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13 Software Design Principles Every Developer Should Know: 1. 𝐊𝐈𝐒𝐒 (𝐊𝐞𝐞𝐩 𝐈𝐭 𝐒𝐢𝐦𝐩𝐥𝐞, 𝐒𝐭𝐮𝐩𝐢𝐝): Design solutions as simply as possible. Avoid unnecessary complexity. 2. 𝐃𝐑𝐘 (𝐃𝐨𝐧'𝐭 𝐑𝐞𝐩𝐞𝐚𝐭 𝐘𝐨𝐮𝐫𝐬𝐞𝐥𝐟): Eliminate duplicate code. Centralize logic to make maintenance easier. 3. 𝐘𝐀𝐆𝐍𝐈 (𝐘𝐨𝐮 𝐀𝐫𝐞𝐧’𝐭 𝐆𝐨𝐧𝐧𝐚 𝐍𝐞𝐞𝐝 𝐈𝐭): Build only what’s needed now, not features you might need “someday.” 4. 𝐄𝐧𝐜𝐚𝐩𝐬𝐮𝐥𝐚𝐭𝐞 𝐖𝐡𝐚𝐭 𝐕𝐚𝐫𝐢𝐞𝐬: Hide changing parts of your code behind stable interfaces. 5. 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 𝐭𝐨 𝐚𝐧 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞, 𝐍𝐨𝐭 𝐚𝐧 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: Depend on abstractions, not concrete classes, for flexibility. 6. 𝐅𝐚𝐯𝐨𝐫 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 𝐎𝐯𝐞𝐫 𝐈𝐧𝐡𝐞𝐫𝐢𝐭𝐚𝐧𝐜𝐞: Combine small, reusable components instead of relying heavily on class hierarchies. 7. 𝐒𝐭𝐫𝐢𝐯𝐞 𝐟𝐨𝐫 𝐋𝐨𝐨𝐬𝐞𝐥𝐲 𝐂𝐨𝐮𝐩𝐥𝐞𝐝 𝐃𝐞𝐬𝐢𝐠𝐧𝐬: Reduce interdependencies so changes in one part don’t break others. 8. 𝐋𝐚𝐰 𝐨𝐟 𝐃𝐞𝐦𝐞𝐭𝐞𝐫 (𝐋𝐨𝐃): Only communicate with direct neighbors. Don’t chain too many calls. 𝐒𝐎𝐋𝐈𝐃 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬: 9. 𝐒𝐑𝐏 (𝐒𝐢𝐧𝐠𝐥𝐞 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞): One class, one job—keep responsibilities clear and focused. 10. 𝐎𝐂𝐏 (𝐎𝐩𝐞𝐧/𝐂𝐥𝐨𝐬𝐞𝐝 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞): Open for extension, closed for modification—add new features without changing core code. 11. 𝐋𝐒𝐏 (𝐋𝐢𝐬𝐤𝐨𝐯 𝐒𝐮𝐛𝐬𝐭𝐢𝐭𝐮𝐭𝐢𝐨𝐧 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞): Subclasses should seamlessly replace their parents without issues. 12. 𝐈𝐒𝐏 (𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞 𝐒𝐞𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞): Create small, specific interfaces instead of large, general ones. 13. 𝐃𝐈𝐏 (𝐃𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐲 𝐈𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞): Depend on abstractions, not details—high-level modules shouldn’t depend on low-level modules. ♻️ Repost to help others in your network. 👉 Get a FREE System Design Interview Handbook by subscribing to my free newsletter: blog.algomaster.io

DNS Record Types You Should Know! Here are the 8 most commonly used DNS Record Types. 1 - A (Address) Record Maps a domain name to an IPv4 address. It is one of the most essential records for translating human-readable domain names into IP addresses. 2 - CNAME (Canonical Name) Record Used to alias one domain name to another. Often used for subdomains, pointing them to the main domain while keeping the actual domain name hidden. 3 - AAAA Record Similar to an A record but maps a domain name to an IPv6 address. They are used for websites and services that support the IPv6 protocol. 4 - PTR Record Provides reverse DNS lookup, mapping an IP address back to a domain name. It is commonly used in verifying the authenticity of a server. 5 - MX Record Directs email traffic to the correct mail server. 6 - NS (Name Server) Record Specifies the authoritative DNS servers for the domain. These records help direct queries to the correct DNS servers for further lookups. 7 - SRV (Service) Record SRV record specifies a host and port for specific services such as VoIP. They are used in conjunction with A records. 8 - TXT (Text) Record Allows the administrator to add human-readable text to the DNS records. It is used to include verification records, like SPF, for email security. Over to you: Which other DNS Record Type have you seen? -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/bbg-social #systemdesign #coding #interviewtips .

