Repository: ashishps1/awesome-system-design-resources Branch: main Commit: 25724090f7dd Files: 24 Total size: 76.9 KB Directory structure: gitextract_sclv_x77/ ├── LICENSE ├── README.md └── implementations/ ├── java/ │ ├── consistent_hashing/ │ │ └── ConsistentHashing.java │ ├── load_balancing_algorithms/ │ │ ├── IPHash.java │ │ ├── LeastConnections.java │ │ ├── LeastResponseTime.java │ │ ├── RoundRobin.java │ │ └── WeightedRoundRobin.java │ └── rate_limiting/ │ ├── FixedWindowCounter.java │ ├── LeakyBucket.java │ ├── SlidingWindowCounter.java │ ├── SlidingWindowLog.java │ └── TokenBucket.java └── python/ ├── consistent_hashing/ │ └── consistent-hashing.py ├── load_balancing_algorithms/ │ ├── ip_hash.py │ ├── least_connections.py │ ├── least_response_time.py │ ├── round_robin.py.py │ └── weighted_round_robin.py └── rate_limiting/ ├── fixed_window_counter.py ├── leaky_bucket.py ├── sliding_window_counter.py ├── sliding_window_log.py └── token_bucket.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: LICENSE ================================================ GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . ================================================ FILE: README.md ================================================ # Awesome System Design Resources

This repository contains free resources to learn System Design concepts and prepare for interviews. 👉 Subscribe to my [AlgoMaster Newsletter](https://bit.ly/amghsd) and get a **FREE System Design Interview Handbook** in your inbox. ✅ If you are new to System Design, start here: [System Design was HARD until I Learned these 30 Concepts](https://blog.algomaster.io/p/30-system-design-concepts) ## ⚙️ Core Concepts - [Scalability](https://algomaster.io/learn/system-design/scalability) - [Availability](https://algomaster.io/learn/system-design/availability) - [Reliability](https://algomaster.io/learn/system-design/reliability) - [SPOF](https://algomaster.io/learn/system-design/single-point-of-failure-spof) - [Latency vs Throughput vs Bandwidth](https://algomaster.io/learn/system-design/latency-vs-throughput) - [Consistent Hashing](https://algomaster.io/learn/system-design/consistent-hashing) - [CAP Theorem](https://algomaster.io/learn/system-design/cap-theorem) - [Failover](https://www.druva.com/glossary/what-is-a-failover-definition-and-related-faqs) - [Fault Tolerance](https://www.cockroachlabs.com/blog/what-is-fault-tolerance/) ## 🌐 Networking Fundamentals - [OSI Model](https://algomaster.io/learn/system-design/osi) - [IP Addresses](https://algomaster.io/learn/system-design/ip-address) - [Domain Name System (DNS)](https://blog.algomaster.io/p/how-dns-actually-works) - [Proxy vs Reverse Proxy](https://blog.algomaster.io/p/proxy-vs-reverse-proxy-explained) - [HTTP/HTTPS](https://algomaster.io/learn/system-design/http-https) - [TCP vs UDP](https://algomaster.io/learn/system-design/tcp-vs-udp) - [Load Balancing](https://blog.algomaster.io/p/load-balancing-algorithms-explained-with-code) - [Checksums](https://algomaster.io/learn/system-design/checksums) ## 🔌 API Fundamentals - [APIs](https://algomaster.io/learn/system-design/what-is-an-api) - [API Gateway](https://blog.algomaster.io/p/what-is-an-api-gateway) - [REST vs GraphQL](https://blog.algomaster.io/p/rest-vs-graphql) - [WebSockets](https://blog.algomaster.io/p/websockets) - [Webhooks](https://algomaster.io/learn/system-design/webhooks) - [Idempotency](https://algomaster.io/learn/system-design/idempotency) - [Rate limiting](https://blog.algomaster.io/p/rate-limiting-algorithms-explained-with-code) - [API Design](https://abdulrwahab.medium.com/api-architecture-best-practices-for-designing-rest-apis-bf907025f5f) ## 🗄️ Database Fundamentals - [ACID Transactions](https://algomaster.io/learn/system-design/acid-transactions) - [SQL vs NoSQL](https://algomaster.io/learn/system-design/sql-vs-nosql) - [Database Indexes](https://algomaster.io/learn/system-design/indexing) - [Database Sharding](https://algomaster.io/learn/system-design/sharding) - [Data Replication](https://redis.com/blog/what-is-data-replication/) - [Database Scaling](https://blog.algomaster.io/p/system-design-how-to-scale-a-database) - [Databases Types](https://blog.algomaster.io/p/15-types-of-databases) - [Bloom Filters](https://algomaster.io/learn/system-design/bloom-filters) - [Database Architectures](https://www.mongodb.com/developer/products/mongodb/active-active-application-architectures/) ## ⚡ Caching Fundamentals - [Caching 101](https://algomaster.io/learn/system-design/what-is-caching) - [Caching Strategies](https://algomaster.io/learn/system-design/caching-strategies) - [Cache Eviction Policies](https://blog.algomaster.io/p/7-cache-eviction-strategies) - [Distributed Caching](https://blog.algomaster.io/p/distributed-caching) - [Content Delivery Network (CDN)](https://algomaster.io/learn/system-design/content-delivery-network-cdn) ## 🔄 Asynchronous Communication - [Pub/Sub](https://algomaster.io/learn/system-design/pub-sub) - [Message Queues](https://algomaster.io/learn/system-design/message-queues) - [Change Data Capture (CDC)](https://algomaster.io/learn/system-design/change-data-capture-cdc) ## 🧩 Distributed System and Microservices - [HeartBeats](https://blog.algomaster.io/p/heartbeats-in-distributed-systems) - [Service Discovery](https://blog.algomaster.io/p/service-discovery-in-distributed-systems) - [Consensus