20+ Pyspark Developer Interview Questions and Answers

Interview questions for a PySpark Developer

• Explained current project and its implementation

• Differentiated between managed and external table

• Described Spark architecture and RDD

• Discussed characteristics of RDD and lazy nature

• Provided insert statement for managed and external table

• Explained deployment related to code in PySpark

• Answered Python related questions

• Explained how to convince manager/scrum master for code changes

• Discussed team size and management

The SQL code for calculating year-on-year growth percentage with year-wise grouping.

• Use the LAG function to get the previous year's value

• Calculate the growth percentage using the formula: ((current year value - previous year value) / previous year value) * 100

• Group by year to get year-wise growth percentage

Coalesce reduces the number of partitions without shuffling data, while repartition increases the number of partitions by shuffling data. Cache and persist are used to persist RDDs in memory.

• Coalesce is used to reduce the number of partitions without shuffling data, while repartition is used to increase the number of partitions by shuffling data.

• Coalesce is more efficient when reducing partitions as it avoids shuffling, while repartition involves shuffling data across the net...read more

SQL query to find the second highest rank in a dataset

• Use the ORDER BY clause to sort the ranks in descending order

• Use the LIMIT and OFFSET clauses to skip the highest rank and retrieve the second highest rank

• Example: SELECT rank FROM dataset ORDER BY rank DESC LIMIT 1 OFFSET 1

Orchestrating code in GCP involves using tools like Cloud Composer or Cloud Dataflow to schedule and manage workflows.

• Use Cloud Composer to create, schedule, and monitor workflows using Apache Airflow

• Utilize Cloud Dataflow for real-time data processing and batch processing tasks

• Use Cloud Functions for event-driven serverless functions

• Leverage Cloud Scheduler for job scheduling

• Integrate with other GCP services like BigQuery, Pub/Sub, and Cloud Storage for data processing and s...read more

Optimization techniques in Apache Spark improve performance and efficiency.

• Partitioning data to distribute work evenly

• Caching frequently accessed data in memory

• Using broadcast variables for small lookup tables

• Optimizing shuffle operations by reducing data movement

• Applying predicate pushdown to filter data early

DataFrame is a higher-level abstraction built on top of RDD, providing more structure and optimization capabilities.

• DataFrames are distributed collections of data organized into named columns, similar to tables in a relational database.

• RDDs are lower-level abstractions representing a collection of objects distributed across a cluster, with no inherent structure.

• DataFrames provide optimizations like query optimization and code generation, making them faster for data processing...read more

To schedule a Pyspark script in a serverless environment, you can use cloud services like AWS Lambda or Azure Functions.

• Use AWS Lambda or Azure Functions to create a serverless function that triggers your Pyspark script.

• Set up a schedule using cloud services like AWS CloudWatch Events or Azure Scheduler to run the function at specified intervals.

• Ensure your Pyspark script is optimized for serverless execution to minimize costs and maximize performance.

Coalesce reduces the number of partitions without shuffling data, while repartition reshuffles data to create a specific number of partitions.

• Coalesce is used to reduce the number of partitions without shuffling data

• Repartition is used to increase or decrease the number of partitions by shuffling data

• Coalesce is more efficient when reducing partitions as it avoids shuffling

• Repartition is useful when you need to explicitly control the number of partitions

• Example: coalesce(5) v...read more

Filter students with marks greater than 40 from a dataset of names, subjects, and marks.

• Use PySpark DataFrame to load the dataset.

• Apply a filter condition to select rows where marks > 40.

• Extract the unique names of students who meet the criteria.

• Example: For the given data, only student 'B' has marks greater than 40.

Yes, you can change a Hive managed table to an external table using SQL commands.

• Use the command: ALTER TABLE table_name SET TBLPROPERTIES ('EXTERNAL'='TRUE');

• You must ensure that the data is moved to a location accessible by Hive.

• Example: ALTER TABLE my_table SET TBLPROPERTIES ('EXTERNAL'='TRUE');

• After conversion, Hive will not manage the data, allowing for external access.

SQL coding for calculating average rolling, lead, and lag functions.

• Use window functions like ROWS BETWEEN and ORDER BY for calculating rolling averages.

• Use LEAD and LAG functions to access data from previous or next rows.

• Example: SELECT col1, AVG(col2) OVER (ORDER BY col1 ROWS BETWEEN 3 PRECEDING AND CURRENT ROW) AS rolling_avg FROM table_name;

The entry point of a pipeline in PySpark is the SparkSession object.

• The entry point of a PySpark pipeline is typically created using the SparkSession object.

• The SparkSession object is used to create DataFrames, register tables, and execute SQL queries.

