PREGIOTEK’s Post

I spent hours reading about design patterns and principles that support large scale systems. Here are the important things: 1. Stateless Architecture - Move session data out of web servers and into persistent storage (e.g., NoSQL databases). - This enables horizontal scaling, facilitates easier auto-scaling and improves system resilience. 2. Load Balancing & CDNs - Use health checks and geo-routing - Serve static assets via CDNs - Enhance security with private IPs for inter-server communication 3. Multi-Tier Caching - Implement caching at CDN, application, and database levels - Use read-through caching for hot data - Consider cache expiration and consistency in multi-region setups 4. Scale Databases through Sharding - Implement horizontal partitioning (sharding) to distribute data across multiple servers. - Choose sharding keys carefully to ensure even data distribution. - Handle challenges like resharding, hotspots, and cross-shard queries. 5. Message Queues - Decouple services using Kafka or RabbitMQ - Enable asynchronous processing - Allow independent scaling of producers and consumers 6. Comprehensive Monitoring - Focus on host-level, aggregated, and business KPI metrics - Implement centralized logging - Invest in automation tools 7. Multi-Region Deployment - Use geo-DNS for intelligent traffic routing - Implement regional data replication - Address data synchronization and deployment consistency challenges 8. Failure-Oriented Design - Build redundancy into every tier of the system. - Implement circuit breakers to fail fast and prevent cascade failures. - Use strategies like bulkhead pattern to isolate failures. 9. Ensure Data Consistency and Integrity - In distributed databases, consider the trade-offs between consistency and availability (CAP theorem). - Implement strategies like read-after-write consistency where necessary. 10. Optimize for Performance - Use asynchronous processing where possible to improve responsiveness. - Implement database indexing strategies for faster queries. - Consider denormalization to improve read performance, weighing it against data integrity needs. 11. Automate Operations - Implement continuous integration and deployment (CI/CD) pipelines. - Use infrastructure-as-code for consistent environment management. - Automate routine tasks like backups, scaling, and failover procedures. Books: - Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann - System Design Interview – An insider's guide by Alex Xu #SystemDesign #DistributedSystems

Day 21 of my system design journey: Understanding Data Consistency and Consistency Levels Today, I explored the critical concept of data consistency in distributed systems. When data is spread across multiple nodes (often for better availability and fault tolerance), data consistency ensures that all nodes reflect the same view of the data. However, achieving this across a distributed architecture comes with challenges like network latency, node failures, and concurrent updates. Here are the key consistency models I learned about: 1. Strong Consistency: Every read operation reflects the most recent write, ensuring no stale data. Although it prioritizes data integrity, it can impact system performance and availability due to the need for synchronization across nodes. 2. Eventual Consistency: This model ensures that all replicas eventually converge to the same state, even if there's a delay. It’s often used in systems where high availability and partition tolerance are more important than immediate consistency, like in NoSQL databases. 3. Causal Consistency: Operations that are causally related are seen in the same order by all nodes. This model maintains a balance between strong consistency and the system’s scalability. 4. Read-Your-Writes Consistency: A client sees the effects of its own writes in subsequent read operations, providing an intuitive user experience. Data consistency is a fascinating topic because it’s all about trade-offs—between speed, availability, and accuracy. Different systems use different models based on their requirements for performance, availability, and fault tolerance. #SystemDesign #DistributedSystems #DataConsistency #LearningJourney