Algorithms](https://medium.com/@sourabhatta1819/consensus-in-distributed-system-ac79f8ba2b8c) - [Distributed Locking](https://martin.kleppmann.com/2016/02/08/how-to-do-distributed-locking.html) - [Gossip Protocol](http://highscalability.com/blog/2023/7/16/gossip-protocol-explained.html) - [Circuit Breaker](https://medium.com/geekculture/design-patterns-for-microservices-circuit-breaker-pattern-276249ffab33) - [Disaster Recovery](https://cloud.google.com/learn/what-is-disaster-recovery) - [Distributed Tracing](https://www.dynatrace.com/news/blog/what-is-distributed-tracing/) ## 🖇️ Architectural Patterns - [Client-Server Architecture](https://algomaster.io/learn/system-design/client-server-architecture) - [Microservices Architecture](https://medium.com/hashmapinc/the-what-why-and-how-of-a-microservices-architecture-4179579423a9) - [Serverless Architecture](https://blog.algomaster.io/p/2edeb23b-cfa5-4b24-845e-3f6f7a39d162) - [Event-Driven Architecture](https://www.confluent.io/learn/event-driven-architecture/) - [Peer-to-Peer (P2P) Architecture](https://www.spiceworks.com/tech/networking/articles/what-is-peer-to-peer/) ## ⚖️ System Design Tradeoffs - [Top 15 Tradeoffs](https://blog.algomaster.io/p/system-design-top-15-trade-offs) - [Vertical vs Horizontal Scaling](https://algomaster.io/learn/system-design/vertical-vs-horizontal-scaling) - [Concurrency vs Parallelism](https://blog.algomaster.io/p/concurrency-vs-parallelism) - [Long Polling vs WebSockets](https://blog.algomaster.io/p/long-polling-vs-websockets) - [Batch vs Stream Processing](https://blog.algomaster.io/p/batch-processing-vs-stream-processing) - [Stateful vs Stateless Design](https://blog.algomaster.io/p/stateful-vs-stateless-architecture) - [Strong vs Eventual Consistency](https://blog.algomaster.io/p/strong-vs-eventual-consistency) - [Read-Through vs Write-Through Cache](https://blog.algomaster.io/p/59cae60d-9717-4e20-a59e-759e370db4e5) - [Push vs Pull Architecture](https://blog.algomaster.io/p/af5fe2fe-9a4f-4708-af43-184945a243af) - [REST vs RPC](https://blog.algomaster.io/p/106604fb-b746-41de-88fb-60e932b2ff68) - [Synchronous vs. asynchronous communications](https://blog.algomaster.io/p/aec1cebf-6060-45a7-8e00-47364ca70761) - [Latency vs Throughput](https://aws.amazon.com/compare/the-difference-between-throughput-and-latency/) ## ✅ [How to Answer a System Design Interview Problem](https://algomaster.io/learn/system-design-interviews/answering-framework) ## 💻 System Design Interview Problems ### Easy - [Design URL Shortener like TinyURL](https://algomaster.io/learn/system-design-interviews/design-url-shortener) - [Design Autocomplete for Search Engines](https://algomaster.io/learn/system-design-interviews/design-instagram) - [Design Load Balancer](https://algomaster.io/learn/system-design-interviews/design-load-balancer) - [Design Content Delivery Network (CDN)](https://www.youtube.com/watch?v=8zX0rue2Hic) - [Design Parking Garage](https://www.youtube.com/watch?v=NtMvNh0WFVM) - [Design Vending Machine](https://www.youtube.com/watch?v=D0kDMUgo27c) - [Design Distributed Key-Value Store](https://www.youtube.com/watch?v=rnZmdmlR-2M) - [Design Distributed Cache](https://www.youtube.com/watch?v=iuqZvajTOyA) - [Design Authentication System](https://www.youtube.com/watch?v=uj_4vxm9u90) - [Design Unified Payments Interface (UPI)](https://www.youtube.com/watch?v=QpLy0_c_RXk) ### Medium - [Design WhatsApp](https://algomaster.io/learn/system-design-interviews/design-whatsapp) - [Design Spotify](https://algomaster.io/learn/system-design-interviews/design-spotify) - [Design Instagram](https://algomaster.io/learn/system-design-interviews/design-instagram) - [Design Notification Service](https://algomaster.io/learn/system-design-interviews/design-notification-service) - [Design Distributed Job Scheduler](https://blog.algomaster.io/p/design-a-distributed-job-scheduler) - [Design Tinder](https://www.youtube.com/watch?v=tndzLznxq40) - [Design Facebook](https://www.youtube.com/watch?v=9-hjBGxuiEs) - [Design Twitter](https://www.youtube.com/watch?v=wYk0xPP_P_8) - [Design Reddit](https://www.youtube.com/watch?v=KYExYE_9nIY) - [Design Netflix](https://www.youtube.com/watch?v=psQzyFfsUGU) - [Design Youtube](https://www.youtube.com/watch?v=jPKTo1iGQiE) - [Design Google Search](https://www.youtube.com/watch?v=CeGtqouT8eA) - [Design E-commerce Store like Amazon](https://www.youtube.com/watch?v=EpASu_1dUdE) - [Design TikTok](https://www.youtube.com/watch?v=Z-0g_aJL5Fw) - [Design Shopify](https://www.youtube.com/watch?v=lEL4F_0J3l8) - [Design Airbnb](https://www.youtube.com/watch?v=YyOXt2MEkv4) - [Design Rate Limiter](https://www.youtube.com/watch?v=mhUQe4BKZXs) - [Design Distributed Message Queue like Kafka](https://www.youtube.com/watch?v=iJLL-KPqBpM) - [Design Flight Booking System](https://www.youtube.com/watch?v=qsGcfVGvFSs) - [Design Online Code Editor](https://www.youtube.com/watch?v=07jkn4jUtso) - [Design an Analytics Platform (Metrics & Logging)](https://www.youtube.com/watch?v=kIcq1_pBQSY) - [Design Payment System](https://www.youtube.com/watch?v=olfaBgJrUBI) - [Design a Digital Wallet](https://www.youtube.com/watch?v=4ijjIUeq6hE) ### Hard - [Design Location Based Service like Yelp](https://www.youtube.com/watch?v=M4lR_Va97cQ) - [Design Uber](https://www.youtube.com/watch?v=umWABit-wbk) - [Design Food Delivery App like Doordash](https://www.youtube.com/watch?v=iRhSAR3ldTw) - [Design Google