👨‍💻VENKATA SAI KIRAN KALASANDRAM’s Post

You’re not prepared for a system design interview if you still don’t have a grip on these fundamental topics. No matter the scale, these topics come into play each time. Prepare these well. - Checksum ↳https://lnkd.in/gJnJpnRF - CAP Theorem ↳https://lnkd.in/gjtcJxeN - SQL vs NoSQL ↳https://lnkd.in/gwCe58TU - Circuit Breaker ↳https://lnkd.in/gvSrR_EA - Load Balancing ↳https://lnkd.in/gefSiXEJ - Fault Tolerance ↳https://lnkd.in/guiDtdXT - API Design ↳https://lnkd.in/g6tHgXr7 - WebSockets ↳https://lnkd.in/gzdAdBNC - API Gateway ↳https://lnkd.in/gWYGxs3S - Failover ↳https://lnkd.in/g-eH-Pvf - Caching ↳https://lnkd.in/gAp-9udf - Scalability ↳https://lnkd.in/gHUfp8x9 - HeartBeat ↳https://lnkd.in/gzfQt74b - Proxy Server ↳https://lnkd.in/gs2ZF8_d - Bloom Filters ↳https://lnkd.in/gnekDkQv - REST vs RPC ↳https://lnkd.in/gVGeh3VV - Idempotency ↳https://lnkd.in/gqGnkZHg - Database Index ↳https://lnkd.in/gE_q5m_g - Data Replication ↳https://lnkd.in/gPBmH5h8 - Database Scaling ↳https://lnkd.in/gX7AKQHY - Data Redundancy ↳https://lnkd.in/gccsuSvr - Service Discovery ↳https://lnkd.in/gEjBT8ZA - Disaster Recovery ↳https://lnkd.in/gEYQ_74X - ACID Transactions ↳https://lnkd.in/g-sjsMwX - Database Sharding ↳https://lnkd.in/gnuxe5pu - Distributed Locking ↳https://lnkd.in/gJrrrqsa - Consistent Hashing ↳https://lnkd.in/gmrHsEzq - Distributed Caching ↳https://lnkd.in/gUH2yV-X - Consistency Patterns ↳https://lnkd.in/gey7tqYh - Latency vs Throughput ↳https://lnkd.in/gDAx6QQd - Serverless Architecture ↳https://lnkd.in/gtS52Sza

Topics which are must to prepare for system design. thanks Shalini Goyal for sharing such a amazing content

You’re not prepared for a system design interview if you still don’t have a grip on these fundamental topics. No matter the scale, these topics come into play each time. Prepare these well. - Checksum ↳https://lnkd.in/gJnJpnRF - CAP Theorem ↳https://lnkd.in/gjtcJxeN - SQL vs NoSQL ↳https://lnkd.in/gwCe58TU - Circuit Breaker ↳https://lnkd.in/gvSrR_EA - Load Balancing ↳https://lnkd.in/gefSiXEJ - Fault Tolerance ↳https://lnkd.in/guiDtdXT - API Design ↳https://lnkd.in/g6tHgXr7 - WebSockets ↳https://lnkd.in/gzdAdBNC - API Gateway ↳https://lnkd.in/gWYGxs3S - Failover ↳https://lnkd.in/g-eH-Pvf - Caching ↳https://lnkd.in/gAp-9udf - Scalability ↳https://lnkd.in/gHUfp8x9 - HeartBeat ↳https://lnkd.in/gzfQt74b - Proxy Server ↳https://lnkd.in/gs2ZF8_d - Bloom Filters ↳https://lnkd.in/gnekDkQv - REST vs RPC ↳https://lnkd.in/gVGeh3VV - Idempotency ↳https://lnkd.in/gqGnkZHg - Database Index ↳https://lnkd.in/gE_q5m_g - Data Replication ↳https://lnkd.in/gPBmH5h8 - Database Scaling ↳https://lnkd.in/gX7AKQHY - Data Redundancy ↳https://lnkd.in/gccsuSvr - Service Discovery ↳https://lnkd.in/gEjBT8ZA - Disaster Recovery ↳https://lnkd.in/gEYQ_74X - ACID Transactions ↳https://lnkd.in/g-sjsMwX - Database Sharding ↳https://lnkd.in/gnuxe5pu - Distributed Locking ↳https://lnkd.in/gJrrrqsa - Consistent Hashing ↳https://lnkd.in/gmrHsEzq - Distributed Caching ↳https://lnkd.in/gUH2yV-X - Consistency Patterns ↳https://lnkd.in/gey7tqYh - Latency vs Throughput ↳https://lnkd.in/gDAx6QQd - Serverless Architecture ↳https://lnkd.in/gtS52Sza

