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

Primary Key in DBMS

Ayushi Trivedi
Ayushi Trivedi Last Updated : 18 Jun, 2024
5 min read

Introduction

With a focus on data integrity and effective retrieval, this article offers a thorough description of primary keys in a database management system (DBMS). It covers types of primary keys, their creation and implementation, and practical applications. The article also discusses best practices for choosing and managing primary keys, highlighting their advantages like uniqueness, referential integrity, performance enhancement, and simplified database design.

Overview

  • Learn the role of primary keys play in maintaining data integrity and speedy data retrieval.
  • Recognize scenarios where each type of primary key is appropriate.
  • Create primary keys during the initial table creation.
  • Maintain consistency and minimalism in primary key selection.
  • Understand how primary keys simplify database design and improve data security.
Primary Key in DBMS

Table of contents

What is Primary Key in DBMS?

An essential component of a DBMS that is necessary for relational database design and administration is the primary key. It guarantees the uniqueness of every record in a database, protecting data integrity and enabling speedy data retrieval.

A primary key is a column, or a set of columns, in a database table that uniquely identifies each row in that table. The primary key must contain unique values and cannot contain null values.

Purpose of Primary Key

  • Uniqueness: makes ensuring that the value of the primary key column (or columns) in a table is unique across all rows.
  • Integrity: guarantees the unique identity of every record, hence upholding entity integrity.
  • Indexing: Often automatically indexed by the database system, which speeds up query performance.
  • Relationships: Serves as a reference point for foreign keys in other tables, helping to establish relationships between tables.

Characteristics of Primary Keys

  • Unique Values: Each value in the primary key column(s) must be unique.
  • Non-null: Primary key columns cannot contain null values.
  • Immutable: The values in a primary key should not change over time.
  • Minimal: Composed of the smallest number of columns necessary to ensure uniqueness.

Types of Primary Keys

Let us now explore types of primary keys in detail and with example.

Single-Column Primary Key

A single-column key is the main key of a database table that has just one column. This column ensures that each entry in the table is distinct and that no two rows have the same value. The simplest kind of primary key is a single-column key, which is frequently employed when a record can be identified just by one feature.

CREATE TABLE employees (
    employee_id INT PRIMARY KEY,
    name VARCHAR(50),
    position VARCHAR(50)
);

Composite Primary Key

A composite primary key is a primary key in a database table that has two or more columns. By combining these columns, it is possible to identify each entry in the table individually. When a single column is insufficient to guarantee each record’s uniqueness, composite primary keys are employed. When used in junction tables and many-to-many connection tables, this kind of primary key is quite helpful.

CREATE TABLE orders (
    order_id INT,
    product_id INT,
    PRIMARY KEY (order_id, product_id)
);

Creating a Primary Key

Let us now learn to create a primary key.

During Table Creation

You can specify a primary key right in the CREATE TABLE line when making a new table. This guarantees that as soon as the table is formed, the primary key constraint is implemented.

CREATE TABLE students (
    student_id INT PRIMARY KEY,
    name VARCHAR(100),
    birthdate DATE
);

By defining the primary key during table creation, the database ensures from the outset that every student has a unique student_id, which helps maintain data integrity and optimizes query performance.

Adding to an Existing Table

If a table doesn’t already contain a primary key constraint, it can be added with the ALTER TABLE statement. This comes in handy when you realize you need a primary key constraint after the table has been created and loaded with data.

ALTER TABLE students
ADD CONSTRAINT pk_student_id PRIMARY KEY (student_id);

The database guarantees that the student_id column now enforces uniqueness and non-null values by adding the primary key constraint to an existing table. This may require verifying and maybe changing existing data to comply with these constraints.

Example

Consider a simple database with two tables: students and enrollments. Each student has a unique student_id and can enroll in multiple courses.