Docs](https://www.youtube.com/watch?v=2auwirNBvGg) - [Design Google Maps](https://www.youtube.com/watch?v=jk3yvVfNvds) - [Design Zoom](https://www.youtube.com/watch?v=G32ThJakeHk) - [Design File Sharing System like Dropbox](https://www.youtube.com/watch?v=U0xTu6E2CT8) - [Design Ticket Booking System like BookMyShow](https://www.youtube.com/watch?v=lBAwJgoO3Ek) - [Design Distributed Web Crawler](https://www.youtube.com/watch?v=BKZxZwUgL3Y) - [Design Code Deployment System](https://www.youtube.com/watch?v=q0KGYwNbf-0) - [Design Distributed Cloud Storage like S3](https://www.youtube.com/watch?v=UmWtcgC96X8) - [Design Distributed Locking Service](https://www.youtube.com/watch?v=v7x75aN9liM) ## 📇 Courses - [System Design Fundamentals](https://algomaster.io/learn/system-design/course-introduction) - [System Design Interviews](https://algomaster.io/learn/system-design-interviews/introduction) ## 📩 Newsletters - [AlgoMaster Newsletter](https://blog.algomaster.io/) ## 📚 Books - [Designing Data-Intensive Applications](https://www.amazon.in/dp/9352135245) ## 📺 YouTube Channels - [Tech Dummies Narendra L](https://www.youtube.com/@TechDummiesNarendraL) - [Gaurav Sen](https://www.youtube.com/@gkcs) - [codeKarle](https://www.youtube.com/@codeKarle) - [ByteByteGo](https://www.youtube.com/@ByteByteGo) - [System Design Interview](https://www.youtube.com/@SystemDesignInterview) - [sudoCODE](https://www.youtube.com/@sudocode) - [Success in Tech](https://www.youtube.com/@SuccessinTech/videos) ## 📜 Must-Read Engineering Articles - [How Discord stores trillions of messages](https://discord.com/blog/how-discord-stores-trillions-of-messages) - [Building In-Video Search at Netflix](https://netflixtechblog.com/building-in-video-search-936766f0017c) - [How Canva scaled Media uploads from Zero to 50 Million per Day](https://www.canva.dev/blog/engineering/from-zero-to-50-million-uploads-per-day-scaling-media-at-canva/) - [How Airbnb avoids double payments in a Distributed Payments System](https://medium.com/airbnb-engineering/avoiding-double-payments-in-a-distributed-payments-system-2981f6b070bb) - [Stripe’s payments APIs - The first 10 years](https://stripe.com/blog/payment-api-design) - [Real time messaging at Slack](https://slack.engineering/real-time-messaging/) ## 🗞️ Must-Read Distributed Systems Papers - [Paxos: The Part-Time Parliament](https://lamport.azurewebsites.net/pubs/lamport-paxos.pdf) - [MapReduce: Simplified Data Processing on Large Clusters](https://research.google.com/archive/mapreduce-osdi04.pdf) - [The Google File System](https://static.googleusercontent.com/media/research.google.com/en//archive/gfs-sosp2003.pdf) - [Dynamo: Amazon’s Highly Available Key-value Store](https://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf) - [Kafka: a Distributed Messaging System for Log Processing](https://notes.stephenholiday.com/Kafka.pdf) - [Spanner: Google’s Globally-Distributed Database](https://static.googleusercontent.com/media/research.google.com/en//archive/spanner-osdi2012.pdf) - [Bigtable: A Distributed Storage System for Structured Data](https://static.googleusercontent.com/media/research.google.com/en//archive/bigtable-osdi06.pdf) - [ZooKeeper: Wait-free coordination for Internet-scale systems](https://www.usenix.org/legacy/event/usenix10/tech/full_papers/Hunt.pdf) - [The Log-Structured Merge-Tree (LSM-Tree)](https://www.cs.umb.edu/~poneil/lsmtree.pdf) - [The Chubby lock service for loosely-coupled distributed systems](https://static.googleusercontent.com/media/research.google.com/en//archive/chubby-osdi06.pdf) ---

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================================================ FILE: implementations/java/consistent_hashing/ConsistentHashing.java ================================================ package implementations.java.consistent_hashing; import java.security.MessageDigest; import java.security.NoSuchAlgorithmException; import java.util.*; public class ConsistentHashing { private final int numReplicas; // Number of virtual nodes per server private final TreeMap ring; // Hash ring storing virtual nodes private final Set servers; // Set of physical servers public ConsistentHashing(List servers, int numReplicas) { this.numReplicas = numReplicas; this.ring = new TreeMap<>(); this.servers = new HashSet<>(); // Add each server to the hash ring for (String server : servers) { addServer(server); } } private long hash(String key) { try { MessageDigest md = MessageDigest.getInstance("MD5"); md.update(key.getBytes()); byte[] digest = md.digest(); return ((long) (digest[0] & 0xFF) << 24) | ((long) (digest[1] & 0xFF) << 16) | ((long) (digest[2] & 0xFF) << 8) | ((long) (digest[3] & 0xFF)); } catch (NoSuchAlgorithmException e) { throw new RuntimeException("MD5 algorithm not found", e); } } public void addServer(String server) { servers.add(server); for (int i = 0; i < numReplicas; i++) { long hash = hash(server + "-" + i); // Unique hash for each virtual node ring.put(hash, server); } } public void removeServer(String server) { if (servers.remove(server)) { for (int i = 0; i < numReplicas; i++) { long hash = hash(server + "-" + i); ring.remove(hash); } } } public String getServer(String key) { if (ring.isEmpty()) { return null; // No servers available } long hash = hash(key); // Find the closest server in a clockwise direction Map.Entry entry = ring.ceilingEntry(hash); if (entry == null) { // If we exceed the highest