🚀 Scaling Your Data Layer for Optimal Performance! 🚀 In today's fast-paced, data-driven world, ensuring that your application's data layer can scale efficiently is critical for success. The ability to manage high loads, maintain consistency, and ensure low-latency responses isn't just a luxury—it's a necessity! Here are some key strategies for scaling your data layer: 💡 Replication Replication ensures high availability and redundancy. Whether you go with leader-follower, multi-leader, or even a leaderless architecture, replication helps balance writes and highly consistent reads across your system. It’s all about reliability and fault tolerance. 💡 Sharding Splitting your monolithic database into smaller, more manageable shards can massively improve performance, scalability, and availability. This is especially useful when dealing with massive datasets and high-throughput systems. 💡 Distributed Caching Caching is key to low-latency responses. Distributed caching ensures data is served faster by storing it closer to the application, reducing load on your databases. Just remember to handle cache invalidation and key distributioneffectively! 💡 CQRS Pattern The Command Query Responsibility Segregation (CQRS) pattern allows you to scale reads and writes separately. This pattern optimizes performance by enabling eventual consistency between write operations (commands) and read operations (queries). When these strategies are combined, you get a robust, scalable architecture capable of handling today's most demanding applications! 🌟 Scaling the data layer is not just about handling growth, but about delivering consistent, reliable, and efficient experiences to your users. #DataScaling #TechArchitecture #CloudComputing #DistributedSystems #CQRS #Replication #Sharding #Caching

➡️ 𝐏𝐫𝐨𝐦𝐞𝐭𝐡𝐞𝐮𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 🔬 Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. Below is a an overview of the Prometheus architecture: 🏮 Components: 🖥 Prometheus Server: - Core for collecting, storing, and querying time-series data. - It’s pull-based and scrapes metrics from targets at regular intervals. - Stores data in a local time-series database. ❤️ Metrics (Targets/Exporters): - Apps or services expose metrics. - Prometheus scrapes metrics from these targets. 📶 Data Model: - Time-series data with metric names and labels. - Example: `http_requests_total{method="GET", status="200"}`. ⚠️ PromQL: - Query language for time-series data. - Allows filtering, grouping, and math operations on metrics. ⚠️ Alertmanager: - Handles alerts from Prometheus. - Manages notifications and integrates with third-party channels. ☁️ Storage: - Uses local on-disk storage. - Data retention policies. - Data is organized in blocks and compacted over time. 🏮 Workflow: 🌐 Configuration: - Targets and scrape intervals defined in Prometheus config files. - Relabeling allows modifying or filtering metrics before storage. 📚 Scraping: - Prometheus Server scrapes metrics from configured targets. - Targets expose metrics typically at /metrics endpoint. ☁️ Storage: - Scraped metrics stored in the local time-series database. - Data organized by metric name and labels. 📚 Querying: - Users utilize PromQL to query and analyze stored metrics. - Grafana or Prometheus's UI visualizes query results. ⚠️ Alerting: - Prometheus evaluates alerting rules based on queries. - Alerts sent to Alertmanager if conditions are met. ⚠️ Alertmanager Handling: - Alertmanager receives alerts and manages their lifecycle. - Handles deduplication, grouping, and sends notifications to configured channels.

It took me 8 years and several failures to get these 30 system design concepts. You can get them in 10 seconds by reading this post. 1. use autoscaling to handle traffic spikes 2. design for scalability from the beginning 3. plan and implement fault tolerance 4. prioritize horizontal scaling for growth 5. implement data partitioning and sharding 6. use data lakes for analytics and reports 7. employ CDNs to reduce global latency 8. make operations idempotent for simplicity 9. use event-driven architecture for flexibility 10. use blob/object storage for media files 11. accept tradeoffs; perfection is unattainable 12. implement data replication and redundancy 13. use rate limiting to protect the system 14. utilize read-through cache for read-heavy applications 15. use write-through cache for write-heavy applications 16. choose NoSQL databases for unstructured data 17. use heartbeat mechanisms to detect failures 18. adopt WebSockets for real-time communication 19. employ database sharding for horizontal scaling 20. clearly define system use cases and constraints 21. consider microservices for flexibility and scalability 22. design for adaptability; expect evolving requirements 23. use database indexing for efficient data retrieval 24. thoroughly understand requirements before designing 25. utilize asynchronous processing for background tasks 26. consider denormalizing databases for read-heavy workloads 27. avoid over-engineering; add features only as needed 28. prefer SQL databases for structured data and transactions 29. use load balancers for high availability and traffic distribution 30. consider message queues for asynchronous communication P.S.: Repost ♻️ to help your network and someone who may need it

