Dragonfly’s Post

What an amazing Article ---- Hudi is a grass-roots, open-source project, and the community is stronger than ever. The Hudi community has a proven track record of industry innovations, supporting some of the largest data lakes and cloud vendors for many years. Hudi is an open data lakehouse platform. Open table formats are essential, but we need open compute services for a no-lock-in data architecture. Minimizing Hudi to just a table format is an inaccurate and unfair characterization. Format standardization is essential but does not prevent the need to solve complex computer science problems related to data processing. Hudi is still better suited for the incremental workloads it is designed for. History often repeats itself. Hudi introduced the world’s first production data lakehouse with a new “headless” compute model that we have all grown to love. Users wanting to get behind innovative technologies early should recognize where innovation has come from historically. You don’t pay for open-source choices until you “pay” for them. If we genuinely desire openness, users must look past what vendors write or market and make educated decisions based on technical facts and business needs. Recognizing Hudi’s rightful place in the data ecosystem requires an understanding of both top-down vendor technology pushes and bottom-up open-source developer adoption. We continue to be constructive and collaborative, like how we kick-started the industry interoperability conversation by creating Onetable, now Apache XTable (Incubating). Next, we are hard at work bringing an open-source data lake database with radically improved storage and compute capabilities to market in the coming months. Read More https://lnkd.in/e-uERRcH

🚀 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

Unbundling Database Transactions: The Genesis of ScalarDB During my tenure at Yahoo!, I was involved in developing a large-scale data streaming collection and processing system known as Data Highway. One of the significant challenges we encountered was the need to update configurations simultaneously across a vast network of hundreds of thousands of machines. The conventional approach seemed cumbersome, prompting me to contemplate a more streamlined solution. The idea crystallized into a simple yet powerful concept: what if we could execute updates across numerous nodes with a single transaction, something akin to { begintx(); for n = 1 … 200000 { update(node[n], config); } committx() }? This notion persisted as I delved into potential research avenues. It wasn't until I stumbled upon a paper titled "Unbundling transactions in the cloud" authored by David Lomet, Alan Fekete, Gerhard Waikum, and Mike Zwilling that the pieces started falling into place. The paper sparked a realization: what if we could unbundle the traditional database system, separating transaction coordination, storage, and logging? This unbundling could potentially facilitate transactions across multiple data items without necessitating direct coordination across applications. Central to this concept was treating each data item as a standalone database unit with its own storage and write-ahead log. These data items could be dispersed across the network, even spanning heterogeneous stores. In this envisioned architecture, transaction coordination would shift to the client side, while logging would become intrinsic to each individual data item. This marked the genesis of my foray into the realm of distributed transactions, culminating in the development of the Cherry Garcia protocol. Subsequently, ScalarDB embraced this paradigm to shape their product. By reimagining the conventional database architecture and leveraging the principles of unbundling, we paved the way for a more efficient and scalable approach to distributed transactions. Through this journey, the core intuition behind ScalarDB was born, propelling innovation in the field of distributed systems. #scalarlabs #scalardb #transactionservices #distributedsystems #distributedcomputing

Legacy systems typically carry technical debt that can hold back government innovation, which can lead to frustrated users, costs and lost opportunities.🖥️ Today, user experience is a key motivation behind current modernisation efforts by governments. Data platform Redis’ Raden Ardhian shares three key features of an application modernisation solution that enables governments to transform their legacy #IT.

Caching with Asynchronous Data Synchronization 🚀 Boosting performance while maintaining data integrity! Ever wondered how applications manage to be lightning-fast while ensuring that your data is always up-to-date? It's all about "caching with asynchronous data synchronization"! In this setup, data is temporarily stored in cache memory to improve performance and reduce the need for frequent server requests. Then, at certain intervals or when specific conditions are met, the cached data is synchronized with the server using a RESTful API. This approach helps maintain data consistency between the client and server while optimizing performance by minimizing unnecessary API calls. 🔍 Pros: ⚡ Faster Performance: Say goodbye to sluggish loading times! Caching stores frequently accessed data in memory, cutting down on repetitive database queries. 🌐 Reduced Server Load: Lightening the server's workload means smoother operations and potentially lower costs. 📶 Offline Access: Even without internet access, you can still access cached data, ensuring uninterrupted productivity. 📈 Scalability: As your user base grows, caching helps distribute the load, ensuring consistent performance. 🛑 Cons: 🔄 Data Consistency: Keeping data consistent across cache and server can be tricky, especially with frequent changes. 🗑️ Cache Invalidation: Managing outdated data in the cache requires careful strategies to avoid serving stale information. 🧩 Increased Complexity: Adding caching and synchronization layers can make the system architecture more complex, requiring careful design. 💻 Resource Overhead: More caching means more system resources like memory and processing power, impacting overall performance. ⏳ Synchronization Delays: Asynchronous synchronization may lead to temporary inconsistencies between client and server data. Mastering this technique requires a balance between speed and accuracy, but when done right, it's a game-changer for app performance! 🚀 #TechTalk #Caching #DataSync #PerformanceBoost

