Parquet format and columnar storage in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a Parquet file into a Spark DataFrame.

Apache Spark

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

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

Acsv

Btext

Cjson

Dparquet

Attempts:

3 left

💡 Hint

Common Mistakes

Using csv or json method instead of parquet.

Forgetting to specify the file path.

The parquet method reads Parquet files into a DataFrame.

2fill in blank

medium

Complete the code to write a DataFrame to Parquet format with overwrite mode.

Apache Spark

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

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

Aoverwrite

Bignore

Cerror

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using append mode when overwrite is needed.

Not specifying mode and causing errors if output exists.

The overwrite mode replaces existing data at the output path.

3fill in blank

hard

Fix the error in the code to select only the 'name' and 'age' columns from a Parquet DataFrame.

Apache Spark

selected_df = df.select([1])

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

A"name", "age"

Bname, age

C["name", "age"]

D['name', 'age']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list of column names instead of separate string arguments.

Using unquoted column names.

The select method accepts multiple column names as separate string arguments.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each column name to its data type from a DataFrame schema.

Apache Spark

col_types = {col.name: col.[1] for col in df.schema.[2]

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

AdataType

Bfields

Ccolumns

Dtypes

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'columns' or 'types' which are not schema attributes.

Accessing dataType directly on schema instead of on fields.

The schema's fields list contains column objects, each with a dataType attribute.

5fill in blank

hard

Fill all three blanks to filter rows where the 'age' column is greater than 30 and select 'name' and 'age' columns.

Apache Spark

filtered_df = df.filter(df.[1] [2] [3]).select("name", "age")

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

Aage

B>

C30

Dage > 30

Attempts:

3 left

💡 Hint

Common Mistakes

Putting the entire condition as one string instead of separate parts.

Using wrong operators like '<' or '==' instead of '>'.

Use the column name age, the operator >, and the value 30 to filter rows.

Practice

1. What is the main advantage of storing data in Parquet format in Apache Spark?

easy

A. It compresses data by converting it to images.

B. It stores data as plain text for easy reading.

C. It stores data only in memory without saving to disk.

D. It stores data column-wise, making queries faster and storage smaller.

Solution

1. Step 1: Understand Parquet storage method

   Parquet stores data in a columnar format, meaning data is saved column by column instead of row by row.

2. Step 2: Benefits of columnar storage

   This column-wise storage allows faster queries on specific columns and reduces storage size due to better compression.

3. Final Answer:

   It stores data column-wise, making queries faster and storage smaller. -> Option D

4. Quick Check:

   Columnar storage = faster queries + smaller files [OK]

Hint: Remember: Parquet = column-wise storage for speed and size [OK]

Common Mistakes:

• Thinking Parquet stores data as plain text
• Confusing row-wise with column-wise storage
• Assuming Parquet only stores in memory

2. Which of the following is the correct way to save a Spark DataFrame named df as a Parquet file?

easy

A. df.saveAsParquet('path/to/file')

B. df.write.parquet('path/to/file')

C. df.write.save('path/to/file', format='csv')

D. df.save('path/to/file', format='parquet')

Solution

1. Step 1: Recall Spark DataFrame write syntax

   In Spark, to save a DataFrame as Parquet, use the write.parquet() method.

2. Step 2: Check each option

   df.write.parquet('path/to/file') uses df.write.parquet('path'), which is correct. Others use incorrect methods or formats.

3. Final Answer:

   df.write.parquet('path/to/file') -> Option B

4. Quick Check:

   Use write.parquet() to save DataFrame [OK]

Hint: Use df.write.parquet(path) to save DataFrame as Parquet [OK]

Common Mistakes:

• Using saveAsParquet which does not exist
• Saving with wrong format like CSV
• Using df.save() without write

3. Given the following Spark code, what will be the output schema of the loaded DataFrame?

df.write.parquet('data.parquet')
df2 = spark.read.parquet('data.parquet')
df2.printSchema()

medium

A. The schema will be the same as the original DataFrame df.

B. The schema will be empty because Parquet does not store schema.

C. The schema will only contain column names but no data types.

D. The schema will be reversed compared to df.

Solution

1. Step 1: Understand Parquet schema storage

   Parquet files store both data and schema (column names and types).

2. Step 2: Loading Parquet preserves schema

   When reading with spark.read.parquet(), the schema is restored exactly as saved.

3. Final Answer:

   The schema will be the same as the original DataFrame df. -> Option A

4. Quick Check:

   Parquet stores schema = schema preserved [OK]

Hint: Parquet files keep schema; reading restores it fully [OK]

Common Mistakes:

• Thinking Parquet files do not store schema
• Assuming schema is lost or incomplete
• Believing schema order changes on read

4. You try to read a Parquet file using spark.read.parquet('file_path') but get an error. Which of the following is the most likely cause?

medium

A. The file path is incorrect or the file does not exist.

B. Parquet files cannot be read by Spark.

C. You must use read.csv() to read Parquet files.

D. The DataFrame was not saved before reading.

Solution

1. Step 1: Check common read errors

   Errors reading Parquet usually happen if the file path is wrong or file is missing.

2. Step 2: Verify other options

   Parquet files are supported by Spark, so B and C are wrong. D is unrelated because reading does not depend on saving in the same session.

3. Final Answer:

   The file path is incorrect or the file does not exist. -> Option A

4. Quick Check:

   File not found = read error [OK]

Hint: Check file path and existence before reading Parquet [OK]

Common Mistakes:

• Assuming Spark cannot read Parquet
• Using wrong read method like read.csv
• Thinking saving is required in same session

5. You have a large Spark DataFrame with many columns but only need to query a few columns frequently. How does using Parquet format help improve performance?

hard

A. Parquet stores data row-wise, so all columns are always read together.

B. Parquet converts all columns to JSON strings for faster parsing.

C. Parquet stores data column-wise, so reading only needed columns is faster and uses less disk I/O.

D. Parquet compresses data by removing all null values automatically.

Solution

1. Step 1: Recall Parquet columnar storage benefits

   Parquet stores data column-wise, allowing queries to read only selected columns.

2. Step 2: Understand impact on performance

   Reading fewer columns reduces disk I/O and speeds up queries, especially on large datasets.

3. Final Answer:

   Parquet stores data column-wise, so reading only needed columns is faster and uses less disk I/O. -> Option C

4. Quick Check:

   Columnar storage = selective fast reads [OK]

Hint: Parquet reads only needed columns, saving time and I/O [OK]

Common Mistakes:

• Thinking Parquet stores data row-wise
• Believing Parquet converts data to JSON
• Assuming Parquet removes nulls automatically