Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

Performance Tuning Practices in Hive

N
Nausheen Last Updated : 22 Mar, 2022
5 min read

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

Introduction

Apache Hive is a data warehouse system built on top of Hadoop which gives the user the flexibility to write complex MapReduce programs in form of SQL- like queries. Performance Tuning is an essential part of running Hive Queries as it helps the programmer to reduce the time and increase the efficiency of the code. Applying correct approaches and using best optimization practices we can achieve great milestones in terms of time and storage of hive queries as well as improve the performance of our cluster, and it is here that Performance Tuning plays an important role. 

In this article, I want to share some of the practices of Performance Tuning configuration and queries, which I have found effective while using Hive.

First of all, let’s understand what are the steps for tuning the Hive queries:

  1. Identify: In this step, we list all the Hive codes which consume more time.
  2. Tune: We find out the best solutions without interfering with the functionality of the code and make sure that the code is improved.
  3. Test and Deploy: We test the changed code and then deploy it in production. We should monitor the running time of the changed code for a few days.
  4. Measure: In this step, we measure if the changed codes are improving at any time. Also, compare the new run timings of the changed code with the past production timings.
  5. Continue improvements: We keep looking for possible solutions to improve further.

Types of Performance Tuning Techniques

There are several Hive optimization techniques to improve its performance which we can implement when we run our hive queries, thereby focusing on the Performance Tuning as well:

1 Avoid locking of tables

It is extremely important to make sure that the tables are being used in any Hive query as sources are not being used by another process. This can lead to locking of the table and our query can be stuck for an unknown time.

We can use the parameters below for making sure that the tables are not being locked:

set hive.support.concurrency=false;
set hive.txn.manager=org.apache.hadoop.hive.ql.lockmgr.DummyTxnManager;
set hive.bin.strict.locking.mode=false;

2 Use the Hive execution engine as TEZ

While executing Hive queries, TEZ execution engine is the preferred choice because it eliminates unnecessary disk access. It takes data from disk once, performs calculations, and produces output, thus saving us from multiple disk traversals. We can consider TEZ to be a much more flexible and powerful successor to the map-reduce framework.

We can set the parameter below for using TEZ engine:

set hive.execution.engine=tez;

3 Use Hive Cost Based Optimizer (CBO)

Apache Hive provides a cost-based optimizer to improve performance. It generates efficient execution plans like how to order joins, which type of join to perform, the degree of parallelism etc. by examining the query cost. These decisions are collected by ANALYZE statements or the metastore itself, ultimately cutting down on query execution time and reducing resource utilization.

We can set the parameter using :

set hive.cbo.enable=true;

4 Parallel execution at a Mapper & Reducer level

We can improve the performance of aggregations, filters, and joins of our hive queries by using vectorized query execution, which means scanning them in batches of 1024 rows at once instead of single row each time.

We should explore the below parameters which will help to bring in more parallelism and which significantly improves query execution time:

set hive.vectorized.execution.enabled=true; 
set hive.exec.parallel=true;

For example:

Select a.*, b.* from
(select * from table1 ) a
Join
(select * from table2 ) b
On a.id=b.id
;

As we can see the two subqueries are independent so this might increase efficiency.

One important thing to note is, parallel execution will increase cluster utilization. If the cluster utilization of a cluster is already very high, parallel execution will not help much.

5 Use STREAMTABLE option

When we are joining multiple tables, we can use STREAMTABLE option. By default, the right-most table gets streamed.

For example: If we are joining 2 tables ‘huge_table’ join ‘small_table’, by default ‘small_table’ gets streamed as it is the rightmost table. In this case, ‘huge_table’ , being the bigger table, will try to get buffered into memory and might cause java heap space issues or the job might run longer. In this case, what we can do is add /*+ STREAMTABLE(‘huge_table’) */ and it will make ‘huge_table’ to be streamed rather than coming into memory.

Hence, in this way, we can be free of remembering the order of joining tables.

6 Use Map Side JOIN Option

If one of the tables in the join is a small table and can be loaded into memory, we can force a MAPSIDE join like shown below:

Select /*+ MAPJOIN(small_table) */ large_table.col1,large_table.col2 from large_table join small_table on large_table.col1 = small_table.col1;

A map side join can be performed within a mapper without using a Map/Reduce step.

Also, We can let the execution engine take care of this by setting auto.convert.join as True.

set auto.convert.join = True :

7 Avoid Calculated Fields in JOIN and WHERE clause

We should avoid using any calculated fields in JOIN and WHERE clauses as they take a long time to run the Hive query. We can use CTE(Create table expression) to handle those functionalities and can optimize our queries.

For example:

Original query:

select a.coll, b.col2 from table1 as a join table2 as b on (a.coll +50 = b.col2);

Optimized query:

with CTE as
(select a.col1 + 50 as C1 FROM table1 )
select CTE.C1, b.col2 from CTE join table2 b on (CTE.C1 = b.col2);

8 Use SORT BY instead of ORDER BY

Hive supports both ORDER BY and SORT BY causes. ORDER BY works on a single reducer and it causes a performance bottleneck. But, SORT BY orders the data only within each reducer and performs a local ordering where each reducer’s output will be sorted ensuring better performance.

9  Select columns which are needed

While we are using Hive, If we need only a few columns from a table, avoid using SELECT * FROM as it adds unnecessary time to the execution.

10 Suitable Input format Selection

Using appropriate file formats on the basis of data can significantly increase our query performance. Hive comes with columnar input formats like RCFile, ORC, etc. On comparing to Text, Sequence, and RC file formats, ORC shows better performance because Hive has a vectorized ORC reader which allows reducing the read operations in analytics queries by allowing each column to be accessed individually.

11 Limit (Filter) the data as early as possible 

This is the fundamental principle for any tuning where we filter or drop records ahead in the process so that we can avoid dealing with long-running of queries and dealing with unnecessary results.

For example :

a join b where a.col !=null

can be written as

 (select * from a where a.col!=null) join b

12 Use Multi Query Inserts

Here we will have a common dataset (Superset) and then we will use that to insert into multiple tables based on specific conditions specified in the WHERE clause. This helps to load multiple tables in parallel when they have the common superset of data.

13 Modularize the code into logical pieces

This helps in terms of the maintenance of the code. Also helps in restarting the jobs in scenarios of failures. This can be used to achieve parallelism by running modules that are independent of each other thus saving time.

Takeaway Notes

Use hive parameters judiciously

We should avoid using a lot of hive parameters if they are not needed. For this we should understand the data well and then use it efficiently else we might end up impacting the overall cluster utilization impacting other processes.

Identify the right targets

Most of these techniques will work on all jobs but spend the effort where it counts. Start with the jobs that will have the biggest impact on performance, that is the ones that take many hours!

Even small gains add up quickly

For example, A 10% gain on a 5-hour job saves 30 minutes of processing time!

Conclusion

In the article, we covered the Hive Optimization Techniques for Hive Queries both configuration-wise and query-wise, and how Performance Tuning can be implemented using hive. I hope this article helped you in understanding how to efficiently tune the performance of hive queries to execute them faster on clusters. Please don’t forget to drop in your comments if you have any doubts.

About the Author

Nausheen Aafreen is a Big Data Engineer and Data Research Analyst working in a multinational artificial intelligence company. She has spent over 5.6 years working in Data Engineering and Data Science projects. She worked in various technologies including big data, automation, and data research.

 

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

N
Nausheen

I am working as a data engineer having a true passion for learning and enhancing my skills. I have started writing my experience based articles in order to benefit the beginners in Data engineering field.

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details