The author argues that abstract architectural discussions about microservices are often unproductive. Instead of focusing on theoretical benefits and drawbacks, conversations should center on concrete business problems and how microservices might address them. Architects tend to get bogged down in ideal scenarios and complex diagrams, losing sight of the practicalities of implementation and the potential negative impact on team productivity. The author advocates for a more pragmatic, iterative approach, starting with a monolith and gradually decomposing it into microservices only when justified by specific business needs, like scaling particular functionalities or enabling independent deployments. This shift in focus from theoretical architecture to measurable business value ensures that microservices serve the organization, not the other way around.
"Architecture Patterns with Python" introduces practical architectural patterns for structuring Python applications beyond simple scripts. It focuses on Domain-Driven Design (DDD) principles and demonstrates how to implement them alongside architectural patterns like dependency injection and the repository pattern to create well-organized, testable, and maintainable code. The book guides readers through building a realistic application, iteratively improving its architecture to handle increasing complexity and evolving requirements. It emphasizes using Python's strengths effectively while promoting best practices for software design, ultimately enabling developers to create robust and scalable applications.
Hacker News users generally expressed interest in "Architecture Patterns with Python," praising its clear writing and practical approach. Several commenters highlighted the book's focus on domain-driven design and its suitability for bridging the gap between simple scripts and complex applications. Some appreciated the free online availability, while others noted the value of supporting the authors by purchasing the book. A few users compared it favorably to other architecture resources, emphasizing its Python-specific examples. The discussion also touched on testing strategies and the balance between architecture and premature optimization. A couple of commenters pointed out the book's emphasis on using readily available tools and libraries rather than introducing new frameworks.
Component simplicity, in the context of functional programming, emphasizes minimizing the number of moving parts within individual components. This involves reducing statefulness, embracing immutability, and favoring pure functions where possible. By keeping each component small, focused, and predictable, the overall system becomes easier to reason about, test, and maintain. This approach contrasts with complex, stateful components that can lead to unpredictable behavior and difficult debugging. While acknowledging that some statefulness is unavoidable in real-world applications, the article advocates for strategically minimizing it to maximize the benefits of functional principles.
Hacker News users discuss Jerf's blog post on simplifying functional programming components. Several commenters agree with the author's emphasis on reducing complexity and avoiding over-engineering. One compelling comment highlights the importance of simple, composable functions as the foundation of good FP, arguing against premature abstraction. Another points out the value of separating pure functions from side effects for better testability and maintainability. Some users discuss specific techniques for achieving simplicity, such as using plain data structures and avoiding monads when unnecessary. A few commenters note the connection between Jerf's ideas and Rich Hickey's "Simple Made Easy" talk. There's also a short thread discussing the practical challenges of applying these principles in large, complex projects.
The blog post "The program is the database is the interface" argues that traditional software development segregates program logic, data storage, and user interface too rigidly. This separation leads to complexities and inefficiencies when trying to maintain consistency and adapt to evolving requirements. The author proposes a more integrated approach where the program itself embodies the database and the interface, drawing inspiration from Smalltalk's image-based persistence and the inherent interactivity of spreadsheet software. This unified model would simplify development by eliminating impedance mismatches between layers and enabling a more fluid and dynamic relationship between data, logic, and user experience. Ultimately, the post suggests this paradigm shift could lead to more powerful and adaptable software systems.
Hacker News users discuss the implications of treating the program as the database and interface, focusing on the simplicity and power this approach offers for specific applications. Some commenters express skepticism, noting potential performance and scalability issues, particularly for large datasets. Others suggest this concept is not entirely new, drawing parallels to older programming paradigms like Smalltalk and spreadsheet software. A key discussion point revolves around the sweet spot for this approach, with general agreement that it's best suited for smaller, self-contained projects or niche applications where flexibility and rapid development are prioritized over complex data management needs. Several users highlight the potential of using this model for prototyping and personal projects.
Listen Notes, a podcast search engine, attributes its success to a combination of technical and non-technical factors. Technically, they leverage a Python/Django backend, PostgreSQL database, Redis for caching, and Elasticsearch for search, all running on AWS. Their focus on cost optimization includes utilizing spot instances and reserved capacity. Non-technical aspects considered crucial are a relentless focus on the product itself, iterative development based on user feedback, SEO optimization, and content marketing efforts like consistently publishing blog posts. This combination allows them to operate efficiently while maintaining a high-quality product.
