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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

Apache Zookeeper Architecture and Installation

T
Trupti Dekate Last Updated : 03 Aug, 2022
6 min read

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

Introduction

Zookeeper in Hadoop can be considered a centralized repository where distributed applications can put data into and retrieve data from. It makes a distributed system work together as a whole using its synchronization, serialization, and coordination goals. For clarity, Zookeeper can be thought of as a file system where we have nodes that store data instead of files or directories that store data. Zookeeper is a Hadoop Admin tool used to manage jobs in a cluster.

Source: – https://en.wikipedia.org/wiki/Apache_ZooKeeper
The formal definition of Apache Zookeeper says that it is a distributed, open-source configuration, synchronization service along with a name registry for distributed applications. Apache Zookeeper is used to manage and coordinate a large cluster of machines. For example, Apache Storm, which Twitter uses to store machine state data, has Apache Zookeeper as a coordinator between machines.

Why do we need a Zookeeper in Hadoop?

Distributed applications are difficult to coordinate and work with because they are much more prone to errors due to the large number of machines connected to the network. Because many machines are involved, race conditions and deadlocks are common problems when implementing distributed applications. A race condition occurs when a machine tries to perform two or more operations at once, and this can be resolved with ZooKeeper’s serialization feature.

A deadlock occurs when two or more computers attempt to access the same shared resource simultaneously. More precisely, they try to access each other’s resources, which leads to a deadlock because neither system releases the resource but waits for the other system to release it. Synchronization in Zookeeper helps resolve deadlocks. Another major problem with a distributed application is partial process failure, leading to data inconsistency. Zookeeper handles this with atomicity, meaning either the entire process terminates, or nothing is left after failure. So Zookeeper is an important part of Hadoop that takes care of these small but important matters so that the developer can focus more on the application’s functionality.

ZooKeeper service
Source: – https://data-flair.training/blogs/zookeeper-architecture/

How does Zookeeper Architecture work in Hadoop?

Hadoop Zookeeper Architecture is a distributed application that follows a simple client-server model, where clients are the nodes that consume the service and servers are the nodes that provide the service. Multiple server nodes are collectively called a ZooKeeper file. A zookeeper client uses at least one server at a given time.
The master node is dynamically selected based on consensus within the ensemble, so the Zookeeper file is usually an odd number, so that’s where the majority of votes are. If the master node fails, another master node is instantly selected and takes over from the previous master node.
In addition to masters and slaves, there are also observers in Zookeeper. Observers were invited to address the scaling issue. The addition of slaves affected writing performance because the voting process was expensive. Observers are, therefore, slaves who do not participate in voting but have similar duties to other slaves.

Writes in Zookeeper Architecture

All writes to Zookeeper Architecture go through the master node, so all writes are guaranteed to be sequential. When performing a write operation to Zookeeper, each server connected to this client stores data along with the master server. This keeps all servers updated with the data. However, this also means that concurrent writes cannot be performed. The linear write guarantee can be problematic if Zookeeper is used for the main write load.
Zookeeper in Hadoop is ideally used to coordinate message exchange between clients, which involves fewer writes and more reads. Zookeeper is useful until the data is shared, but if the application has concurrent data writes, Zookeeper can get in the way and impose a strict order of operations.

Reads in ZookeeperArchitecture

Zookeeper is best at reading because reading can be concurrent. Concurrent reads are performed because each client is connected to a different server, and all clients can read from the servers simultaneously. However, concurrent reads result in ultimate consistency because the master server is not involved. There may be cases where the client may have an outdated view that updates with a small delay.

How to use Apache ZooKeeper to build distributed applications?

Zookeeper Architecture does all the above details, and the user does not need to do anything. A commander is chosen, observers are set, and the stage is set for users to use Zookeeper.
Compared to earlier users, Zookeeper can be used as a file system where directories can be created and data stored. Like any other file system, the directories created above can also have children and grandchildren. This file system is stored centrally and can be accessed from anywhere.
An example of Apache Zookeeper might be a data model. Each directory in our example is called a znode in Zookeeper. Stores statistical data such as version details and user data up to 1 Mb in size. This small information storage space clearly shows that Zookeeper is not used to store data like a database but instead to store small amounts of data such as configuration data that must be shared.
There are 2 types of snobs:
  • Persistent: This is the default node type in every Zookeeper. Persistent nodes are always present and contain important configuration details. When a new node is added to Zookeeper, it goes to the permanent node and gets configuration information.
  • Transient: These are session nodes that are created when the application starts and are deleted when the application ends. This is mainly useful for checking client applications in case of failure. When the app crashes, znode dies.

Installing Apache ZooKeeper

Steps to download and install Zookeeper 3.4.6 with configuration for 3 Zookeeper nodes:
  1. Download and install the JDK from http://www.oracle.com/technetwork/java/javase/downloads/index.html or http://www.guru99.com/install-java.html – if not already installed. The Apache ZooKeeper server runs on the JVM, an important prerequisite.
  2. Go to http://zookeeper.apache.org/ and download Zookeeper from the release page.
  3. Select download from mirrors and select the first mirror.
  4. Go to the stable folder and download zookeeper-3.4.6.tar.gz
  5. Unzip the tarball with tar –zxvf zookeeper-3.4.6.tar.gz
  6. Create the directory with mkdir/usr/local/zookeeper/data. You can create this directory as root and change the owner to any user you need.
  7.  Create a zookeeper configuration file using sudo vi / usr/local/zookeeper/conf/zoo.cfg and insert the following code: 
    tickTime = 2000

sync limit = 5

dataDir = /usr/local/zookeeper/data

clientPort t= 2181

server.1 = Master : 2888 : 3888

server.2 = Slave1 : 2888 : 3888

server.3 = Slave2 : 2888 : 3888
  8. Create a file named myid in the data folder using sudo vi/usr/local/zookeeper/data/myid and write “1” without quotes in this file and save it.
  9. Do steps 1 to 7 for the other 2 servers, but change the myid data to 2 for servers 2 and 3 for server 3.
  10. Use the zkServer.sh start command to start Zookeeper on all servers
  11. To confirm that Zookeeper has started, type jps and check QuorumPeerMain.
  12. Use the command zkCli.sh -server Slave1:2181 to start the client

Using Zookeeper

The below-listed points explain how to use Zookeeper-

• Store configuration data and settings in a centralized repository for access from anywhere.
• Message queue for asynchronous communication, for example, a user clicks a button to place an order on a website, and it takes some time to generate that order, so instead of the user having to wait, the order can be placed in a message queue, and the user can continue shopping.
Zookeeper Architecture Hadoop can be run as a watchdog, and other nodes can be notified of data changes in one node through notifications.

Conclusion

Zookeeper Architecture goes through the master node, so all writes are guaranteed to be sequential. When performing a write operation to Zookeeper, each server connected to this client stores data along with the master server. This keeps all servers updated with the data. However, this also means that concurrent writes cannot be performed.

  • Apache Zookeeper might be a data model. Each directory in our example is called a znode in Zookeeper. Stores statistical data such as version details and user data up to 1 Mb in size.
  • If the master node fails, another master node is instantly selected and takes over from the previous master node. In addition to masters and slaves, there are also observers in Zookeeper.
  • Multiple server nodes are collectively called a ZooKeeper file. A Zookeeper Architecture client uses at least one server at a given time.

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T
Trupti Dekate

I am an Accountant at Global private Analytics Services working with the Data Analysis Team for handling the budget of various Growing Companies. We provide service of analytics and made the work of new tech companies easy by helping them manage their total investment and giving suggestions.

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