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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

A Beginner’s Guide to CAP Theorem for Data Engineering

Aniruddha Bhandari
Aniruddha Bhandari Last Updated : 05 Apr, 2024
9 min read

Introduction

We have had significant advances in distributed databases to handle the proliferation of data. This has allowed us to handle increased traffic with lower latency, allowed an easier expansion of the database system, provided better fault tolerance in terms of replication, and so much more on CAP Theorem.

The NoSQL databases have inadvertently been at the forefront of this shift in the domain of distributed databases. And they have been providing you with lots of flexibility in terms of handling your data. Plus there is a plethora of them out there!

However, the real question is which one to use? The answer to this question lies not only in the properties of these databases but also in understanding a fundamental theorem. A theorem that has gained renewed attention since the advent of such databases in the realm of databases. Yes, I’m talking about the CAP theorem!

Understanding CAP theorem

In simple terms, the CAP theorem lets you determine how you want to handle your distributed database systems when a few database servers refuse to communicate with each other due to some fault in the system. However, there exists some misunderstanding. So, in this article, we will try to understand the CAP theorem and how it helps to choose the right distributed database system.

But first, let’s get a basic understanding of distributed database systems.

Table of contents

What is the CAP Theorem?

In the last section, you understood what each term means in the CAP theorem. Now let us understand the theorem itself.

The CAP theorem states that a distributed database system has to make a tradeoff between Consistency and Availability when a Partition occurs.

A distributed database system is bound to have partitions in a real-world system due to network failure or some other reason. Therefore, partition tolerance is a property we cannot avoid while building our system. So a distributed system will either choose to give up on Consistency or Availability but not on Partition tolerance.

For example in a distributed system, if a partition occurs between two nodes, it is impossible to provide consistent data on both the nodes and availability of complete data. Therefore, in such a scenario we either choose to compromise on Consistency or on Availability. Hence, a NoSQL distributed database is either characterized as CP or AP. CA type databases are generally the monolithic databases that work on a single node and provide no distribution. Hence, they require no partition tolerance.

Distributed Database Systems

In a NoSQL type distributed database system, multiple computers, or nodes, work together to give an impression of a single working database unit to the user. They store the data in these multiple nodes. And each of these nodes runs an instance of the database server and they communicate with each other in some way.

When a user wants to write to the database, the data is appropriately written to a node in the distributed database. The user may not be aware of where the data is written. Similarly, when a user wants to retrieve the data, it connects to the nearest node in the system which retrieves the data for it, without the user knowing about this.

This way, a user simply interacts with the system as if it is interacting with a single database. Internally the nodes communicate with each other, retrieving data that the user is looking for, from the relevant node, or storing the data provided by the user.

Now, the benefits of a distributed system are quite obvious. With the increase in traffic from the users, we can easily scale our database by adding more nodes to the system. Since these nodes are commodity hardware, they are relatively cheaper than adding more resources to each of the nodes individually. That is, horizontal scaling is cheaper than vertical scaling.

This horizontal scaling makes replication of data cheaper and easier. This means that now the system can easily handle more user traffic by appropriately distributing the traffic amongst the replicated nodes that.

Distributed databases Vertical vs Horizontal scaling

So now the real problem comes while choosing the appropriate distribution system for a task. To answer such a question, we need to understand the CAP theorem.

Understanding CAP theorem with an Example

CAP theorem, also known as Brewer’s theorem, stands for Consistency, Availability and Partition Tolerance. But let’s try to understand each, with an example.

Availability

Imagine there is a very popular mobile operator in your city and you are its customer because of the amazing plans it offers. Besides that, they also provide an amazing customer care service where you can call anytime and get your queries and concerns answered quickly and efficiently. Whenever a customer calls them, the mobile operator is able to connect them to one of their customer care operators.

The customer is able to elicit any information required by her/him about his accounts like balance, usage, or other information. We call this Availability because every customer is able to connect to the operator and get the information about the user/customer.

CAP theorem Availability

Consistency

Now, you have recently shifted to a new house in the city and you want to update your address registered with the mobile operator. You decide to call the customer care operator and update it with them. When you call, you connect with an operator. This operator makes the relevant changes in the system. But once you have put down the phone, you realize you told them the correct street name but the old house number (old habits die hard!).

So you frantically call the customer care again. This time when you call, you connect with a different customer care operator but they are able to access your records as well and know that you have recently updated your address. They make the relevant changes in the house number and the rest of the address is the same as the one you told the last operator.

We call this as Consistency because even though you connect to a different customer care operator, they were able to retrieve the same information.

CAP theorem Consistency

Partition tolerance

Recently you have noticed that your current mobile plan does not suit you. You do not access that much mobile data any longer because you have good wi-fi facilities at home and at the office, and you hardly step outside anywhere. Therefore, you want to update your mobile plan. So you decide to call the customer care once again.

On connecting with the operator this time, they tell you that they have not been able to update their records due to some issues. So the information lying with the operator might not be up to date, therefore they cannot update the information. We can say here that the service is broken or there is no Partition tolerance.

CAP theorem Partition

Understanding the Terms of the CAP theorem

Now let’s take up these terms one by one and try to understand them in a more formal manner.

Consistency

Consistency means that the user should be able to see the same data no matter which node they connect to on the system. This data is the most recent data written to the system. So if a write operation has occurred on a node, it should be replicated to all its replicas. So that whenever a user connects to the system, they can see that same information.

