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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

An Ultimate Manual to Apache Oozie

Vibha
Vibha Last Updated : 03 Feb, 2023
5 min read

Introduction

Big data processing is crucial today. Big data analytics and learning help corporations foresee client demands, provide useful recommendations, and more. Hadoop, the Open-Source Software Framework for scalable and scattered computation of massive data sets, makes it easy. While MapReduce, Hive, Pig, and Cascading are all useful tools, completing all necessary processing or computing in a single job is seldom possible. Many MapReduce tasks are commonly linked together to create and collect intermediate data and manage processing flow. To overcome this, Yahoo developed Oozie, a foundation for managing multi-step processes in MapReduce, Pig, etc.

Oozie
Source: oreilley.com

Learning Objectives

  1. Understand what it is apache oozie and what are its types
  2. We are going to learn how it works
  3. What is a workflow engine?

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

Table of Contents

  1. What is Apache Oozie?
  2. Types of Oozie Jobs
  3. Features of Oozie
  4. How does Oozie work?
  5. Deployment of Workflow Application
  6. Wrapping up
  7. What is Apache Oozie?

What is Apache Oozie?

Apache Oozie is a workflow scheduler system for running and managing Hadoop jobs in a scattered environment. It grants the processing of multiple complex jobs in a successive way to carry out a larger job. Two or more duties in a job sequence can also be programmed to operate concurrently. It is basically an Open-Source Java Web Application licensed under the Apache 2.0 license. It is in charge of initiating workflow operations, which are then processed by the Hadoop processing engine. As a result, Oozie may use the current Hadoop infrastructure for load balancing, fail-over, and so on.

It can be used to quickly schedule MapReduce, Sqoop, Pig, or Hive tasks. Many different types of jobs can be integrated using Apache oozie, and a job pipeline of one’s choice can be quickly established.

Types of Oozie Jobs

  • Oozie Workflow Jobs: Apache Oozie workflow is a set of action and control nodes organized in a DAG. DAG is a directed acyclic graph (DAG) that captures control dependency, with each action representing a Hadoop job, Pig, Hive, Sqoop, or Hadoop DistCp job. Aside from Hadoop tasks, there are other operations like Java apps, shell scripts, and email notifications.
  • Oozie Coordinator Jobs: To resolve trigger-based workflow computation, the Apache Oozie coordinator is employed. It provides a foundation for providing triggers or predictions, after which it schedules the workflow depending on those established triggers. It allows officials to monitor and regulate workflow processes in response to group conditions and application-specific constraints.
  • Oozie Bundle: It is a group of Oozie coordinator apps that give instructions on when to launch that coordinator. Users can start, stop, resume, suspend, and rerun at the bundle level, giving them complete control. Bundles are also defined using an XML-based language called the Bundle Specification Language. It is a very useful degree of abstraction in many major corporations.
Types of Oozie Jobs

It is indeed quite flexible. Jobs can be begun, paused, interrupted, and restarted with ease. Rerunning failed workflows is a breeze with Oozie.

Features of Oozie

  • Jobs can be controlled from anywhere using its Web Service APIs.
  • It contains a client API and a command-line interface that can be used from a Java application to initiate, control, and monitor jobs.
  • It provides the ability to perform jobs that are scheduled to run regularly.
  • It has the ability to send email reminders when jobs are completed.

How does Oozie work?

It is a service that runs in the Hadoop group and on client computers. It sends workflow definitions for immediate or delayed processing. The workflow is mainly made up of action and control-flow nodes.

An action node represents a workflow job, like transferring files into HDFS, running MapReduce, Pig, or Hive jobs, importing data with Sqoop, or processing a shell script of a Java program.

A control-flow node manages the workflow processing between actions by allowing features like conditional logic, which allows alternative branches to be followed based on the outcome of a former action node.

How does Oozie work?
Source: oreilly.com

This group of nodes includes the Start Node, End Node, and Error Node.

  1. The Start Node indicates the beginning of the workflow job.
  2. The End Node indicates the end of the job.
  3. The Error Node denotes the existence of an error and the associated error message to be written.

It uses an HTTP callback at the end of a workflow process to notify the client of the workflow status. The callback may be caused by entering or exiting an action node too.

Deployment of Workflow Application

The workflow description and each connected resource, like Pig scripts, MapReduce Jar files, and so forth, make up a workflow application. The workflow application must adhere to a simple directory structure deployed to HDFS so that it can access it.

Directory Structure

/
??? lib/
? ??? hadoop-application-examples.jar
??? workflow.xml

Workflow.xml (a workflow definition file) must be kept in the top-level directory (parent directory). Jar files containing MapReduce classes can be found under the Lib directory. Any build tool, like Ant or Maven, can be used to create a workflow application that adheres to this pattern.

The command for copying files to HDFS

% hadoop fs -put hadoop-examples/target/ name of workflow

Run Oozie Workflow Job

To run the jobs, we’ll need to use the Oozie command-line tool, which is an important client program that talks to the Oozie server.

Step 1: Export the OOZIE URL environment variable to define the Oozie command that sets the Oozie server to use for processing.

% export OOZIE_URL="http://localhost:11000/oozie"

Step 2: Use the -config option to run the Oozie workflow Job, which refers to a local Java properties file. The file includes definitions for the parameters used in the workflow XML file.

% oozie job -config ch05/src/main/resources/max-temp-workflow.properties -run

Step 3: The oozie.wf.application.path. It informs the Oozie of the location of the workflow application in HDFS.

nameNode=hdfs://localhost:8020
jobTracker=localhost:8021
oozie.wf.application.path=${nameNode}/user/${user.name}/

Step 4: The status of a workflow job can be determined by using the subcommand ‘job’ with the ‘-info’ option, which requires giving the job id after ‘-info’, as explained below. Depending on job status, RUNNING, KILLED, or SUCCEED will be shown.

      % oozie job -info

Step 5: To receive the result of successful workflow computation, we must run the following Hadoop command.

      % hadoop fs -cat

Conclusion

We learn how to deploy workflow apps and operate the workflow application. It initiates work process operations using the Hadoop processing engine to carry out several tasks. It uses modern Hadoop hardware for load balancing, failover, etc. Oozie is responsible for determining the completion of tasks through callbacks and polling. Insights from the article:

  • We Apache Oozie schedule Hadoop jobs in a scattered environment.
  • A workflow engine stores and runs Hadoop workflows like MapReduce, Pig, etc.
  • Control-flow nodes provide conditional logic to facilitate process coordination.
  • Java apps may use the Oozie command line interface and client API to manage and keep tabs on processes.

We hope you liked this post; please share your thoughts in the comments below.

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

Apache HadoopApache OozieAPIblogathondeploymentMapreducePig
Vibha
Vibha

I am an engineering student. Currently, I am pursing Btech from Vellore Institute of Technology. I am very passionate about programming and constantly eager to expand my knowledge in Data Science and Machine Learning.

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Apache Oozie: Scheduler System to Manage & Perform Hadoop Jobs
Apache Oozie: Scheduler System to Manage & Perform Hadoop Jobs
Top Interview Questions & Answers for Apache Oozie
Top Interview Questions & Answers for Apache Oozie

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