node, wrap around to the first node entry = ring.firstEntry(); } return entry.getValue(); } public static void main(String[] args) { List servers = Arrays.asList("S0", "S1", "S2", "S3", "S4", "S5"); ConsistentHashing ch = new ConsistentHashing(servers, 3); // Step 2: Assign requests (keys) to servers System.out.println("UserA is assigned to: " + ch.getServer("UserA")); System.out.println("UserB is assigned to: " + ch.getServer("UserB")); // Step 3: Add a new server dynamically ch.addServer("S6"); System.out.println("UserA is now assigned to: " + ch.getServer("UserA")); // Step 4: Remove a server dynamically ch.removeServer("S2"); System.out.println("UserB is now assigned to: " + ch.getServer("UserB")); } } ================================================ FILE: implementations/java/load_balancing_algorithms/IPHash.java ================================================ import java.util.List; public class IPHash { private List servers; public IPHash(List servers) { this.servers = servers; } public String getNextServer(String clientIp) { int hash = clientIp.hashCode(); int serverIndex = Math.abs(hash) % servers.size(); return servers.get(serverIndex); } public static void main(String[] args) { List servers = List.of("Server1", "Server2", "Server3"); IPHash ipHash = new IPHash(servers); List clientIps = List.of("192.168.0.1", "192.168.0.2", "192.168.0.3"); for (String ip : clientIps) { System.out.println(ip + " is mapped to " + ipHash.getNextServer(ip)); } } } ================================================ FILE: implementations/java/load_balancing_algorithms/LeastConnections.java ================================================ import java.util.HashMap; import java.util.List; import java.util.Map; public class LeastConnections { private Map serverConnections; public LeastConnections(List servers) { serverConnections = new HashMap<>(); for (String server : servers) { serverConnections.put(server, 0); } } public String getNextServer() { return serverConnections.entrySet().stream() .min(Map.Entry.comparingByValue()) .map(Map.Entry::getKey) .orElse(null); } public void releaseConnection(String server) { serverConnections.computeIfPresent(server, (k, v) -> v > 0 ? v - 1 : 0); } public static void main(String[] args) { List servers = List.of("Server1", "Server2", "Server3"); LeastConnections leastConnectionsLB = new LeastConnections(servers); for (int i = 0; i < 6; i++) { String server = leastConnectionsLB.getNextServer(); System.out.println(server); leastConnectionsLB.releaseConnection(server); } } } ================================================ FILE: implementations/java/load_balancing_algorithms/LeastResponseTime.java ================================================ import java.util.ArrayList; import java.util.List; import java.util.Random; public class LeastResponseTime { private List servers; private List responseTimes; public LeastResponseTime(List servers) { this.servers = servers; this.responseTimes = new ArrayList<>(servers.size()); for (int i = 0; i < servers.size(); i++) responseTimes.add(0.0); } public String getNextServer() { double minResponseTime = responseTimes.get(0); int minIndex = 0; for (int i = 1; i < responseTimes.size(); i++) { if (responseTimes.get(i) < minResponseTime) { minResponseTime = responseTimes.get(i); minIndex = i; } } return servers.get(minIndex); } public void updateResponseTime(String server, double responseTime) { int index = servers.indexOf(server); responseTimes.set(index, responseTime); } public static double simulateResponseTime(String server) { // Simulating response time with random delay Random random = new Random(); double delay = 0.1 + (1.0 - 0.1) * random.nextDouble(); try { Thread.sleep((long) (delay * 1000)); } catch (InterruptedException e) { e.printStackTrace(); } return delay; } public static void main(String[] args) { List servers = List.of("Server1", "Server2", "Server3"); LeastResponseTime leastResponseTimeLB = new LeastResponseTime(servers); for (int i = 0; i < 6; i++) { String server = leastResponseTimeLB.getNextServer(); System.out.println("Request " + (i + 1) + " -> " + server); double responseTime = simulateResponseTime(server); leastResponseTimeLB.updateResponseTime(server, responseTime); System.out.println("Response Time: " + String.format("%.2f", responseTime) + "s"); } } } ================================================ FILE: implementations/java/load_balancing_algorithms/RoundRobin.java ================================================ import java.util.List; import java.util.concurrent.atomic.AtomicInteger; public class RoundRobin { private List servers; private AtomicInteger index; public RoundRobin(List servers) { this.servers = servers; this.index = new AtomicInteger(-1); } public String getNextServer() { int currentIndex = index.incrementAndGet() % servers.size(); return servers.get(currentIndex); } public static void main(String[] args) { List servers = List.of("Server1", "Server2", "Server3"); RoundRobin roundRobinLB = new RoundRobin(servers); for (int i = 0; i < 6; i++) { System.out.println(roundRobinLB.getNextServer()); } } } ================================================ FILE: implementations/java/load_balancing_algorithms/WeightedRoundRobin.java ================================================ import java.util.List; public class WeightedRoundRobin { private List servers; private List weights; private int currentIndex; private int currentWeight; public