Fill in the Blanks: Mastering Testing and Debugging PySpark Applications


Fill in the Blanks Mastering Testing and Debugging PySpark ApplicationsOnline version Drills to master testing and debugging of PySpark applications by Good Sam 1 import unittest value expected_data getOrCreate result_df.collect() == expected_df.collect @classmethod expected_df = self.spark.createDataFrame def test_add_doubled_column(self if __name__ == "__main__ self.assertTrue appName("PySparkTesting from pyspark.sql.functions import col expected_data = [(1, 2), (2, 4), (3, 6 cls.spark = SparkSession.builder unittest.main master("local[2 def setUpClass(cls df = self.spark.createDataFrame def add_doubled_column(df, column_name @classmethod return df.withColumn("doubled", col(column_name) * 2 value", "doubled 1,), (2,), (3 cls.spark.stop def tearDownClass(cls from pyspark.sql import SparkSession result_df = add_doubled_column(df, "value class PySparkTest(unittest.TestCase Scenario 1 : Unit Testing a Data Transformation Function in PySpark Problem : You have developed a function that transforms a DataFrame by adding a new column which doubles the values of an existing column . You need to write a unit test to validate that this function performs as expected . Solution : import unittest from pyspark . sql import SparkSession from pyspark . sql . functions import col def add_doubled_column ( df , column_name ) : return df . withColumn ( " doubled " , col ( column_name ) * 2 ) class PySparkTest ( unittest . TestCase ) : @classmethod def setUpClass ( cls ) : cls . spark = SparkSession . builder \ . appName ( " PySparkTesting " ) \ . master ( " local [ 2 ] " ) \ . getOrCreate ( ) @classmethod def tearDownClass ( cls ) : cls . spark . stop ( ) def test_add_doubled_column ( self ) : # Create a sample DataFrame df = self . spark . createDataFrame ( [ ( 1 , ) , ( 2 , ) , ( 3 , ) ] , [ " value " ] ) # Apply the transformation result_df = add_doubled_column ( df , " value " ) # Expected DataFrame expected_data = [ ( 1 , 2 ) , ( 2 , 4 ) , ( 3 , 6 ) ] expected_df = self . spark . createDataFrame ( expected_data , [ " value " , " doubled " ] ) # Assert that the transformed DataFrame matches the expected DataFrame self . assertTrue ( result_df . collect ( ) = = expected_df . collect ( ) , " The transformed DataFrame does not match the expected DataFrame . " ) # Run the tests if __name__ = = " __main__ " : unittest . main ( ) - - - ) : ) ) : ) : \ . " ) \ . ] " ) \ . ( ) ) : ( ) ) : # Create a sample DataFrame ( [ ( , ) ] , [ " " ] ) # Apply the transformation " ) # Expected DataFrame ) ] ( , [ " " ] ) # Assert that the transformed DataFrame matches the expected DataFrame ( ( ) , " The transformed DataFrame does not match the expected DataFrame . " ) # Run the tests " : ( ) Explanation : Function Definition : add_doubled_column takes a DataFrame and a column name , then returns the DataFrame with an additional column where the values are doubled . Unit Test Setup : Using unittest framework to structure PySpark tests , with setUpClass and tearDownClass for initializing and stopping the Spark session . Test Method : test_add_doubled_column creates a sample DataFrame , applies the transformation , and compares the result with an expected DataFrame to ensure the function works correctly . Assertions : Uses assertTrue with a condition that checks if the collected data from the result DataFrame matches the expected DataFrame , providing clear feedback if the test fails . This unit testing approach is a fundamental part of developing reliable PySpark applications , allowing you to validate transformations and logic independently of the complete ETL pipeline . 