Writing output with partitioning in Apache Spark - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of partitioned write with multiple columns

What will be the folder structure created when writing a DataFrame partitioned by two columns?

Consider this Spark code:

df.write.partitionBy("year", "month").parquet("/data/events")

The DataFrame has data for years 2022 and 2023, and months 1 and 2.

Which folder paths will be created inside /data/events?

A /data/events/year=2022/month=1/, /data/events/year=2022/month=2/, /data/events/year=2023/month=1/, /data/events/year=2023/month=2/

B /data/events/month=1/year=2022/, /data/events/month=2/year=2022/, /data/events/month=1/year=2023/, /data/events/month=2/year=2023/

C /data/events/year=2022/, /data/events/year=2023/, /data/events/month=1/, /data/events/month=2/

D /data/events/year=2022_month=1/, /data/events/year=2022_month=2/, /data/events/year=2023_month=1/, /data/events/year=2023_month=2/

💡 Hint

Partition folders are created in the order of the columns specified in partitionBy.

When writing with partitionBy("year", "month"), Spark creates nested folders first by year, then by month inside each year folder.

❓ data_output

intermediate

Number of files created with partitioning

You write a DataFrame with 3 partitions using partitionBy("category"). The DataFrame has 2 unique categories: 'A' and 'B'.

If the DataFrame has 6 total partitions before writing, how many parquet files will be created inside the output folder?

A 2 files

B 6 files

C 3 files

D 1 file

💡 Hint

Each partition of the DataFrame writes one file per partition folder.

Even though there are 2 categories, the DataFrame has 6 partitions. Each partition writes one file, so total files = 6.

🔧 Debug

advanced

Error when writing partitioned data with unsupported column

What error will this Spark code produce?

df.write.partitionBy("nonexistent_column").parquet("/output/path")

The DataFrame df does not have a column named nonexistent_column.

A AnalysisException: 'Partition column nonexistent_column not found in schema'

B NullPointerException

C No error, writes data ignoring the partition column

D FileNotFoundException

💡 Hint

Partition columns must exist in the DataFrame schema.

Spark throws an AnalysisException if the partition column is missing from the DataFrame.

🚀 Application

advanced

Choosing partition columns for efficient queries

You have a large sales dataset with columns: date, region, product, and sales_amount.

You want to write the data partitioned to speed up queries filtering by region and date. Which partitioning strategy is best?

A Partition by <code>product</code>

B Partition by <code>date</code> only

C Partition by <code>region</code> only

D Partition by <code>region</code> and <code>date</code> (in that order)

💡 Hint

Partition columns should match common filter columns in queries.

Partitioning by both region and date helps queries filtering on these columns run faster by pruning partitions.

🧠 Conceptual

expert

Effect of partitioning on data skew and shuffle

Which statement best describes the impact of partitioning output data on data skew and shuffle during a Spark write?

A Partitioning always reduces shuffle and eliminates data skew

B Partitioning increases shuffle and always causes data skew

C Partitioning can reduce shuffle but may cause data skew if partition keys are unevenly distributed

D Partitioning has no effect on shuffle or data skew

💡 Hint

Think about how data distribution affects partitioning.

Partitioning can reduce shuffle by grouping data but if partition keys are uneven, some partitions become very large causing skew.

Practice

1. What does the partitionBy method do when writing data in Apache Spark?

easy

A. It sorts the data before saving to disk.

B. It compresses the output data to save disk space.

C. It splits the output data into folders based on column values.

D. It encrypts the output data for security.

Solution

1. Step 1: Understand the purpose of partitionBy

   The partitionBy method organizes output files into folders named by unique values of specified columns.

2. Step 2: Recognize its effect on data storage

   This splitting helps in faster data reading and better organization by grouping similar data together.

3. Final Answer:

   It splits the output data into folders based on column values. -> Option C

4. Quick Check:

   partitionBy splits data folders by column values [OK]

Hint: partitionBy creates folders by column values when saving [OK]

Common Mistakes:

• Thinking partitionBy compresses data
• Confusing partitionBy with sorting
• Assuming partitionBy encrypts data

2. Which of the following is the correct syntax to write a DataFrame df partitioned by column country in Spark?

easy

A. df.write.save.partitionBy("country")("path/to/output")

B. df.partitionBy.write("country").save("path/to/output")

C. df.write.save("path/to/output").partitionBy("country")

D. df.write.partitionBy("country").save("path/to/output")

Solution

1. Step 1: Recall the method chaining order

   In Spark, partitionBy is called on the write object before save.