• Example: spark = SparkSession.builder.appName('example').getOrCreate()

Spark architecture is based on a master-slave model with a cluster manager and a distributed file system.

• Spark has a driver program that communicates with a cluster manager to coordinate tasks.

• The cluster manager allocates resources to worker nodes, which execute tasks in parallel.

• Spark uses a distributed file system, such as HDFS, to store and access data across the cluster.

• Spark also includes a variety of libraries and APIs for data processing, machine learning, and streami...read more

Azure linked services are connections to external data sources, while Azure datasets are structured data objects within Azure Data Factory.

• Azure linked services are used to connect to external data sources such as databases, storage accounts, and SaaS applications.

• Azure datasets are structured data objects within Azure Data Factory that represent data from linked services or other sources.

• Linked services define the connection information and credentials required to connect to...read more

Actions and transformations are two types of operations in PySpark. Actions return a value to the driver program, while transformations create a new RDD.

• Actions are operations that trigger the execution of the Spark job, such as collect(), count(), and saveAsTextFile().

• Transformations are operations that create a new RDD from an existing one, such as map(), filter(), and reduceByKey().

• Actions are lazy operations, meaning they are not executed until an action is called, while ...read more

RDD is a distributed collection of objects, while DataFrame is a distributed collection of data organized into named columns.

• RDD is more low-level and requires manual schema definition, while DataFrame provides a higher-level API with schema inference.

• DataFrames are optimized for processing structured data, while RDDs are more suitable for unstructured data.

• DataFrames support SQL queries and optimizations through Catalyst optimizer, while RDDs do not have built-in optimizatio...read more

A stage is a collection of tasks that perform a specific computation, while a task is a unit of work that is executed on a single executor.

• Stage is a higher-level unit of work that can be broken down into multiple tasks.

• Tasks are individual units of work that are executed on a single executor.

• Stages are used to organize and coordinate tasks in a Spark job.

• Tasks are the actual units of computation that are performed by executors.

• Example: A stage may involve reading data from a...read more

RDDs and DataFrames are data structures in Apache Spark for processing and analyzing large datasets.

• RDDs (Resilient Distributed Datasets) are the fundamental data structure of Spark, representing a collection of elements that can be operated on in parallel.

• DataFrames are distributed collections of data organized into named columns, similar to a table in a relational database.

• DataFrames are built on top of RDDs, providing a more user-friendly API for structured data processing...read more

Null handling in Spark involves handling missing or null values in data processing.

• Use functions like coalesce, na.fill, na.drop to handle null values

• Consider using when and otherwise functions for conditional null handling

• Be cautious of potential null pointer exceptions when working with null values

Spark is used for big data processing due to its speed, scalability, and ease of use.

• Spark is used for processing large volumes of data quickly and efficiently.

• It offers in-memory processing which makes it faster than traditional MapReduce.

• Spark provides a wide range of libraries for diverse tasks like SQL, streaming, machine learning, and graph processing.

• It can run on various platforms like Hadoop, Kubernetes, and standalone clusters.

• Spark's ease of use and compatibility wi...read more

Optimisation in PySpark involves improving performance and efficiency of Spark jobs.

• Use partitioning to distribute data evenly across nodes

• Avoid shuffling data between nodes as much as possible

• Use broadcast variables for small lookup tables

• Cache intermediate results to avoid recomputation

• Optimize transformations and actions for better performance

Transformations are lazy operations that create new RDDs, while Actions are operations that trigger computation and return results.

• Transformations are operations like map, filter, and reduceByKey that create a new RDD from an existing one.

• Actions are operations like count, collect, and saveAsTextFile that trigger computation on an RDD and return results.

• Transformations are lazy and are only executed when an action is called, allowing for optimization of computations.

• Actions a...read more

Airflow operators are used to define the tasks to be executed in a workflow.

• Operators are classes that define the logic to execute a task in Airflow.

• There are various types of operators such as BashOperator, PythonOperator, and more.

• Operators can be customized to suit specific task requirements.

• Operators can be chained together to create complex workflows.

• Example: BashOperator executes a bash command, PythonOperator runs a Python function.

A program to count the occurrences of each word in a text document.

• Use Spark RDD to read the text file and split the lines into words

• Apply transformations like map and reduceByKey to count the occurrences of each word

• Handle punctuation and case sensitivity to ensure accurate word count results

Exploding JSON data in PySpark allows you to transform nested structures into flat tables for easier analysis.

• Use the `explode` function to flatten arrays in JSON data.

• Example: df.select(explode(df.json_column)).show() to display exploded rows.

• For nested JSON, combine `explode` with `selectExpr` to access deeper levels.

• Example: df.selectExpr('explode(nested_array) as exploded').show() for nested arrays.