.format("socket") .format("socket") \
.option("host", "localhost") .option("host", "localhost") \
.option("port", "12345") .option("port", "12345") \
.load() .load() \
linesDf.printSchema() linesDf.printSchema()

// process # process
val wordsDf=linesDf.selectExpr("explode(split(value,' ')) as word") wordsDf=linesDf.selectExpr("explode(split(value,' ')) as word")
val countsDf =wordsDf.groupBy("word").count() countsDf =wordsDf.groupBy("word").count()

// write to the sink # write to the sink

val wordCountQuery = countsDf.writeStream wordCountQuery = countsDf.writeStream \
.format('console') .format('console') \
.outputMode('complete') .outputMode('complete') \
.option("checkpointLocation","checkpoint-location1") .option("checkpointLocation","checkpoint-location1") \
.start() .start()
wordCountQuery.awaitTermination() wordCountQuery.awaitTermination()

Add below two bold lines and run again, you can see the performance gain. NO change in pyspark

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spark = SparkSession.builder spark = SparkSession.builder \
.master("local[2]") .master("local[2]") \
.appName("My Streaming Application") .appName("My Streaming Application") \
.config("spark.sql.shuffle.partitions",3) .config("spark.sql.shuffle.partitions",3) \
.config("spark.streaming.stopGracefullyOnShutdown","true" .config("spark.streaming.stopGracefullyOnShutdown","true") \
.getOrCreate() .getOrCreate()

Add below bold line to Set triggering time. Small difference in pyspark
wordCountQuery = countsDf.writeStream wordCountQuery = countsDf.writeStream \
.format('console') .format('console') \
.outputMode('complete') .outputMode('complete') \
.option("checkpointLocation","checkpoint-location2") .option("checkpointLocation","checkpoint-location2") \
.trigger(Trigger.ProcessingTime("5 seconds")) .trigger(processingTime='5 seconds') \
.start() .start()

Specific changes that are required in above program

1. Import is different
2. Need to remove all val keywords
3. Add \ at end of line to continue code to next line.
4. .trigger(Trigger.ProcessingTime("5 seconds")) in scala is changed to .trigger(processingTime='5 seconds') \ in pyspark

WEEK 16 – Spark Streaming Part 2 TRENDY TECH

TRENDY TECH

Problem Statement: Read json file and write json file using spark structured streaming
Solution:
Scala Spark Program PySpark Program
import org.apache.spark.sql.SparkSession from pyspark.sql import SparkSession

val spark = SparkSession.builder spark = SparkSession.builder \

.master("local[2]") .master("local[2]") \
.appName("My Streaming Application") .appName("My Streaming Application") \
.config("spark.sql.shuffle.partitions",3) .config("spark.sql.shuffle.partitions",3) \
.config("spark.streaming.stopGracefullyOnShutdown","true") .config("spark.streaming.stopGracefullyOnShutdown","true") \
.config("spark.sql.streaming.schemaInference","true") .config("spark.sql.streaming.schemaInference","true") \
.getOrCreate() .getOrCreate()

// read from file source # read from file source

val ordersDf = spark.readStream ordersDf = spark.readStream \
.format("json") .format("json") \
.option("path", "myinputfolder") .option("path", "myinputfolder") \
.load() .load() \

// process # process
val ordersDf.createOrReplaceTempView("orders") ordersDf.createOrReplaceTempView("orders")
val completeOrders =spark.sql("select * from orders where completeOrders =spark.sql("select * from orders where
order_status='COMPLETE'") order_status='COMPLETE'")

// write to the sink # write to the sink

val wordCountQuery = completeOrders.writeStream wordCountQuery = completeOrders.writeStream \
.format('json') .format('json') \
.outputMode('append') .outputMode('append') \
.option("path", "myoutputfolder") .option("path", "myoutputfolder") \
.option("checkpointLocation","checkpoint-location5") .option("checkpointLocation","checkpoint-location5") \
.trigger(Trigger.ProcessingTime("5 seconds")) .trigger(processingTime='30 seconds') \
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.start() .start()

wordCountQuery.awaitTermination() wordCountQuery.awaitTermination()

Add bold line to set micro batch of one file. NO change in pyspark
val ordersDf = spark.readStream ordersDf = spark.readStream \
.format("json") .format("json") \
.option("path", "myinputfolder") .option("path", "myinputfolder") \
.option("maxFilesPerTrigger",1) .option("maxFilesPerTrigger",1) \
.load() .load()\

Specific changes that are required in above program

1. Import is different
2. Need to remove all val keywords
3. Add \ at end of line to continue code to next line.
4. .trigger(Trigger.ProcessingTime("5 seconds")) in scala is changed to .trigger(processingTime='5 seconds') \ in pyspark

