Integration testing pipelines in Apache Spark - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Spark DataFrame after pipeline transformation

Given the following Spark pipeline code, what is the output DataFrame content after applying the transformations?

Apache Spark

from pyspark.sql import SparkSession
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, VectorAssembler

spark = SparkSession.builder.master('local').appName('Test').getOrCreate()
data = [(0, 'cat'), (1, 'dog'), (2, 'cat'), (3, 'bird')]
schema = ['id', 'animal']
df = spark.createDataFrame(data, schema)

indexer = StringIndexer(inputCol='animal', outputCol='animalIndex')
assembler = VectorAssembler(inputCols=['animalIndex'], outputCol='features')
pipeline = Pipeline(stages=[indexer, assembler])
model = pipeline.fit(df)
result = model.transform(df).select('id', 'animal', 'animalIndex', 'features')
result.show()

A [Row(id=0, animal='cat', animalIndex=0.0, features=DenseVector([0.0])), Row(id=1, animal='dog', animalIndex=1.0, features=DenseVector([1.0])), Row(id=2, animal='cat', animalIndex=0.0, features=DenseVector([0.0])), Row(id=3, animal='bird', animalIndex=2.0, features=DenseVector([2.0]))]

B [Row(id=0, animal='cat', animalIndex=1.0, features=DenseVector([0.0])), Row(id=1, animal='dog', animalIndex=2.0, features=DenseVector([1.0])), Row(id=2, animal='cat', animalIndex=1.0, features=DenseVector([0.0])), Row(id=3, animal='bird', animalIndex=0.0, features=DenseVector([2.0]))]

C [Row(id=0, animal='cat', animalIndex=2.0, features=DenseVector([2.0])), Row(id=1, animal='dog', animalIndex=0.0, features=DenseVector([0.0])), Row(id=2, animal='cat', animalIndex=2.0, features=DenseVector([2.0])), Row(id=3, animal='bird', animalIndex=1.0, features=DenseVector([1.0]))]

D [Row(id=0, animal='cat', animalIndex=1.0, features=DenseVector([1.0])), Row(id=1, animal='dog', animalIndex=2.0, features=DenseVector([2.0])), Row(id=2, animal='cat', animalIndex=1.0, features=DenseVector([1.0])), Row(id=3, animal='bird', animalIndex=0.0, features=DenseVector([0.0]))]

💡 Hint

Remember StringIndexer assigns indices based on label frequency descending order.

StringIndexer assigns indices starting from 0 for the most frequent label. 'cat' appears twice, so it gets index 0 because it is the most frequent. 'dog' appears once and 'bird' appears once, so they get indices 1 and 2 respectively based on label ordering. VectorAssembler creates a vector from the animalIndex column.

❓ data_output

intermediate

Number of rows after filtering in Spark pipeline

Consider a Spark DataFrame with 1000 rows. A pipeline stage filters rows where the column 'age' is greater than 30. If 400 rows have age > 30, how many rows remain after the pipeline transformation?

Apache Spark

from pyspark.sql import SparkSession
from pyspark.ml import Pipeline
from pyspark.sql.functions import col

spark = SparkSession.builder.master('local').appName('Test').getOrCreate()
data = [(i, 20 + (i % 50)) for i in range(1000)]
schema = ['id', 'age']
df = spark.createDataFrame(data, schema)

class AgeFilter:
    def __init__(self, age_limit):
        self.age_limit = age_limit
    def transform(self, df):
        return df.filter(col('age') > self.age_limit)
    def fit(self, df):
        return self

filter_stage = AgeFilter(30)
pipeline = Pipeline(stages=[filter_stage])
model = pipeline.fit(df)
result = model.transform(df)
count = result.count()

A 1000

B 400

C 600

D 0

💡 Hint

Count how many rows satisfy the condition age > 30.

Since ages cycle from 20 to 69, exactly 400 rows have age > 30, so after filtering, 400 rows remain.

🔧 Debug

advanced

Identify the error in Spark pipeline code

What error does the following Spark pipeline code raise when executed?

Apache Spark

from pyspark.sql import SparkSession
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer

spark = SparkSession.builder.master('local').appName('Test').getOrCreate()
data = [(0, 'apple'), (1, 'banana')]
schema = ['id', 'fruit']
df = spark.createDataFrame(data, schema)

indexer = StringIndexer(inputCol='fruit', outputCol='fruitIndex')
pipeline = Pipeline(stages=[indexer])
model = pipeline.fit(df)
result = model.transform(df).select('id', 'fruitIndex')
result.show()

A No error, outputs DataFrame with 'id' and 'fruitIndex' columns

B TypeError: 'StringIndexer' object is not callable

C AnalysisException: cannot resolve 'fruitIndex' given input columns: [id, fruit]

D AttributeError: 'Pipeline' object has no attribute 'fit'

💡 Hint

Check if the pipeline stages are correctly defined and used.

The code correctly fits the pipeline and transforms the DataFrame. The output DataFrame contains 'id' and 'fruitIndex' columns without error.

🚀 Application

advanced

Best approach to test Spark pipeline integration

Which approach is best to test the integration of multiple Spark pipeline stages in a data science project?

A Only test the pipeline on production data to ensure real-world correctness

B Test each pipeline stage separately with unit tests and skip full pipeline testing

C Run the full pipeline on a small sample dataset and verify the final output matches expected results

D Manually inspect intermediate DataFrame outputs without automated tests

💡 Hint

Integration testing means testing combined parts together.

Integration testing verifies that all pipeline stages work together correctly by running the full pipeline on sample data and checking outputs.

🧠 Conceptual

expert

Effect of caching in Spark pipeline integration tests

In Spark pipeline integration tests, what is the main benefit of caching intermediate DataFrames during the pipeline execution?