2 partition_sizes = df.withColumn("partition_id", spark_partition_id()).groupBy("partition_id").agg(count("").alias("records_per_partition partition_sizes.show from pyspark.sql.functions import spark_partition_id, count Scenario 2 : Identifying and Resolving Data Skew in a Spark Job Debugging Common Issues in Spark Jobs Problem : You notice that some tasks in your Spark job are taking significantly longer than others , suggesting a possible data skew . Solution : from pyspark . sql . functions import spark_partition_id , count # Analyze the distribution of data across partitions partition_sizes = df . withColumn ( " partition_id " , spark_partition_id ( ) ) . groupBy ( " partition_id " ) . agg ( count ( " " ) . alias ( " records_per_partition " ) ) # Check the output to identify skewed partitions partition_sizes . show ( ) # Analyze the distribution of data across partitions " ) ) # Check the output to identify skewed partitions ( ) Explanation : Identify Skew : Use spark_partition_id ( ) to add a column indicating the partition ID for each row , then group by this ID and count the records in each partition . Large discrepancies in these counts suggest skew . Resolution : Depending on the findings , consider redistributing the data either by repartitioning or using techniques like salting keys that have a large number of records . 3 from pyspark.sql.functions import udf from pyspark.sql.types import DoubleType df = df.withColumn("result", complex_formula_udf(df["input_column return (x * 2.54) / (x ** 0.5 + 2 complex_formula_udf = udf(complex_formula, DoubleType def complex_formula(x Scenario 4 : ( Advanced Topics ) Applying a Custom Calculation to a DataFrame Column Using a UDF Problem : You need to apply a complex mathematical formula to a column in a DataFrame , which is not directly supported by built - in Spark functions . Solution : from pyspark . sql . functions import udf from pyspark . sql . types import DoubleType # Define the UDF def complex_formula ( x ) : return ( x * 2 . 54 ) / ( x * * 0 . 5 + 2 ) # Register the UDF complex_formula_udf = udf ( complex_formula , DoubleType ( ) ) # Apply the UDF to a DataFrame df = df . withColumn ( " result " , complex_formula_udf ( df [ " input_column " ] ) ) - - - - - - # Define the UDF ) : ) # Register the UDF ( ) ) # Apply the UDF to a DataFrame " ] ) ) Explanation : UDF Definition : Create a Python function complex_formula that performs the calculation . UDF Registration : Convert the Python function into a UDF that Spark can use , specifying DoubleType ( ) as the return type to ensure correct data handling . Application : Use withColumn to apply the UDF to the DataFrame , creating a new column with the results of the calculation . 4 secure_df = mask_data(df, "sensitive_column from pyspark.sql.functions import col, sha2 return df.withColumn(column_name, sha2(col(column_name), 256 def mask_data(df, column_name Scenario 6 : ( Advanced Topics ) Enforcing Data Masking on Sensitive Columns Data Security and Governance in Spark Problem : You need to ensure that sensitive data , such as personal identifiers , is masked or anonymized in the dataset before it is used for analysis to comply with privacy regulations . Solution : from pyspark . sql . functions import col , sha2 # Function to mask sensitive data def mask_data ( df , column_name ) : return df . withColumn ( column_name , sha2 ( col ( column_name ) , 256 ) ) # Applying data masking to the DataFrame secure_df = mask_data ( df , " sensitive_column " ) - - - - # Function to mask sensitive data ) : ) ) # Applying data masking to the DataFrame " ) Explanation : Data Masking Function : sha2 is used to hash the sensitive column data , here using a 256 - bit SHA - 2 algorithm . Hashing transforms the sensitive data into a fixed - size string , which appears random and cannot be easily reversed . This ensures that the sensitive data is anonymized while maintaining a unique identifier for records . Application : The mask_data function applies this transformation to any specified column of the DataFrame . This method helps in protecting sensitive data and is part of data governance practices to comply with data security standards and regulations . This solution provides a practical approach to enhancing data security in PySpark through data masking , essential for protecting sensitive information and adhering to compliance requirements in data processing workflows . 