Commenters on Hacker News largely praised the Listen Notes post for its transparency and detailed breakdown of its tech stack. Several appreciated the honesty regarding the challenges faced and the evolution of their infrastructure, particularly the shift away from Kubernetes. Some questioned the choice of Python/Django given its resource intensity, suggesting alternatives like Go or Rust. Others offered specific technical advice, such as utilizing a vector database for podcast search or exploring different caching strategies. The cost of running the service also drew attention, with some surprised by the high AWS bill. Finally, the founder's candidness about the business model and the difficulty of monetizing a podcast search engine resonated with many readers.
This presentation compares and contrasts Fuchsia's component architecture with Linux containers. It explores how both technologies approach isolation, resource management, and inter-process communication. The talk delves into the underlying mechanisms of each, highlighting Fuchsia's capability-based security model and its microkernel design as key differentiators from containerization solutions built upon Linux's monolithic kernel. The goal is to provide a clear understanding of the strengths and weaknesses of each approach, allowing developers to better evaluate which technology best suits their specific needs.
HN commenters generally expressed skepticism about Fuchsia's practical advantages over Linux containers. Some pointed out the significant existing investment in container technology and questioned whether Fuchsia offered enough improvement to justify switching. Others noted Fuchsia's apparent complexity and lack of clear benefits in terms of security or performance. A few commenters raised concerns about software availability on Fuchsia, specifically mentioning the lack of common tools like strace and gdb. The overall sentiment leaned towards a "wait and see" approach, with little enthusiasm for Fuchsia as a container replacement.
This 1972 paper by Parnas compares two system decomposition strategies: one based on flowcharts and step-wise refinement, and another based on information hiding. Parnas argues that decomposing a system into modules based on hiding design decisions behind interfaces leads to more stable and flexible systems. He demonstrates this by comparing two proposed modularizations of a KWIC (Key Word in Context) indexing system. The information hiding approach results in modules that are less interconnected and therefore less affected by changes in implementation details or requirements. This approach prioritizes minimizing inter-module communication and dependencies, making the resulting system easier to modify and maintain in the long run.
HN commenters discuss Parnas's modularity paper, largely agreeing with its core principles. Several highlight the enduring relevance of information hiding and minimizing inter-module dependencies to reduce complexity and facilitate change. Some commenters share anecdotes about encountering poorly designed systems violating these principles, reinforcing the paper's importance. The concept of "secrets" as the basis of modularity resonated, with discussions about how it applies to various levels of software design, from low-level functions to larger architectural components. A few commenters also touch upon the balance between pure theory and practical application, acknowledging the complexities of real-world software development.
This post explores architectural patterns for adding realtime functionality to web applications. It covers techniques ranging from simple polling and long-polling to more sophisticated approaches like Server-Sent Events (SSE) and WebSockets. The author emphasizes choosing the right tool for the job based on factors like data volume, connection latency, and server resource constraints. They also discuss the importance of considering connection management, message ordering, and error handling. The post provides practical advice and code examples using JavaScript and Node.js to illustrate the different patterns, highlighting their strengths and weaknesses. Ultimately, it aims to give developers a clear understanding of the available options for building realtime features and empower them to make informed decisions based on their specific needs.
HN users generally praised the article for its clear explanations and practical approach to building realtime features. Several commenters highlighted the value of the "pull vs. push" breakdown and the discussion of different polling strategies. Some questioned the long-term viability of polling-based solutions and advocated for WebSockets or server-sent events for true real-time experiences. A few users shared their own experiences and preferences with specific technologies like LiveView and Elixir's Phoenix Channels. There was also some discussion about the trade-offs between complexity, performance, and scalability when choosing different realtime approaches.
The blog post argues for an intermediate representation (IR) layer in query compilers between the logical plan and the physical plan, called the "relational algebra IR." This layer would represent queries in a standardized, relational algebra form, enabling greater portability and reusability of optimization rules across different physical execution engines. Currently, optimization logic is often tightly coupled to specific physical plans, making it difficult to adapt to new engines or hardware. By introducing this standardized relational algebra IR, query compilers can achieve better modularity and extensibility, simplifying development and allowing for easier experimentation with new optimization strategies without needing to rewrite code for each backend. This ultimately leads to more efficient query execution across diverse environments.
HN commenters generally agree with the author's premise that a middle tier is missing in query compilers, sitting between logical optimization and physical optimization. This tier would handle "cross-physical plan" optimizations, allowing for better cost-based decisions that consider different physical plan choices holistically rather than sequentially. Some discuss the challenges in implementing this, particularly the explosion of search space and the difficulty in accurately costing plans. Others offer specific examples where such a tier would be beneficial, such as selecting join algorithms based on data distribution or optimizing for specific hardware like GPUs. A few commenters mention existing systems that implement similar concepts, though not necessarily as a distinct tier, suggesting the idea is already being explored in practice. Some debate the practicality of the proposed solution, suggesting alternative approaches like adaptive query execution or learned optimizers.
