9 Essential Software Architecture Patterns for Scalable Distributed Systems in 2026

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

image_credit - ByteByteGo

Hello friends, in modern software development, distributed systems are very popular but architects and developers face the challenge of designing systems that efficiently manage data and facilitate seamless communication between various components.

Architectural patterns provide proven solutions to common problems encountered in distributed systems, ensuring reliability, scalability, and maintainability.

Among these patterns, some patterns stand out as fundamental for managing data and communication flow effectively, which we will see in this article.

These are also important topic for System design interviews and knowledge of these pattern goes a long way in solving System design problem and impressing your interviewer.

Apart from preparing common System design questions like API Gateway vs load balancer, Forward Proxy vs Reverse Proxy as well common System Design problem , it make sense to know about these patterns as well.

Let's find out more about these patterns to understand their principles and applications.

By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io , bugfree.ai, and Udemy which have many great System design courses

Also, a solid knowledge of various Architecture patterns like Peer to Peer Pattern, API Gateway goes a long way in designing systems that can withstand the test of time on production. On that note, here is a nice diagram from DesignGuru.io on Microservices architecture:

9 Best Architectural Patterns for Distributed Systems

In the past, you have learned about essential Microservice design patterns like Event Sourcing, SAGA, Database Per Microservices, API Gateway, Circuit-Breaker and also shared best practices to design Microservices , now its time to see the brief overview of common architecture patterns for Data communication.

1. Peer-to-Peer (P2P) Pattern

The Peer-to-Peer pattern fosters direct communication between two or more components without the need for a central coordinator.

In this decentralized model, each node in the network can act as both a client and a server, enabling efficient resource sharing and collaboration.

P2P architectures are commonly used in file sharing systems, decentralized applications (DApps), and blockchain networks, where resilience and scalability are paramount.

Here is how P2P architecture looks like:

---

2. API Gateway Pattern

An API Gateway serves as a unified entry point for client requests to access backend services within an application.

By consolidating multiple APIs into a single interface, it simplifies client-server interactions and enforces security, authentication, and rate limiting policies.

API Gateways are essential components in microservices architectures, enabling service discovery, load balancing, and protocol translation while abstracting the complexities of backend systems.

Here is how it looks:

If you like to watch, here is another great video from ByteByteGo which explains API Gateway

---

3. Pub-Sub (Publish-Subscribe)

The Pub-Sub pattern decouples message producers (publishers) from consumers (subscribers) through a message broker or event bus like Kafka, Solace, RabbitMQ, or ActiveMQ.

Publishers broadcast messages to predefined topics or channels, while subscribers express interest in specific topics and receive relevant messages asynchronously.

Pub-Sub architectures facilitate loose coupling, scalability, and fault tolerance, making them ideal for real-time messaging systems, event-driven microservices, and IoT platforms.

Here is how Pub-sub pattern looks like:

---

4. Request-Response Pattern

The Request-Response pattern represents the fundamental interaction model in distributed systems, where a client sends a request to a server and awaits a corresponding response.

This synchronous communication paradigm is prevalent in web applications, RESTful APIs, and RPC (Remote Procedure Call) frameworks.

Request-Response interactions ensure predictable behavior and enable error handling, making them suitable for transactional workflows and user-facing interfaces.

Here is how Request-Response model looks like in action:

---

5. Event Sourcing Pattern

Event Sourcing is a distributed system pattern for persisting the state of an application as a sequence of immutable events.

Instead of storing current state directly, events representing state transitions are stored and replayed to reconstruct the application state when needed.

Event Sourcing enables auditability, temporal querying, and replayability, making it well-suited for financial systems, collaborative editing tools, and domain-driven designs where historical data is crucial.

Here is how a Event Sourcing pattern looks like:

And, if you like to watch, here is a nice video on Event Sourcing which is worth watching:

---

6. ETL (Extract, Transform, Load) Pattern

ETL is a data integration pattern used to extract data from multiple sources, transform it into a standardized format, and load it into a destination database or data warehouse.

This pattern is essential for data migration, synchronization, and consolidation tasks in business intelligence, data analytics, and data warehousing projects.

ETL pipelines automate data workflows, handle data quality issues, and support batch processing of large datasets.

Here is how ETL looks lin action:

---

7. Batching Pattern

Batching involves accumulating data over a period or until a certain threshold is reached before processing it as a single unit.

By aggregating multiple operations into larger batches, it reduces overhead and improves efficiency in data processing pipelines.

Batching is commonly employed in data ingestion, ETL processes, and distributed computing frameworks to optimize resource utilization and minimize latency.