5 def update_accumulator(value def zero(self, initialValue from pyspark import AccumulatorParam class DictAccumulatorParam(AccumulatorParam return value print(my_accumulator.value df.rdd.map(update_accumulator).collect my_accumulator.add({value: 1 v1[key] = v2[key def addInPlace(self, v1, v2 if key in v1 else v1[key] += v2[key for key in v2 my_accumulator = spark.sparkContext.accumulator({}, DictAccumulatorParam return v1 return Scenario 3 : Using Accumulators to Track Execution Metrics Monitoring and Optimizing Spark Job Performance Problem : You want to monitor specific operations within your Spark job , such as counting certain events or tracking the occurrence of specific conditions . Solution : from pyspark import AccumulatorParam class DictAccumulatorParam ( AccumulatorParam ) : def zero ( self , initialValue ) : return { } def addInPlace ( self , v1 , v2 ) : for key in v2 : if key in v1 : v1 [ key ] + = v2 [ key ] else : v1 [ key ] = v2 [ key ] return v1 # Create an accumulator my_accumulator = spark . sparkContext . accumulator ( { } , DictAccumulatorParam ( ) ) # Example usage in a transformation def update_accumulator ( value ) : my_accumulator . add ( { value : 1 } ) return value df . rdd . map ( update_accumulator ) . collect ( ) # Access the accumulated values print ( my_accumulator . value ) - - - - ) : ) : { } ) : : : ] : ] # Create an accumulator ( ) ) # Example usage in a transformation ) : } ) ( ) # Access the accumulated values ) Explanation : Custom Accumulator : Define a custom accumulator that operates on dictionaries , allowing you to count occurrences of various values throughout your job . Monitoring : Use the accumulator in a map operation or similar to increment counts based on data values or conditions encountered during job execution . This provides a way to track detailed metrics about what's happening within your Spark tasks . Insights : After the job , inspect the values of the accumulator to gain insights into the distribution or frequency of events , which can help in debugging and understanding job behavior . These scenarios equip you with practical methods for debugging and monitoring PySpark jobs , ensuring you can identify performance bottlenecks and operational issues , and apply effective solutions to maintain and improve job efficiency . 6 format("kafka start readStream kafka_topic_name = "stream_topic query = df_stream.writeStream outputMode("append option("subscribe", kafka_topic_name query.awaitTermination option("kafka.bootstrap.servers", kafka_bootstrap_servers df_stream.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING df_stream = spark appName("KafkaStreaming from pyspark.sql import SparkSession format("console kafka_bootstrap_servers = 'localhost:9092' getOrCreate spark = SparkSession.builder load Scenario 5 : ( Advanced Topics ) Consuming Streaming Data from Apache Kafka Integrating with Apache Kafka for Streaming Data Problem : You need to read streaming data from a Kafka topic for real - time processing in PySpark . Solution : from pyspark . sql import SparkSession # Create a Spark session spark = SparkSession . builder \ . appName ( " KafkaStreaming " ) \ . getOrCreate ( ) # Define Kafka parameters kafka_topic_name = " stream_topic " kafka_bootstrap_servers = 'localhost : 9092' # Read from Kafka df_stream = spark \ . readStream \ . format ( " kafka " ) \ . option ( " kafka . bootstrap . servers " , kafka_bootstrap_servers ) \ . option ( " subscribe " , kafka_topic_name ) \ . load ( ) # Processing the stream # Here you can define transformations , for example , parsing the Kafka message df_stream . selectExpr ( " CAST ( key AS STRING ) " , " CAST ( value AS STRING ) " ) # Start streaming query = df_stream . writeStream \ . outputMode ( " append " ) \ . format ( " console " ) \ . start ( ) query . awaitTermination ( ) - - # Create a Spark session \ . " ) \ . ( ) # Define Kafka parameters " # Read from Kafka \ . \ . " ) \ . ) \ . ) \ . ( ) # Processing the stream # Here you can define transformations , for example , parsing the Kafka message ) " ) # Start streaming \ . " ) \ . " ) \ . ( ) ( ) Explanation : Session Creation : Initialize a Spark session to handle streaming data . Kafka Configuration : Specify the Kafka bootstrap servers and the topic you are subscribing to . Streaming Read : Use readStream to connect to Kafka and continuously read the incoming stream . Data Processing : Apply any necessary transformations , here converting Kafka byte messages to strings for easier handling . Stream Output : Start the stream processing and output to the console for demonstration purposes . In production , this could be directed to databases , file systems , or other storage services . These solutions demonstrate how to extend PySpark's capabilities to handle custom data transformations with UDFs and integrate with real - time data streams from Apache Kafka , essential skills for advanced data engineering tasks in dynamic and scalable environments . 