If you're a coder, these common warning signs in your codebase will probably look familiar. Give it a read. 👇 Before we start, The term 'smell' => is a metaphor, comparing problematic code to a foul odor. Just as a bad smell indicates something rotten, a code smell suggests potential underlying issues with the design or implementation. What constitutes a code smell can be subjective and vary depending on - the programming language - development methodology - personal preferences of developers. 📌 𝐖𝐡𝐲 𝐂𝐚𝐫𝐞 𝐀𝐛𝐨𝐮𝐭 𝐂𝐨𝐝𝐞 𝐒𝐦𝐞𝐥𝐥𝐬? Think of code smells as little warning signs that your code could use a tune-up. [1.] Maintainability ◾ Code smells make your code a mess, slowing you down and making bugs more likely. [2.] Scalability ◾ Smelly code is like a ticking time bomb, making it hard to grow your application when you need to. [3.] Technical Debt ◾ Ignoring code smells is like putting off maintenance on your car – it'll cost you more in the long run. 📌 𝐂𝐨𝐝𝐞 𝐒𝐦𝐞𝐥𝐥𝐬 [1.] Bloated Code ◾ Long Methods/Classes Break down into smaller, more focused units. ◾ Excessive Comments Focus on self-documenting code, not excessive explanations. [2.] Object-Oriented Abuses ◾ Switch Statements Consider replacing with polymorphism when dealing with type-specific behavior. ◾ Temporary Fields Avoid fields that are only used in specific scenarios. Refactor into local variables or parameters. [3.] Rigidity ◾ Divergent Change A class changed in different ways for unrelated reasons. Split it up. ◾ Shotgun Surgery A change that requires modifications to multiple classes. Aim for better modularity. [4.] Unnecessary Complexity ◾ Duplicate Code Extract common functionality into reusable methods or functions. ◾ Dead Code Remove code that is no longer used. ◾ Speculative Generality Avoid writing code for features that might never be needed. [5.] Tight Coupling ◾ Feature Envy A method that accesses another object's data more than its own. Consider moving it. ◾ Inappropriate Intimacy Classes that rely too heavily on each other's internals. Enforce stricter encapsulation. 📌 𝐋𝐞𝐭'𝐬 𝐭𝐚𝐥𝐤 𝐚𝐛𝐨𝐮𝐭 𝐬𝐨𝐦𝐞 𝐚𝐝𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐨𝐝𝐞 𝐒𝐦𝐞𝐥𝐥𝐬. ◾ Magic Numbers Use named constants instead of unexplained numbers. ◾ Deep Nesting Simplify complex nested structures for better readability. ◾ Long Parameter Lists Consider using parameter objects or refactoring the method signature. 📌 𝐇𝐨𝐰 𝐭𝐨 𝐃𝐞𝐚𝐥 𝐰𝐢𝐭𝐡 𝐂𝐨𝐝𝐞 𝐒𝐦𝐞𝐥𝐥𝐬? ◾ Refactoring The primary solution is to improve the design without changing the external behavior. ◾ Incremental Changes Don't try to fix everything at once. Start with small, focused refactorings. ◾ Testing Ensure you have adequate tests in place before refactoring to catch any regressions. ______ 👍 Follow - Mayank Ahuja 🗒️ Newsletter - https://lnkd.in/dJByxEYY Recommended Read - Refactoring book by <Martin Fowler> #code #softwaredevelopment

𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 is a solid choice for building scalable and reliable apps. PostgreSQL was born in the mid-80s at UC Berkeley. Professor Stonebraker, who'd previously created the Ingres database, wanted something better => a database that could handle more than just simple numbers and text. The team behind POSTGRES (as it was called then) built the system from scratch using the C language. In the 90s, the project went open-source and was renamed PostgreSQL. Today, PostgreSQL is a top choice for all sorts of applications. PostgreSQL => ◾ runs as a multi-process system ◾ strictly follows the ACID principles ◾ optimized for speed and efficiency 📌 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋'𝐬 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐥𝐬 [1.] Client Connection ◾ A user or application connects to the PostgreSQL server, establishing a communication channel. [2.] Postmaster ◾ The postmaster process - the main PostgreSQL process - receives the connection request and authenticates the user based on credentials and permissions. [3.] Backend Process ◾ The postmaster then forks a new backend process dedicated to handling this specific client's session. => 1 backend process per client. [4.] Query Parsing & Planning ◾ The backend process receives SQL queries from the client. ◾ It first parses the query to check syntax and then generates an execution plan. ◾ The query planner, using statistics and cost estimates, determines the most efficient way to execute the query. [5.] Execution & Data Access The backend process executes the plan. ◾ Reading from Shared Buffers If the required data is already in the shared memory cache, it's quickly retrieved. ◾ Fetching from Disk If not in the cache, the backend process reads the data blocks from disk into the shared buffers. ◾ Index Usage If the query involves filtering or sorting, relevant indexes are used to speed up data retrieval. ◾ MVCC The backend process reads the appropriate row versions based on Multi-Version Concurrency Control (MVCC), ensuring transaction isolation. [6.] Data Manipulation If the query involves updates, inserts or deletes - ◾ New Row Versions Created Instead of directly modifying data, PostgreSQL creates new row versions with the changes, ensuring other transactions see a consistent view. ◾ Write-Ahead Logging (WAL) Changes are first logged to the WAL for durability before being written to the main data files. [7.] Result Transmission ◾ The backend process sends the query results back to the client. [8.] Transaction Management ◾ If the query is part of a transaction, the backend process manages transaction state and isolation. ◾ Upon COMMIT, changes are made permanent & upon ROLLBACK, they are discarded. [9.] Vacuuming ◾ Vacuum process => periodically cleans up outdated row versions and unused space. [10.] Checkpointing ◾ Data is periodically saved from memory to disk for faster recovery. 👍 Follow - Mayank Ahuja 🗒️ Newsletter - https://lnkd.in/dJByxEYY #softwaredevelopment

"How did your startup grow so fast?" I've been getting that question a lot, and I believe it comes down to a single word: momentum. We tried to codify momentum and came up with a new concept called... >> The Heartbeat Framework Getting momentum is extremely hard, and losing it is very easy. As makers, we not only have to ship software. We have to ship and talk about it. We have to push new content constantly if we want to create a feeling of “always shipping”. That's why we think about our efforts as a heartbeat. It's a rhythm that we follow to expose internal momentum to the outside world. Just like a heartbeat, there are big peaks followed by small peaks. It's important to have a balance between them, but the beat must be always present for people to know we are "alive." In contrast, if there's a flat line, it means we are "dead" and don't have momentum anymore. The framework is divided into: A) Launch Weeks B) Mini-Launches C) Change log D) Blog E) Customer story F) Social post A) Launch Weeks 🩺 Frequency: 2-3x/year Launch Weeks are the big peaks. It's when we launch 5 new features in 5 days. As the biggest "sign of life", launch weeks are often the most impactful way we share our momentum. But we are also careful not to do them too frequently, since they require a heavy "cost" from the team. 👉 Real-life example: https://lnkd.in/dFVU7_xv B) Mini-Launch 🩺 Frequency: 4-5x/year Mini-Launches are small wins that are worth sharing in a more detailed way, but don't require the same level of planning and coordination as Launch Weeks. 👉 Real-life example: https://lnkd.in/ge4mDz6q C) Change log 🩺 Frequency: 1-2x/month As we update the product, we post regular change logs to highlight how the product is evolving. A fun exercise is to create an internal streak to motivate the team, so you don't miss a single week/month without shipping. 👉 Real-life example: https://lnkd.in/d3pPvTDw D) Blog 🩺 Frequency: 1-2x/month There's nothing more telling than going to a company blog and seeing the last post from 1 year ago. 👉 Real-life example: https://lnkd.in/dr7TKiiu E) Customer stories 🩺 Frequency: 1-2x/month We exist to serve real developers, so it's important for us to talk to our customers and share their experiences. These are not only crucial for attracting new customers, but also to keep the team motivated with real stories of how our product is helping people. 👉 Real-life example: https://lnkd.in/gUmjHi84 F) Social posts 🩺 Frequency: 1x/day We try to be very active in only a few places. We found that X and LinkedIn are the most effective places for us to be at this moment. 👉 Real-life example: this post ;) Startups are all about momentum, so make sure you don't lose it once you find it. I hope this was helpful.