Here is how a Batching pattern looks like:

---

9. Streaming Processing Pattern

Streaming Processing enables the continuous ingestion, processing, and analysis of data streams in real-time. Unlike batch processing, which operates on static datasets, streaming systems handle infinite data streams with low latency and high throughput.

Streaming architectures support event-driven processing, complex event processing (CEP), and real-time analytics applications in domains such as finance, IoT, and cybersecurity.

Here is a nice diagram from Hazlecast which shows Stream Processing in action:

---

10. Orchestration Pattern

Orchestration involves a central coordinator (an orchestrator) managing the interactions between distributed components or services to execute a workflow or business process.

By coordinating task execution, handling exceptions, and enforcing dependencies, orchestration ensures the orderly execution of complex workflows spanning multiple systems.

Orchestration engines are used in workflow automation, business process management (BPM), and microservices orchestration to streamline operations and improve agility.

Here is how it looks by using Saga Orchestrator Pattern

And, here is a nice diagram from ByteByteGowhich explains all these architecture styles in a more visual way

---

Best System Design Interviews Resources

And, here are curated list of best system design books, online courses, and practice websites which you can check to better prepare for System design interviews. Most of these courses also answer questions I have shared here.

1. DesignGuru's Grokking System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

2. Codemia.io : This is another great platform to practice System design problems for interviews. It got more than 120+ System design problems, many of them are free and also a proper structure to solve them.

3. "System Design Interview" by Alex Xu: This book provides an in-depth exploration of system design concepts, strategies, and interview preparation tips.

4. "Designing Data-Intensive Applications" by Martin Kleppmann: A comprehensive guide that covers the principles and practices for designing scalable and reliable systems.

5. Bugfree.ai: Bugfree AI is a popular platform for technical interview preparation. It's a LeetCode-style platform to practice System Design and Coding interview questions. It includes a variety of questions to practice.

6. "System Design Primer" on GitHub: A curated list of resources, including articles, books, and videos, to help you prepare for system design interviews.

7. Educative's System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

8. High Scalability Blog: A blog that features articles and case studies on the architecture of high-traffic websites and scalable systems.

9. YouTube Channels: Check out channels like "Gaurav Sen" and "Tech Dummies" for insightful videos on system design concepts and interview preparation.

10. ByteByteGo: A live book and course by Alex Xu for System design interview preparation. It contains all the content of the System Design Interview book volumes 1 and 2, and will be updated with volume 3 which is coming soon.

11. Exponent: A specialized site for interview prep, especially for MAANG companies like Amazon and Google, They also have a great system design course and many other material which can help you crack FAANG interviews.

image_credit - ByteByteGo

You should also combine theoretical knowledge with practical application by working on real-world projects and participating in mock interviews. Continuous practice and learning will undoubtedly enhance your proficiency in system design interviews.

---

That's all about 9 essential Software architecture patterns. Most of these patterns are also applicable to distributed systems, and they are also quite important for system design interviews.

In short, the effective management of data and communication flow is critical for building robust and scalable distributed systems.

Architectural patterns such as Peer-to-Peer, API Gateway, Pub-Sub, Request-Response, Event Sourcing, ETL, Batching, Streaming Processing, and Orchestration offer valuable solutions to address diverse challenges in system design and implementation.

By understanding these software architecture and distributed system patterns and their respective strengths and trade-offs, architects and developers can make informed decisions to design systems that meet the evolving needs of their applications and users.

Caching Strategies in System Design: Types, Patterns, Trade-offs & Best Practices

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

image_credit - DesignGuru.io

Hello friends, Caching is not just an important topic for System design interviews, its also technique in software development, enabling faster data retrieval, reducing load times, and enhancing user experience.

For developers, mastering caching concepts is crucial as it can significantly optimize application performance and scalability.

In the past, I have talked about common system design questions like API Gateway vs Load Balancer and Horizontal vs Vertical Scaling, Forward proxy vs reverse proxy as well common System Design problems and in this article we will explore the fundamentals of caching in system design and learn different caching strategies that are essential knowledge for technical interviews.

It's also one of the essential System design topics for interview and you must prepare it well.

In this article, you will learn ten essential caching concepts, ranging from client-side and server-side strategies to more advanced techniques like distributed caching and cache replacement policies

So what are we waiting for? let's start

By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io and Udemy which have many great System design courses and a System design interview template like this which you can use to answer any System Design question.

If you need more choices, you can also see this list of best System Deisgn courses, books, and websites

P.S. Keep reading until the end. I have a free bonus for you.

What is Caching? Which data to Cache? Where to Cache?

While designing distributed system, caching should be strategically placed to optimize performance, reduce latency, and minimize load on backend services.

Caching can be implemented at multiple layers like

1. Client-Side Cache
   This involves storing frequently accessed data on the client device, reducing the need for repeated requests to the server. It is effective for data that doesn't change frequently and can significantly improve user experience by reducing latency.