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

🚀 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐑𝐮𝐥𝐞𝐬 𝐨𝐟 𝐓𝐡𝐮𝐦𝐛 𝐟𝐨𝐫 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 🚀 When it comes to scaling architectures, there are several key considerations to keep in mind to ensure optimal performance and cost-efficiency: 𝑪𝒐𝒔𝒕 𝒂𝒏𝒅 𝑺𝒄𝒂𝒍𝒂𝒃𝒊𝒍𝒊𝒕𝒚: Scaling an architecture often involves adding resources such as servers, bandwidth, or storage, which can quickly become expensive. It's crucial to balance the desired level of scalability with the available budget to avoid unnecessary expenses. 𝑬𝒗𝒆𝒓𝒚 𝑺𝒚𝒔𝒕𝒆𝒎 𝑪𝒐𝒏𝒄𝒆𝒂𝒍𝒔 𝒂 𝑩𝒐𝒕𝒕𝒍𝒆𝒏𝒆𝒄𝒌 𝑺𝒐𝒎𝒆𝒘𝒉𝒆𝒓𝒆: In any architecture, there's always a bottleneck waiting to be discovered. Identifying this bottleneck is the first step towards achieving effective scalability. It could be a particular component, database, or even a specific code segment that limits performance. 𝑺𝒍𝒐𝒘 𝑺𝒆𝒓𝒗𝒊𝒄𝒆𝒔 𝑷𝒐𝒔𝒆 𝑮𝒓𝒆𝒂𝒕𝒆𝒓 𝑪𝒉𝒂𝒍𝒍𝒆𝒏𝒈𝒆𝒔 𝑻𝒉𝒂𝒏 𝑭𝒂𝒊𝒍𝒆𝒅 𝑺𝒆𝒓𝒗𝒊𝒄𝒆𝒔: Slow services can be more detrimental to your system's performance than outright service failures. They can cause delays and timeouts for independent services, impacting the entire system. Users often prefer services that fail fast and gracefully, as it allows for quicker error recovery and ensures a better user experience. 𝑺𝒄𝒂𝒍𝒊𝒏𝒈 𝒕𝒉𝒆 𝑫𝒂𝒕𝒂 𝑻𝒊𝒆𝒓 𝑷𝒓𝒆𝒔𝒆𝒏𝒕𝒔 𝒕𝒉𝒆 𝑮𝒓𝒆𝒂𝒕𝒆𝒔𝒕 𝑪𝒉𝒂𝒍𝒍𝒆𝒏𝒈𝒆: Scaling the data tier, especially relational databases, can be one of the most challenging aspects of architecture. As data grows, managing databases and ensuring their performance becomes increasingly complex. Techniques like database sharding, replication, and caching can help address data tier scalability challenges. 𝑪𝒂𝒄𝒉𝒆 𝑬𝒙𝒕𝒆𝒏𝒔𝒊𝒗𝒆𝒍𝒚 𝒕𝒐 𝑶𝒑𝒕𝒊𝒎𝒊𝒛𝒆 𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆: By storing frequently accessed data in memory, you can reduce the load on the data tier and improve response times. Caching can be applied at various levels, including application-level caches and content delivery networks (CDNs). 𝑬𝒇𝒇𝒆𝒄𝒕𝒊𝒗𝒆 𝑴𝒐𝒏𝒊𝒕𝒐𝒓𝒊𝒏𝒈 𝒊𝒔 𝑽𝒊𝒕𝒂𝒍 𝒇𝒐𝒓 𝑺𝒄𝒂𝒍𝒂𝒃𝒍𝒆 𝑺𝒚𝒔𝒕𝒆𝒎𝒔: Effective monitoring provides real-time insights into system performance, resource utilization, and potential issues. By employing monitoring tools and setting up alerts, you can proactively identify and address problems before they impact users. Implementing these rules of thumb can help you build scalable and efficient systems that meet the demands of a growing user base. #SolutionArchitecture #Scalability #TechInnovation #CostEfficiency #PerformanceOptimization

