Idempotent pipeline design in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a parquet file into a DataFrame.

Apache Spark

df = spark.read.[1]("/data/input.parquet")

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

Atext

Bcsv

Cparquet

Djson

Attempts:

3 left

💡 Hint

Common Mistakes

Using csv or json instead of parquet causes errors or wrong data.

Forgetting to specify the file path as a string.

To read a parquet file, use spark.read.parquet().

2fill in blank

medium

Complete the code to write a DataFrame as a parquet file with overwrite mode.

Apache Spark

df.write.mode([1]).parquet("/data/output.parquet")

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

A"overwrite"

B"error"

C"append"

D"ignore"

Attempts:

3 left

💡 Hint

Common Mistakes

Using append mode causes duplicate data on reruns.

Forgetting to set mode leads to errors if file exists.

Use mode("overwrite") to replace existing data and ensure idempotency.

3fill in blank

hard

Fix the error in the code to drop duplicates from a DataFrame.

Apache Spark

df_clean = df.[1]()

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

Adropna

BremoveDuplicates

Cdistincts

DdropDuplicates

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna removes rows with nulls, not duplicates.

Using non-existent methods causes runtime errors.

The correct method to remove duplicate rows is dropDuplicates().

4fill in blank

hard

Fill both blanks to create a DataFrame with only unique user IDs and save it idempotently.

Apache Spark

unique_users = df.select([1]).[2]()
unique_users.write.mode("overwrite").parquet("/data/users_unique.parquet")

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

A"user_id"

BdropDuplicates

Cdistinct

D"id"

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropDuplicates without selecting a column may not work as expected.

Selecting wrong column names causes empty results.

Select the user_id column and use distinct() to get unique rows.

5fill in blank

hard

Fill all three blanks to build an idempotent pipeline that reads, filters, and writes data.

Apache Spark

df = spark.read.[1]("/data/raw.parquet")
filtered = df.filter(df[[2]] > 100)
filtered.write.mode([3]).parquet("/data/filtered.parquet")

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

Aparquet

B"value"

C"overwrite"

Dcsv

Attempts:

3 left

💡 Hint

Common Mistakes

Using csv format when data is parquet causes errors.

Forgetting overwrite mode causes duplicate or error on rerun.

Filtering on wrong column name returns empty or wrong data.

Read parquet data, filter rows where value is greater than 100, and write with overwrite mode for idempotency.

Practice

1. What does it mean for a data pipeline to be idempotent in Apache Spark?

easy

A. It requires manual cleanup after each run.

B. It runs faster each time it is executed.

C. It only processes new data and ignores old data.

D. It produces the same output even if run multiple times with the same input.

Solution

1. Step 1: Understand the meaning of idempotency in data pipelines

   Idempotency means running the pipeline multiple times with the same input should not change the output after the first run.

2. Step 2: Relate to Apache Spark pipeline behavior

   In Spark, an idempotent pipeline ensures consistent results by handling duplicates and overwriting data safely.

3. Final Answer:

   It produces the same output even if run multiple times with the same input. -> Option D

4. Quick Check:

   Idempotent = Same output every run [OK]

Hint: Idempotent means repeat runs give same results [OK]

Common Mistakes:

• Thinking idempotent means faster runs
• Assuming it only processes new data
• Believing manual cleanup is needed

2. Which Spark write mode helps achieve idempotency by replacing existing data safely?

easy

A. append

B. ignore

C. overwrite

D. update

Solution

1. Step 1: Review Spark write modes

   Append adds new data without removing old data, overwrite replaces existing data, ignore skips writing if data exists, update is not a standard Spark write mode.

2. Step 2: Identify which mode replaces data for idempotency

   Overwrite mode replaces the existing data, ensuring the output is consistent after multiple runs.

3. Final Answer:

   overwrite -> Option C

4. Quick Check:

   Overwrite mode = Replace data safely [OK]

Hint: Overwrite mode replaces data for consistent output [OK]

Common Mistakes:

• Choosing append which adds duplicates
• Selecting ignore which skips writes
• Thinking update is a valid write mode

3. Given this Spark code snippet, what will be the output count after running twice?

df = spark.createDataFrame([(1, 'a'), (2, 'b'), (1, 'a')], ['id', 'value'])
dedup_df = df.dropDuplicates()
dedup_df.write.mode('overwrite').save('/tmp/data')
loaded_df = spark.read.load('/tmp/data')
print(loaded_df.count())

medium

A. 2

B. 0

C. 1

D. 3

Solution

1. Step 1: Understand the input data and deduplication

   The input DataFrame has 3 rows with one duplicate (id=1, value='a'). dropDuplicates() removes duplicates, leaving 2 unique rows.

2. Step 2: Analyze the write and read operations

   Using overwrite mode saves the deduplicated data, replacing any previous data. Reading it back will load 2 rows.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Deduplication + overwrite = 2 rows [OK]

Hint: Deduplicate then overwrite keeps unique rows only [OK]

Common Mistakes:

• Counting duplicates as separate rows
• Assuming append mode adds duplicates
• Ignoring overwrite effect on saved data

4. You have a Spark pipeline that appends data without deduplication. What issue can this cause when rerunning the pipeline?

medium

A. Data duplication leading to incorrect results

B. Pipeline runs faster each time

C. Data is automatically cleaned up

D. Output files are overwritten safely

Solution

1. Step 1: Understand append mode without deduplication

   Appending data adds new rows without removing duplicates, so rerunning the pipeline adds repeated data.

2. Step 2: Identify the impact on results

   Repeated runs cause duplicated data, which can lead to wrong analytics or reports.

3. Final Answer:

   Data duplication leading to incorrect results -> Option A

4. Quick Check:

   Append without deduplication = duplicates [OK]

Hint: Appending without deduplication causes duplicates [OK]

Common Mistakes:

• Thinking pipeline runs faster
• Assuming data cleans automatically
• Believing output files overwrite safely

5. You want to design an idempotent Spark pipeline that processes daily sales data. Which combination ensures idempotency when rerunning for the same day?
1. Read raw daily data
2. Remove duplicates
3. Write results to storage
Choose the best write mode and deduplication approach.

hard

A. Use append mode and no deduplication

B. Use overwrite mode and remove duplicates before writing

C. Use ignore mode and append duplicates

D. Use append mode and remove duplicates after writing

Solution

1. Step 1: Identify deduplication timing

   Removing duplicates before writing ensures only unique data is saved, preventing repeated entries.

2. Step 2: Choose write mode for idempotency

   Overwrite mode replaces existing data for the day, so rerunning does not add duplicates.

3. Final Answer:

   Use overwrite mode and remove duplicates before writing -> Option B

4. Quick Check:

   Deduplicate + overwrite = idempotent pipeline [OK]

Hint: Deduplicate first, then overwrite to ensure idempotency [OK]

Common Mistakes:

• Appending duplicates causing repeated data
• Removing duplicates after writing is too late
• Using ignore mode skips writing data