7 groupBy kafka_topic_name = "sales_data window(col("timestamp"), "1 minute option("subscribe", kafka_topic_name amount readStream alias("total_sales selectExpr("CAST(value AS STRING) as raw_data sales_data = sales_stream.select spark = SparkSession.builder writeStream col("store_id getOrCreate query.awaitTermination start query = aggregated_sales option("kafka.bootstrap.servers", kafka_bootstrap_servers appName("Real-Time Sales Aggregation get_json_object(col("raw_data"), "$.amount").cast("double").alias sales_stream = spark sum("amount get_json_object(col("raw_data"), "$.timestamp").alias("timestamp from pyspark.sql.functions import window, col get_json_object(col("raw_data"), "$.store_id").alias("store_id format("console kafka_bootstrap_servers = 'localhost:9092' option("startingOffsets", "earliest load format("kafka aggregated_sales = sales_data outputMode("complete from pyspark.sql import SparkSession Real - World Scenario : Processing Streaming Data from Apache Kafka with PySpark Scenario : Real - Time Data Aggregation from Multiple Sources Problem : You need to aggregate real - time sales data coming from multiple store locations via Apache Kafka to monitor overall sales performance and generate timely reports . Solution : from pyspark . sql import SparkSession from pyspark . sql . functions import window , col # Initialize Spark Session spark = SparkSession . builder \ . appName ( " Real - Time Sales Aggregation " ) \ . getOrCreate ( ) # Define Kafka parameters kafka_topic_name = " sales_data " kafka_bootstrap_servers = 'localhost : 9092' # Read streaming data from Kafka sales_stream = spark \ . readStream \ . format ( " kafka " ) \ . option ( " kafka . bootstrap . servers " , kafka_bootstrap_servers ) \ . option ( " subscribe " , kafka_topic_name ) \ . option ( " startingOffsets " , " earliest " ) \ . load ( ) \ . selectExpr ( " CAST ( value AS STRING ) as raw_data " ) # Parse the streaming data sales_data = sales_stream . select ( get_json_object ( col ( " raw_data " ) , " $ . store_id " ) . alias ( " store_id " ) , get_json_object ( col ( " raw_data " ) , " $ . amount " ) . cast ( " double " ) . alias ( " amount " ) , get_json_object ( col ( " raw_data " ) , " $ . timestamp " ) . alias ( " timestamp " ) ) # Aggregate data over a 1 - minute window aggregated_sales = sales_data \ . groupBy ( window ( col ( " timestamp " ) , " 1 minute " ) , col ( " store_id " ) ) \ . sum ( " amount " ) \ . alias ( " total_sales " ) # Write the results to a sink , e . g . , console for testing query = aggregated_sales \ . writeStream \ . outputMode ( " complete " ) \ . format ( " console " ) \ . start ( ) query . awaitTermination ( ) - - - # Initialize Spark Session \ . " ) \ . ( ) # Define Kafka parameters " # Read streaming data from Kafka \ . \ . " ) \ . ) \ . ) \ . " ) \ . ( ) \ . " ) # Parse the streaming data ( " ) , ( " " ) , " ) ) # Aggregate data over a 1 - minute window \ . ( " ) , " ) ) \ . " ) \ . " ) # Write the results to a sink , e . g . , console for testing \ . \ . " ) \ . " ) \ . ( ) ( ) Explanation : Kafka Integration : The solution begins by connecting to a Kafka topic that streams sales data , ensuring all messages from the earliest are consumed . Data Parsing : Using Spark's get_json_object to extract relevant fields from JSON formatted messages ensures that each piece of data is correctly interpreted and used for further processing . Window - Based Aggregation : Grouping data by a time window and store ID , and then summing up sales amounts within each window , allows for real - time monitoring of sales performance across different store locations . Output : The aggregated data is output to the console , which can be replaced with more robust storage or reporting solutions for production environments . This solution is an example of how to handle real - time data streams efficiently , providing actionable insights that can support dynamic business operations and decision - making .