The blog post "Common mistakes in architecture diagrams (2020)" identifies several pitfalls that make diagrams ineffective. These include using inconsistent notation and terminology, lacking clarity on the intended audience and purpose, including excessive detail that obscures the key message, neglecting important elements, and poor visual layout. The post emphasizes the importance of using the right level of abstraction for the intended audience, focusing on the key message the diagram needs to convey, and employing clear, consistent visuals. It advocates for treating diagrams as living documents that evolve with the architecture, and suggests focusing on the "why" behind architectural decisions to create more insightful and valuable diagrams.
HN commenters largely agreed with the author's points on diagram clarity, with several sharing their own experiences and preferences. Some emphasized the importance of context and audience when choosing a diagram style, noting that highly detailed diagrams can be overwhelming for non-technical stakeholders. Others pointed out the value of iterative diagramming and feedback, suggesting sketching on a whiteboard first to get early input. A few commenters offered additional tips like using consistent notation, avoiding unnecessary jargon, and ensuring diagrams are easily searchable and accessible. There was some discussion on specific tools, with Excalidraw and PlantUML mentioned as popular choices. Finally, several people highlighted the importance of diagrams not just for communication, but also for facilitating thinking and problem-solving.
Cell-based architecture offers a robust approach to designing complex systems by compartmentalizing them into independent "cells". Like a walled city protecting against a zombie horde, each cell operates autonomously with its own data and logic, communicating with other cells through well-defined interfaces. This isolation prevents cascading failures; if one cell gets "infected" (compromised or buggy), the infection is contained, preventing it from spreading and bringing down the entire system. This modularity also facilitates independent development, deployment, and scaling of individual cells, making the system more adaptable and resilient to change. By sacrificing some global optimization for localized control, cell-based architecture prioritizes stability and evolvability in the face of unforeseen challenges.
Hacker News users generally praised the article for its clear and engaging explanation of cell-based architecture using the zombie analogy. Several commenters appreciated the novelty and effectiveness of the analogy, finding it memorable and helpful for understanding complex systems. Some discussed the practical applications of cell-based architecture, mentioning its use in game development and other software projects. A few users offered alternative analogies or pointed out minor inaccuracies, but the overall sentiment was positive, with many thanking the author for the insightful and entertaining read. One commenter highlighted the importance of fault tolerance, a key benefit of cell-based systems, which the zombie analogy effectively illustrates.
Scaling WebSockets presents challenges beyond simply scaling HTTP. While horizontal scaling with multiple WebSocket servers seems straightforward, managing client connections and message routing introduces significant complexity. A central message broker becomes necessary to distribute messages across servers, introducing potential single points of failure and performance bottlenecks. Various approaches exist, including sticky sessions, which bind clients to specific servers, and distributing connections across servers with a router and shared state, each with tradeoffs. Ultimately, choosing the right architecture requires careful consideration of factors like message frequency, connection duration, and the need for features like message ordering and guaranteed delivery. The more sophisticated the features and higher the performance requirements, the more complex the solution becomes, involving techniques like sharding and clustering the message broker.
HN commenters discuss the challenges of scaling WebSockets, agreeing with the article's premise. Some highlight the added complexity compared to HTTP, particularly around state management and horizontal scaling. Specific issues mentioned include sticky sessions, message ordering, and dealing with backpressure. Several commenters share personal experiences and anecdotes about WebSocket scaling difficulties, reinforcing the points made in the article. A few suggest alternative approaches like server-sent events (SSE) for simpler use cases, while others recommend specific technologies or architectural patterns for robust WebSocket deployments. The difficulty in finding experienced WebSocket developers is also touched upon.
Successful abstractions manage complexity by isolating it. They provide a simplified interface that hides intricate details, allowing users to interact with a system without needing to understand its inner workings. A good abstraction chooses which details to expose and which to conceal, offering just enough information for effective use. This simplification reduces cognitive load and allows for easier composition and reuse of components. The key is finding the right balance: too much abstraction leads to leaky abstractions where the underlying complexity seeps through, while too little provides insufficient simplification.