Ways to Improve API Performance [1.] Caching Store frequently used data for lightning-fast retrieval. ◾ Server-Side Caching Use tools like => Redis, Memcached etc. ◾ Client-Side Caching Leverage HTTP headers => Cache-Control, ETag ◾ Content Delivery Networks (CDNs) [2.] Database Optimization ◾ Indexing Create indexes on frequently queried columns to speed up data retrieval. ◾ Query Tuning Analyze slow queries using tools like EXPLAIN and rewrite them for better performance. ◾ Batching ◾ ORM Optimization (be mindful of => N+1 query problem => application executes an initial query to retrieve a set of entities, then performs additional queries for each entity to fetch its related data) [3.] Data Serialization Choose the right format to balance speed and size. ◾ JSON Optimization ◾ Protocol Buffers (Protobuf) ◾ MessagePack [4.] Network Optimization ◾ Content Compression Enable Gzip or Brotli compression etc. ◾ Reduce Network Hops ◾ Upgrade to HTTP/2 multiplexing, header compression etc. [5.] Server-Side Optimizations ◾ Rate Limiting Avoid abuse and ensure fair usage. ◾ Asynchronous Processing Use background tasks or message queues. ◾ Load Balancing Distribute traffic across multiple servers. ◾ Connection Pooling Reuse database connections to reduce connection overhead. [6.] API Design ◾ Pagination Break down large datasets into smaller chunks for faster responses and reduced server load. ◾ Filtering and Sorting [7.] Architectural Considerations ◾ API Gateway ◾ Caching Authorization Cache authorization decisions to avoid redundant checks. => Never miss : - Monitoring - Profiling - Load Testing Optimizing API performance is an ongoing process. 👍 👉 Credit: Amigoscode 😍 Learn Web Development with JavaScript Mastery and W3Schools.com🔥 💯 Follow Prince Meghani For Development, Programming Tips & Tricks, and Job Opportunities. #systemdesign #interview #coding #api #apidesign