🚀 Sharding, Partitioning, and Replication : Often confused concepts of system design 🛠️ (Disclaimer : A small Note of my personal understanding only, not any company's views :)) Checkout: #TBEB System design is the backbone of scalable and resilient architectures, yet it's rife with concepts that often cause confusion. Today, let's shed light on three key concepts: sharding, partitioning, and replication. 1️⃣ Sharding: Sharding involves breaking down a large database into smaller, more manageable parts called "shards." Each shard holds a subset of the data, distributing the load across multiple servers or nodes. Think of sharding as dividing an encyclopedia into volumes, with each volume containing distinct information. For instance, in a social media platform, user data could be sharded based on geographical regions. This allows for efficient data retrieval and scalability, as queries can be distributed across shards. 2️⃣ Partitioning: Partitioning is akin to sharding but operates at a finer level within a database or data structure. It involves dividing data into smaller partitions based on specific criteria, such as ranges of values or hash functions. For example, in a messaging application, messages could be partitioned based on the sender's ID or message timestamp. Partitioning enables better data organization and retrieval efficiency. It's like organizing books in a library by genre or author, making it easier to locate relevant information quickly. 3️⃣ Replication: Replication involves creating and maintaining multiple copies of data across different nodes or data centers. These copies, or replicas, serve as backups and provide fault tolerance and high availability. Consider replication as making photocopies of important documents: if one copy gets lost or damaged, you can always rely on another. In a distributed system, replication ensures data durability and resilience against node failures or network issues. In summary, sharding, partitioning, and replication are indispensable techniques in system design for achieving scalability, performance, and fault tolerance. By understanding these concepts and applying them judiciously, engineers can architect robust and efficient systems capable of handling immense loads and ensuring uninterrupted service delivery. Think Big Execute Bigger (#TBEB) 🙂 Let's continue demystifying system design one concept at a time! 💡💻 #SystemDesign #Scalability #Architecture #TechTalk #interviewprep

➡️ 𝐏𝐫𝐨𝐦𝐞𝐭𝐡𝐞𝐮𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 🔬 Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. Below is a an overview of the Prometheus architecture: 🏮 Components: 🖥 Prometheus Server: - Core for collecting, storing, and querying time-series data. - It’s pull-based and scrapes metrics from targets at regular intervals. - Stores data in a local time-series database. ❤️ Metrics (Targets/Exporters): - Apps or services expose metrics. - Prometheus scrapes metrics from these targets. 📶 Data Model: - Time-series data with metric names and labels. - Example: `http_requests_total{method="GET", status="200"}`. ⚠️ PromQL: - Query language for time-series data. - Allows filtering, grouping, and math operations on metrics. ⚠️ Alertmanager: - Handles alerts from Prometheus. - Manages notifications and integrates with third-party channels. ☁️ Storage: - Uses local on-disk storage. - Data retention policies. - Data is organized in blocks and compacted over time. 🏮 Workflow: 🌐 Configuration: - Targets and scrape intervals defined in Prometheus config files. - Relabeling allows modifying or filtering metrics before storage. 📚 Scraping: - Prometheus Server scrapes metrics from configured targets. - Targets expose metrics typically at /metrics endpoint. ☁️ Storage: - Scraped metrics stored in the local time-series database. - Data organized by metric name and labels. 📚 Querying: - Users utilize PromQL to query and analyze stored metrics. - Grafana or Prometheus's UI visualizes query results. ⚠️ Alerting: - Prometheus evaluates alerting rules based on queries. - Alerts sent to Alertmanager if conditions are met. ⚠️ Alertmanager Handling: - Alertmanager receives alerts and manages their lifecycle. - Handles deduplication, grouping, and sends notifications to configured channels. ♥️ Prometheus#tool#alert#grafana#scraping#

