Mayank Ahuja

Scalable Application Design: Building for Growth As a software architect, designing for scalability is crucial in today's rapidly evolving tech landscape. Here's an in-depth look at key principles for creating scalable applications: 1. Modular Architecture:   - Implement microservices   - Use APIs for inter-service communication   - Ensure each module can be scaled independently   - Example: Split monolithic e-commerce app into separate services for inventory, orders, and user management 2. Stateless Design:   - Store session data in distributed caches like Redis   - Use JWT or similar tokens for authentication   - Offload state to the client when possible   - Benefit: Any server can handle any request, enabling easier horizontal scaling 3. Caching Strategies:   - Implement multi-level caching (application, distributed, CDN)   - Use read-through and write-through caching patterns   - Consider eventual consistency for non-critical data   Tools: Redis, Memcached, Varnish 4. Asynchronous Processing:   - Utilize message queues for background jobs   - Implement event-driven architectures   - Use webhooks for third-party integrations   Technologies: RabbitMQ, Apache Kafka, AWS SQS 5. Database Optimization:   - Choose appropriate DB type (relational vs NoSQL)   - Implement database sharding for horizontal scaling   - Use read replicas to offload queries   - Optimize indexes and query patterns   - Consider polyglot persistence for diverse data needs 6. Load Balancing:   - Implement layer 4 (transport) and layer 7 (application) load balancing   - Use consistent hashing for distributed caching   - Consider global load balancing for multi-region deployments   Tools: NGINX, HAProxy, cloud-native solutions (ELB, GCP Load Balancing) 7. Monitoring and Auto-scaling:   - Implement comprehensive logging and tracing   - Use time-series databases for metrics (e.g., Prometheus)   - Set up alerts for critical thresholds   - Implement predictive auto-scaling based on historical patterns   Tools: ELK stack, Grafana, Datadog 8. Content Delivery and Edge Computing:   - Utilize CDNs for static content delivery   - Implement edge computing for low-latency requirements   - Consider multi-region deployments for global user bases 9. Resilience and Fault Tolerance:   - Implement circuit breakers to prevent cascading failures   - Use retry mechanisms with exponential backoff   - Design for graceful degradation of services   Tools: Hystrix, Resilience4j 10. Performance Testing and Optimization:   - Conduct regular load testing and stress testing   - Profile your application to identify bottlenecks   - Optimize critical paths in your code   Tools: JMeter, Gatling, Locust Remember, scalability is an ongoing process. Continuously monitor, test, and refine your architecture that meets the growing demands of your users and business. Do you face any scalability challenges in your projects? #SoftwareArchitecture #ScalableDesign #TechLeadership #SystemDesign

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if you are working on unix based machine don't rename folders from camel or snake case to pascal, GitHub does not see any difference between "lesson" and "Lesson", such a nightmare and not convenient choice for software developing platform. You can resolve such issue by changing name to something completely different and change back again 😐 #github #opensource

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The Lifecycle of a Pod in Kubernetes: From Creation to Termination #WorkSmartWithK8s #kubernetes #pod #lifecycle #tutorial https://lnkd.in/eCffFmhG

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Should .terraform.lock.hcl file be added to .gitignore or committed to Git repo? https://lnkd.in/eEK9smAY #git #gitignore #HashiCorp #Terraform

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Null values causing chaos? Verbose conditionals? Bloated initialization code? 🧹Clean up your code with the Null-Coalescing Assignment Operator!🧹 C# 8 introduced the Null-Coalescing Assignment Operator: `??=` 👍Advantages 1. Concise Code: Reduces the need for verbose conditional checks. 2. Readability: Makes the intent clear and the code easier to read. 3. Efficiency: Streamlines initialization or defaulting of variables. 👎Disadvantages 1. Limited Use Case: Only applicable when checking for null values. 2. Less Explicit: The code is harder to understand when the reader does not know the operator. ✅When to Use - Initializing Variables: Ensures variables have default values. - Simplifying Null Checks: Replaces verbose null checks with a single line. ❌When Not to Use - Complex Conditions: When more complex logic is needed beyond null checks. - Readability Concerns: In cases where explicit null checks improve understanding of the code. 💡Takeaways The null-coalescing assignment operator (`??=`)—introduced in C# 8—simplifies null checks, making code more concise and readable. Use it to ensure variables have default values while avoiding verbose conditional statements. 💬 How has the null-coalescing assignment operator improved your code (or not)? Comment below! #ASPNETCore #SoftwareArchitecture #dotnet #csharp #DesignAndArchitecture #CodeDesign

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8 Essential Load Balancing Algorithms Load balancing is the unsung hero of high-performance web applications. 1. Round Robin Distributes requests sequentially across a list of servers, like taking turns. 2. Least Connections Directs requests to the server with the fewest active connections. 3. Weighted Round Robin Similar to Round Robin but assigns weights to servers based on their capacity, directing more traffic to more powerful servers. 4. Weighted Least Connections Combines the benefits of Least Connections and Weighted Round Robin, considering both server load and capacity. 5. IP Hash Uses the client's IP address to consistently direct their requests to the same server. 6. Least Response Time Directs requests to the server with the quickest response time and the lowest number of active connections. 7. Random Assigns requests to servers randomly. 8. Least Bandwidth Sends requests to the server consuming the least amount of network bandwidth. --- Credit: Design Gurus