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TRENDY TECH
Problem Statement: Write a real time tumbling window program to read json file.
Solution:
Scala Spark Program PySpark Program
import org.apache.spark.SparkConf from pyspark.sql import SparkSession
import org.apache.spark.sql.SparkSession from pyspark.sql.functions import from_json, window
import org.apache.log4j.Level from pyspark.sql.types import StructType, StructField, IntegerType ,
import org.apache.log4j.Logger StringType
import java.sql.Timestamp from pyspark.sql import functions as F
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.functions._
import org.apache.spark.sql.streaming.Trigger

object TumblingDemo extends App {

Logger.getLogger("org").setLevel(Level.ERROR)

val spark = SparkSession.builder() spark = SparkSession.builder \

.master("local[2]") .master("local[2]") \
.appName("Tumbling") .appName("Tumbling") \
.config("spark.streaming.stopGracefullyOnShutdown","true") .config("spark.streaming.stopGracefullyOnShutdown", "true") \
.config("spark.sql.shuffle.partitions",3) .config("spark.sql.shuffle.partitions", 3) \
.getOrCreate() .getOrCreate()

//1. read # 1. read

val ordersDf = spark.readStream ordersDf = spark.readStream \
.format("socket") .format("socket") \
.option("host","localhost") .option("host", "localhost") \
.option("port","12345") .option("port", "12345") \
.load .load()

//define own schema instead of infering it # define own schema instead of infering it
val orderSchema= StructType(List( orderSchema = StructType([
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StructField("order_id", IntegerType), StructField("order_id", IntegerType()),
StructField("order_date", StringType), StructField("order_date", StringType()),
StructField("order_customer_id", IntegerType), StructField("order_customer_id", IntegerType()),
StructField("order_status", StringType), StructField("order_status", StringType()),
StructField("amount", IntegerType), StructField("amount", IntegerType()),
)) ])

//2. process # 2. process

val valueDF = ordersDf.select(from_json(col("value"), valueDF = ordersDf.select(from_json(F.col("value"),
orderSchema).alias("value")) orderSchema).alias("value"))

val refinedOrderDF = valueDF.select("value.*") refinedOrderDF = valueDF.select("value.*")

val windowAggDF = refinedOrderDF windowAggDF = refinedOrderDF \

.groupBy(window(col("order_date"),"15 minute")) .groupBy(window(F.col("order_date"), "15 minute")) \
.agg(sum("amount") .alias("totalInvoice")) .agg(F.sum("amount").alias("totalInvoice"))

val opDf= windowAggDF.select("window.start","window.end","totalInvoice") opDf = windowAggDF.select("window.start", "window.end", "totalInvoice")

val ordersQuery = opDf.writeStream ordersQuery = opDf.writeStream \

.format("console") .format("console") \
.outputMode("update") .outputMode("update") \
.option("checkpointLocation","chk-Loc1") .option("checkpointLocation", "chk-Loc1") \
.trigger(Trigger.ProcessingTime("15 second")) .trigger(processingTime="15 second")\
.start() .start()

ordersQuery.awaitTermination() ordersQuery.awaitTermination()

Add bold line to use Watermark NO change in pyspark

val windowAggDF = refinedOrderDF windowAggDF = refinedOrderDF \
.withWatermark("order_date","30 minute") .withWatermark("order_date","30 minute") \
.groupBy(window(col("order_date"),"15 minute")) .groupBy(window(F.col("order_date"), "15 minute")) \
.agg(sum("amount") .agg(F.sum("amount").alias("totalInvoice"))
.alias("totalInvoice"))

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Add bold argument to make the window sliding NO change in pyspark
windowAggDF = refinedOrderDF windowAggDF = refinedOrderDF \
.withWatermark("order_date","30 .withWatermark("order_date","30 minute") \
minute").groupBy(window(F.col("order_date"), "15 minute","1 minute")) .groupBy(window(F.col("order_date"), "15 minute","1 minute")) \
.agg(F.sum("amount").alias("totalInvoice")) .agg(F.sum("amount").alias("totalInvoice"))

Specific changes that are required in above program

1. Import is different
2. Need to remove all val keywords
3. Add \ at end of line to continue code to next line.
4. .trigger(Trigger.ProcessingTime("15 seconds")) in scala is changed to .trigger(processingTime='15 seconds') \ in pyspark
5. In pyspark some functions such as col and sum cannot be used directly hence we add from pyspark.sql import functions
as F and then use F.col or F.sum
6. List() is changed to []
7. Replace load with load(), same for IntegerType(),StringType()
8. Replace builder() to builder

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TRENDY TECH

Problem Statement: Write a real time join program using spark structured streaming
Solution:
Scala Spark Program PySpark Program
import org.apache.spark.sql.SparkSession from pyspark.sql import SparkSession
import org.apache.log4j.Level from pyspark.sql.functions import from_json, window
import org.apache.log4j.Logger from pyspark.sql.types import StructType, StructField, IntegerType,
import java.sql.Timestamp StringType, TimestampType
import org.apache.spark.sql.types.StructType from pyspark.sql import functions as F
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.types.TimestampType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.functions._
import org.apache.spark.sql.streaming.Trigger

val spark = SparkSession.builder() spark = SparkSession.builder \

.master("local[2]") .master("local[2]") \
.appName("join") .appName("join") \
.config("spark.streaming.stopGracefullyOnShutdown", "true") .config("spark.streaming.stopGracefullyOnShutdown", "true") \
.config("spark.sql.shuffle.partitions", 3) .config("spark.sql.shuffle.partitions", 3) \
.getOrCreate() .getOrCreate()