2. Edge Cache (Content Delivery Network - CDN)
   CDNs cache content at the edge nodes closest to the end-users, which helps in delivering static content like images, videos, and stylesheets faster by serving them from geographically distributed servers.

3. Application-Level Cache
   This includes in-memory caches such as Redis or Memcached within the application layer. These caches store results of expensive database queries, session data, and other frequently accessed data to reduce the load on the database and improve application response times.

4. Database Cache
   Techniques such as query caching in the database layer store the results of frequent queries. This reduces the number of read operations on the database and speeds up data retrieval.

5. Distributed Cache
   In a distributed system, a distributed cache spans multiple nodes to provide high availability and scalability. It ensures that the cached data is consistent across the distributed environment and can handle the high throughput required by large-scale systems.

When designing a caching strategy, it's crucial to determine what data to cache by analyzing usage patterns, data volatility, and access frequency.

Implementing an appropriate cache eviction policy (such as LRU - Least Recently Used, or TTL - Time to Live) ensures that stale data is purged, maintaining the cache's relevance.

Moreover, considering consistency models and cache invalidation strategies is vital to ensure that cached data remains accurate and up-to-date across the system.

And, here is a nice diagram on caching from DesignGuru.io to illustrate what I just said.

---

10 Caching Basics for System Design Interview

Here are 10 essential caching related basics and concepts every programmer must know before going for any System design interview.

1) client-side caching

Client-side caching is a fundamental technique where data is stored on the user's device to minimize server requests and improve load times. Two primary methods include:

• Browser Cache: Stores resources like CSS, JavaScript, and images locally to reduce page load times on subsequent visits.
• Service Workers: Enable offline access by caching responses, allowing applications to function without an internet connection.

In short:

• browser cache: stores CSS, js, images to reduce load time\
  • service workers: enable offline access by caching response

Here is how client side caching looks like:

---

2) server-side caching

This is another type of caching which involves storing data on the server to expedite response times for user requests.

Key strategies include:

• Page Caching: Saves entire web pages, allowing faster delivery on subsequent requests .

• Fragment Caching:
  Caches specific parts of a page, such as sidebars or navigation bars, to enhance loading efficiency.

• Object Caching:
  Stores expensive query results to prevent repeated calculations

In short:

• page caching: cache the entire web page
  • fragment caching: cache page components like sidebars, navigation bar\
  • object caching: cache expensive query results

Here is how server side caching looks like:

image_credit --- ByteByteGo

---

3) Database caching

Database caching is crucial for reducing database load and improving query performance. Important techniques include:

• Query Caching:
  Stores the results of database queries to quickly serve repeat requests.

• Row Level Caching:
  Caches frequently accessed rows to avoid repeated database fetches.

In short:

• query caching: cache db query results to reduce load
  • row level caching: cache popular rows to avoid repeated fetches

Here is an example of database caching on AWS:

---

4) application-level caching

Application-level caching focuses on caching within the application to reduce computation and data retrieval times. Strategies include:

• Data Caching: Stores specific data points or entire datasets for quick access.
• Computational Caching: Caches the results of expensive computations to avoid repeated processing.

In short:

• data caching: cache specific data points or entire datasets\
  • computational caching: cache expensive computation results to avoid recalculation

---

5) Distributed caching

Distributed caching enhances scalability by spreading cache data across multiple servers, allowing high availability and fault tolerance.

In short, this type of caching just spreads cache across many servers for scalability

Here is how a distributed cache with Redis looks like:

---

6) CDN

Content Delivery Networks (CDNs) are used to cache static files close to users via edge servers, significantly reducing latency and speeding up content delivery.

In short, CDN store static files near users using edge servers for low latency

Also, here is a nice diagram on how CDN Works by DeisgnGuru.io

---

7) cache replacement policies

Cache replacement policies determine how caches handle data eviction. Common policies include:

• Least Recently Used (LRU): Evicts the least recently accessed items first.
• Most Recently Used (MRU): Evicts the most recently accessed items first.
• Least Frequently Used (LFU): Evicts items that are accessed least often.

In short:

- LRU: removes the least recently accessed items first\
- MRU: removes the most recently accessed items first\
- LFU: removes items that are accessed least often

---

8) hierarchical caching

Hierarchical caching involves multiple cache levels (e.g., L1, L2) to balance speed and storage capacity. This model is quit popular on CPU.

In short:

• caching at many levels (L1, L2 caches) for speed and capacity

---

9) cache invalidation

Cache invalidation ensures that stale data is removed from the cache. Methods include:

• Time-to-Live (TTL): Sets an expiry time for cached data.
• Event-based Invalidation: Triggers invalidation based on specific events or conditions.
• Manual Invalidation: Allows developers to manually update the cache using tools.