1

import unittest value expected_data getOrCreate result_df.collect() == expected_df.collect @classmethod expected_df = self.spark.createDataFrame def test_add_doubled_column(self if name == "main self.assertTrue appName("PySparkTesting from pyspark.sql.functions import col expected_data = [(1, 2), (2, 4), (3, 6 cls.spark = SparkSession.builder unittest.main master("local[2 def setUpClass(cls df = self.spark.createDataFrame def add_doubled_column(df, column_name @classmethod return df.withColumn("doubled", col(column_name) * 2 value", "doubled 1,), (2,), (3 cls.spark.stop def tearDownClass(cls from pyspark.sql import SparkSession result_df = add_doubled_column(df, "value class PySparkTest(unittest.TestCase

Scenario 1 : Unit Testing a Data Transformation Function in PySpark
Problem : You have developed a function that transforms a DataFrame by adding a new column which doubles the values of an existing column . You need to write a unit test to validate that this function performs as expected .

Solution :

import unittest
from pyspark . sql import SparkSession
from pyspark . sql . functions import col

def add_doubled_column ( df , column_name ) :
return df . withColumn ( " doubled " , col ( column_name ) * 2 )

class PySparkTest ( unittest . TestCase ) :
@classmethod
def setUpClass ( cls ) :
cls . spark = SparkSession . builder \
. appName ( " PySparkTesting " ) \
. master ( " local [ 2 ] " ) \
. getOrCreate ( )

@classmethod
def tearDownClass ( cls ) :
cls . spark . stop ( )

def test_add_doubled_column ( self ) :
# Create a sample DataFrame
df = self . spark . createDataFrame (
[ ( 1 , ) , ( 2 , ) , ( 3 , ) ] ,
[ " value " ]
)

# Apply the transformation
result_df = add_doubled_column ( df , " value " )

# Expected DataFrame
expected_data = [ ( 1 , 2 ) , ( 2 , 4 ) , ( 3 , 6 ) ]
expected_df = self . spark . createDataFrame (
expected_data ,
[ " value " , " doubled " ]
)

# Assert that the transformed DataFrame matches the expected DataFrame
self . assertTrue (
result_df . collect ( ) = = expected_df . collect ( ) ,
" The transformed DataFrame does not match the expected DataFrame . "
)

# Run the tests
if __name__ = = " __main__ " :
unittest . main ( )

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# Create a sample DataFrame
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# Assert that the transformed DataFrame matches the expected DataFrame
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Explanation :

Function Definition : add_doubled_column takes a DataFrame and a column name , then returns the DataFrame with an additional column where the values are doubled .
Unit Test Setup : Using unittest framework to structure PySpark tests , with setUpClass and tearDownClass for initializing and stopping the Spark session .
Test Method : test_add_doubled_column creates a sample DataFrame , applies the transformation , and compares the result with an expected DataFrame to ensure the function works correctly .
Assertions : Uses assertTrue with a condition that checks if the collected data from the result DataFrame matches the expected DataFrame , providing clear feedback if the test fails .
This unit testing approach is a fundamental part of developing reliable PySpark applications , allowing you to validate transformations and logic independently of the complete ETL pipeline .

2

partition_sizes = df.withColumn("partition_id", spark_partition_id()).groupBy("partition_id").agg(count("*").alias("records_per_partition partition_sizes.show from pyspark.sql.functions import spark_partition_id, count

Scenario 2 : Identifying and Resolving Data Skew in a Spark Job

Debugging Common Issues in Spark Jobs

Problem : You notice that some tasks in your Spark job are taking significantly longer than others , suggesting a possible data skew .