// define own schema instead of infering it # define own schema instead of infering it
val impressionSchema = StructType(List( impressionSchema = StructType([
StructField("impressionID", StringType), StructField("impressionID", StringType()),
StructField("ImpressionTime", TimestampType), StructField("ImpressionTime", TimestampType()),
StructField("CampaignName", StringType), StructField("CampaignName", StringType()),
)) ])

val clickSchema = StructType(List( clickSchema = StructType([

StructField("clickID", StringType), StructField("clickID", StringType()),
StructField("ClickTime", TimestampType), StructField("ClickTime", TimestampType()),
)) ])

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// read the stream # read the stream
val impressionsDf = spark.readStream impressionsDf = spark.readStream \
.format("socket") .format("socket") \
.option("host", "localhost") .option("host", "localhost") \
.option("port", "12342") .option("port", "12342") \
.load() .load()

val clicksDf = spark.readStream clicksDf = spark.readStream \

.format("socket") .format("socket") \
.option("host", "localhost") .option("host", "localhost") \
.option("port", "12343") .option("port", "12343") \
.load() .load()

// structure the data based on the schema defined - impressionDf # structure the data based on the schema defined - impressionDf
val valueDF1 = impressionsDf.select(from_json(col("value"), valueDF1 = impressionsDf.select(from_json(F.col("value"),
impressionSchema).alias("value")) impressionSchema).alias("value"))
val impressionDfNew = valueDF1.select("value.*") impressionDfNew = valueDF1.select("value.*")

// structure the data based on the schema defined - clickDf # structure the data based on the schema defined - clickDf
val valueDF2 = clicksDf.select(from_json(col("value"), valueDF2 = clicksDf.select(from_json(F.col("value"),
clickSchema).alias("value")) clickSchema).alias("value"))
val clickDfNew = valueDF2.select("value.*") clickDfNew = valueDF2.select("value.*")

// join condition #join condition

val joinExpr = impressionDfNew.col("ImpressionID") === joinExpr = impressionDfNew["ImpressionID"] == clickDfNew["clickID"]
clickDfNew.col("clickID")
#join type
// join type joinType="inner"
val joinType="inner"

// joining both the streaming data frames #joining both the streaming data frames
val joinedDf = impressionDfNew.join(clickDfNew,joinExpr,joinType) joinedDf = impressionDfNew.join(clickDfNew,joinExpr,joinType) \
.drop(clickDfNew.col("clickID")) .drop(clickDfNew["clickID"])

// output to the sink #output to the sink

val campaignQuery = joinedDf.writeStream campaignQuery = joinedDf.writeStream \
.format("console") .format("console") \
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.outputMode("append") .outputMode("append") \
.option("checkpointLocation", "chk-Loc2") .option("checkpointLocation", "chk-Loc2") \
.trigger(processingTime="15 second") .trigger(processingTime="15 second")\
.start() .start()

campaignQuery.awaitTermination() campaignQuery.awaitTermination()

Add bold code to use watermark No change in pyspark except F.col

// structure the data based on the schema defined - impressionDf # structure the data based on the schema defined - impressionDf
valueDF1 = impressionsDf.select(from_json(col("value"), valueDF1 = impressionsDf.select(from_json(F.col("value"),
impressionSchema).alias("value")) impressionSchema).alias("value"))
impressionDfNew = impressionDfNew =
valueDF1.select("value.*").withWatermark("impressionTime","30 minute") valueDF1.select("value.*").withWatermark("impressionTime","30 minute")
// structure the data based on the schema defined - clickDf # structure the data based on the schema defined - clickDf
valueDF2 = clicksDf.select(from_json(col("value"), clickSchema).alias("value")) valueDF2 = clicksDf.select(from_json(F.col("value"),
clickDfNew = valueDF2.select("value.*").withWatermark("clickTime","30 clickSchema).alias("value"))
minute") clickDfNew = valueDF2.select("value.*").withWatermark("clickTime","30
minute")

Specific changes that are required in above program

1. Import is different
2. .trigger(Trigger.ProcessingTime("15 seconds")) in scala is changed to .trigger(processingTime='15 seconds') \ in pyspark
3. In pyspark some functions such as col cannot be used directly hence we add from pyspark.sql import functions as F and
then use F.col
4. List() is changed to []
5. Replace load with load(), same for IntegerType(),StringType()
6. Replace builder() to builder
7. In pyspark dataframe does not have col function , hence we give column name using []. Also Replace === with ==
impressionDfNew["ImpressionID"] == clickDfNew["clickID"]

WEEK 16 – Spark Streaming Part 2 TRENDY TECH