In short:

- TTL: set expiry time\
- event based: invalidate based on events or conditions\
- manual: update cache using tools

Here is a nice System design cheat sheet about cache invalidation methods by DesignGuru.io to understand this concept better:

---

10) caching patterns

Finally, caching patterns are strategies for synchronizing cache with the database. Common patterns include:

• Write-through: Writes data to both the cache and the database simultaneously.
• Write-behind: Writes data to the cache immediately and to the database asynchronously.
• Write-around: Directly writes data to the database, bypassing the cache to avoid cache misses on subsequent reads.

In short:

- write-through: data is written to the cache and the database at once\
- write-behind: data is written to the cache and asynchronously to database\
- write-around: data is written directly to the database, bypassing the cache

Here is another great diagram to understand various caching strategies, courtesy DesignGuru.io, one of the best place to learn System Design.

Best System Design Interviews Resources

And, here are curated list of best system design books, online courses, and practice websites which you can check to better prepare for System design interviews. Most of these courses also answer questions I have shared here.

1. DesignGuru's Grokking System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

2. Codemia.io : This is another great platform to practice System design problems for interviews. It got more than 120+ System design problems, many of them are free and also a proper structure to solve them.

3. "System Design Interview" by Alex Xu: This book provides an in-depth exploration of system design concepts, strategies, and interview preparation tips.

4. "Designing Data-Intensive Applications" by Martin Kleppmann: A comprehensive guide that covers the principles and practices for designing scalable and reliable systems.

5. LeetCode System Design Tag: LeetCode is a popular platform for technical interview preparation. The System Design tag on LeetCode includes a variety of questions to practice.

6. "System Design Primer" on GitHub: A curated list of resources, including articles, books, and videos, to help you prepare for system design interviews.

7. Educative's System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

8. High Scalability Blog: A blog that features articles and case studies on the architecture of high-traffic websites and scalable systems.

9. YouTube Channels: Check out channels like "Gaurav Sen" and "Tech Dummies" for insightful videos on system design concepts and interview preparation.

10. ByteByteGo: A live book and course by Alex Xu for System design interview preparation. It contains all the content of System Design Interview book volume 1 and 2 and will be updated with volume 3 which is coming soon.

11. Exponent: A specialized site for interview prep especially for FAANG companies like Amazon and Google, They also have a great system design course and many other material which can help you crack FAAN interviews.

image_credit - ByteByteGo

Conclusion

That's all about 10 essential Cache related concepts for System design interview. Caching can improve the performance and scalability of your application. So use it carefully. Understanding and implementing these caching concepts can significantly enhance application performance, scalability, and user satisfaction.

Other System Design Articles and Resources you may like

• Is DesignGuru's System Design Course worth it
• 16 best Resources to Prepare for System Design Interview
• 100+ System Design Interview Questions and Problems
• Is Exponent's System Design Course worth it?
• 16 Best System Design Interview Resources for Developers
• Is System Design Interview RoadMap by DesignGuru worth it?
• Top 5 Places to learn System design and Software design
• 10 Reasons to Learn System Design in 2024
• 6 Best System Design and API Design Interactive Courses
• Top 5 System Design YouTube Channels for Engineers
• 10 Best Places to Learn System Design in 2024
• How to Prepare for System Design Interview in 2024
• Is ByteByteGo worth the hype?
• 10 Software Design Courses for Developers
• 5 Best System Design Newsletters for Interviews
• My Favorite Software Design Courses for 2024
• 3 Places to Practice System Design Mock interviews
• Is Designing Data-intensive application book worth reading?

Thanks for reading this article so far. If you like this Twitter system design interview solution then please share it with your friends and colleagues. If you have any questions feel free to ask in the comments.

Bonus\
As promised, here is the bonus for you, a free book. I just found a new free book to learn Distributed System Design, you can also read it here on Microsoft --- https://info.microsoft.com/rs/157-GQE-382/images/EN-CNTNT-eBook-DesigningDistributedSystems.pdf

Database Sharding 101: The One Topic You Must Nail in Every System Design Interview

Hello friends, in this data driven world, the ability to efficiently handle vast amounts of data is crucial for businesses and organizations. Traditional monolithic databases often struggle to keep pace with the demands of modern applications and services and become performance bottleneck. This is where database sharding comes into play, offering a powerful solution for horizontally scaling your data. If you don't know what is Sharding? Well, Sharding is a database architecture technique that involves partitioning a large database into smaller, more manageable pieces, called "shards," which are distributed across multiple servers.