🚀 Mastering System Design: Key Insights for Efficient Architecture. Some essential system design principles that are critical for anyone looking to excel in building scalable, resilient, and high-performing systems. ✨ Breaking Problems into Modules: A top-down approach is crucial. Simplify complexity by dividing the problem into manageable pieces. ⚙️ Trade-offs Matter: There is no perfect solution. Always consider system constraints and the end-user impact when making architectural decisions. 🌐 Key Architectural Concepts: 1. Consistent Hashing 2. CAP Theorem 3. Load Balancing (Hardware & Software) 4. SQL vs NoSQL 5. Data Partitioning & Sharding 6. Caching & Queue Systems 🛠️ Smart Load Balancers & Proxies: From internal platform scaling to managing traffic, balancing resources is the backbone of high availability. 💾 Databases – SQL vs NoSQL: Understanding when to use structured vs. unstructured data can save both time and resources. Learn how to optimize for ACID compliance or leverage the flexibility of NoSQL for big data scenarios. 🔑 Caching, Replication, & Redundancy: Ensure data consistency and fault tolerance by employing proper caching strategies and replication models. 🔄 Queues for Asynchronous Processing: Efficiently handle large-scale distributed systems by utilizing queues to balance high loads. Follow Devkant Bhagat for amazing content Credit- Respected owner #SystemDesign #SoftwareArchitecture #ScalableSystems #Devkantbhagat #TechArchitecture #DistributedSystems #DesignPatterns #TechLeadership #BackendDevelopment #CloudArchitecture #HighAvailability #DevkantBhagat #TechInfrastructure #TechSkills #ScalableArchitecture #Microservices #EngineeringExcellence

How to Scale Data Layer The scalability of a system is heavily dependent on the data layer.  No matter how much effort is made to scale the API or the application layer, it is limited by the scalability of the data layer. Also, scaling the data layer is often the most difficult task during application design. Horizontally scaling the data layer of an application, also known as “scaling out”, involves distributing the data and load across multiple servers or nodes.  This approach is particularly effective for handling large volumes of data and high traffic loads, but it also adds multiple orders of complexity. Since the data is distributed, many issues regarding transactions and consistency that don’t appear in monolithic databases become quite common. Several techniques are available for horizontally scaling the data layer, each having pros and cons with specific nuances worth considering. In this post, we’ll explore the major techniques for scaling the data layer horizontally along with examples. Also, we will understand the advantages and disadvantages of each technique to get a better idea of when to use a particular approach over another choice.

In today's data-driven world, businesses are dealing with an unprecedented influx of data. One of the major challenges that we've encountered recently revolves around the architectural design of our database to handle dynamic scaling without compromising performance or data integrity. We noticed that as our user base rapidly expanded, our existing database architecture was struggling to keep up with the increased load, resulting in slower response times and occasional downtime. The core of the problem lay in our monolithic database design, which wasn't built with scalability in mind. With our users demanding high availability and seamless performance, we realized a shift to a more flexible and robust solution was imperative. Here's how we tackled the problem: 1. **Database Sharding**: We decomposed our monolithic database into smaller, more manageable pieces called shards. Each shard operates as an independent database, responsible for a subset of the data, allowing us to distribute the load more evenly and improve access times. 2. **Geographical Distribution**: By strategically placing database shards closer to our major user bases, we reduced latency and enhanced data accessibility. This way, users experience faster performance regardless of their location. 3. **Implementing Caching Layers**: To further alleviate the load on the primary databases, we deployed caching layers using distributed cache systems like Redis. This step significantly boosted query response times by storing frequently accessed data closer to the application layer. 4. **Adopting a Microservices Architecture**: By decoupling our services, we allowed each microservice to have its own dedicated database, further optimizing resource allocation and improving scalability. 5. **Automated Monitoring and Scaling**: Leveraging tools for real-time monitoring and auto-scaling, we're able to respond to spikes in user activity promptly, ensuring continuous service availability and performance stability. Transitioning to this redesigned architecture has offered significant improvements in our system's reliability and efficiency. Our users now enjoy a seamless experience, and we're better equipped to handle future growth. If you're facing similar database challenges, consider embracing a scalable and distributed approach to ensure your architecture can meet evolving demands. Let's embrace the future of data management together! #DatabaseArchitecture #Scalability #DataManagement #TechSolutions