WeightedRoundRobin(List servers, List weights) { this.servers = servers; this.weights = weights; this.currentIndex = -1; this.currentWeight = 0; } public String getNextServer() { while (true) { currentIndex = (currentIndex + 1) % servers.size(); if (currentIndex == 0) { currentWeight--; if (currentWeight <= 0) { currentWeight = getMaxWeight(); } } if (weights.get(currentIndex) >= currentWeight) { return servers.get(currentIndex); } } } private int getMaxWeight() { return weights.stream().max(Integer::compare).orElse(0); } public static void main(String[] args) { List servers = List.of("Server1", "Server2", "Server3"); List weights = List.of(5, 1, 1); WeightedRoundRobin weightedRoundRobinLB = new WeightedRoundRobin(servers, weights); for (int i = 0; i < 7; i++) { System.out.println(weightedRoundRobinLB.getNextServer()); } } } ================================================ FILE: implementations/java/rate_limiting/FixedWindowCounter.java ================================================ package implementations.java.rate_limiting; import java.time.Instant; public class FixedWindowCounter { private final long windowSizeInSeconds; // Size of each window in seconds private final long maxRequestsPerWindow; // Maximum number of requests allowed per window private long currentWindowStart; // Start time of the current window private long requestCount; // Number of requests in the current window public FixedWindowCounter(long windowSizeInSeconds, long maxRequestsPerWindow) { this.windowSizeInSeconds = windowSizeInSeconds; this.maxRequestsPerWindow = maxRequestsPerWindow; this.currentWindowStart = Instant.now().getEpochSecond(); this.requestCount = 0; } public synchronized boolean allowRequest() { long now = Instant.now().getEpochSecond(); // Check if we've moved to a new window if (now - currentWindowStart >= windowSizeInSeconds) { currentWindowStart = now; // Start a new window requestCount = 0; // Reset the count for the new window } if (requestCount < maxRequestsPerWindow) { requestCount++; // Increment the count for this window return true; // Allow the request } return false; // We've exceeded the limit for this window, deny the request } } ================================================ FILE: implementations/java/rate_limiting/LeakyBucket.java ================================================ package implementations.java.rate_limiting; import java.time.Instant; import java.util.LinkedList; import java.util.Queue; public class LeakyBucket { private final long capacity; // Maximum number of requests the bucket can hold private final double leakRate; // Rate at which requests leak out of the bucket (requests per second) private final Queue bucket; // Queue to hold timestamps of requests private Instant lastLeakTimestamp; // Last time we leaked from the bucket public LeakyBucket(long capacity, double leakRate) { this.capacity = capacity; this.leakRate = leakRate; this.bucket = new LinkedList<>(); this.lastLeakTimestamp = Instant.now(); } public synchronized boolean allowRequest() { leak(); // First, leak out any requests based on elapsed time if (bucket.size() < capacity) { bucket.offer(Instant.now()); // Add the new request to the bucket return true; // Allow the request } return false; // Bucket is full, deny the request } private void leak() { Instant now = Instant.now(); long elapsedMillis = now.toEpochMilli() - lastLeakTimestamp.toEpochMilli(); int leakedItems = (int) (elapsedMillis * leakRate / 1000.0); // Calculate how many items should have leaked // Remove the leaked items from the bucket for (int i = 0; i < leakedItems && !bucket.isEmpty(); i++) { bucket.poll(); } lastLeakTimestamp = now; } } ================================================ FILE: implementations/java/rate_limiting/SlidingWindowCounter.java ================================================ package implementations.java.rate_limiting; import java.time.Instant; public class SlidingWindowCounter { private final long windowSizeInSeconds; // Size of the sliding window in seconds private final long maxRequestsPerWindow; // Maximum number of requests allowed in the window private long currentWindowStart; // Start time of the current window private long previousWindowCount; // Number of requests in the previous window private long currentWindowCount; // Number of requests in the current window public SlidingWindowCounter(long windowSizeInSeconds, long maxRequestsPerWindow) { this.windowSizeInSeconds = windowSizeInSeconds; this.maxRequestsPerWindow = maxRequestsPerWindow; this.currentWindowStart = Instant.now().getEpochSecond(); this.previousWindowCount = 0; this.currentWindowCount = 0; } public synchronized boolean allowRequest() { long now = Instant.now().getEpochSecond(); long timePassedInWindow = now - currentWindowStart; // Check if we've moved to a new window if (timePassedInWindow >= windowSizeInSeconds) { previousWindowCount = currentWindowCount; currentWindowCount = 0; currentWindowStart = now; timePassedInWindow = 0; } // Calculate the weighted count of requests double weightedCount = previousWindowCount * ((windowSizeInSeconds - timePassedInWindow) / (double) windowSizeInSeconds) + currentWindowCount; if (weightedCount < maxRequestsPerWindow) { currentWindowCount++; // Increment the count for this window return true; // Allow the request } return false; // We've exceeded the limit, deny the request } } ================================================ FILE: implementations/java/rate_limiting/SlidingWindowLog.java ================================================ package implementations.java.rate_limiting; import java.time.Instant; import java.util.LinkedList; import java.util.Queue; public class SlidingWindowLog { private final long windowSizeInSeconds; // Size of the sliding window in seconds private final long maxRequestsPerWindow; // Maximum number of requests allowed in the window private final Queue requestLog; // Log of request timestamps public SlidingWindowLog(long windowSizeInSeconds, long maxRequestsPerWindow) { this.windowSizeInSeconds = windowSizeInSeconds; this.maxRequestsPerWindow = maxRequestsPerWindow; this.requestLog = new LinkedList<>(); } public synchronized boolean allowRequest() { long now = Instant.now().getEpochSecond(); long windowStart = now - windowSizeInSeconds; // Remove timestamps that are outside of the current window while (!requestLog.isEmpty() && requestLog.peek() <= windowStart) { requestLog.poll(); } if (requestLog.size() < maxRequestsPerWindow) { requestLog.offer(now); // Log this request return true; // Allow the request } return false; // We've exceeded the limit for this window, deny the request } } ================================================ FILE: implementations/java/rate_limiting/TokenBucket.java ================================================ package implementations.java.rate_limiting; import java.time.Instant; public class TokenBucket { private final long capacity; // Maximum number of tokens the bucket can hold private final double fillRate; // Rate at which tokens are added to the bucket (tokens per second) private double tokens; // Current number of tokens in the bucket private Instant lastRefillTimestamp; // Last time we refilled the bucket public TokenBucket(long capacity, double fillRate) { this.capacity = capacity; this.fillRate = fillRate; this.tokens = capacity; // Start with a full bucket this.lastRefillTimestamp = Instant.now(); } public synchronized boolean allowRequest(int tokens) { refill(); // First, add any new tokens based on elapsed time if (this.tokens < tokens) { return false; // Not enough tokens, deny the request } this.tokens -= tokens; // Consume the tokens return true; // Allow the request } private void refill() { Instant now = Instant.now(); // Calculate how many tokens to add based on the time elapsed double tokensToAdd = (now.toEpochMilli() - lastRefillTimestamp.toEpochMilli()) * fillRate / 1000.0; this.tokens = Math.min(capacity, this.tokens + tokensToAdd); // Add tokens, but don't exceed capacity this.lastRefillTimestamp = now; } } ================================================ FILE: implementations/python/consistent_hashing/consistent-hashing.py ================================================ import hashlib import bisect class ConsistentHashing: def __init__(self, servers, num_replicas=3): """ Initializes the consistent hashing ring. - servers: List of initial server names (e.g., ["S0", "S1", "S2"]) - num_replicas: Number of virtual nodes per server for better load balancing """ self.num_replicas = num_replicas # Number of virtual nodes per server self.ring = {} # Hash ring storing virtual node mappings self.sorted_keys = [] # Sorted list of hash values (positions) on the ring self.servers = set() # Set of physical servers (used for tracking) # Add each server to the hash ring for server in servers: self.add_server(server) def _hash(self, key): """Computes a hash value for a given key using MD5.""" return int(hashlib.md5(key.encode()).hexdigest(), 16) def add_server(self, server): """ Adds a server to the hash ring along with its virtual nodes. - Each virtual node is a different hash of the server ID to distribute load. - The server is hashed multiple times and placed at different positions. """ self.servers.add(server) for i in range(self.num_replicas): # Creating multiple virtual nodes hash_val = self._hash(f"{server}-{i}") # Unique hash for each virtual node self.ring[hash_val] = server # Map hash to the server bisect.insort(self.sorted_keys, hash_val) # Maintain a sorted list for efficient lookup def remove_server(self, server): """ Removes a server and all its virtual nodes from the hash ring. """ if server in self.servers: self.servers.remove(server) for i in range(self.num_replicas): hash_val = self._hash(f"{server}-{i}") # Remove each virtual node's hash self.ring.pop(hash_val, None) # Delete from hash ring self.sorted_keys.remove(hash_val) # Remove from sorted key list def get_server(self, key): """ Finds the closest server for a given key. - Hash the key to get its position on the ring. - Move clockwise to find the nearest server. - If it exceeds the last node, wrap around to the first node. """ if not self.ring: return None # No servers available hash_val = self._hash(key) # Hash the key index = bisect.bisect(self.sorted_keys, hash_val) % len(self.sorted_keys) # Locate nearest server return self.ring[self.sorted_keys[index]] # Return the assigned server # ----------------- Usage Example ------------------- # Step 1: Initialize Consistent Hashing with servers servers = ["S0", "S1", "S2", "S3", "S4", "S5"] ch = ConsistentHashing(servers) # Step 2: Assign requests (keys) to servers print(ch.get_server("UserA")) # Maps UserA to a server print(ch.get_server("UserB")) # Maps UserB to a server # Step 3: Add a new server dynamically ch.add_server("S6") print(ch.get_server("UserA")) # Might