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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

Architecture and Components of Apache YARN

Chetan
Chetan Last Updated : 25 Jul, 2022
5 min read

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

Introduction

YARN is an open-source project for Apache representing “Yet Another Resource Negotiator”. Hadoop Collection Manager is responsible for sharing resources (such as CPU, memory, disk, and network), and organizing and monitoring tasks throughout the Hadoop collection. Previous versions of Hadoop only support MapReduce functionality in the Hadoop collection; However, the advance of YARN has also made it possible to use other large data solution frameworks such as Spark, Flink, and Samza and many more in the Hadoop Cluster. YARN supports a wide variety of tasks such as broadcast processing, cluster processing, graph processing and duplicate processing.

What is YARN Architecture?

YARN resource manager

 

Apache YARN consists of two main components: Resource Manager and Node Manager. One Resource Manager per set while the Node Manager daemon works on all employee nodes.
A resource Manager is a daemon that is responsible for distributing resources to the collection. It has two main components which are the Editor and the Application Manager. The scheduler is responsible for organizing applications throughout the collection based on CPU memory and needs. There is only one Service Manager per set.
The Application Manager accepts tasks and creates specific Applicants and restarts them in the event of a failure.
Node Manager is a daemon that works on all staff nodes and manages resources at the machine level. Node Manager defines the resources available on the node and keeps track of usage. It also monitors the life of the nodes and if it is found to be unhealthy refer it to the resource manager. The Node Manager communicates with the resource manager to send regular reports about the usage of the report and liaises with the application manager to generate a JVM to perform the task.

Hadoop YARN Architecture

Application Master is responsible for managing the entire application life cycle from resource consultation, to tracking and monitoring of the work environment.

 

How Does YARN Support Frameworks?

YARN is not limited to Hadoop MapReduceHadoop MapReduce; can be used to run one of the most promising frameworks for data consulting services like Spark, Flink, Samza, and many more. The list below represents all the components that can currently operate on YARN.

YARN Scheduler

YARN supports three planning policies namely FIFO, Power, and Effective Planning that determine how revenue activities will be prioritized or prioritized.

FIFO Scheduler

In the FIFO Editorial Policy, requests are provided for “First Outgoing” but this policy may result in job starvation if the collection is shared between multiple users. Therefore, this policy is not valid for shared collections. By default, YARN is always set to FIFO policy.

Capacity Scheduler

In Capacity Scheduler, various organizations share their Hadoop collection to increase the use of the collection. Although organizations share their collection, Position Planners make sure that each organization receives its required volume.
Capacity Scheduler provides power guarantees, flexibility, resource-based planning, critical planning, multiple hires, and much more. We must configure the structure below in the conf / yarn-site.xml file to enable the Skill Editor in YARN.

Fair Scheduler

The Positive Planning Policy ensures that all active functions receive approximately equal shares in use (memory or CPU). Tasks are divided into rows and resources are distributed equally in those rows. It always ensures a small amount of queue sharing and when the queue is empty, excess resources are still distributed through operations running on other lines. We can also define a set of rules that apply to applications submitted for applications to enter the appropriate lines for further processing.

Reservation System

Users can store certain apps on YARN using the booking system so that important apps can always access resources on time. We can mark any leaf line as a booking line in Fair & Capacity Scheduler (fair-scheduler.xml / capacity-scheduler.xml). Let’s see how it works.
The user submits a booking creation request and receives a booking Id. In the next step, users submit a booking request and booking id and a Reservation Agent called GREE will create bookings in the System (The system is a data framework that monitors and tracks all bookings).
Next time a user makes a request with a booking id, the organizer will ensure that the app receives the reserved resources. However, if resources do not work they can be used to run other applications as well.

High Availability

Earlier in Hadoop 2.4, Cable Manager was a single point of failure in the YARN collection. However, after Hadoop 2.4 Resource Manager operates in active mode / StandBy to provide error tolerance and high availability.
The Standby Resource Manager regularly monitors all possible changes to the active Resource Manager and may replace them in the event of a failure. The Service Manager works closely with the veterinarian to record his status and decide which Service Manager should work in the event of a failure.
Failure The transition from active mode to standby mode may be automatic or automatic. Manual failover transmission can be done by the controller using the “yarn rmadmin” cli while in automatic failover transmission zookeeper daemon is used.

Yarn Federation

The Yarn Federation is a program to combine YARN’s smaller collections into a larger group. Applications running on the federated clusters can be configured in any sub-cluster location. There will be multiple resource managers for each collection. Such structures offer greater flexibility and scalability as a separate Service Manager will manage part of the collection thus increasing overall planning and monitoring performance.

 

YARN Versus Mesos

YARN vs MESOS

  • Apache Mesos is another well-known resource manager in the market. There are a few important differences between the two.
  • YARN is written in Java Mesos written in C ++
  • By default, YARN is based on memory configuration only. By default, Apache Mesos has memory and editing CPU
  • Apache YARN is a monolithic editor which means we follow a single step of planning and feeding for work Apache Mesos is a non-monolithic process that follows a two-step process of planning and feeding.

Conclusion

Undoubtedly, YARN is a robust, flexible, flexible engine management software that supports more than 15 large data structures. Allows the external application to run the Hadoop Distributed file system. It is in great demand and is widely used throughout the industry.
  • The Architecture of YARN and the various components that we use to work with YARN i.e Resource manager, Application manager, Node Manager
  • various frameworks supported by YARN – YARN scheduler, FIFO scheduler, Capacity Scheduler, and Fair Scheduler.
  • The reservation system includes High Availability & High Federation. This was all you need to get started with YARN.

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Apache HadoopblogathonYARN
Chetan
Chetan

Data Analyst who love to drive insights by visualizing the data and extracting the knowledge from it. Automating various tasks using python & builds Real time Dashboard's using tech like React and node.js. Capable of Creaking complex SQL queries to fetch the accurate data.

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