PySpark Interview Questions and Answers
Intermediate / 1 to 5 years experienced level questions & answers
Ques 1. Explain the concept of Resilient Distributed Datasets (RDD) in PySpark.
RDD is the fundamental data structure in PySpark, representing an immutable distributed collection of objects. It allows parallel processing and fault tolerance.
Example:
data = [1, 2, 3, 4, 5]
rdd = spark.sparkContext.parallelize(data)
Ques 2. What is the difference between a DataFrame and an RDD in PySpark?
DataFrame is a higher-level abstraction on top of RDD, providing a structured and tabular representation of data. It supports various optimizations and operations similar to SQL.
Example:
df = spark.createDataFrame([(1, 'John'), (2, 'Jane')], ['ID', 'Name'])
Ques 3. What is the purpose of the 'cache' operation in PySpark?
The 'cache' operation is used to persist a DataFrame or RDD in memory, enhancing the performance of iterative algorithms or repeated operations.
Example:
df.cache()
Ques 4. How can you handle missing or null values in a PySpark DataFrame?
You can use the 'na' functions like 'drop' or 'fill' to handle missing values in a PySpark DataFrame.
Example:
df.na.drop()
Ques 5. What is the purpose of the 'explode' function in PySpark?
The 'explode' function is used to transform a column with arrays or maps into multiple rows, duplicating the values of the other columns.
Example:
from pyspark.sql.functions import explode
exploded_df = df.select('ID', explode('items').alias('item'))
Ques 6. Explain the purpose of the 'persist' operation in PySpark.
'Persist' is used to persist a DataFrame or RDD in memory or on disk, allowing faster access to the data in subsequent operations.
Example:
df.persist()
Ques 7. What is the purpose of the 'explode' function in PySpark?
The 'explode' function is used to transform a column with arrays or maps into multiple rows, duplicating the values of the other columns.
Example:
from pyspark.sql.functions import explode
exploded_df = df.select('ID', explode('items').alias('item'))
Ques 8. How can you handle missing or null values in a PySpark DataFrame?
You can use the 'na' functions like 'drop' or 'fill' to handle missing values in a PySpark DataFrame.
Example:
df.na.drop()
Ques 9. Explain the difference between 'cache' and 'persist' operations in PySpark.
'Cache' is a shorthand for 'persist(memory_only=True)', while 'persist' allows more flexibility by specifying storage levels (memory-only, disk-only, etc.).
Example:
df.cache()
Ques 10. What is the purpose of the 'agg' method in PySpark?
The 'agg' method is used for aggregating data in a PySpark DataFrame. It allows you to perform various aggregate functions like sum, avg, max, min, etc., on specified columns.
Example:
result = df.agg({'Sales': 'sum', 'Quantity': 'avg'})
Ques 11. Explain the purpose of the 'coalesce' method in PySpark.
The 'coalesce' method is used to reduce the number of partitions in a PySpark DataFrame. It helps in optimizing the performance when the number of partitions is unnecessarily large.
Example:
df_coalesced = df.coalesce(5)
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