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🌟 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 101: 𝗧𝗵𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗕𝗹𝗼𝗰𝗸𝘀 𝗼𝗳 𝗠𝗼𝗱𝗲𝗿𝗻 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 🌟 💡 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝗳𝗼𝗿𝗰𝗲 𝗯𝗲𝗵𝗶𝗻𝗱 𝗺𝗼𝗱𝗲𝗿𝗻 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. 𝗛𝗲𝗿𝗲’𝘀 𝗮 𝗾𝘂𝗶𝗰𝗸 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗼𝗳 𝗛𝗼𝘄 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗪𝗼𝗿𝗸𝘀: 1️⃣ 𝗖𝗹𝘂𝘀𝘁𝗲𝗿: The Kubernetes cluster is a set of worker machines, called nodes, that run containerized applications. 2️⃣ 𝗡𝗼𝗱𝗲𝘀: Each node is a unit of computing that runs containerized apps, ensuring scalability. Every Kubernetes cluster has at least one worker node. 3️⃣ 𝗣𝗼𝗱𝘀: Worker nodes host pods, the smallest deployable units, which contain your application’s containers. 4️⃣ 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗣𝗹𝗮𝗻𝗲: This is the brain behind Kubernetes. It manages the worker nodes and pods via the API server, ensuring your application stays in its desired state. 🌐 Whether you’re 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀, 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗻𝗲𝘄 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀, 𝗼𝗿 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗺𝘂𝗹𝘁𝗶-𝗰𝗹𝗼𝘂𝗱 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀, Kubernetes is essential for managing the complexity of containerized applications with ease. #CloudNative #Kubernetes #DevOps #Containers #Microservices #CloudComputing #CloudArchitecture #KubernetesCluster #ITInfrastructure #TechTransformation Azure DevOps AWS DevOps KodeKloud

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dyk: icymi: Tuki Logic Pvt. Ltd. Techrida Crestha Holding AmazonQ best but microsoft copilot worst: me stll do coding in rust go java php python react oracle mysql sqlserver mongo elasticsearch hadoop .net asp blockchain docker kubernetes k8 test k6 javascript css html alike Devs gaining little (if anything) from AI coding assistants Feature Sep 26, 2024 6 mins Generative AI Software Development Code analysis firm sees no major benefits from AI dev tool when measuring key programming metrics, though others report incremental gains from coding copilots with emphasis on code review. Photo of young blond bearded guy it support specialist wear plaid shirt sitting armchair confused looking at bug website indoors office Credit: Roman Samborskyi / Shutterstock Coding assistants have been an obvious early use case in the generative AI gold rush, but promised productivity improvements are falling short of the mark — if they exist at all. Many developers say AI coding assistants make them more productive, but a recent study set forth to measure their output and found no significant gains. Use of GitHub Copilot also introduced 41% more bugs, according to the study from Uplevel, a company providing insights from coding and collaboration data. The study measured pull request (PR) cycle time, or the time to merge code into a repository, and PR throughput, the number of pull requests merged. It found no significant improvements for developers using Copilot. Uplevel, using data generated by its customers, compared the output of about 800 developers using GitHub Copilot over a three-month period to their output in a three-month period before adoption. Measuring burnout In addition to measuring productivity, the Uplevel study looked at factors in developer burnout, and it found that GitHub Copilot hasn’t helped there, either. The amount of working time spent outside of standard hours decreased for both the control group and the test group using the coding tool, but it decreased more when the developers weren’t using Copilot. Uplevel’s study was driven by curiosity over claims of major productivity gains as AI coding assistants become ubiquitous, says Matt Hoffman, product manager and data analyst at the company. A GitHub survey published in August found that 97% of software engineers, developers, and programmers reported using AI coding assistants. “We’ve seen different studies of people saying, ‘This is really helpful for our productivity,’” he says. “We’ve also seen some people saying, ‘You know what? I’m kind of having to be more of a [code] reviewer.’” A representative of GitHub Copilot didn’t have a comment on the study, but pointed to a recent study saying developers were able to write code 55% faster using the coding assistant. The Uplevel team also went into its study expecting to see some productivity gains, Hoffman says. https://lnkd.in/dWynxKxD

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Hello #techies I have one doubt. In system design we always avoid a single point of failure, so suppose we had done horizontal scaling of n server and we have a load balancer to route the request to servers. If the load balancer failed or crashed then ??? Gaurav Sen #systemdesign.