2. Step 2: Verify correct syntax

   The correct syntax is df.write.partitionBy("country").save("path"). Other options misuse method order or call.

3. Final Answer:

   df.write.partitionBy("country").save("path/to/output") -> Option D

4. Quick Check:

   partitionBy called after write, before save [OK]

Hint: Use df.write.partitionBy(...).save(...) syntax [OK]

Common Mistakes:

• Calling partitionBy after save
• Using partitionBy on df instead of df.write
• Incorrect method chaining order

3. Given the following code, what folder structure will be created when saving the DataFrame?

df.write.partitionBy("year", "month").mode("overwrite").parquet("/data/events")

Assuming year has values 2023 and 2024, and month has values 1 and 2.

medium

A. /data/events/year=2023/month=1/, /data/events/year=2023/month=2/, /data/events/year=2024/month=1/, /data/events/year=2024/month=2/

B. /data/events/2023/1/, /data/events/2023/2/, /data/events/2024/1/, /data/events/2024/2/

C. /data/events/year/month/2023/1/, /data/events/year/month/2023/2/, /data/events/year/month/2024/1/, /data/events/year/month/2024/2/

D. /data/events/year=month=2023_1/, /data/events/year=month=2023_2/, /data/events/year=month=2024_1/, /data/events/year=month=2024_2/

Solution

1. Step 1: Understand partition folder naming

   Spark creates folders named by each partition column and its value, nested in order of columns.

2. Step 2: Apply to given columns and values

   Folders will be nested as /year=2023/month=1/ etc., not just values or combined keys.

3. Final Answer:

   /data/events/year=2023/month=1/, /data/events/year=2023/month=2/, /data/events/year=2024/month=1/, /data/events/year=2024/month=2/ -> Option A

4. Quick Check:

   Partition folders named as column=value pairs [OK]

Hint: Partition folders use column=value format nested by columns [OK]

Common Mistakes:

• Using only values without column names in folders
• Combining columns into one folder name
• Adding extra folder levels incorrectly

4. Identify the error in this Spark code that tries to write a DataFrame partitioned by category:

df.write.save("/output/path").partitionBy("category")

medium

A. The path should be a directory, not a file.

B. partitionBy should be called before save, not after.

C. partitionBy requires a list of columns, not a single string.

D. The DataFrame must be cached before writing.

Solution

1. Step 1: Check method chaining order

   In Spark, partitionBy must be called on the write object before save.

2. Step 2: Identify the error in the code

   The code calls save first, then partitionBy, which is invalid and causes an error.

3. Final Answer:

   partitionBy should be called before save, not after. -> Option B

4. Quick Check:

   partitionBy before save is required [OK]

Hint: Call partitionBy before save to avoid errors [OK]

Common Mistakes:

• Calling partitionBy after save
• Passing a single string instead of list (both allowed but string is valid)
• Confusing caching with writing order

5. You have a large DataFrame df with columns country, year, and sales. You want to save it partitioned by country and year to speed up queries filtering by these columns. Which approach is best to write the data efficiently?

hard

A. Use df.write.partitionBy("country", "year").mode("overwrite").parquet("/data/sales") to save partitioned parquet files.

B. Save the DataFrame without partitioning and filter later during reading.

C. Use df.write.mode("overwrite").csv("/data/sales") without partitioning for simplicity.

D. Write the DataFrame multiple times, once per country and year combination.

Solution

1. Step 1: Understand partitioning benefits

   Partitioning by country and year organizes data into folders, speeding up queries filtering on these columns.

2. Step 2: Evaluate options for efficient writing

   Use df.write.partitionBy("country", "year").mode("overwrite").parquet("/data/sales") to save partitioned parquet files. This uses partitionBy with parquet format and overwrite mode, which is efficient and standard practice.

3. Step 3: Reject less efficient options

   Options B and C do not partition, causing slower queries. Writing the DataFrame multiple times, once per country and year combination, is inefficient and error-prone.

4. Final Answer:

   Use df.write.partitionBy("country", "year").mode("overwrite").parquet("/data/sales") to save partitioned parquet files. -> Option A

5. Quick Check:

   partitionBy with parquet speeds queries [OK]

Hint: Partition by query filter columns using parquet format [OK]

Common Mistakes:

• Not partitioning data before saving
• Writing multiple times instead of once
• Using CSV without partitioning for large data