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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
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Most Asked Interview Questions on Apache Spark

Kishan Yadav
Kishan Yadav Last Updated : 08 Jun, 2023
7 min read

Introduction

Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark’s in-memory data processing capabilities make it 100 times faster than Hadoop. It has the ability to process a huge amount of data in such a short period. The most important feature of Spark is in-memory data processing. Here is a list of interview questions on Apache Spark.

This article was published as a part of the Data Science Blogathon.

Table of contents

Apache Spark Interview Questions

Spark is one of the interviewer’s favorite topics in big data interviews, so in this blog, we’ll go over the most important and frequently asked interview questions about Apache Spark. Let’s begin…

Apache Spark

1. What is the spark?

Spark is a general-purpose in-memory compute engine. You can connect it with any storage system like a Local storage system, HDFS, Amazon S3, etc. It also provides you the freedom to use a resource manager of your choice, whether Yarn, Mesos, Kubernetes, or standalone. It is designed to manage batch application workloads, iterative algorithms, interactive queries, and streaming. Spark supports high-level APIs such as Java, Scala, Python, and R. It is built in Scala language.

2. What is RDD in Apache Spark?

RDDs stand for Resilient Distributed Dataset. It is the most important building block of any spark application. It is immutable. RDD properties are:-

  • Resilient:- It has fault tolerance properties and can quickly recover lost data.
  • Distributed:- Data are distributed across multiple nodes for faster processing.
  • Dataset:- Collection of data points on which we perform operations.

RDD provides fault tolerance through a lineage graph. A lineage graph keeps track of transformations to be executed after an action has been called. The Lineage graph helps to recompute any missing or damaged RDD because of node failures. RDDs are used for low-level transformations and actions.

3. What is the Difference between SparkContext Vs. SparkSession?

In Spark 1.x version, we must create different contexts for each API. For example:-

  • SparkContext
  • SQLContext
  • HiveContext 

While in the spark 2.x version, a new entry point named SparkSession was introduced, single-handedly covering all of the functionality. No need to create different contexts for the entry points.

SparkContext is the main entry point for accessing the spark functionalities. It represents a spark cluster’s connection which is useful in building RDDs, accumulators, and broadcast variables on the cluster. We can access the default object of SparkContext in spark-shell, which is present in a variable name “sc.”

SparkSession:- Before the spark 2.0 version, we need different contexts to access the different functionalities in spark. While in spark 2.0, we have a unified entry point named SparkSession. It contains SQLContext, HiveContext, and StreamingContext. No need to create separate ones. The APIs accessible in those contexts are likewise available in SparkSession, and SparkSession includes a SparkContext for real computation.

4. What is the broadcast variable?

Broadcast variables in Spark are a mechanism for sharing the data across the executors to be read-only. Without broadcast variables, we have to ship the data to each executor whenever they perform any type of transformation and action, which can cause network overhead. While in the case of broadcast variables, they are shipped once to all the executors and are cached over there for future reference.

Broadcast Variables Use case

Suppose we are doing transformations and need to look up a larger table of zipping codes/pin codes. It’s not feasible to send the data to each executor when needed, and we can’t query it each time to the database. So, in this case, we can convert this lookup table into the broadcast variables, and Spark will cache it in every executor.

5. Explain Pair RDD?

The Spark Paired RDDs are a collection of key-value pairs. There is two data item in a key-value pair (KVP). The key is the identifier, while the value is the data corresponding to the key value. A few special operations are available on RDDs of key-value pairs, such as distributed “shuffle” operations, grouping, or aggregating the elements by a key.

val spark = SparkSession.builder()

.appName("PairedRDDCreation")

.master("local")

.getOrCreate()

val rdd = spark.sparkContext.parallelize(
List("Germany India USA","USA India Russia","India Brazil Canada China"))
val wordsRdd = rdd.flatMap(_.split(" "))
val pairRDD = wordsRdd.map(f=>(f,1))
pairRDD.foreach(println)

Output:-

(Germany,1)
(India,1)
(USA,1)
(USA,1)
(India,1)
(Russia,1)
(India,1)
(Brazil,1)
(Canada,1)
(China,1)

6. What is the difference between RDD persist() and cache() methods?

The persistence and caching mechanisms are the optimization techniques. It may be used for Interactive as well as Iterative computation. Iterative means to reuse the results over multiple computations. Interactive means allowing a two-way flow of information. These mechanisms help us to save the results so that upcoming stages can use them. We can save the RDDs either in Memory(most preferred) or on Disk((less Preferred because of its slow access speed).

Persist():- We know that RDDs are re-computable on each action due to their default behavior. To avoid the re-computation, we can persist with the RDDs. Now, whenever we call an action on RDD, no-re-computation takes place.

In persist() method, computations results get stored in its partitions. The persistent method will store the data in JVM when working with Java and Scala. While in python, when we call persist method, the serialization of the data takes place. We can store the data either in memory or on the disk. A combination of both is also possible.