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Unlike other joins, semi and anti joins only return rows from one of the tables SEMI JOIN => A row matches ANTI JOIN => NO row matches You can write these using (NOT) EXISTS subqueries SELECT ... FROM t1 WHERE [NOT] EXISTS ( SELECT ... FROM t2 WHERE ... ) GeeksforGeeks explains

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Working with Microservices architecture is awesome. Designing multi factor aspects and solving multiple realtime production issues. Like cascading failures and circuit breakers, even securing applications with different - different security implementations like SSO using WSO2 server or keycloak and OAuth etc and lot’s of things. #spring #springbooot #springcloud #springsecurity #microservices

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Sharing a small excerpt from the article I'm actively working on - "Why Azure SQL DBs become Inaccessible": Some of the most common reasons that we observe during on-call, which lead to DBs becoming inaccessible, are: - Too restrictive NSG rules that block traffic to AKV (or Managed HSM). Commonly happens when switching from Public access to Private Link. - Misconfigurations in Private Link to AKV (usually related to DNS misconfiguration) - Dropping Managed Identity and losing access to AKV. - Switching to Service-managed Key and immediately dropping AKV / Managed HSM keys. Problematic because transition to Service-managed key does not happen instantenously and there are usually VLFs that require TDE-CMK key for a while. - Swiching to new AKV / Managed HSM key and immediately dropping old one. Problematic due to exact same reason as stated above. - Misconfiguring DNS servers such that they return different IPs (usually Private DNS returning private endpoint (PE) IP, while public DNS returns Public IP). This is especially problematic if there's an active Firewall on AKV / Managed HSM side that only allows traffic via PE. - Setting up GeoDR scenario and not having same keys on Geo-primary and Geo-secondary. - Using NVAs and Firewalls that drop packets to AKV / Managed HSM. - etc. Do note that pretty much every AKV/Managed HSM error is observable in SQL's error log. Just search for "Azure Key Vault" and "AzureKeyVault" keywords. And that's about it for today :)

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The best way to manage secrets for applications running on Kubernetes is to store your secret in a secrets manager like AWS Secret Manager or Hashicorp Vault and access the secret from the application using the SDK. The secret is fetched when the pod starts, stays in volatile memory (RAM) while the pod is running and gets removed once the pod is deleted. Kubernetes secrets is not the right way to store the secret. Except for RBAC mechanisms, there is no security. Anyone who gets master access to the cluster shall be able to access the secrets which provides a huge surface area for attack. Follow me for more such Devops and Kubernetes related content. #devops #kubernetes

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Microservices pattern play an important role in effectively using Microservices architecture, here are common patterns which experienced developer should be aware of 1. Service Registry 2. API Gateway 3. CQRS 4. SAGA 5. Event Sourcing 6. BFF 7. Database per Service 8. Sidecar and much more

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TCA 1.16.1 got released 🚀 What's Changed ⚒️ Address a potential race condition in shared file storage. This fixes a race condition between mutating @Shared(.fileStorage) directly and the file system notifying it of changes on ⚒️ Add @ViewBuilder to NavigationStack root view. ⚒️ Address potential crash in KVO shared app storage observation during cancellation. PS: Please find the link to the full changelog in the comments. --- If you're looking to improve your TCA skills, check out my free interactive tutorial on TCA best practices: https://lnkd.in/dJHaxm4E

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You don't always need Prometheus on #kubernetes to collect metrics. Sometimes you need to collect metrics as logs. Well, you can do so with CronJobs. Head on to this article to know more. #Observability #Monitoring https://lnkd.in/giQWNggk

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Attention: Update for Developer learning and for Springboot developers The blog post "Introduction to Htmx for Spring Boot Developers" discusses how Htmx can simplify front-end development for Spring Boot applications. Key points include: - Htmx Overview: Htmx is a library that enhances HTML with attributes, enabling developers to create interactive user experiences without heavy front-end frameworks. It promotes a declarative programming style, allowing HTML elements to trigger server requests and update the DOM dynamically. - Benefits of Htmx: It helps developers avoid the complexities of modern JavaScript frameworks by allowing them to leverage existing Spring Boot knowledge. Htmx facilitates faster development and reduces the overhead associated with JavaScript tooling. - Implementation Examples: The post provides practical examples, such as creating a button that triggers a server request and a search box that filters results based on user input. It details the necessary Spring Boot controller methods and HTML templates to implement these features. - Community and Resources: The post highlights the growing Htmx community and offers links to additional resources and tutorials for further learning. Overall, Htmx is presented as a valuable tool for developers seeking to streamline their front-end development process within Spring Boot applications. #springboot #springframework #java #framework #html #html Citations: Introduction to Htmx for Spring Boot Developers | The IntelliJ IDEA Blog https://lnkd.in/gjPsbVEJ

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Configure Scheduled Trigger in GitHub Actions using Cron expression https://lnkd.in/gwfm8mMt #CRON #GitHubActions #Scheduled #workflow #YAML

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Configure Scheduled Trigger in GitHub Actions using Cron expression https://lnkd.in/gwfm8mMt #CRON #GitHubActions #Scheduled #workflow #YAML

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