🔥 Self-explanatory Who's been in the little car? 🙋♂️ raise your hand! #sre #devops #technology #production #fun

Concepts every developer should know: concurrency is 𝗡𝗢𝗧 parallelism. Parallelism and concurrency are two terms that often create confusion. One is about managing multiple tasks at once, intermixing them to optimize resource usage. The other involves executing multiple tasks simultaneously. As Rob Pike (one of the creators of Golang) succinctly put it: “𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 is about 𝐝𝐞𝐚𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 lots of things at once. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺 is about 𝐝𝐨𝐢𝐧𝐠 lots of things at once." 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆? In modern systems, concurrency is driven by design principles that ensure tasks or processes run efficiently, whether the hardware has one or multiple processors. Even with a single CPU, concurrency patterns allow tasks to share processor time effectively. This creates an illusion of parallel execution. These patterns also enable parts of a program to be executed out of sequence or in partial order, while still preserving the intended behavior of the program. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺? While concurrency is about dealing with many tasks at once (task management). Parallelism is about doing many tasks at once (task execution). Parallelism requires hardware support, such as multi-core or multi-processor systems, to allow different tasks to run at the same time. This distinction between concurrency (task management) and parallelism (task execution) significantly impacts application performance and efficiency. Parallelism is particularly beneficial for compute-intensive applications, where tasks can be distributed across multiple processors to be executed simultaneously, leading to faster and more efficient processing. 𝗔𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗼𝘂𝘀 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 is used to 𝗮𝗰𝗵𝗶𝗲𝘃𝗲 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗶𝗻 𝘀𝗶𝗻𝗴𝗹𝗲-𝘁𝗵𝗿𝗲𝗮𝗱𝗲𝗱 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀. This approach enables a program to initiate tasks without waiting for previous ones to finish, managing multiple tasks in a non-blocking manner. A great 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝘀 𝗡𝗼𝗱𝗲.𝗷𝘀, which handles concurrency in a single-threaded model using callbacks and event loops. Meanwhile, 𝗺𝘂𝗹𝘁𝗶-𝘁𝗵𝗿𝗲𝗮𝗱𝗲𝗱 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 (𝗲𝗴; 𝗖#) 𝗳𝗮𝗰𝗶𝗹𝗶𝘁𝗮𝘁𝗲 𝗯𝗼𝘁𝗵 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺. They facilitate both concurrent task execution and true parallel execution across multiple processors or cores simultaneously. Understanding concurrency and parallelism is an important distinction for building high-performing and efficient software solutions. ~~ Thanks to our partner Kestra who keeps our content free to the community. 𝗔𝗿𝗲 𝗿𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗺𝗮𝗻𝘂𝗮𝗹 𝘁𝗮𝘀𝗸𝘀 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝘆𝗼𝘂 𝗱𝗼𝘄𝗻? Kestra’s intuitive UI makes it effortless to automate and manage your workflows visually. Check it out: https://lnkd.in/gBE2cFJQ

Understanding AI agents will be one of the most valuable skills in 2025. Here’s what you need to know: AI agents are broader systems that leverage LLMs. They’re beginning to reshape the way we build applications, automate workflows, enhance data processing, and more. 𝗪𝗵𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲? They're set for a lot of growth in 2025. This means understanding and working with them will be one of the most valuable skills in 2025. 𝗪𝗵𝗮𝘁 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗮𝗿𝗲 𝘁𝗵𝗲𝘆? This article explains it well: https://lnkd.in/g6PDrzCQ In simple terms, AI agents are computational systems that can perceive their environment, reason about tasks, and take actions autonomously to achieve specific objectives. Think of them as self-directed problem solvers that can collaborate, adapt, and execute tasks independently. 𝗖𝗼𝗿𝗲 𝗰𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿𝗶𝘀𝘁𝗶𝗰𝘀 𝗼𝗳 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀: 🔹 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 — can make decisions and operate without human intervention 🔹 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 & 𝗿𝗲𝗮𝗰𝘁𝗶𝘃𝗲 — adapt to environmental changes and feedback; anticipate needs and takes initiative to fulfil objectives 🔹 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 — engages with humans, other agents, or systems to collaborate and refine outcomes 🔹 𝗔𝗰𝘁𝗶𝗼𝗻-𝗼𝗿𝗶𝗲𝗻𝘁𝗲𝗱 — execute tasks using tools, APIs, and systems to achieve goals Understanding the evolving space of AI agents can be challenging. However, I came across a fantastic article by MongoDB that makes them simple to understand. Learn more here: https://lnkd.in/gFnGAmSS