Each shard contains a subset of the data, and together they form the complete dataset. This approach enhances performance and scalability by distributing the workload, reducing latency, and enabling parallel processing.

Top 10 Data Structures and Algorithms for System Design Interviews

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

Hi there, if you are preparing for a System Design Interview, then one thing you should focus on is learning different System Design Algorithms and what problems they solve in Distributed Systems and Microservices.

In the past, I have shared 6 System Design Problems and 10 Essential System Design topics and in this article, I am going to tell you 10 System Design algorithms and distributed data structures which every developer should learn.

Without any further ado, here are the 10 System Design algorithms and distributed Data Structures you can use to solve large-scale distributed system problems:

1. Consistent Hashing
2. MapReduce
3. Distributed Hash Tables (DHT)
4. Bloom Filters
5. Two-phase commit (2PC)
6. Paxos
7. Raft
8. Gossip protocol
9. Chord:
10. CAP theorem

These algorithms and distributed data structures are just a few examples of the many techniques that can be used to solve large-scale distributed system problems.

By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io, bugfree.ai and Udemy which have many great System design courses, and these popular System design YouTube channels, which have many great System design courses and tutorials.

10 Distributed Data Structure and System Design Algorithms for Programmers

It's important to have a good understanding of these algorithms and how to apply them effectively in different scenarios.

So, let's deep dive into each of them and find out what they are, how they work, and when to use them.

1. Consistent Hashing

Consistent hashing is a technique used in distributed systems to efficiently distribute data among multiple nodes.

It is used to minimize the amount of data that needs to be transferred between nodes when a node is added or removed from the system.

The basic idea behind consistent hashing is to use a hash function to map each piece of data to a node in the system. Each node is assigned a range of hash values, and any data that maps to a hash value within that range is assigned to that node.

When a node is added or removed from the system, only the data that was assigned to that node needs to be transferred to another node. This is achieved by using a concept called virtual nodes.

Instead of assigning each physical node a range of hash values, multiple virtual nodes are assigned to each physical node.

Each virtual node is assigned a unique range of hash values, and any data that maps to a hash value within that range is assigned to the corresponding physical node.

When a node is added or removed from the system, only the virtual nodes that are affected need to be reassigned, and any data that was assigned to those virtual nodes is transferred to another node.

This allows the system to scale dynamically and efficiently, without requiring a full redistribution of data each time a node is added or removed.

Overall, consistent hashing provides a simple and efficient way to distribute data among multiple nodes in a distributed system. It is commonly used in large-scale distributed systems, such as content delivery networks and distributed databases, to provide high availability and scalability.

---

2. Map reduce

MapReduce is a programming model and framework for processing large datasets in a distributed system. It was originally developed by Google and is now widely used in many big data processing systems, such as Apache Hadoop.

The basic idea behind MapReduce is to break a large dataset into smaller chunks, distribute them across multiple nodes in a cluster, and process them in parallel. The processing is divided into two phases: a Map phase and a Reduce phase.

In the Map phase, the input dataset is processed by a set of Map functions in parallel. Each Map function takes a key-value pair as input and produces a set of intermediate key-value pairs as output.

These intermediate key-value pairs are then sorted and partitioned by key, and sent to the Reduce phase.

In the Reduce phase, the intermediate key-value pairs are processed by a set of Reduce functions in parallel. Each Reduce function takes a key and a set of values as input, and produces a set of output key-value pairs.

Here is an example of how MapReduce can be used to count the frequency of words in a large text file:

1. Map phase: Each Map function reads a chunk of the input file and outputs a set of intermediate key-value pairs, where the key is a word and the value is the number of occurrences of that word in the chunk.
2. Shuffle phase: The intermediate key-value pairs are sorted and partitioned by key, so that all the occurrences of each word are grouped together.
3. Reduce phase: Each Reduce function takes a word and a set of occurrences as input, and outputs a key-value pair where the key is the word and the value is the total number of occurrences of that word in the input file.

The MapReduce framework takes care of the parallel processing, distribution, and fault tolerance of the computation. This allows it to process large datasets efficiently and reliably, even on clusters of commodity hardware.

---

3. Distributed Hash Tables (DHT)

A Distributed Hash Table (DHT) is a distributed system that provides a decentralized key-value store. It is used in peer-to-peer (P2P) networks to store and retrieve information in a scalable and fault-tolerant manner.

In a DHT, each participating node stores a subset of the key-value pairs, and a mapping function is used to assign keys to nodes.

This allows nodes to locate the value associated with a given key by querying only a small subset of nodes, typically those responsible for keys close to the given key in the mapping space.

DHTs provide several desirable properties, such as self-organization, fault-tolerance, load-balancing, and efficient routing. They are commonly used in P2P file sharing systems, content distribution networks, and distributed databases.

