InfoQ Homepage Distributed Systems Content on InfoQ
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Reactive Real-Time Notifications with SSE, Spring Boot, and Redis Pub/Sub
Explore the power of reactive programming for building scalable real-time notification systems. Using Spring Boot Reactive and Spring WebFlux, leverage non-blocking operations to handle high-volume, asynchronous data flows efficiently. Discover how Redis Pub/Sub enables event-driven messaging and how the SSE protocol provides persistent connections for instant client updates without polling.
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Taking Advantage of Cell-Based Architectures to Build Resilient and Fault-Tolerant Systems
Cell-based architectures offer a robust approach to building resilient systems. They achieve this through the core principles of isolation, autonomy, and replication. Each cell manages its resources and makes decisions autonomously. Observability for cell-based architecture requires a tailored approach to address the unique challenges and opportunities presented by this distributed system design.
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Article Series: Cell-Based Architectures: How to Build Scalable and Resilient Systems
In this article series, we take readers on a journey of discovery and provide a comprehensive overview and in-depth analysis of many key aspects of cell-based architectures, as well as practical advice for applying this approach to existing and new architectures.
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How Cell-Based Architecture Enhances Modern Distributed Systems
Cell-based architecture has emerged as a response to many challenges associated with distributed systems. It employs the bulkhead pattern to isolate failures to a fraction of the affected infrastructure footprint and prevent widespread impact. Cells can also help organize large architectures into domain-bound deployment and delivery units, which provides essential sociotechnical benefits.
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Zero-Knowledge Proofs for the Layman
This article will introduce you to zero-knowledge proofs, a kind of cryptography you can use to provide the proof you know a secret, such as a private key or the solution to a problem, without ever sharing it to an interested party. While many articles exist on the topic, this will not require any high math knowledge.
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The False Dichotomy of Monolith vs. Microservices
Taking sides in the debate of microservices v. monolith gets in the way of doing the right thing for our customers. Sometimes, we need microservices. Sometimes, we need a monolith. Most of the time we are better off somewhere between these extremes.
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Migrate a RMI-Based Legacy Application to WebSocket
Technical debt, especially in enterprise software, is a relevant problem that developers recurrently have to face. This article provides a use case related to removing technical debt in a large enterprise application based on an old fashioned Remote Method Invocation (RMI) protocol, and migrating it toward modern cloud-aware communication technologies.
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Location, Location, Location: MVA Considerations for Distributed Processing and Data
Even when designing a Minimum Viable Architecture (MVA), developers must consider resource location, especially when mobile apps are part of a distributed system. Distributing the data and processing can introduce new challenges if location is not part of the decision-making criteria.
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Seven Ways to Fail at Microservices
At QCon Plus last November, I presented some of the ways microservices can go wrong. I’m a consultant for IBM, and part of my job is helping businesses get cloud-native. These problems are based on my experience – which, unfortunately, I see repeatedly in the field.
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Forensic Monitoring of Blockchains is Key for Broader Industry Adoption
This article presents a new blockchain consensus mechanism, XinFin Delegated Proof-of-Stake Consensus Protocol 2.0, that provides a higher level of trust by creating an efficient judiciary system. It has been designed to keep all verifying nodes accountable and allow for bad actors to be identified and effectively neutralized.
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Building Reliable Software Systems with Chaos Engineering
Advances in large-scale, distributed software systems are changing the game for software engineering. As an industry, we are quick to adopt practices that improve flexibility and improve feature velocity. If we can move quickly, can we do so without breaking things? Chaos Engineering practices can be used to navigate complexity and build more reliable systems.
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Can We Trust the Cloud Not to Fail?
I will start with the theory behind failure detection, and then review a couple of real-world examples of how the mechanism works in a real cloud - on Azure. Even though this article includes real-world applications of failure detection within Azure, the same notions could also apply to GCP, AWS, or any other distributed system.