❇️ 𝗟𝗢𝗚 𝗣𝗔𝗥𝗦𝗜𝗡𝗚 𝗖𝗛𝗘𝗔𝗧 𝗦𝗛𝗘𝗘𝗧 ❇️ Log parsing is the process of analyzing and extracting useful information from log files. Log files are records of events and activities generated by software applications, servers, and other systems. These files often contain a wealth of data, including timestamps, error messages, transaction details, user activities, and system performance metrics. 𝗟𝗼𝗴 𝗽𝗮𝗿𝘀𝗶𝗻𝗴 𝗶𝗻𝘃𝗼𝗹𝘃𝗲𝘀 𝘀𝗲𝘃𝗲𝗿𝗮𝗹 𝘀𝘁𝗲𝗽𝘀: 1. Reading Log Files 2. Filtering Data 3. Extracting Information 4. Formatting Data 5. Analyzing Data 6. Automating Processes Log parsing is crucial for troubleshooting, monitoring system performance, ensuring security, and gaining insights into application behavior. Tools commonly used for log parsing include GREP, AWK, SED, Logstash, Splunk, and ELK Stack (Elasticsearch, Logstash, Kibana). 𝗦𝗼𝗺𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗼𝗻𝗹𝘆 𝘂𝘀𝗲𝗱 𝗹𝗼𝗴-𝗽𝗮𝗿𝘀𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝗮𝗿𝗲 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗲𝗱 𝗯𝗲𝗹𝗼𝘄; 👇 1. 𝗚𝗥𝗘𝗣    GREP searches any given input files, selecting lines that match one or more patterns.     2. 𝗖𝗨𝗧    CUT cuts out selected portions of each line from each file and writes them to the standard output.     3. 𝗦𝗘𝗗    SED reads the specified files, modifying the input as specified by a list of commands.     4. 𝗔𝗪𝗞    AWK scans each input file for lines that match any of a set of patterns.     5. 𝗦𝗢𝗥𝗧    SORT sorts text and binary files by lines.     6. 𝗨𝗡𝗜𝗤    UNIQ reads the specified input file comparing adjacent lines and writes a copy of each unique input line to the output file. These commands are often used in combination to quickly find useful information from the log files. For example, the below commands list the timestamps (column 2) when there is an exception happening for xxService. grep “xxService” service.log | grep “Exception” | cut -d” “ -f 𝗧𝗵𝗲 𝗱𝗶𝗮𝗴𝗿𝗮𝗺 𝗯𝗲𝗹𝗼𝘄 𝗹𝗶𝘀𝘁𝘀 𝘁𝗵𝗲 𝘁𝗼𝗽 𝟲 𝗹𝗼𝗴-𝗽𝗮𝗿𝘀𝗶𝗻𝗴 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀. 👇 𝗧𝗵𝗮𝗻𝗸 𝘆𝗼𝘂 𝗳𝗼𝗿 𝗿𝗲𝗮𝗱𝗶𝗻𝗴 📖 Want to know more? 𝐅𝐨𝐥𝐥𝐨𝐰 𝐦𝐞 𝐨𝐫 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 🤝 Please don't forget to 𝐋𝐈𝐊𝐄 ❤️, 𝐂𝐎𝐌𝐌𝐄𝐍𝐓 💭 and 𝐑𝐞𝐩𝐨𝐬𝐭 ♻️ #LinuxCommands #ShellScripting #GREP #CUT #SED #AWK #SORT #UNIQ #LogAnalysis #TechTips #CommandLine #SysAdmin #DevOps #CodingTips #TechKnowledge #ITAutomation #Programming #LinuxTips #SysAdminTools

When designing a High-Level Design (HLD) during a system design interview, be sure to highlight and clearly explain the following components: 📗 API Gateway: A centralized entry point for clients to access multiple microservices, handling request routing, security, and analytics. 📗Microservices: Independent services that manage specific business functionalities, allowing for modular development and deployment. 📗Content Delivery Network (CDN): A distributed network of servers that delivers web content to users based on geographic location, improving load times and reducing latency. 📗Caching Layer: Stores frequently accessed data in memory to reduce database load and improve response times. 📗Database: The primary storage component that manages application data, which can be relational (e.g., MySQL) or NoSQL (e.g., MongoDB). 📗Queue: An asynchronous communication system (e.g., RabbitMQ or Kafka) that decouples services and improves scalability. 📗External Services: Third-party APIs or systems your application interacts with, such as payment gateways or authentication services. 📗Cron Job: A scheduled task that automates the execution of scripts or commands at specified intervals. It can be used for regular maintenance, data processing, or sending notifications. #programming #highleveldesign #systemdesign