HN commenters largely agreed with the author's premise that good abstractions hide complexity. Several pointed out that "leaky abstractions" are a common problem, where the underlying complexity bleeds through and negates the abstraction's benefits. One commenter highlighted the difficulty of finding the right balance, where an abstraction is neither too complex nor too simplistic, using the example of an overly abstracted car where the driver has no control over engine specifics. The value of predictable behavior within an abstraction was also emphasized, along with the importance of choosing the right level of abstraction for the task at hand, suggesting different levels for different users (e.g., library user vs. library developer). Some discussion focused on the definition of "complexity" itself, with suggestions that "complications" or "implementation details" might be more accurate terms. The lack of mention of Postel's Law (be conservative in what you send, liberal in what you accept) was noted by one commenter as a surprising omission.
This blog post explores using Go's strengths for web service development while leveraging Python's rich machine learning ecosystem. The author details a "sidecar" approach, where a Go web service communicates with a separate Python process responsible for ML tasks. This allows the Go service to handle routing, request processing, and other web-related functionalities, while the Python sidecar focuses solely on model inference. Communication between the two is achieved via gRPC, chosen for its performance and cross-language compatibility. The article walks through the process of setting up the gRPC connection, preparing a simple ML model in Python using scikit-learn, and implementing the corresponding Go service. This architectural pattern isolates the complexity of the ML component and allows for independent scaling and development of both the Go and Python parts of the application.
HN commenters discuss the practicality and performance implications of the Python sidecar approach for ML in Go. Some express skepticism about the added complexity and overhead, suggesting gRPC or REST might be overkill for simple tasks and questioning the performance benefits compared to pure Python or using GoML libraries directly. Others appreciate the author's exploration of different approaches and the detailed benchmarks provided. The discussion also touches on alternative solutions like using shared memory or embedding Python in Go, as well as the broader topic of language interoperability for ML tasks. A few comments mention specific Go ML libraries like gorgonia/tensor as potential alternatives to the sidecar approach. Overall, the consensus seems to be that while interesting, the sidecar approach may not be the most efficient solution in many cases, but could be valuable in specific circumstances where existing Go ML libraries are insufficient.
Summary of Comments ( 37 )
https://news.ycombinator.com/item?id=43515563
Hacker News commenters generally agreed with the author's premise that architects often over-engineer microservice architectures. Several pointed out that the drive towards microservices often comes from vendors pushing their products and technologies, rather than actual business needs. Some argued that "architect" has become a diluted title, often held by those lacking practical experience. A compelling argument raised was that good architecture should be invisible, enabling developers, rather than dictating complex structures. Others shared anecdotes of overly complex microservice implementations that created more problems than they solved, emphasizing the importance of starting simple and evolving as needed. A few commenters, however, defended the role of architects, suggesting that the article painted with too broad a brush and that experienced architects can add significant value.
The Hacker News post titled "Why I'm No Longer Talking to Architects About Microservices" (linking to an article on the same topic) generated a moderate amount of discussion, with several commenters sharing their perspectives on the role of architects in microservice adoption and the challenges of implementing them effectively.
Several commenters echoed the sentiment of the original article, expressing frustration with architects who promote microservices as a silver bullet without a deep understanding of the complexities involved. One commenter noted how some architects focus excessively on theoretical purity and complex diagrams, losing sight of the practical considerations and trade-offs required in real-world projects. They highlighted the importance of understanding the problem domain and choosing the right architecture for the specific needs, rather than blindly following trends.
Another commenter pointed out the potential disconnect between architects who design systems and the developers who actually build and maintain them. They emphasized the need for architects to be more hands-on and involved in the implementation process, gaining practical experience with the technologies they advocate. This, they argued, would lead to more realistic and effective architectural decisions.
The discussion also touched upon the importance of communication and collaboration between architects and developers. One comment suggested that the issue isn't necessarily architects themselves, but rather a lack of clear communication and shared understanding. They advocated for a more collaborative approach, where architects and developers work together to define the architecture and ensure its successful implementation.
A different perspective was offered by a commenter who argued that architects do have a valuable role to play in microservice adoption, particularly in large organizations. They suggested that architects can help establish guidelines, patterns, and best practices for implementing microservices consistently across different teams. However, they acknowledged the risk of architects becoming detached from the realities of development, reinforcing the need for practical experience and ongoing communication.
Some commenters also discussed the potential downsides of microservices, such as increased complexity, operational overhead, and the need for robust monitoring and logging. They cautioned against adopting microservices prematurely or without a clear understanding of the benefits and trade-offs.
While there was a general agreement on the challenges associated with microservice adoption and the importance of pragmatic architectural decisions, the comments also highlighted the diverse perspectives on the role of architects in this process. The discussion emphasized the need for collaboration, communication, and a deep understanding of both the technical and organizational context when implementing microservices.