Data serialization (Avro, Parquet, ORC) in Hadoop - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the Parquet file format when saving a DataFrame in Spark.

Hadoop

df.write.format("[1]").save("/data/output")

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

Acsv

Bjson

Ctext

Dparquet

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'csv' or 'json' instead of 'parquet' will save in the wrong format.

Forgetting to specify the format causes default saving behavior.

To save data in Parquet format, use format("parquet").

2fill in blank

medium

Complete the code to read an Avro file into a Spark DataFrame.

Hadoop

spark.read.format("[1]").load("/data/input.avro")

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

Aavro

Bparquet

Corc

Dcsv

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'parquet' or 'orc' format to read Avro files causes errors.

Not having the Avro package installed in Spark can cause failures.

To read Avro files, use format("avro") with Spark.

3fill in blank

hard

Fix the error in the code to write a DataFrame in ORC format.

Hadoop

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

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

AsaveAsTextFile

Bsave

Corc

DsaveAsOrcFile

Attempts:

3 left

💡 Hint

Common Mistakes

Using saveAsTextFile for ORC files causes wrong output.

Using non-existent methods like saveAsOrcFile causes errors.

Use df.write.orc(path) to write ORC files in Spark.

4fill in blank

hard

Fill both blanks to create a DataFrame from a Parquet file and select only the 'name' column.

Hadoop

df = spark.read.[1]("/data/users.parquet").select("[2]")

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

Aparquet

Bavro

Cname

Dage

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'avro' format to read Parquet files causes errors.

Selecting a wrong column name returns empty or errors.

Use parquet to read Parquet files and select the 'name' column.

5fill in blank

hard

Fill all three blanks to write a DataFrame in Avro format with overwrite mode and save to '/data/avro_output'.

Hadoop

df.write.mode("[1]").format("[2]").save("[3]")

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

Aappend

Boverwrite

Cavro

D/data/avro_output

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'append' mode overwrites incorrectly.

Using wrong format causes save errors.

Saving to wrong path causes confusion.

Use mode("overwrite") to overwrite, format("avro") for Avro, and the correct path to save.

Practice

1. Which data serialization format stores data in a row-wise manner and includes a schema for each record?

easy

A. Avro

B. Parquet

C. ORC

D. CSV

Solution

1. Step 1: Understand Avro's storage method

   Avro stores data row-wise, meaning it saves one record at a time along with its schema.

2. Step 2: Compare with other formats

   Parquet and ORC store data column-wise, optimizing for query speed, while CSV has no schema.

3. Final Answer:

   Avro -> Option A

4. Quick Check:

   Row-wise with schema = Avro [OK]

Hint: Row-wise + schema means Avro format [OK]

Common Mistakes:

• Confusing Parquet or ORC as row-wise
• Thinking CSV has schema
• Mixing storage orientation concepts

2. Which of the following is the correct way to specify a Parquet file format in a Hadoop job configuration?

easy

A. job.setOutputFormatClass(TextOutputFormat.class);

B. job.setOutputFormatClass(AvroOutputFormat.class);

C. job.setOutputFormatClass(OrcOutputFormat.class);

D. job.setOutputFormatClass(ParquetOutputFormat.class);

Solution

1. Step 1: Identify the output format class for Parquet

   Parquet files require setting the output format to ParquetOutputFormat in Hadoop jobs.

2. Step 2: Eliminate other options

   TextOutputFormat is for text files, AvroOutputFormat for Avro files, and OrcOutputFormat for ORC files.

3. Final Answer:

   job.setOutputFormatClass(ParquetOutputFormat.class); -> Option D

4. Quick Check:

   Parquet output format = ParquetOutputFormat [OK]

Hint: Parquet uses ParquetOutputFormat class in Hadoop [OK]

Common Mistakes:

• Using TextOutputFormat for Parquet files
• Confusing AvroOutputFormat with ParquetOutputFormat
• Mixing ORC and Parquet output formats

3. Given the following PySpark code snippet, what will be the output schema of the saved file?

df.write.format('orc').save('/data/output')

medium

A. Data saved in ORC format with schema preserved

B. Data saved in CSV format without schema

C. Data saved in Parquet format with schema preserved

D. Data saved in Avro format with schema preserved

Solution

1. Step 1: Identify the format used in the code

   The code uses df.write.format('orc'), which means data is saved in ORC format.

2. Step 2: Understand ORC properties

   ORC stores data column-wise and preserves schema for efficient queries.

3. Final Answer:

   Data saved in ORC format with schema preserved -> Option A

4. Quick Check:

   ORC format with schema = Data saved in ORC format with schema preserved [OK]

Hint: format('orc') means ORC with schema saved [OK]

Common Mistakes:

• Assuming default is CSV
• Confusing ORC with Parquet or Avro
• Ignoring schema preservation in ORC

4. You try to read a Parquet file in Spark using this code:

spark.read.format('parquet').load('/data/file.orc')

What is the main issue here?

medium

A. File extension does not match format specified

B. Parquet format cannot read ORC files

C. Spark cannot read Parquet files

D. No issue, code will run fine

Solution

1. Step 1: Check the file format and extension

   The code tries to read a file with .orc extension using Parquet format.

2. Step 2: Understand format compatibility

   Parquet reader cannot read ORC files because they have different internal structures.

3. Final Answer:

   Parquet format cannot read ORC files -> Option B

4. Quick Check:

   Parquet reader ≠ ORC file [OK]

Hint: Parquet reader can't read ORC files [OK]

Common Mistakes:

• Ignoring file extension mismatch
• Assuming Spark auto-detects format
• Thinking Parquet and ORC are interchangeable

5. You have a large dataset with many columns but only a few are needed for analysis. Which data serialization format is best to optimize query speed and storage, and why?

hard

A. CSV, because it is simple and easy to read

B. Avro, because it stores data row-wise with schema

C. Parquet, because it stores data column-wise allowing fast column queries

D. JSON, because it supports nested data

Solution

1. Step 1: Understand the dataset and query needs

   Many columns exist but only a few are needed, so reading only those columns is important.

2. Step 2: Compare serialization formats for columnar access

   Parquet stores data column-wise, enabling fast queries on selected columns and saving storage.

3. Step 3: Eliminate other options

   Avro is row-wise, so it reads all columns; CSV and JSON lack efficient columnar storage and schema.

4. Final Answer:

   Parquet, because it stores data column-wise allowing fast column queries -> Option C

5. Quick Check:

   Column-wise storage = Parquet best for selective queries [OK]

Hint: Column-wise format like Parquet speeds up column queries [OK]

Common Mistakes:

• Choosing Avro for column queries
• Picking CSV or JSON for big data efficiency
• Ignoring storage and query speed trade-offs