A common question in System design interviews is, What is CAP theorem? Let's try to understand this in easy language. CAP (Consistency, Availability, Partition Tolerance) states that at a point in time in a distributed system only two of these three properties can be achieved Note*: In a distributed system our data is present at different nodes as a result of data replication. Let's try to understand all three properties of the CAP theorem one by one: Consistency: It means regardless of all data nodes present at different locations, all clients should see the same data at once. Any change in data should immediately get replicated to other nodes. Availability: It means that for any reason if one or more nodes of data get down, the client should still receive data through some other nodes, in short system shouldn't get down due to the absence of data. Partition Tolerance: In a distributed system, the partition is a break in communications - a disconnection or delay between nodes due to connectivity issues. As I mentioned before, we can have only two properties at a time, AP (Availability & PT): An AP database provides availability and partition tolerance but not consistency in the event of a failure. All nodes remain available when a partition occurs, but some might return an older version of the data. CouchDB and Cassandra. CP (Consistency and PT): A CP database offers consistency and partition tolerance but sacrifices availability. The practical result is that when a partition occurs, the system must make the inconsistent node unavailable until it can resolve the partition. MongoDB and Redis are examples of CP databases. CA (Consistency and Availability): CA databases enable consistency and availability across all nodes. It's an assumption that partition will not happen between any two nodes. But in real life partition happens due to connectivity So it's not possible for any NoSQL databases to have CA. The CAP theorem highlights the trade-offs developers must consider when designing distributed systems, helping them decide which properties are most critical for their use case. #systemdesign #microservices #java #fullstack

Caching 101: The Must-Know Caching Strategies Fetching data is 𝘀𝗹𝗼𝘄. Caching speeds things up by storing frequently accessed data for quick reads. But how do you populate and update the cache? That's where strategies come in. Read Strategies: 𝗖𝗮𝗰𝗵𝗲 𝗔𝘀𝗶𝗱𝗲 (Lazy Loading) - How it works: Tries cache first, then fetches from DB on cache miss - Usage: When cache misses are rare or the latency of a cache miss + DB read is acceptable 𝗥𝗲𝗮𝗱 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 - How it works: Cache handles DB reads, transparently fetching missing data on cache miss - Usage: Abstracts DB logic from app code. Keeps cache consistently populated by handling misses automatically Write Strategies: 𝗪𝗿𝗶𝘁𝗲 𝗔𝗿𝗼𝘂𝗻𝗱 - How it works: Writes bypass the cache and go directly to the DB - Usage: When written data won't immediately be read back from cache 𝗪𝗿𝗶𝘁𝗲 𝗕𝗮𝗰𝗸 (Delayed Write) - How it works: Writes to cache first, async write to DB later - Usage: In write-heavy environments where slight data loss is tolerable 𝗪𝗿𝗶𝘁𝗲 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 - How it works: Immediate write to both cache and DB - Usage: When data consistency is critical Real-Life Usage: 𝗖𝗮𝗰𝗵𝗲 𝗔𝘀𝗶𝗱𝗲 + 𝗪𝗿𝗶𝘁𝗲 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 This ensures consistent cache/DB sync while allowing fine-grained cache population control during reads. Immediate database writes might strain the DB. 𝗥𝗲𝗮𝗱 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 + 𝗪𝗿𝗶𝘁𝗲 𝗕𝗮𝗰𝗸 This abstracts the DB and handles bursting write traffic well by delaying sync. However, it risks larger data loss if the cache goes down before syncing the buffered writes to the database. credit : https://x.com/sahnlam #caching #javascript

