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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

Difference Between SQL Commit and SQL Rollback

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 09 Jul, 2024
4 min read

Introduction

Managing data transactions is an important skill to have while working with databases. Tools like Structured Query Language (SQL) help you do this efficiently. It offers an array of built-in commands that can handle transactions, ensuring data integrity and consistency. Two most commonly used commands in this context are COMMIT and ROLLBACK. In this article, we will try to understand the differences between the COMMIT and ROLLBACK commands in SQL, as we learn about them in detail. We will also explore their usage through practical examples to master transaction management in SQL.

If you’re just starting out to explore SQL, here’s a beginner’s guide to help you: SQL For Data Science: A Beginner Guide

Difference Between SQL Commit and SQL Rollback

Overview

  • Understand the concept of transactions in SQL.
  • Learn what the COMMIT and ROLLBACK commands do in SQL. 
  • Know the key differences between COMMIT and ROLLBACK commands.
  • Learn how to practically use these commands in SQL.

Table of Contents

What is a Transaction in SQL?

In SQL, a transaction is a sequence of one or more operations treated as a single unit of work. In other words, it’s a set or combination of commands or actions (such as INSERT, UPDATE, DELETE, etc.), that together form a process. The point to note here is that if even one of these commands is not completed, the entire process will be canceled. Hence, all the operations must be completed for the transaction to be done.

Transactions follow the below properties, collectively termed as ACID:

  • Atomicity: They ensure that all operations within the transaction are completed; if not, the transaction is aborted.
  • Consistency: They ensure that the database remains in a valid state before and after the transaction.
  • Isolation: They ensure that concurrent transactions do not interfere with each other.
  • Durability: They ensure that once a transaction is committed, it is permanently applied to the database.
SQL transactions

SQL COMMIT Command

The COMMIT command in SQL is used to save all changes made during the current transaction. Once a COMMIT command is issued, the changes become permanent and visible to other users.

Syntax: COMMIT;

Key Points

  • The COMMIT command finalizes the transaction, making all changes made by the transaction permanent.
  • The COMMIT operation is irreversible.
  • Once executed, the changes will be visible to other users and sessions.

Practical Example

START TRANSACTION;

INSERT INTO employees (name, position, salary) VALUES ('Alice', 'Engineer', 70000);
UPDATE employees SET salary = salary + 5000 WHERE name = 'Alice';

COMMIT;

In this example, the transaction inserts a new employee and updates the salary. The COMMIT command saves these changes.

SQL ROLLBACK Command

The ROLLBACK command in SQL is used to undo all the changes made during the current transaction. You can use this if an error occurs during a transaction, or if you change your mind about the operations performed. It reverts the database to its previous state before the transaction began.

Syntax: ROLLBACK;

Key Points

  • The ROLLBACK command reverses all changes made by the current transaction.
  • It restores the database to the state it was in before the transaction started.
  • It’s very handy for handling errors and maintaining data integrity.
  • Some database systems support partial rollbacks to a savepoint. This allows you to roll back only part of a transaction.

Practical Example

START TRANSACTION;

INSERT INTO employees (name, position, salary) VALUES ('Bob', 'Manager', 90000);
UPDATE employees SET salary = salary + 5000 WHERE name = 'Bob';

ROLLBACK;

Here, the transaction inserts a new employee and updates the salary. However, the ROLLBACK command reverts these changes.

Differences Between COMMIT and ROLLBACK in SQL

Feature COMMIT ROLLBACK
Function Saves all changes made in the transaction Reverts all changes made in the transaction
Durability Ensures changes are permanent Ensures changes are not saved
Usage Used when all operations are successful Used when an error occurs or transaction fails
Syntax COMMIT; ROLLBACK;
Reversibility Irreversible once executed Can be executed multiple times if transaction fails

Conclusion

Understanding and using COMMIT and ROLLBACK commands effectively are essential for managing transactions in SQL. By using COMMIT, you make your changes permanent and visible to others. Meanwhile, ROLLBACK allows you to undo changes and revert the database to its previous state. Together, these commands help maintain data integrity, handle errors, and ensure that the database remains in a consistent state. Whether you are developing a new application or managing an existing database, mastering COMMIT and ROLLBACK will help you maintain control over your data and ensure that your transactions are executed correctly.

Learn More: SQL: A Full Fledged Guide from Basics to Advanced Level

Frequently Asked Questions

Q1. What happens if you don’t use COMMIT in a transaction?

A. If you don’t use COMMIT, the changes made in the transaction will not be saved and will be lost once the session ends or a ROLLBACK is issued.

Q2. Can you use ROLLBACK after COMMIT?

A. No, once a COMMIT is issued, the changes are permanent and cannot be undone with ROLLBACK.

Q3. What is the difference between ROLLBACK and SAVEPOINT?

A. ROLLBACK undoes all changes made in the transaction, while SAVEPOINT allows you to set a point within a transaction to which you can later roll back.

Q4. How does COMMIT work in an auto-commit mode?

A. In auto-commit mode, every individual SQL statement is treated as a transaction and is automatically committed right after it is executed.

Q5. Is it necessary to use COMMIT and ROLLBACK in every SQL operation?

A. Not necessarily. These commands are used for managing explicit transactions. In auto-commit mode, each SQL statement is committed automatically.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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