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Reading list

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
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
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
Model Deployment in Android Model Deployment in Iphone

YARN for Large Scale Computing: Beginner’s Edition

aditya8495d
aditya8495d Last Updated : 31 Jan, 2023
6 min read

Introduction

YARN stands for Yet Another Resource Negotiator. It is a powerful resource management system for a horizontal server environment. It is designed to be more flexible and generic than the original Hadoop MapReduce system, making it an attractive choice for companies looking to implement Hadoop. It allows companies to process data types and run various data processing engines on the same cluster, like Spark, Hive, Pig, etc.

YARN
Source: bigdataschool

One of the critical features of YARN is its ability to handle multiple types of workloads. This is achieved using two key components: the Resource Manager and the Node Manager. The Resource Manager allocates resources to different applications, while the Node Manager manages the resources on individual nodes in the cluster.

YARN has many applications and use cases, including batch processing, interactive SQL, real-time streaming, machine learning, graph processing, long-running services, and big data processing.

Learning Objectives:

  1. We will get a basic introduction to YARN.
  2. Then you will learn about the pros and cons of using it.
  3. Discuss the applications and use cases.
  4. Understand the critical difference between YARN and MapReduce.
  5. And finally, you will get hands-on with various coded and practical examples.

Table of Contents

1. Advantages and Disadvantages of YARN

2. Applications and Use Cases of YARN

3. Learn the Difference: YARN vs. Map Reduce

4. Understanding the Concept With Basic Code Examples

5. Conclusion

Advantages and Disadvantages of Using YARN

There are benefits to using YARN for extensive systems, but it has many disadvantages too. Below are some of the pros and cons.

Advantages of YARN:

  • Improved Isolation and Management of Resources: It provides better isolation and management of resources, which can improve the stability and performance of the cluster.
  • Flexibility: It can handle multiple types of workloads, which allows companies to run different types of applications on the same work, leading to improved consumption of resources and cost savings.
  • Support for Many Computer Applications: It supports many distributed computing applications, including batch processing, interactive SQL, real-time streaming, and machine learning.
  • Scalability: It can dynamically scale up and down the cluster, allowing organizations to adapt to changing workloads.
  • Rolling Upgrades: It supports rolling upgrades, which makes companies improve their work without interruption to service.
  • Multi-version Support: It allows organizations to run multiple versions of Hadoop on the same cluster.

Disadvantages of YARN:

  • Complexity: It can be complex to set up and configure and requires a good understanding of the cluster setup and configurations to utilize its capabilities thoroughly.
  • Limited Support for Legacy Applications: It only supports some of the legacy applications built for the original Hadoop MapReduce system.
  • Resource Allocation: Its resource allocation algorithm may sometimes be optimal for specific workloads.
  • Resource Allocation Fairness: Its resource allocation algorithm may only sometimes be fair for different applications and users.
YARN
Source: educba

Applications and Use Cases of YARN

It has many applications and use cases that are highly beneficial for current tech giants. Some of them are discussed below.

  • Batch Processing: YARN can enable batch processing jobs, such as data warehousing and ETL (extract, transform, load). This allows organizations to process large amounts of data efficiently and cost-effectively.
  • Interactive SQL: YARN can run interactive SQL queries on large data sets. This allows organizations to perform real-time analytics and gain insights from their data.
  • Real-time Streaming: YARN can process real-time streaming data, such as log data, sensor data, and social media data. This allows organizations to gain insights and make decisions in near real-time.
  • Machine Learning: YARN can be used to allocate machine learning algorithms on large data sets. This allows organizations to build and train models for various applications, such as image and speech recognition, natural language processing, and predictive analytics.
  • Graph Processing: YARN can process graph data like social networks and recommendation systems. This allows organizations to analyze relationships and connections within their data.
  • Long-Running Services: YARN can allocate long-running services like web services and data pipelines. This allows organizations to deploy and manage these services in a scalable and fault-tolerant manner.
  • Big Data Processing: YARN enables different data processing engines to run on the same cluster. This allows organizations to process structured, semi-structured, and unstructured data using tools like Spark, Hive, Pig, etc.
YARN
Source: educba

 

Learn the Difference: YARN vs. Map Reduce

This section will discuss the difference between YARN and Map Reduce.