Solution :

from pyspark . sql . functions import spark_partition_id , count

# Analyze the distribution of data across partitions
partition_sizes = df . withColumn ( " partition_id " , spark_partition_id ( ) ) . groupBy ( " partition_id " ) . agg ( count ( " * " ) . alias ( " records_per_partition " ) )

# Check the output to identify skewed partitions
partition_sizes . show ( )

# Analyze the distribution of data across partitions
" ) )

# Check the output to identify skewed partitions
( )

Explanation :

Identify Skew : Use spark_partition_id ( ) to add a column indicating the partition ID for each row , then group by this ID and count the records in each partition . Large discrepancies in these counts suggest skew .
Resolution : Depending on the findings , consider redistributing the data either by repartitioning or using techniques like salting keys that have a large number of records .

3

from pyspark.sql.functions import udf from pyspark.sql.types import DoubleType df = df.withColumn("result", complex_formula_udf(df["input_column return (x * 2.54) / (x ** 0.5 + 2 complex_formula_udf = udf(complex_formula, DoubleType def complex_formula(x

Scenario 4 : ( Advanced Topics ) Applying a Custom Calculation to a DataFrame Column Using a UDF

Problem : You need to apply a complex mathematical formula to a column in a DataFrame , which is not directly supported by built - in Spark functions .

Solution :

from pyspark . sql . functions import udf
from pyspark . sql . types import DoubleType

# Define the UDF
def complex_formula ( x ) :
return ( x * 2 . 54 ) / ( x * * 0 . 5 + 2 )

# Register the UDF
complex_formula_udf = udf ( complex_formula , DoubleType ( ) )

# Apply the UDF to a DataFrame
df = df . withColumn ( " result " , complex_formula_udf ( df [ " input_column " ] ) )

- - - - - -

# Define the UDF
) :
)

# Register the UDF
( ) )

# Apply the UDF to a DataFrame
" ] ) )

Explanation :

UDF Definition : Create a Python function complex_formula that performs the calculation .
UDF Registration : Convert the Python function into a UDF that Spark can use , specifying DoubleType ( ) as the return type to ensure correct data handling .
Application : Use withColumn to apply the UDF to the DataFrame , creating a new column with the results of the calculation .

4

secure_df = mask_data(df, "sensitive_column from pyspark.sql.functions import col, sha2 return df.withColumn(column_name, sha2(col(column_name), 256 def mask_data(df, column_name

Scenario 6 : ( Advanced Topics ) Enforcing Data Masking on Sensitive Columns

Data Security and Governance in Spark

Problem : You need to ensure that sensitive data , such as personal identifiers , is masked or anonymized in the dataset before it is used for analysis to comply with privacy regulations .

Solution :

from pyspark . sql . functions import col , sha2

# Function to mask sensitive data
def mask_data ( df , column_name ) :
return df . withColumn ( column_name , sha2 ( col ( column_name ) , 256 ) )

# Applying data masking to the DataFrame
secure_df = mask_data ( df , " sensitive_column " )

- - - -

# Function to mask sensitive data
) :
) )

# Applying data masking to the DataFrame
" )

Explanation :

Data Masking Function : sha2 is used to hash the sensitive column data , here using a 256 - bit SHA - 2 algorithm . Hashing transforms the sensitive data into a fixed - size string , which appears random and cannot be easily reversed . This ensures that the sensitive data is anonymized while maintaining a unique identifier for records .
Application : The mask_data function applies this transformation to any specified column of the DataFrame . This method helps in protecting sensitive data and is part of data governance practices to comply with data security standards and regulations .
This solution provides a practical approach to enhancing data security in PySpark through data masking , essential for protecting sensitive information and adhering to compliance requirements in data processing workflows .

5

def update_accumulator(value def zero(self, initialValue from pyspark import AccumulatorParam class DictAccumulatorParam(AccumulatorParam return value print(my_accumulator.value df.rdd.map(update_accumulator).collect my_accumulator.add({value: 1 v1[key] = v2[key def addInPlace(self, v1, v2 if key in v1 else v1[key] += v2[key for key in v2 my_accumulator = spark.sparkContext.accumulator({}, DictAccumulatorParam return v1 return

Scenario 3 : Using Accumulators to Track Execution Metrics

Monitoring and Optimizing Spark Job Performance

Problem : You want to monitor specific operations within your Spark job , such as counting certain events or tracking the occurrence of specific conditions .

