Top 10 Pyspark Interview Questions and Answers

Pyspark is a distributed computing framework for big data processing, while Pandas is a library for data manipulation in Python.

• Pyspark is designed for big data processing and distributed computing, while Pandas is more suitable for smaller datasets ...read more

A program to count the number of words in a text file using PySpark.

• Read the text file using SparkContext

• Split the lines into words using flatMap

• Map each word to a tuple of (word, 1)

• Reduce by key to count the occurrences of each word

• Save the output t...read more

Use Pyspark for big data processing and distributed computing, use pandas for smaller datasets and data manipulation.

• Use Pyspark for handling large datasets that don't fit into memory

• Use pandas for data manipulation and analysis on smaller datasets t...read more

RDD stands for Resilient Distributed Datasets in Pyspark, which are fault-tolerant collections of elements that can be processed in parallel.

• RDDs are the fundamental data structure in Pyspark.

• They are immutable and can be cached in memory for faster ...read more

Use dropDuplicates() function in pyspark to remove duplicate rows based on primary key.

• Use dropDuplicates() function on the DataFrame with the primary key column specified.

• Specify the subset parameter in dropDuplicates() to specify the primary key co...read more

PySpark architecture is based on the Apache Spark architecture, with additional components for Python integration.

• PySpark architecture includes Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX.

• It allows Python developers to interact with Spa...read more

Data is processed using PySpark by creating Resilient Distributed Datasets (RDDs) and applying transformations and actions.

• Data is loaded into RDDs from various sources such as HDFS, S3, or databases.

• Transformations like map, filter, reduceByKey, etc...read more

Use the withColumn method in PySpark to combine two columns in a DataFrame.

• Use the withColumn method to create a new column by combining two existing columns

• Specify the new column name and the expression to combine the two columns

• Example: df = df.wit...read more

Large data processing in Pyspark involves partitioning, caching, and optimizing transformations for efficient processing.

• Partitioning data to distribute workload evenly across nodes

• Caching intermediate results to avoid recomputation

• Optimizing transfo...read more

Data manipulations using pyspark involve processing and transforming large datasets using the PySpark framework.

• Use PySpark functions like select, filter, groupBy, and join to manipulate data

• Utilize RDDs (Resilient Distributed Datasets) and DataFrame...read more

Use Pyspark to remove regex characters from column values

• Use the regexp_replace function in Pyspark to remove regex characters from column values

• Specify the regex pattern to match and the replacement string

• Apply the regexp_replace function to the des...read more

Use window function to find products with 3 consecutive years sales in Pyspark

• Use window function to partition by product and order by year

• Filter the results where the count of consecutive years is 3