One popular DHT algorithm is the Chord protocol, which uses a ring-based topology and a consistent hashing function to assign keys to nodes. Another widely used DHT is the Kademlia protocol, which uses a binary tree-like structure to locate nodes responsible for a given key.

---

4. Bloom Filters

Bloom Filters are a probabilistic data structure used for efficient set membership tests. They were introduced by Burton Howard Bloom in 1970.

A Bloom Filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set or not. It uses a bit array and a set of hash functions to store and check for the presence of an element in a set.

The process of adding an element to a Bloom Filter involves passing the element through a set of hash functions which returns a set of indices in the bit array. These indices are then set to 1 in the bit array.

To check whether an element is present in the set or not, the same hash functions are applied to the element and the resulting indices are checked in the bit array.

If all the bits at the indices are set to 1, then the element is considered to be present in the set. However, if any of the bits are not set, the element is considered to be absent from the set.

Since Bloom Filters use hash functions to index the bit array, there is a possibility of false positives, i.e., the filter may incorrectly indicate that an element is present in the set when it is not.

However, the probability of a false positive can be controlled by adjusting the size of the bit array and the number of hash functions used.

The false negative rate, i.e., the probability of a Bloom filter failing to identify an element that is actually present in the set, is zero.

Bloom Filters are widely used in various fields such as networking, databases, and web caching to perform efficient set membership tests.

---

5. 2 Phase Commit

Two-phase commit (2PC) is a protocol used to ensure the atomicity and consistency of transactions in distributed systems. It is a way to guarantee that all nodes participating in a transaction either commit or rollback together.

The two-phase commit protocol works in two phases:

1. Prepare Phase: In the prepare phase, the coordinator node sends a message to all participating nodes, asking them to prepare to commit the transaction.

Each participant responds with a message indicating whether it is prepared to commit or not. If any participant cannot prepare, it responds with a message indicating that it cannot participate in the transaction.

1. Commit Phase: If all participants are prepared to commit, the coordinator sends a message to all nodes asking them to commit. Each participant commits the transaction and sends an acknowledgement to the coordinator.

If any participant cannot commit, it rolls back the transaction and sends a message to the coordinator indicating that it has rolled back.

If the coordinator receives acknowledgements from all participants, it sends a message to all nodes indicating that the transaction has been committed.

If the coordinator receives a rollback message from any participant, it sends a message to all nodes indicating that the transaction has been rolled back.

The two-phase commit protocol ensures that all nodes in a distributed system agree on the outcome of a transaction, even in the presence of failures.

However, it has some drawbacks, including increased latency and the possibility of deadlock. Additionally, it requires a coordinator node, which can be a single point of failure.

---

6. Paxos

Paxos is a distributed consensus algorithm that allows a group of nodes to agree on a common value, even in the presence of failures. It was introduced by Leslie Lamport in 1998 and has become a fundamental algorithm for distributed systems.

The Paxos algorithm is designed to handle a variety of failure scenarios, including message loss, duplication, reordering, and node failures.

The algorithm proceeds in two phases: the prepare phase and the accept phase. In the prepare phase, a node sends a prepare message to all other nodes, asking them to promise not to accept any proposal with a number less than a certain value.

Once a majority of nodes have responded with promises, the node can proceed to the accept phase. In the accept phase, the node sends an accept message to all other nodes, proposing a certain value.

If a majority of nodes respond with an acceptance message, the value is considered accepted.

Paxos is a complex algorithm, and there are several variations and optimizations of it, such as Multi-Paxos, Fast Paxos, and others.

These variations aim to reduce the number of messages exchanged, optimize the latency of the algorithm, and reduce the number of nodes that need to participate in the consensus. Paxos is widely used in distributed databases, file systems, and other distributed systems where a high degree of fault tolerance is required.

---

7. Raft

Raft is a consensus algorithm designed to ensure fault-tolerance in distributed systems. It is used to maintain a replicated log that stores a sequence of state changes across multiple nodes in a cluster.

Raft achieves consensus by electing a leader, which coordinates the communication among the nodes and ensures that the log is consistent across the cluster.

The Raft algorithm consists of three main components: leader election, log replication, and safety. In the leader election phase, nodes in the cluster elect a leader using a randomized timeout mechanism.

The leader then coordinates the log replication by receiving state changes from clients and replicating them across the nodes in the cluster. Nodes can also request entries from the leader to ensure consistency across the cluster.

The safety component of Raft ensures that the algorithm is resilient to failures and ensures that the log is consistent across the cluster.

Raft achieves safety by ensuring that only one node can be the leader at any given time and by enforcing a strict ordering of log entries across the cluster.