Solution :

from pyspark import AccumulatorParam

class DictAccumulatorParam ( AccumulatorParam ) :
def zero ( self , initialValue ) :
return { }

def addInPlace ( self , v1 , v2 ) :
for key in v2 :
if key in v1 :
v1 [ key ] + = v2 [ key ]
else :
v1 [ key ] = v2 [ key ]
return v1

# Create an accumulator
my_accumulator = spark . sparkContext . accumulator ( { } , DictAccumulatorParam ( ) )

# Example usage in a transformation
def update_accumulator ( value ) :
my_accumulator . add ( { value : 1 } )
return value

df . rdd . map ( update_accumulator ) . collect ( )

# Access the accumulated values
print ( my_accumulator . value )

- - - -

) :
) :
{ }

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:
:
]
:
]

# Create an accumulator
( ) )

# Example usage in a transformation
) :
} )

( )

# Access the accumulated values
)

Explanation :

Custom Accumulator : Define a custom accumulator that operates on dictionaries , allowing you to count occurrences of various values throughout your job .
Monitoring : Use the accumulator in a map operation or similar to increment counts based on data values or conditions encountered during job execution . This provides a way to track detailed metrics about what's happening within your Spark tasks .
Insights : After the job , inspect the values of the accumulator to gain insights into the distribution or frequency of events , which can help in debugging and understanding job behavior .
These scenarios equip you with practical methods for debugging and monitoring PySpark jobs , ensuring you can identify performance bottlenecks and operational issues , and apply effective solutions to maintain and improve job efficiency .

6

format("kafka start readStream kafka_topic_name = "stream_topic query = df_stream.writeStream outputMode("append option("subscribe", kafka_topic_name query.awaitTermination option("kafka.bootstrap.servers", kafka_bootstrap_servers df_stream.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING df_stream = spark appName("KafkaStreaming from pyspark.sql import SparkSession format("console kafka_bootstrap_servers = 'localhost:9092' getOrCreate spark = SparkSession.builder load

Scenario 5 : ( Advanced Topics ) Consuming Streaming Data from Apache Kafka

Integrating with Apache Kafka for Streaming Data

Problem : You need to read streaming data from a Kafka topic for real - time processing in PySpark .

Solution :

from pyspark . sql import SparkSession

# Create a Spark session
spark = SparkSession . builder \
. appName ( " KafkaStreaming " ) \
. getOrCreate ( )

# Define Kafka parameters
kafka_topic_name = " stream_topic "
kafka_bootstrap_servers = 'localhost : 9092'

# Read from Kafka
df_stream = spark \
. readStream \
. format ( " kafka " ) \
. option ( " kafka . bootstrap . servers " , kafka_bootstrap_servers ) \
. option ( " subscribe " , kafka_topic_name ) \
. load ( )

# Processing the stream
# Here you can define transformations , for example , parsing the Kafka message
df_stream . selectExpr ( " CAST ( key AS STRING ) " , " CAST ( value AS STRING ) " )

# Start streaming
query = df_stream . writeStream \
. outputMode ( " append " ) \
. format ( " console " ) \
. start ( )

query . awaitTermination ( )

- -

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# Define Kafka parameters
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# Read from Kafka
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# Processing the stream
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# Start streaming
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Explanation :

Session Creation : Initialize a Spark session to handle streaming data .
Kafka Configuration : Specify the Kafka bootstrap servers and the topic you are subscribing to .
Streaming Read : Use readStream to connect to Kafka and continuously read the incoming stream .
Data Processing : Apply any necessary transformations , here converting Kafka byte messages to strings for easier handling .
Stream Output : Start the stream processing and output to the console for demonstration purposes . In production , this could be directed to databases , file systems , or other storage services .

These solutions demonstrate how to extend PySpark's capabilities to handle custom data transformations with UDFs and integrate with real - time data streams from Apache Kafka , essential skills for advanced data engineering tasks in dynamic and scalable environments .