A It speeds up repeated access to intermediate results, reducing test runtime

B It automatically fixes data quality issues in intermediate DataFrames

C It prevents the pipeline from running any transformations

D It increases memory usage without any performance benefit

💡 Hint

Think about how caching affects performance in Spark.

Caching stores intermediate results in memory, so repeated accesses during tests are faster, reducing overall test time.

Practice

1. What is the main goal of integration testing pipelines in Apache Spark?

easy

A. To check if all parts of the data pipeline work together correctly

B. To test only individual functions separately

C. To deploy the pipeline to production without testing

D. To write documentation for the pipeline

Solution

1. Step 1: Understand integration testing purpose

   Integration testing checks if different parts of a system work together as expected.

2. Step 2: Apply to Spark pipelines

   In Spark, integration testing ensures all pipeline stages (data reading, transformation, writing) function together correctly.

3. Final Answer:

   To check if all parts of the data pipeline work together correctly -> Option A

4. Quick Check:

   Integration testing = check all parts together [OK]

Hint: Integration tests check combined parts, not single functions [OK]

Common Mistakes:

• Confusing integration testing with unit testing
• Skipping tests before deployment
• Thinking integration tests only check code style

2. Which of the following is the correct way to create a small sample DataFrame for integration testing in Spark using Python?

easy

A. spark.makeDataFrame([(1, 'a'), (2, 'b')], ['id', 'value'])

B. spark.createDataFrame([(1, 'a'), (2, 'b')], ['id', 'value'])

C. spark.newDataFrame([(1, 'a'), (2, 'b')], ['id', 'value'])

D. spark.DataFrame.create([(1, 'a'), (2, 'b')], ['id', 'value'])

Solution

1. Step 1: Recall Spark DataFrame creation syntax

   The correct method to create a DataFrame from a list in PySpark is spark.createDataFrame(data, schema).

2. Step 2: Match options with correct syntax

   Only spark.createDataFrame([(1, 'a'), (2, 'b')], ['id', 'value']) uses spark.createDataFrame correctly with data and column names.

3. Final Answer:

   spark.createDataFrame([(1, 'a'), (2, 'b')], ['id', 'value']) -> Option B

4. Quick Check:

   Use spark.createDataFrame() to make test data [OK]

Hint: Remember: spark.createDataFrame() is the correct method [OK]

Common Mistakes:

• Using non-existent methods like newDataFrame or makeDataFrame
• Calling createDataFrame on DataFrame class instead of spark session
• Passing schema incorrectly

3. Given the following Spark code for integration testing, what will be the output of result_df.collect()?

input_df = spark.createDataFrame([(1, 10), (2, 20)], ['id', 'value'])
result_df = input_df.withColumn('value_plus_one', input_df['value'] + 1)

medium

A. [(1, 10), (2, 20)]

B. [(1, 11), (2, 21)]

C. [(1, 10, 11), (2, 20, 21)]

D. Error: withColumn requires a string as first argument

Solution

1. Step 1: Understand withColumn usage

   withColumn adds a new column with the given name and values. Here, 'value_plus_one' is added as 'value' + 1.

2. Step 2: Calculate new column values

   For id=1, value=10, value_plus_one=11; for id=2, value=20, value_plus_one=21.

3. Final Answer:

   [(1, 10, 11), (2, 20, 21)] -> Option C

4. Quick Check:

   withColumn adds new column with value+1 [OK]

Hint: withColumn adds new column; check column names carefully [OK]

Common Mistakes:

• Thinking withColumn replaces existing columns
• Ignoring the new column added
• Confusing collect() output format

4. You wrote this integration test code but it fails with an error. What is the mistake?

test_df = spark.createDataFrame([(1, 'x'), (2, 'y')], ['id', 'value'])
result_df = test_df.filter(test_df['value'] > 1)
result_df.show()

medium

A. DataFrame creation syntax is wrong

B. Column name 'value' is incorrect

C. Missing import for filter function

D. Cannot compare string column with '>' operator in Spark

Solution

1. Step 1: Analyze filter condition

   The filter uses '>' to compare string column 'value' with 1. Spark does not support '>' between string and integer.

2. Step 2: Understand Spark comparisons

   For mixed types, cast the column e.g. F.col('value').cast('int') or ensure matching types, not direct '>' operator.

3. Final Answer:

   Cannot compare string column with '>' operator in Spark -> Option D

4. Quick Check:

   Compatible types required for '>' in Spark filters [OK]

Hint: Cast types before using comparison operators in Spark [OK]

Common Mistakes:

• Assuming '>' works between strings and numbers in Spark
• Misnaming columns
• Forgetting to import Spark functions

5. You want to write an integration test for a Spark pipeline that reads JSON data, filters records with age > 30, and writes the result. Which approach best ensures your test is reliable and fast?

hard

A. Use a small in-memory DataFrame with sample JSON data and compare output DataFrame to expected results

B. Run the full pipeline on the entire production dataset every time

C. Manually check output files after running the pipeline

D. Skip testing and fix bugs after deployment

Solution

1. Step 1: Consider test data size and speed

   Using a small in-memory DataFrame makes tests fast and easy to run repeatedly.

2. Step 2: Compare actual vs expected outputs

   Comparing DataFrames programmatically ensures correctness without manual checks.

3. Step 3: Avoid full production runs or skipping tests

   Running full production data is slow; skipping tests risks bugs in production.

4. Final Answer:

   Use a small in-memory DataFrame with sample JSON data and compare output DataFrame to expected results -> Option A

5. Quick Check:

   Small sample data + compare outputs = reliable fast tests [OK]

Hint: Test small samples and compare outputs, not full data [OK]

Common Mistakes:

• Testing on full production data every time
• Manual output verification
• Skipping tests before deployment