Apache Spark Interview Questions for various topics.
In Apache Spark, caching (or persisting) a DataFrame allows you to store it in-memory, reducing the need to recompute it each time it is accessed. However, if you want to free up the memory, you need to unpersist (or drop) the DataFrame from the cache. Dropping a Spark DataFrame from Cache To drop a DataFrame …
How to Easily Drop a Spark DataFrame from Cache? Read More »
When working with Apache Spark, viewing the contents of a Resilient Distributed Dataset (RDD) can be useful for debugging or inspecting the data. Let’s explore various methods to achieve this in PySpark (Python Spark). 1. Using the `collect()` Method The `collect()` method retrieves the entire RDD data to the driver node. This method is useful …
Apache Spark on YARN stores logs in specific locations depending on various factors such as the configuration of the cluster, the Hadoop and YARN configurations, and the location of the application logs. Understanding where these logs are stored is crucial for debugging and monitoring purposes. Log Storage in Spark on YARN When Spark runs on …
Converting rows in a Scala DataFrame into case class instances is a common requirement in Spark applications, particularly when you want to take advantage of compile-time type safety and immutability provided by case classes. Here’s how to efficiently achieve this using Scala and Spark. Step-by-Step Explanation: Define the Case Class First, you need to define …
How to Efficiently Convert a Scala DataFrame Row into a Case Class? Read More »
In an Apache Spark YARN (Yet Another Resource Negotiator) client mode, maintaining executors and preventing them from getting lost is crucial for ensuring the smooth running of your Spark application. Executors might get lost due to various reasons such as resource contention, node failures, network issues, or configuration problems. Below, we’ll explore various strategies and …
How to Prevent Spark Executors from Getting Lost in Yarn Client Mode? Read More »
Using User-Defined Functions (UDFs) in PySpark allows you to create custom functions that can be used in Spark transformations. Applying UDFs on grouped data involves a few steps: defining the UDF, registering it, and then applying it to the grouped data. Below is a step-by-step example in Python using PySpark. Step 1: Setting Up PySpark …
How to Apply UDFs on Grouped Data in PySpark: A Step-by-Step Python Example Read More »
Joining DataFrames is a common operation in data processing that combines rows from two or more DataFrames based on a related column between them, often referred to as the “key.” Efficiently joining DataFrames in Spark requires understanding of the join strategies and optimizations available in Spark. Here’s a detailed explanation of how to perform joins …
How to Join Spark DataFrames on Keys Efficiently? Read More »
To fix the AttributeError: Can’t Get Attribute ‘new_block’ in Pandas, we need to understand what is causing this issue. This usually occurs when there’s a mismatch in the versions of the pickle file and the environment trying to load it. Specifically, it often happens when a Pandas DataFrame object has been pickled with one version …
How to Fix the AttributeError: Can’t Get Attribute ‘new_block’ in Pandas? Read More »
Apache Spark provides two primary libraries for machine learning: MLlib and Spark ML. Understanding their differences is crucial for effectively leveraging Spark for your machine learning tasks. Spark MLlib vs. Spark ML Both libraries offer machine learning capabilities, but they differ significantly in their design, ease of use, and compatibility with newer functionality. Here’s a …
What’s the Difference Between Spark ML and MLlib Packages? Read More »
When working with Apache Spark, joining DataFrames based on multiple column conditions is a common requirement, especially in data analysis and ETL (Extract, Transform, Load) processes. This can be achieved using various languages supported by Spark, such as PySpark, Scala, and Java. Below, I’ll show you how to do this using PySpark as an example. …
How to Specify Multiple Column Conditions for Dataframe Join in Spark? Read More »
