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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
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

Data Engineering for Beginners – Partitioning vs Bucketing in Apache Hive

Lakshay arora
Lakshay arora Last Updated : 19 Nov, 2020
4 min read

Overview

  • Understand the meaning of partitioning and bucketing in the Hive in detail.
  • We will see, how to create partitions and buckets in the Hive

 

Introduction

You might have seen an encyclopedia in your school or college library. It is a set of books that will give you information about almost anything. Do you know what is the best thing about the encyclopedia?

Partitioning vs Bucketing

Yes, you guessed it correctly. The words are arranged alphabetically.  For example, you have a word in mind  “Pyramids”. You will directly go and pick up the book with the title “P”. You don’t have to search that in other books. Can you imagine how tough would the task be to search for a single book if they were stored without any order?

Here storing the words alphabetically represents indexing, but using a different location for the words that start from the same character is known as bucketing.

Similar kinds of storage techniques like partitioning and bucketing are there in Apache Hive so that we can get faster results for the search queries. In this article, we will see what is partitioning and bucketing, and when to use which one?

 

Table of Contents

  1. What is Partitioning?
  2. When to use Partitioning?
  3. What is Bucketing?
  4. When to use Bucketing?

 

What is Partitioning?

Apache Hive allows us to organize the table into multiple partitions where we can group the same kind of data together. It is used for distributing the load horizontally. Let’s understand it with an example:

Suppose we have to create a table in the hive which contains the product details for a fashion e-commerce company. It has the following columns:

Data

Now, the first filter that most of the customer uses is Gender then they select categories like Shirt, its size, and color. Let’s see how to create the partitions for this example.

CREATE TABLE products ( product_id string,
                        brand      string,
                        size       string,
                        discount   float,
                        price      float )
PARTITIONED BY (gender string,
                category string,
                color string);

Now, the hive will store the data in the directory structure like:

/user/hive/warehouse/mytable/gender=male/category=shoes/color=black

 

Partitioning

Partitioning the data gives us performance benefits and also helps us in organizing the data. Now, let’s see when to use the partitioning in the hive.

 

When to use Partitioning?

  • When the column with a high search query has low cardinality. For example, if you create a partition by the country name then a maximum of 195 partitions will be made and these number of directories are manageable by the hive.
  • On the other hand, do not create partitions on the columns with very high cardinality. For example- product IDs, timestamp, and price because will create millions of directories which will be impossible for the hive to manage.
  • It is effective when the data volume in each partition is not very high. For example, if you have the airline data and you want to calculate the total number of flights in a day. In that case, the result will take more time to calculate over the partition “Dubai” as it has one of the busiest airports in the world whereas for the country like “Albania” will return results quicker.

 

What is Bucketing?

In the above example, we know that we cannot create a partition over the column price because its data type is float and there is an infinite number of unique prices are possible.

Hive will have to generate a separate directory for each of the unique prices and it would be very difficult for the hive to manage these. Instead of this, we can manually define the number of buckets we want for such columns.

Bucketing

In bucketing, the partitions can be subdivided into buckets based on the hash function of a column. It gives extra structure to the data which can be used for more efficient queries.

CREATE TABLE products ( product_id string,
                        brand string,
                        size string,
                        discount float,
                        price float )
PARTITIONED BY (gender string,
                category string,
                color string)
CLUSTERED BY (price) INTO 50 BUCKETS;

Now, only 50 buckets will be created no matter how many unique values are there in the price column. For example, in the first bucket, all the products with a price [ 0 – 500 ] will go, and in the next bucket products with a price [ 500 – 200 ] and so on.

 

When to use Bucketing?

  • We cannot do partitioning on a column with very high cardinality. Too many partitions will result in multiple Hadoop files which will increase the load on the same node as it has to carry the metadata of each of the partitions.
  • If some map-side joins are involved in your queries, then bucketed tables are a good option. Map side join is a process where two tables are joins using the map function only without any reduced function. I would recommed you to go through this article for more understanding about map-side joins: Map Side Joins in Hive

 

End Notes

In this article, we have seen what is partitioning and bucketing, how to create them, and are pros and cons of them.

I would highly recommend you go through the following resources to learn more about Apache Hive:

If you have any questions related to this article do let me know in the comments section below.

Lakshay arora
Lakshay arora

Ideas have always excited me. The fact that we could dream of something and bring it to reality fascinates me. Computer Science provides me a window to do exactly that. I love programming and use it to solve problems and a beginner in the field of Data Science.

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Responses From Readers

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akash kumar
akash kumar

Very nice article .I read from many places but no one explain in this simple and easy way.liked the article.

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akash kumar
akash kumar

Very nice article .I read from many places but no one explain in this simple and easy way.

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