Raft is widely used in distributed systems to provide fault-tolerance and high availability. It is often used in systems that require strong consistency guarantees, such as distributed databases and key-value stores.

---

8. Gossip

The gossip protocol is a peer-to-peer communication protocol used in distributed systems to disseminate information quickly and efficiently.

It is a probabilistic protocol that allows nodes to exchange information about their state with their neighbors in a decentralized manner.

The protocol gets its name from the way it spreads information like a rumor or gossip.

In a gossip protocol, nodes randomly select a set of other nodes to exchange information with. When a node receives information from another node, it then forwards that information to a subset of its neighbors, and the process continues.

Over time, the entire network becomes aware of the information as it spreads from node to node.

One of the key benefits of the gossip protocol is its fault-tolerance. Since the protocol relies on probabilistic communication rather than a central authority, it can continue to function even if some nodes fail or drop out of the network.

This makes it a useful tool in distributed systems where reliability is a critical concern.

Gossip protocols have been used in a variety of applications, including distributed databases, peer-to-peer file sharing networks, and large-scale sensor networks.

They are particularly well-suited to applications that require fast and efficient dissemination of information across a large number of nodes.

---

9. Chrod

Chord is a distributed hash table (DHT) protocol used for decentralized peer-to-peer (P2P) systems. It provides an efficient way to locate a node (or a set of nodes) in a P2P network given its identifier.

Chord allows P2P systems to scale to very large numbers of nodes while maintaining low overhead.

In a Chord network, each node is assigned an identifier, which can be any m-bit number. The nodes are arranged in a ring, where the nodes are ordered based on their identifiers in a clockwise direction.

Each node is responsible for a set of keys, which can be any value in the range of 0 to 2^m-1.

To find a key in the network, a node first calculates its hash value and then contacts the node whose identifier is the first clockwise successor of that hash value.

If the successor node does not have the desired key, it forwards the request to its successor, and so on, until the key is found. This process is known as a finger lookup, and it typically requires a logarithmic number of messages to find the desired node.

To maintain the consistency of the network, Chord uses a protocol called finger tables, which store information about other nodes in the network.

Each node maintains a finger table that contains the identifiers of its successors at increasing distances in the ring. This allows nodes to efficiently locate other nodes in the network without having to maintain a complete list of all nodes.

Chord also provides mechanisms for maintaining consistency when nodes join or leave the network. When a node joins the network, it notifies its immediate successor, which updates its finger table accordingly.

When a node leaves the network, its keys are transferred to its successor node, and the successor node updates its finger table to reflect the departure.

Overall, Chord provides an efficient and scalable way to locate nodes in a P2P network using a simple and decentralized protocol.

---

10. CAP Theorem

The CAP theorem, also known as Brewer's theorem, is a fundamental concept in distributed systems that states that it is impossible for a distributed system to simultaneously guarantee all of the following three properties:

1. Consistency: Every read receives the most recent write or an error.
2. Availability: Every request receives a response, without guarantee that it contains the most recent version of the information.
3. Partition tolerance: The system continues to function and provide consistent and available services even when network partitions occur.

In other words, a distributed system can only provide two out of the three properties mentioned above.

This theorem implies that in the event of a network partition, a distributed system must choose between consistency and availability.

For example, in a partitioned system, if one node cannot communicate with another node, it must either return an error or provide a potentially stale response.

The CAP theorem has significant implications for designing distributed systems, as it requires developers to make trade-offs between consistency, availability, and partition tolerance.

Conclusion

That's all about the essential System Design Data Structure, Algorithms and Protocol You can learn in 2023. In conclusion, system design is an essential skill for software engineers, especially those working on large-scale distributed systems.

These ten algorithms, data structure, and protocols provide a solid foundation for tackling complex problems and building scalable, reliable systems. By understanding these algorithms and their trade-offs, you can make informed decisions when designing and implementing systems.

Additionally, learning these algorithms can help you prepare for system design interviews and improve their problem-solving skills. However, it's important to note that these algorithms are just a starting point, and you should continue to learn and adapt as technology evolves.

By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io, bugfree.ai and Udemy which have many great System design courses, and these popular System design YouTube channels, which have many great System design courses and tutorials.

Also, here is a nice System design template from DesignGuru which you can use to answer any System design question on interviews. It highlights key software architecture components and allows you to express your knowledge well.

All the best for your System design interviews!!

Forward Proxy vs Reverse Proxy in System design

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

image_credit - DesignGurus.io

Hello folks, in last few article, I was answering popular System design questions like API Gateway vs Load Balancer and Horizontal vs Vertical Scaling, and today, we are going to take a look at another interesting System design question, Reverse Proxy vs Forward Proxy.

