Integration testing pipelines in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a SparkSession for testing.

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName([1]).getOrCreate()

Drag options to blanks, or click blank then click option'

A"DataPipeline"

B"ProductionApp"

C"TestApp"

D"MainApp"

Attempts:

3 left

💡 Hint

Common Mistakes

Using production app names in test code.

Not setting an app name at all.

The app name 'TestApp' clearly indicates this SparkSession is for testing purposes.

2fill in blank

medium

Complete the code to read a CSV file into a DataFrame for testing.

Apache Spark

df = spark.read.format([1]).option("header", "true").load("test_data.csv")

Drag options to blanks, or click blank then click option'

A"csv"

B"text"

C"parquet"

D"json"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'json' or 'parquet' format for CSV files.

Forgetting to set header option to true.

The data file is a CSV, so the format must be 'csv' to read it correctly.

3fill in blank

hard

Fix the error in the assertion that checks row count in the test DataFrame.

Apache Spark

assert df.count() [1] 100, "Row count should be 100"

Drag options to blanks, or click blank then click option'

A!=

B<

C>

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' which checks for inequality.

Using '<' or '>' which check for less or more rows.

To verify the DataFrame has exactly 100 rows, use '==' in the assertion.

4fill in blank

hard

Fill both blanks to filter the DataFrame for rows where 'age' is greater than 30.

Apache Spark

filtered_df = df.filter(df.[1] [2] 30)

Drag options to blanks, or click blank then click option'

Aage

B>

C<

Dsalary

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name like 'salary'.

Using '<' instead of '>' operator.

To filter rows where age is greater than 30, use df.age > 30.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps column names to their data types for columns with type 'string'.

Apache Spark

string_cols = { [1]: df.schema[[2]].dataType.simpleString() for [3] in df.columns if df.schema[[3]].dataType.simpleString() == "string" }

Drag options to blanks, or click blank then click option'

Acol

Didx

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Using an index variable instead of column names.

Use 'col' as the variable name consistently to map column names to their data types.

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