YARN Map Reduce
1. YARN (Yet Another Resource Negotiator) is a resource management system for Hadoop that was introduced in Hadoop 2.0. 1. MapReduce is a programming model and software framework for processing large data sets in parallel.
2. YARN is responsible for managing resources, such as CPU and memory, in a Hadoop cluster and scheduling tasks to execute those resources. 2. MapReduce is responsible for processing the data and generating the results.
3. YARN has a centralized architecture that allows for better scalability and resource management, while MapReduce has a more decentralized architecture. It provides more flexibility regarding how resources are allocated and used. 3. MapReduce is more rigid in resource allocation and usage.
4. YARN allows for real-time processing of data streams, while MapReduce is mainly used for batch processing of data. It also allowed for better cluster utilization and increased performance. 4. MapReduce can lead to underutilization of resources and lower performance in some cases
YARN
Source: slideplaye

Understanding the Concept with Basic Code Examples

This section will discuss some basic examples that beginners can easily understand, like running a simple word count job using YARN and MapReduce or running a Pig script using YARN.

Example 1: Running a simple word count job using YARN and MapReduce:

$ hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount /input/text.txt /output/wordcount

In this example, we are using the built-in wordcount example job that comes with Hadoop. We pass two arguments, the input file /input/text.txt and the output directory /output/wordcount, where the job results will be stored. This job will count the number of occurrences of each word in the input text file and write the results to the output directory.

Example 2: Running a Pig script using YARN:

$ pig -x yarn -f /path/to/pig/script.pig

In this example, we use the Pig command-line interface to run a Pig script located at “/path/to/pig/script.pig” on a YARN cluster. The “-x yarn” option tells Pig to use YARN as the execution mode. This can be used to run Pig scripts that perform data processing, data cleaning, and data transformation tasks.

Example 3: Running a Hive query using YARN:

$ hive -e "SELECT * FROM mytable LIMIT 10"

In this example, we use the Hive command-line interface to run a SQL-like query on a table named “mytable.” The query selects all columns and rows from the table and returns only the first ten rows. The results of the question will be printed on the console. This can be used for interactive data querying and exploration.

Example 4: Running a Spark job using YARN:

$ spark-submit --class org.example.MySparkJob --master yarn --deploy-mode client --executor-memory 512m --num-executors 10 /path/to/jar/my-spark-job.jar arg1 arg2

In this example, we are using the Spark command-line interface to submit a Spark job named “MySparkJob” that is packaged in a jar file located at “/path/to/jar/my-spark-job.jar”. The job takes two arguments, “arg1” and “arg2”, passed to the main method. The “–master yarn” option tells Spark to use YARN as the cluster manager. “–deploy-mode client” means spark to run the driver program on the client machine, and “–executor-memory 512m” and “–num-executors 10” are used to configure the amount of memory per executor and the number of executors respectively.

Conclusion

In conclusion, YARN is a powerful resource management system for Hadoop. It allows different types of data processing workloads, such as batch processing, interactive queries, real-time streaming, and machine learning, to run on the same cluster. It gives a centralized management system for a Hadoop cluster, making it easier to manage and monitor resources. However, it’s important to note that it has limitations, such as added complexity, inefficiencies in resource allocation, and limited support by specific frameworks and tools.

Key takeaways of this article:
1. Firstly, we have discussed the YARN framework. It is a resource management system for large-scale distributed computing in Hadoop that allows multiple data processing frameworks to run on the same cluster.
2. Then, we discussed the advantages and disadvantages of using it. We have discussed scalability, the benefits of cluster-making, etc.
3. After that, we discussed its use cases and wrote the code of some of its primary applications, like using a pig script or running a simple command for word count.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Apache HadoopApache YARNblogathonHadoop MapReduceMapreduceSparkYARN
aditya8495d
aditya8495d

As a content writer with experience in data science and data engineering, I am skilled in creating clear, concise and engaging written materials for a variety of audiences. My technical knowledge in these fields allows me to accurately convey complex ideas and concepts in an easy-to-understand manner. In addition, I am constantly seeking to learn and stay up-to-date on the latest trends and developments in data science and data engineering.

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The Tale of Apache Hadoop YARN!
The Tale of Apache Hadoop YARN!

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