These questions are different than system design problems like how to design WhatsApp and YouTube but they are equally important and if you have knowledge of them you can mention in most of the system design problems.

Now coming back to the topic, In network architecture world, proxies play a pivotal role in managing and securing communication between clients and servers.

There are two common types of proxies, forward and reverse proxies, they serve distinct purposes and operate at different layers of the networking stack. Forward proxies are used to shield clients from external networks while Reverse proxy acts as a frontend Facade for backend Servers, much like API Gatewawy and load balancers.

Let's go deep into the intricacies of forward and reverse proxies to know their differences and understand their respective roles in system design.

By the way, if you are in hurry then below diagram from DesignGuru.io, one of the best resource for system design interviews and creator of Grokking the System Design Interview nicely explain it:

---

What is Forward Proxy?

A forward proxy, also known as an outbound proxy, acts as an intermediary between clients and external servers, intercepting outbound requests from clients and forwarding them to their intended destinations.

Here is what forward proxies do for you:

1. Client-Side Proxying
   Forward proxies are typically deployed on the client side of a network, serving as a gateway for outbound traffic. Clients configure their network settings to route traffic through the forward proxy, which then forwards requests to external servers on behalf of the clients.

2. Anonymity and Privacy
   Forward proxies can enhance user privacy and anonymity by masking the IP addresses of clients. External servers only see the IP address of the forward proxy, making it difficult to trace the origin of requests back to individual clients.

3. Content Filtering and Caching
   Forward proxies can implement content filtering policies to restrict access to certain websites or content categories based on predefined rules. Additionally, they can cache frequently accessed content, reducing bandwidth usage and improving performance for subsequent requests.

4. Security and Access Control
   Forward proxies can also enforce security policies and access controls, allowing organizations to regulate access to external resources, block malicious websites, and inspect outbound traffic for threats or policy violations.

You can see in the diagram below that the forward proxy routes user requests to back-end servers

By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io, bugfree.ai and Udemy which have many great System design courses

Now that we know what a forward proxy is let's take a look at a reverse proxy and what services it provides:

---

What is a Reverse Proxy?

A reverse proxy, also known as an inbound proxy, operates on the server side of a network, serving as a front-end facade for backend servers.

It intercepts incoming requests from clients and forwards them to the appropriate back-end servers based on predefined rules.

Key aspects of reverse proxies include:

1. Server-Side Proxying
   Reverse proxies are deployed on the server side of a network, typically in front of backend web servers or application servers. They accept incoming requests from clients on behalf of backend servers and forward them internally.

2. Load Balancing and Traffic Distribution
   Reverse proxies can distribute incoming traffic across multiple backend servers to improve scalability, reliability, and performance. They use algorithms such as round-robin, least connections, or weighted distribution to evenly distribute requests.

3. SSL Termination and Encryption
   Reverse proxies can handle SSL/TLS termination, offloading the encryption and decryption process from backend servers. This simplifies management of SSL certificates and improves performance by reducing the computational overhead on backend servers.

4. Content Delivery and Optimization
   Reverse proxies can cache static content, compress data, and optimize delivery to clients, reducing latency and bandwidth usage. They can also perform content rewriting or transformation to adapt content for different client devices or browsers.

Here is also a nice diagram which shows how reverse proxy work which is quite useful for system design interview, and if you are preparing for one, Educative.io's Modern System Design Guide is another awesome resource I recommend.

---

Difference between Forward and Reverse Proxies and Use Cases

While both forward and reverse proxies act as intermediaries in network communication, their primary objectives and deployment scenarios differ:

For example, Forward proxy is primarily used to shield clients from external networks, enhance privacy and security, and enforce access controls and it's ideal for individual users, organizations, or networks requiring outbound traffic management and anonymity.

On the other hand, Reverse Proxy is primarily used to front-end backend servers, improve scalability and performance, and provide centralized management of incoming traffic.

It is ideal for web servers, application servers, or microservices architectures requiring load balancing, SSL termination, and content optimization.

And, here is a nice diagram which highlights the difference between Forward Proxy and Reverse Proxy from ByteByteGo, one of the best places to learn System Design for interviews. If you are preparing for a system design interview, you should definitely check it out. They also have an awesome YouTube channel.

Conclusion

In conclusion, both forward and reverse proxies are indispensable components in modern network architectures, each serving unique purposes and offering distinct capabilities.

While forward proxies focus on client-side traffic management and security, reverse proxies excel at server-side load balancing, scalability, and optimization.

Understanding their differences is essential for designing resilient, efficient, and secure systems that meet the diverse needs of modern applications and services.

And, if you are preparing for a system design interview, then you may also like my previous articles

By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io, bugfree.ai and Udemy which have many great System design courses

Thank you !!