be reassigned if affected # Step 4: Remove a server dynamically ch.remove_server("S2") print(ch.get_server("UserB")) # Might be reassigned if affected ================================================ FILE: implementations/python/load_balancing_algorithms/ip_hash.py ================================================ import hashlib class IPHash(): def __init__(self, servers): self.servers = servers def get_next_server(self, client_ip): hash_value = hashlib.md5(client_ip.encode()).hexdigest() index = int(hash_value, 16) % len(self.servers) return self.servers[index] # Example usage servers = ["Server1", "Server2", "Server3"] load_balancer = IPHash(servers) client_ips = ["192.168.0.1", "192.168.0.2", "192.168.0.3", "192.168.0.4"] for ip in client_ips: server = load_balancer.get_next_server(ip) print(f"Client {ip} -> {server}") ================================================ FILE: implementations/python/load_balancing_algorithms/least_connections.py ================================================ import random class LeastConnections: def __init__(self, servers): self.servers = {server: 0 for server in servers} def get_next_server(self): # Find the minimum number of connections min_connections = min(self.servers.values()) # Get all servers with the minimum number of connections least_loaded_servers = [server for server, connections in self.servers.items() if connections == min_connections] # Select a random server from the least loaded servers selected_server = random.choice(least_loaded_servers) self.servers[selected_server] += 1 return selected_server def release_connection(self, server): if self.servers[server] > 0: self.servers[server] -= 1 # Example usage servers = ["Server1", "Server2", "Server3"] load_balancer = LeastConnections(servers) for i in range(6): server = load_balancer.get_next_server() print(f"Request {i + 1} -> {server}") load_balancer.release_connection(server) ================================================ FILE: implementations/python/load_balancing_algorithms/least_response_time.py ================================================ import time import random class LeastResponseTime: def __init__(self, servers): self.servers = servers self.response_times = [0] * len(servers) def get_next_server(self): min_response_time = min(self.response_times) min_index = self.response_times.index(min_response_time) return self.servers[min_index] def update_response_time(self, server, response_time): index = self.servers.index(server) self.response_times[index] = response_time # Simulated server response time function def simulate_response_time(): # Simulating response time with random delay delay = random.uniform(0.1, 1.0) time.sleep(delay) return delay # Example usage servers = ["Server1", "Server2", "Server3"] load_balancer = LeastResponseTime(servers) for i in range(6): server = load_balancer.get_next_server() print(f"Request {i + 1} -> {server}") response_time = simulate_response_time() load_balancer.update_response_time(server, response_time) print(f"Response Time: {response_time:.2f}s") ================================================ FILE: implementations/python/load_balancing_algorithms/round_robin.py.py ================================================ class RoundRobin: def __init__(self, servers): self.servers = servers self.current_index = -1 def get_next_server(self): self.current_index = (self.current_index + 1) % len(self.servers) return self.servers[self.current_index] # Example usage servers = ["Server1", "Server2", "Server3"] load_balancer = RoundRobin(servers) for i in range(6): server = load_balancer.get_next_server() print(f"Request {i + 1} -> {server}") ================================================ FILE: implementations/python/load_balancing_algorithms/weighted_round_robin.py ================================================ class WeightedRoundRobin: def __init__(self, servers, weights): self.servers = servers self.weights = weights self.current_index = -1 self.current_weight = 0 def get_next_server(self): while True: self.current_index = (self.current_index + 1) % len(self.servers) if self.current_index == 0: self.current_weight -= 1 if self.current_weight <= 0: self.current_weight = max(self.weights) if self.weights[self.current_index] >= self.current_weight: return self.servers[self.current_index] # Example usage servers = ["Server1", "Server2", "Server3"] weights = [5, 1, 1] load_balancer = WeightedRoundRobin(servers, weights) for i in range(7): server = load_balancer.get_next_server() print(f"Request {i + 1} -> {server}") ================================================ FILE: implementations/python/rate_limiting/fixed_window_counter.py ================================================ import time class FixedWindowCounter: def __init__(self, window_size, max_requests): self.window_size = window_size # Size of the window in seconds self.max_requests = max_requests # Maximum number of requests per window self.current_window = time.time() // window_size self.request_count = 0 def allow_request(self): current_time = time.time() window = current_time // self.window_size # If we've moved to a new window, reset the counter if window != self.current_window: self.current_window = window self.request_count = 0 # Check if we're still within the limit for this window if self.request_count < self.max_requests: self.request_count += 1 return True return False # Usage example limiter = FixedWindowCounter(window_size=60, max_requests=5) # 5 requests per minute for _ in range(10): print(limiter.allow_request()) # Will print True for the first 5 requests, then False time.sleep(0.1) # Wait a bit between requests time.sleep(60) # Wait for the window to reset print(limiter.allow_request()) # True ================================================ FILE: implementations/python/rate_limiting/leaky_bucket.py ================================================ from collections import deque import time class LeakyBucket: def __init__(self, capacity, leak_rate): self.capacity = capacity # Maximum number of requests in the bucket self.leak_rate = leak_rate # Rate at which requests leak (requests/second) self.bucket = deque() # Queue to hold request timestamps self.last_leak = time.time() # Last time we leaked from the bucket def allow_request(self): now = time.time() # Simulate leaking from the bucket leak_time = now - self.last_leak leaked = int(leak_time * self.leak_rate) if leaked > 0: # Remove the leaked requests from the bucket for _ in range(min(leaked, len(self.bucket))): self.bucket.popleft() self.last_leak = now # Check if there's capacity and add the new request if len(self.bucket) < self.capacity: self.bucket.append(now) return True return False # Usage example limiter = LeakyBucket(capacity=5, leak_rate=1) # 5 requests, leak 1 per second for _ in range(10): print(limiter.allow_request()) # Will print True for the first 5 requests, then False time.sleep(0.1) # Wait a bit between requests time.sleep(1) # Wait for bucket to leak print(limiter.allow_request()) # True ================================================ FILE: implementations/python/rate_limiting/sliding_window_counter.py ================================================ import time class SlidingWindowCounter: def __init__(self, window_size, max_requests): self.window_size = window_size # Size of the sliding window in seconds self.max_requests = max_requests # Maximum number of requests per window self.current_window = time.time() // window_size self.request_count = 0 self.previous_count = 0 def allow_request(self): now = time.time() window = now // self.window_size # If we've moved to a new window, update the counts if window != self.current_window: self.previous_count = self.request_count self.request_count = 0 self.current_window = window # Calculate the weighted request count window_elapsed = (now % self.window_size) / self.window_size threshold = self.previous_count * (1 - window_elapsed) + self.request_count # Check if we're within the limit if threshold < self.max_requests: self.request_count += 1 return True return False # Usage example limiter = SlidingWindowCounter(window_size=60, max_requests=5) # 5 requests per minute for _ in range(10): print(limiter.allow_request()) # Will print True for the first 5 requests, then gradually become False time.sleep(0.1) # Wait a bit between requests time.sleep(30) # Wait for half the window to pass print(limiter.allow_request()) # Might be True or False depending on the exact timing ================================================ FILE: implementations/python/rate_limiting/sliding_window_log.py ================================================ import time from collections import deque class SlidingWindowLog: def __init__(self, window_size, max_requests): self.window_size = window_size # Size of the sliding window in seconds self.max_requests = max_requests # Maximum number of requests per window self.request_log = deque() # Log to keep track of request timestamps def allow_request(self): now = time.time() # Remove timestamps that are outside the current window while self.request_log and now - self.request_log[0] >= self.window_size: self.request_log.popleft() # Check if we're still within the limit if len(self.request_log) < self.max_requests: self.request_log.append(now) return True return False # Usage example limiter = SlidingWindowLog(window_size=60, max_requests=5) # 5 requests per minute for _ in range(10): print(limiter.allow_request()) # Will print True for the first 5 requests, then False time.sleep(0.1) # Wait a bit between requests time.sleep(60) # Wait for the window to slide print(limiter.allow_request()) # True ================================================ FILE: implementations/python/rate_limiting/token_bucket.py ================================================ import time class TokenBucket: def __init__(self, capacity, fill_rate): self.capacity = capacity # Maximum number of tokens the bucket can hold self.fill_rate = fill_rate # Rate at which tokens are added (tokens/second) self.tokens = capacity # Current token count, start with a full bucket self.last_time = time.time() # Last time we checked the token count def allow_request(self, tokens=1): now = time.time() # Calculate how many tokens have been added since the last check time_passed = now - self.last_time self.tokens = min(self.capacity, self.tokens + time_passed * self.fill_rate) self.last_time = now # Check if we have enough tokens for this request if self.tokens >= tokens: self.tokens -= tokens return True return False # Usage example limiter = TokenBucket(capacity=10, fill_rate=1) # 10 tokens, refill 1 token per second for _ in range(15): print(limiter.allow_request()) # Will print True for the first 10 requests, then False time.sleep(0.1) # Wait a bit between requests time.sleep(5) # Wait for bucket to refill print(limiter.allow_request()) # True