🤔 Distributed and high-scale system design has become simple nowadays. I took a mock interview recently and the answers blew my mind. 🚀 First 10 minutes of introduction and problem statement discussion 🔹 Candidate seems to be asking questions deliberately as part of some template. 🧑🦰 Then the candidate started with: 🔹A few FRs 🔹A few NFRs 🔹Scale in millions to start with 🔹Few minutes spent on scale and impact etc. 🔸This took around 8-10 minutes and again was sort of template-based. 🔸Resiliency was never touched upon. 🎨 Design 🔹CDN, LB was the default starting point. 🔹Application servers 🔹Then NoSQL DB 🔹Auth Service with SQL DB 🔹Cache in front of DB 🔹A lot about use cases (trying to skip DB modeling, DB selection, Cache strategy, etc.) 🔸This was the design and it took around 20 minutes. 🔸The candidate after asking about DB, chose Mongodb for ease of use and its scaling superiority. Whereas, the solution demanded columnar DB. 🔸No use of queue (solution demanded a shock absorber as it was a write-heavy system to feedback data to an analytics system) 🔸CDN was not required as reads will be internal to the enterprise and use little static assets. The response was analytics reports so CDN can't do anything there. 🔸etc. 🔴 Overall, around 45+ minutes were gone. I see people designing at 30 K feet level and to avoid going in-depth, many things were touched upon (trying to showcase breadth) and most of them were kept superficial. 🙏 Please don't do that. An interview is not a LinkedIn post. 🔵 This leads to loss of opportunity and frustration. My 2 cents: 🔹You can't cover everything in 45-60 minutes. 🔹You need to negotiate important features beforehand and be crisp. 🔹Start designing iteratively (but tell the interviewer about it) 🔹Do not jump right away at 100 million to 10 billion. Ask about the expectations. 🔹Do not build straightaway for scale (even if you have to handle it). 🔹Design a simple system component or actor-based interactions diagram. 🔹Do not just read a few articles or watch videos but instead design systems with friends or tech groups. 🏃♀️I hope I got you running on track. If yes, share your thoughts and repost to help others ♥️ #SystemDesign #DistributedSystems #HighScale

𝙄 𝙝𝙖𝙫𝙚 𝙡𝙚𝙨𝙨 𝙩𝙝𝙖𝙣 2 𝙮𝙚𝙖𝙧 𝙤𝙛 𝙚𝙭𝙥𝙚𝙧𝙞𝙚𝙣𝙘𝙚, 𝙩𝙝𝙚𝙧𝙚 𝙞𝙨 𝙣𝙤 𝙎𝙮𝙨𝙩𝙚𝙢 𝘿𝙚𝙨𝙞𝙜𝙣 𝙧𝙤𝙪𝙣𝙙 𝙛𝙤𝙧 𝙢𝙚? Earlier it used to be true, but nowadays its not, many with 1year of exp too this round is happening. So if your Next Round: System Design 1. For searching some text in millions of record: Use inverted index 2. For ACID property: use RDBMS. 3. For Unstructured data: use NoSQL 4. Database Scaling: Horizontal (Preferred for NoSQL) or Vertical Sharding(Preferred for RDBMS) 5. Read Heavy System: use Read through Cache 6. Low Latency Requirement: CDN + Load Balancer + Cache 7. Write-Heavy System: Use ASYNC Processing (kafka message queues) 8. Handle Complex Data like Videos, Images, Files etc: Go for Object storage (ex: Amazon S3) 9. For High Availability, Performance, & Throughput: Use Load Balancer 10. Global Data Delivery: Use CDN. 11. For Fast DB Queries: Database Indexing 12. Load Management on a Component: Use Rate Limiter. 13. Avoid Single Point of Failure: Set up Disaster Recovery Data Centre 14. Fault-Tolerance: Write though cache + Master Slave Architecture. 15. Peer to Peer communication: Use WebSockets. 16. For VideoCall : Use WebRTC 17. For Data Integrity between 2 system: Use Checksum Algorithm. 18. Efficient Managing of the Servers: Use Consistent Hashing. 19. High Availability and Consistency Trade-Off: Eventual Consistency. 20. Cache Eviction Policy: LRU cache (generally preferred, but there are more) For full details about these concept, sharing the below resources: HLD Playlist: https://lnkd.in/d8eDwYVA LLD Playlist: https://lnkd.in/dJkgzKxf Java Playlist: https://lnkd.in/dUNA6vsU SpringBoot Playlist: https://lnkd.in/gz2A5ih2 Big thanks to Shrayansh Jain for sharing this amazing content. follow for more Shashank Garg #softwareengineer #systemdesign Shashank Garg