However, having a system that maintains consistency instantaneously and globally is near impossible. Therefore, the goal is to make this transition fast enough so that it is hardly noticeable.

Consistency is of importance when it is required that all the clients or users view the same data. This is important in places that deal with financial or personal information. For example, your bank account should reflect the same balance whether you view it from your PC, tablet, or smartphone!

Availability

Availability means that every request from the user should elicit a response from the system. Whether the user wants to read or write, the user should get a response even if the operation was unsuccessful. This way, every operation is bound to terminate.

For example, when you visit your bank’s ATM, you are able to access your account and its related information. Now even if you go to some other ATM, you should still be able to access your account. If you are only able to access your account from one ATM and not another, this means that the information is not available with all the ATMs.

Availability is of importance when it is required that the client or user be able to access the data at all times, even if it is not consistent. For example, you should be able to see your friend’s Whatsapp status even if you are viewing an outdated one due to some network failure.

Partition Tolerance

Partition refers to a communication break between nodes within a distributed system. Meaning, if a node cannot receive any messages from another node in the system, there is a partition between the two nodes. Partition could have been because of network failure, server crash, or any other reason.

So, if Partition means a break in communication then Partition tolerance would mean that the system should still be able to work even if there is a partition in the system. Meaning if a node fails to communicate, then one of the replicas of the node should be able to retrieve the data required by the user.

This is handled by keeping replicas of the records in multiple different nodes. So that even if a partition occurs, we are able to retrieve the data from its replica. As you must have guessed already, partition tolerance is a must for any distributed database system.

Understanding CP with MongoDB

Let’s try to understand how a distributed system would work when it decides to give up on Availability during a partition with the help of MongoDB.

CAP theorem CP with Mongodb

MongoDB is a NoSQL database that stores data in one or more Primary nodes in the form of JSON files. Each Primary node has multiple replica sets that update themselves asynchronously using the operation log file of their respective primary node. The replica set nodes in the system send a heartbeat (ping) to every other node to keep track if other replicas or primary nodes are alive or dead. If no heartbeat is received within 10 seconds, then that node is marked as inaccessible.

If a Primary node becomes inaccessible, then one of the secondary nodes needs to become the primary node. Till a new primary is elected from amongst the secondary nodes, the system remains unavailable to the user to make any new write query. Therefore, the MongoDB system behaves as a Consistent system and compromises on Availability during a partition.

Understanding AP with Cassandra

Now let’s also look at how a system compromises on Consistency. For this, we will look at the Cassandra database which is called a highly available database.

Cassandra is a peer-to-peer system. It consists of multiple nodes in the system. And each node can accept a read or write request from the user. Cassandra maintains multiple replicas of data in separate nodes. This gives it a masterless node architecture where there are multiple points of failure instead of a single point.

The replication factor determines the number of replicas of data. If the replication factor is 3, then we will replicate the data in three nodes in a clockwise manner.

CAP_theorem AP with Cassandra

A situation can occur where a partition occurs and the replica does not get an updated copy of the data. In such a situation the replica nodes will still be available to the user but the data will be inconsistent. However, Cassandra also provides eventual consistency. Meaning, all updates will reach all the replicas eventually. But in the meantime, it allows divergent versions of the same data to exist temporarily. Until we update them to the consistent state.

Therefore, by allowing nodes to be available throughout and allowing temporarily inconsistent data to existing in the system, Cassandra is an AP database that compromises on consistency.

Note that I have considered the MongoDB and Cassandra databases to be in their default settings.

Conclusion

The trade-offs between consistency, availability, and partition tolerance are highlighted by the CAP theorem, a key idea in distributed databases. NoSQL databases frequently give up one of these characteristics in order to obtain the others; Cassandra prioritizes availability and partition tolerance, whereas MongoDB prioritizes consistency. In order to choose the best distributed database system for your unique needs and guarantee a solid and dependable data management solution, it is essential to comprehend the CAP theorem.

Frequently Asked Questions?

Q1. What is CAP theorem explain?

A. The CAP theorem, also known as Brewer’s theorem, is a fundamental principle in distributed systems that states that it is impossible for a distributed data system to simultaneously provide more than two out of three guarantees: consistency, availability, and partition tolerance.

Q2. What is the CAP theorem basic?

A. At its basic level, the CAP theorem asserts that in a distributed system, you can’t achieve all three of the following simultaneously: consistency (all nodes see the same data at the same time), availability (a guarantee that every request receives a response about whether it succeeded or failed), and partition tolerance (the system continues to operate despite network partitions).

Q3. What is the CAP theorem in economics?

A. In economics, the CAP theorem can be seen as an analogy to the trade-offs faced in economic decision-making. Just as the CAP theorem suggests that distributed systems must choose between consistency, availability, and partition tolerance, economic decisions often involve trade-offs between competing goals such as efficiency, equity, and sustainability.

Q4. What is the CAP theorem in geometry?

A. In geometry, the CAP theorem refers to a theorem in spherical trigonometry. It states that on a sphere, given a circle C and a point P outside the circle, there are at most two great circles that pass through P and are tangent to C. This theorem has applications in navigation and geodesy.

CAP TheoremCassandraDistributed Database SystemMongoDB
Aniruddha Bhandari
Aniruddha Bhandari

I am on a journey to becoming a data scientist. I love to unravel trends in data, visualize it and predict the future with ML algorithms! But the most satisfying part of this journey is sharing my learnings, from the challenges that I face, with the community to make the world a better place!

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