Implementing a modern distributed database is not just an option - it's a necessity for businesses looking to stay ahead. Managing geo-distributed transactions is a delicate balance between consistency, availability, and performance. #DataManagement https://lnkd.in/eVmTMsN6

𝐄𝐯𝐞𝐫 𝐰𝐨𝐧𝐝𝐞𝐫𝐞𝐝 𝐡𝐨𝐰 𝐃𝐢𝐬𝐜𝐨𝐫𝐝 𝐦𝐚𝐧𝐚𝐠𝐞𝐬 𝐭𝐨 𝐬𝐭𝐨𝐫𝐞 𝐭𝐫𝐢𝐥𝐥𝐢𝐨𝐧𝐬 𝐨𝐟 𝐦𝐞𝐬𝐬𝐚𝐠𝐞𝐬? 𝐋𝐞𝐭'𝐬 𝐝𝐢𝐯𝐞 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐟𝐚𝐬𝐜𝐢𝐧𝐚𝐭𝐢𝐧𝐠 𝐰𝐨𝐫𝐥𝐝 𝐨𝐟 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 𝐬𝐭𝐨𝐫𝐚𝐠𝐞! Discord, the popular communication platform, handles an enormous volume of messages every day. To achieve this, they utilize a robust backend infrastructure and innovative storage solutions. 𝐒𝐡𝐚𝐫𝐝𝐢𝐧𝐠: Discord employs sharding, where data is distributed across multiple servers or shards. This horizontal scaling approach allows for efficient distribution of workload and ensures reliability and fault tolerance. 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: Discord's microservices architecture enables them to break down complex functionalities into smaller, manageable services. Each service is responsible for specific tasks, such as message storage, indexing, and retrieval. 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬: Discord utilizes distributed databases like Cassandra or DynamoDB, which are designed to handle massive amounts of data across multiple nodes. These databases offer high availability, scalability, and fault tolerance, crucial for storing trillions of messages. 𝐃𝐚𝐭𝐚 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐚𝐧𝐝 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠: To optimize storage space and retrieval performance, Discord employs data compression techniques and indexing mechanisms. This allows for efficient storage and quick retrieval of messages based on various parameters. 𝐃𝐚𝐭𝐚 𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐁𝐚𝐜𝐤𝐮𝐩: Discord ensures data durability and reliability through data replication and regular backups. Multiple copies of data are stored across different servers and data centers to prevent data loss and ensure availability in case of failures. 𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Discord leverages real-time analytics to gain insights into user behavior, message trends, and system performance. This helps them make informed decisions and optimize their storage infrastructure accordingly. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Discord prioritizes data security and compliance with regulations such as GDPR. They implement encryption, access controls, and auditing mechanisms to protect user data and ensure privacy. Overall, Discord's approach to storing trillions of messages exemplifies the importance of scalability, reliability, and innovation in managing large-scale data.

I spent the day yesterday at the inaugural InterSystems Analyst Day. Here are some key takeaways: 1)       Although I had thought that I had not heard of InterSystems, after the company’s retrospective presentation, I realized that I was familiar with the company and prior deployment of their products (e.g., Cache), I was not aware of the extent to which they had re-engineered and redeployed their products in ways that continually increased performance. In their own words, “the company has reinvented itself twice over the past 40 years.” 2)       Their platform is a critical infrastructure component that powers many applications, including ones that lots of people use on a regular basis. 3)       Their technology stack presentation demonstrated that the company understands the current hybrid + multi cloud data distribution paradigms, they also have deliberate awareness of the corresponding performance bottlenecks and how integrated software caching techniques can hide data access latency to speed data access performance. 4)       They have a strategy for integrating machine learning, traditional AI, and Generative AI into the platform architecture while adhering to protocols for ensuring “trusted AI.” More to the point: For the past 5 years I have been teaching conference workshops and tutorials advocating the adoption of a #DataFabric approach to enterprise #DataIntegration and provision that incorporates a #SemanticLayer for #interoperability, integrated #DataGovernance and data protection, and smart caching for performance. I was pleasantly surprised to see the degree to which their architecture diagrams mirrored those I have on my own tutorial slides. It is a breath of fresh air to see platform vendors whose architects and engineers have a profound understanding of the issues organizations face in powering interoperable application development and understand how to address those issues. Kudos to Bobby D'Arcy and Kim Dossey for putting together an excellent analyst day!