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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

Partitioning and Bucketing in Hive

TK
TK Last Updated : 30 Jun, 2022
5 min read

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

Introduction

Hive is a popular data warehouse built on top of Hadoop that is used by companies like Walmart, Tiktok, and AT&T. It is an important technology for data engineers to learn and master. It uses a declarative language called HQL, also known as Hive Query Language, which is almost similar to SQL. Hence, this makes SQL database engineers and newbies with SQL knowledge work on data engineering tasks. Internally, HQL acts as an abstraction to the map-reduce code, which many developers find difficult to write and debug. Since the map-reduce code is replaced with HQL, the development time reduces drastically, which is seen as a major advantage. All the data is stored in a structured format and looks and feels like a normal MySQL database, which simplifies things for developers. As mentioned earlier, Hive is a data warehouse and supports batch processing only. The reason why Hive does not support real-time data processing and tracking is because of the very high latency and humongous amount of data. Also, Hive performs very poorly on small datasets.

In this article, we will explore partitioning and bucketing in Hive and how they can be implemented using HQL.

Partitioning in Hive

Partitioning is one of the popular strategies to improve the performance of Hive. In essence, partitioning is just a formal way to store data inside multiple folders by segregating them using some specific criteria instead of storing everything inside a single folder. As the data is segregated, querying performance improves by leaps and bounds as the number of records reduces drastically. This also helps in reducing the computational complexity of queries. For example, consider you have a hive table containing 1000 food items and related information and one column named “vegetarian” that specifies if the type of food item is vegetarian or not. Assume that there are 300 vegetarian food items and 700 non-vegetarian food items. Also, assume that for vegetarian food items, the value is “1” and “0” otherwise. If we make a query that is specific to vegetarian food items, the hive server has to scan all the 1000 records in the hive table and return the response to the query. Now, let’s say we have partitioned the table using the “vegetarian” column. The records would have been segregated into two folders, one containing the vegetarian food items and the other containing the non-vegetarian food items, and if we try to run the same query, the hive server would have to scan only the folder containing the vegetarian food items and so the total number of records being evaluated is reduced drastically, that is 300 records. I hope that comparing the number of records scanned before and after partitioning a table gives a better picture of how it helps in improving the performance and also, reduces the computational complexity of the queries. Also, the partitioning can extend up to any level like a hierarchy. To understand this better, in the example explained above, assume that there is a field that specifies the type of meat used in preparing the food item like ‘Red meat’, ‘Seafood’, ‘Poultry’, etc. Now we can further partition the table using this field as well. If done so, there would be folders created inside the non-vegetarian folder named ‘Red meat’, ‘Seafood’, etc. Hence, the hierarchy can keep extending and you can have granular control over the partitions to improve performance.

partitioning and bucketing

Hive supports static and dynamic partitioning. In dynamic partitioning, the partitions are created automatically and the data is segregated and loaded in their respective directories by Hive. In static partitioning, the partitions should be created manually by us and the respective data should also be loaded by us. Dynamic partitioning is used when the number of partitions is large, but in terms of loading time, static partitioning performs better. Now that we know what partitioning is, let’s look at some queries for performing dynamic and static partitioning in Hive using HQL.

To enable dynamic partitioning in Hive, we need to run the following command.

$ hive.exec.dynamic.partition=true;

To set the maximum number of dynamic partitions, we need to run the following command. You can set any number of maximum dynamic partitions.

$ hive.exec.max.dynamic.partitions=1000;

At times, Hive does not use the partitions to improve performance and scans across all the records. To make sure that Hive takes the partitioning into account while responding to queries, we need to run the following command.

$ set hive.mapred.mode='strict';

To undo the changes made above, we need to run the following command.

$ set hive.mapred.mode='nonstrict';
$ create table {table_name}({column_name} {data_type}, {column_name} {data_type}, {column_name} {data_type}) partitioned by ({partition_column_name} {data_type}, {partition_column_name} {data_type}, {partition_column_name} {data_type});

The above query is used to create dynamic partitions in Hive. The columns used for partitioning a table should be included inside the create table command as they would be automatically added by Hive.

To view all the partitions on a table in Hive, run the following.

$ show partitions {table_name};

To create partitions statically, we first need to set the dynamic partition property to false.

$ hive.exec.dynamic.partition=false;

Once that is done, we need to create the table and then load the data. To create a hive partition table, run the following query.

$ create table {table_name} ({column_name} {data_type}, {column_name} {data_type}, {column_name} {data_type}) partitioned by ({partition_column_name} {data_type}, {partition_column_name} {data_type}, {partition_column_name} {data_type});

Now, to load the data inside the table, run the following query.

$ insert into {table_name} partition ({partition_col}='condition_1', {partition_col}='condition_2', {partition_col}='condition_3') select {column_name}, {column_name}, {column_name} from {unpartitioned_table_name} where (({partition_col}='condition_1') and ({partition_col}='condition_2') and ({partition_col}='condition_3'));

Bucketing in Hive

Bucketing is another strategy used for performance improvement in Hive. Bucketing is usually applied to columns that have a very high number of unique values. Bucketing segregates records into a number of files or buckets. Internally, a hash value is generated for every unique value in the column used for bucketing. The hash value generated for every record determines the bucket it belongs to. It is designed using the concept of hash table data structure. Bucketing requires us to specify the number of buckets we want the data to be stored in. The preferred number of buckets is generally calculated using the total data size and the size of each block in HDFS, which is 128MB by default.

partitioning and bucketing

To perform bucketing on a table, we need to run the following query.

create table {table_name} ({column_name} {data_type}, {column_name} {data_type}, {column_name} {data_type}) clustered by ({bucketing_column_name}) into {n} buckets;

Conclusion

In this article, we covered the following.

  • What is Hive?
  • What is partitioning in Hive?
  • How to partition a table using HQL?
  • What is bucketing in Hive?
  • How to perform bucketing using HQL?

Hive is being used in major tech companies for data warehousing to store huge amounts of data and has become an important member in the domain of data engineering as it enables developers with SQL backgrounds to write map-reduce code using HQL and improves productivity and reduces the development time. Hive has become a must for data engineers these days. This article discusses only two topics among the lot. If you are interested in data engineering and wish to learn the basics of Hive, refer to this article. For learning advanced concepts in Hive, refer to this article.

That’s it for this article. Hope you enjoyed reading this article and learned something new. Thanks for reading and happy learning!

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TK
TK

I love exploring ML, DL, Machine Vision, Databases, and Full-stack Web Development. I've worked on multiple projects with different stacks and I also hold a patent in the domain of machine vision for manufacturing.

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