7

groupBy kafka_topic_name = "sales_data window(col("timestamp"), "1 minute option("subscribe", kafka_topic_name amount readStream alias("total_sales selectExpr("CAST(value AS STRING) as raw_data sales_data = sales_stream.select spark = SparkSession.builder writeStream col("store_id getOrCreate query.awaitTermination start query = aggregated_sales option("kafka.bootstrap.servers", kafka_bootstrap_servers appName("Real-Time Sales Aggregation get_json_object(col("raw_data"), "$.amount").cast("double").alias sales_stream = spark sum("amount get_json_object(col("raw_data"), "$.timestamp").alias("timestamp from pyspark.sql.functions import window, col get_json_object(col("raw_data"), "$.store_id").alias("store_id format("console kafka_bootstrap_servers = 'localhost:9092' option("startingOffsets", "earliest load format("kafka aggregated_sales = sales_data outputMode("complete from pyspark.sql import SparkSession

Real - World Scenario : Processing Streaming Data from Apache Kafka with PySpark
Scenario : Real - Time Data Aggregation from Multiple Sources
Problem : You need to aggregate real - time sales data coming from multiple store locations via Apache Kafka to monitor overall sales performance and generate timely reports .

Solution :

from pyspark . sql import SparkSession
from pyspark . sql . functions import window , col

# Initialize Spark Session
spark = SparkSession . builder \
. appName ( " Real - Time Sales Aggregation " ) \
. getOrCreate ( )

# Define Kafka parameters
kafka_topic_name = " sales_data "
kafka_bootstrap_servers = 'localhost : 9092'

# Read streaming data from Kafka
sales_stream = spark \
. readStream \
. format ( " kafka " ) \
. option ( " kafka . bootstrap . servers " , kafka_bootstrap_servers ) \
. option ( " subscribe " , kafka_topic_name ) \
. option ( " startingOffsets " , " earliest " ) \
. load ( ) \
. selectExpr ( " CAST ( value AS STRING ) as raw_data " )

# Parse the streaming data
sales_data = sales_stream . select (
get_json_object ( col ( " raw_data " ) , " $ . store_id " ) . alias ( " store_id " ) ,
get_json_object ( col ( " raw_data " ) , " $ . amount " ) . cast ( " double " ) . alias ( " amount " ) ,
get_json_object ( col ( " raw_data " ) , " $ . timestamp " ) . alias ( " timestamp " )
)

# Aggregate data over a 1 - minute window
aggregated_sales = sales_data \
. groupBy (
window ( col ( " timestamp " ) , " 1 minute " ) ,
col ( " store_id " )
) \
. sum ( " amount " ) \
. alias ( " total_sales " )

# Write the results to a sink , e . g . , console for testing
query = aggregated_sales \
. writeStream \
. outputMode ( " complete " ) \
. format ( " console " ) \
. start ( )

query . awaitTermination ( )

- - -

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# Define Kafka parameters
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# Read streaming data from Kafka
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# Parse the streaming data
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# Aggregate data over a 1 - minute window
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Explanation :

Kafka Integration : The solution begins by connecting to a Kafka topic that streams sales data , ensuring all messages from the earliest are consumed .
Data Parsing : Using Spark's get_json_object to extract relevant fields from JSON formatted messages ensures that each piece of data is correctly interpreted and used for further processing .
Window - Based Aggregation : Grouping data by a time window and store ID , and then summing up sales amounts within each window , allows for real - time monitoring of sales performance across different store locations .
Output : The aggregated data is output to the console , which can be replaced with more robust storage or reporting solutions for production environments .
This solution is an example of how to handle real - time data streams efficiently , providing actionable insights that can support dynamic business operations and decision - making .

Mastering Testing and Debugging PySpark Applications

Use these credentials to integrate the game into an LMS compatible with LTI 1.1 or LTI 1.3 such as Canvas, Moodle, or Blackboard. This way, the scores will be automatically saved into the platform’s gradebook.

Mastering Testing and Debugging PySpark Applications