Storage levels of Persisted RDDs:-

1. MEMORY_ONLY(DEFAULT LEVEL)

2. MEMORY_AND_DISK

3. MEMORY_ONLY_SER

4. MEMORY_ONLY_DISK_SER

5. DISC_ONLY

Cache():- It is the same as the persist method; the only difference is cache stores the computations result in the default storage level i.e. Memory. Persist will work the same as a cache when the storage level is set to MEMORY_ONLY.

Syntax to un-persist the RDDs:-

RDD.unpersist( )

7. What is Spark Core?

Spark Core is the foundational unit of all spark applications. It performs the following functionalities: memory management, fault recovery, scheduling, distributing & monitoring jobs, and interaction with storage systems. Spark Core can be accessed through an application programming interface (APIs) built in Java, Scala, Python, and R. It contains APIs that help to define and manipulate the RDDs. This APIs help to hide the complexity of distributed processing behind simple, high-level operators. It provides basic connectivity with different data sources, like AWS S3, HDFS, HBase, etc.

Apache Spark

8. What is RDD Lineage?

RDD Lineage (RDD operator graph or RDD dependency graph) is a graph that contains all the parent RDDs of an RDD.

RDD Lineage

The following transformations can generate the above graph:-

val r00 = sc.parallelize(0 to 9)
val r01 = sc.parallelize(0 to 90 by 10)
val r10 = r00.cartesian(r01)
val r11 = r00.map(n => (n, n))
val r12 = r00.zip(r01)
val r13 = r01.keyBy(_ / 20)
val r20 = Seq(r11, r12, r13).foldLeft(r10)(_ union _)

1. The RDD lineage gets created when we apply different types of transformations to an RDD, creating a so-called Logical Execution Plan.

2. The lineage graph contains information on all the transformations that need to be applied when action gets called.

3. A logical execution plan starts with the earliest RDD and finishes with the RDD, producing the final result on which action has been called.

9. What is the difference between RDD and DataFrame?

DataFrame:-

A data frame stores the data in tabular format. It is a distributed collection of data in rows and columns. The columns can store data types like numeric, logical, factor, or character. It makes the processing of larger datasets easier. Developers can impose a structure onto a distributed collection of data with the help of a data frame. It also provides a high-level abstraction over the distributed data.

RDD:-

RDD(Resilient Distributed Dataset) is a collection of elements distributed across multiple cluster nodes. RDDs are immutable and fault tolerant. RDDs, once created, can’t get changed, but we can perform several transformations to generate new RDDs from them.

10. Explain Accumulator shared variables in Spark?

Accumulators are read-only shared variables. They are only “added” through an associative and commutative operation. They can be used to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of numeric types, and you can also add support for new types.

Accumulators are incremental variables. The tasks running on nodes can add to it while the driver program can read the value. Tasks running on different machines can increment their value, and this aggregated information is available back to the driver.

Frequently Asked Questions

Q1. How can I prepare for an Apache Spark interview?

A. To prepare for an Apache Spark interview, follow these steps:
1. Understand Spark’s architecture, components, and RDDs.
2. Master Spark transformations and actions.
3. Practice writing Spark code in languages like Scala or Python.
4. Learn about Spark SQL, data frames, and data manipulation techniques.
5. Gain hands-on experience with Spark by working on projects or exercises.
6. Study optimization techniques and performance tuning in Spark.
7. Stay updated with the latest advancements and trends in Spark technology.

Q2. How can I optimize performance in Apache Spark?

A. To optimize performance in Apache Spark, you can consider the following techniques:
1. Use efficient data structures like DataFrames and Datasets.
2. Employ partitioning and caching to reduce data shuffling and improve data locality.
3. Utilize broadcast variables for small data sets.
4. Optimize the Spark execution plan by applying transformations and actions strategically.
5. Use appropriate hardware configurations and resource allocation.
6. Monitor and tune Spark configurations based on your workload.

Conclusion

In this article, we have discussed the most important and frequently asked interview questions on Apache Spark. We started our discussion with fundamental questions like what a spark is, RDD, Dataset, and DataFrame. Then, we move towards intermediate and advanced topics like a broadcast variable, cache and persist method in spark, accumulators, and pair RDDs. We have a different variety of questions related to spark.

Key takeaways from this article are:-

  • 1. We learn the difference between the most used terms in Apache Spark, i.e., RDD, DAG, DataFrame, Dataset, etc.
  • 2. We understood Structured APIs and how they are used to perform different operations on data.
  • 3. We also learn about pair RDD, lineage, broadcast variables, and accumulators.
  • 4. Other learnings are sparkcontext v/s sparksession, RDD v/s DataFrame, and Spark core.

I hope this article helps you in your interview preparation relating to Apache Spark. If you have any opinions or questions, then comment down below. Connect with me on LinkedIn for further discussion.

Keep Learning!!!

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Kishan Yadav
Kishan Yadav

Hello 👋,
I am a Data Engineer with a proven track record of working in the information technology and services industry. I am skilled in Apache Spark, Hive, SQL, Python, Hadoop, Databricks and Cloud.

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