Students Table

CREATE TABLE students (
    student_id INT PRIMARY KEY,
    name VARCHAR(100),
    birthdate DATE
);

Enrollments Table

CREATE TABLE enrollments (
    enrollment_id INT PRIMARY KEY,
    student_id INT,
    course_id INT,
    FOREIGN KEY (student_id) REFERENCES students(student_id)
);

Inserting Data into the Students Table

INSERT INTO students (student_id, name, birthdate) VALUES
(1, 'Alice Johnson', '2000-05-15'),
(2, 'Bob Smith', '1999-11-23'),
(3, 'Charlie Brown', '2001-02-10');

Inserting Data into the Enrollments Table

INSERT INTO enrollments (enrollment_id, student_id, course_id) VALUES
(101, 1, 101),
(102, 2, 101),
(103, 1, 102),
(104, 3, 102);

Retrieving Data

Now, let’s retrieve data from both tables to see the output:

Query to Retrieve Data

SELECT * FROM students;

Output:

+------------+---------------+------------+
| student_id | name          | birthdate  |
+------------+---------------+------------+
| 1          | Alice Johnson | 2000-05-15 |
| 2          | Bob Smith     | 1999-11-23 |
| 3          | Charlie Brown | 2001-02-10 |
+------------+---------------+------------+

Query to Retrieve Data from Enrollments Table

SELECT * FROM enrollments;

Output:

+---------------+------------+-----------+
| enrollment_id | student_id | course_id |
+---------------+------------+-----------+
| 101           | 1          | 101       |
| 102           | 2          | 101       |
| 103           | 1          | 102       |
| 104           | 3          | 102       |
+---------------+------------+-----------+

Best Practices

  • Choose Natural Keys When Possible: Use the primary key if there is a naturally occurring unique attribute (such as the Social Security number or the ISBN for publications).
  • Use Surrogate Keys When Necessary: Use a surrogate key (such as an auto-incremented integer) if there isn’t a natural key.
  • Consistency: Keep the primary keys’ names constant across all tables.
  • Normalization: To prevent redundancies and dependence problems, make sure the database is appropriately normalized.

Advantages of Primary Key in DBMS

Here are the key advantages of using primary keys in a Database Management System, summarized in simple points:

  • Uniqueness
    • Ensures each record is uniquely identifiable.
    • Prevents duplicate records.
  • Efficient Data Retrieval
    • Automatically indexed by the DBMS.
    • Speeds up search queries.
  • Supports Referential Integrity
    • Used to establish relationships between tables.
    • Maintains data integrity through foreign keys.
  • Prevents Null Values
    • Primary keys cannot contain null values.
    • Ensures every record has a valid identifier.
  • Data Integrity and Consistency
    • Enforces unique and non-null constraints.
    • Maintains consistent data across the database.
  • Simplifies Database Design
    • Provides a clear structure for the database.
    • Easier to understand and manage the database schema.
  • Enhances Performance
    • Optimizes queries that filter, join, or sort data.
    • Improves overall database performance.
  • Supports Data Integrity in Distributed Systems
    • Ensures unique identification across different nodes.
    • Useful in distributed databases.
  • Facilitates Updates and Deletions
    • Efficient and straightforward to update or delete records.
    • Avoids ambiguity in data modifications.
  • Enables Data Consistency Across Transactions
    • Ensures consistent operations in transactions.
    • Important for transactional integrity.
  • Provides a Foundation for Normalization
    • Essential for reducing redundancy.
    • Helps in organizing data logically.
  • Enhances Data Security
    • Improves access control mechanisms.
    • Ensures only authorized modifications.

Conclusion

In relational database architecture, primary keys play a critical role in guaranteeing data integrity, uniqueness, and quick retrieval. To guarantee consistency between records, they are defined during the construction or updating of tables. They improve database speed and maintain referential integrity. Following best practices results in a more robust and dependable DBMS by streamlining database design, enhancing performance, and fortifying data security.

Frequently Asked Questions

Q1. What is a primary key in a DBMS?

A. A primary key guarantees that every entry in a database table is distinct by acting as a unique identifier for each record. It is used to maintain entity integrity and speed up effective data retrieval; it cannot include null values.

Q2. Why is a primary key important?

A. A primary key preserves data integrity, guarantees record uniqueness, and facilitates effective querying. Additionally, it strengthens the relational database structure by establishing associations between tables via foreign keys.

Q3. Can a primary key contain null values?

A. No, null values are not allowed in a primary key. In order to guarantee the integrity and uniqueness of the data, every value in a primary key column needs to be distinct and not null.

beginnersdatabaseDBMSprimary key
Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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
Congratulations, You Did It!
Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
Library Management System using MYSQL
Library Management System using MYSQL
What Are Composite Keys in DBMS?
What Are Composite Keys in DBMS?

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