The hottest Microservices Substack posts right now

And their main takeaways
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Top Technology Topics

Scaling Distributed Systems with the Scatter-Gather Pattern

Engineering At Scale • 60 implied HN points • 15 Feb 25
🕹 Technology Distributed Systems Microservices Cloud Computing Software Design System Architecture
  1. The Scatter-Gather pattern helps speed up data retrieval by splitting requests to multiple servers at once, rather than one after the other. This makes systems respond faster, especially when lots of data is needed.
  2. Using this pattern can improve system efficiency by preventing wasted time waiting for responses from each service. This means the system can handle more requests at once.
  3. However, implementing Scatter-Gather can be tricky. It requires careful handling of errors and managing different data sources to ensure the information is accurate and reliable.

How Adaptive AI Microcontainers Outmaneuver Modern Cybersecurity Threats in AI Workloads

Phoenix Substack • 14 implied HN points • 20 Feb 25
🕹 Technology AI Security Cybersecurity Microservices Software Development Cloud Computing
  1. AI workloads are important for businesses but are also very attractive targets for cyber threats. This means we need better ways to protect them.
  2. Traditional security methods struggle because they can be predictable and static, making it easier for hackers to get in and steal data or disrupt systems.
  3. Adaptive AI Microcontainers offer a modern solution by constantly changing and healing themselves, making it much harder for cybercriminals to succeed.

How Adaptive AI Microcontainers Outmaneuver Modern Cybersecurity Threats in AI Workloads

Phoenix Substack • 42 implied HN points • 06 Feb 25
🕹 Technology Cybersecurity AI Microservices Software Enterprise
  1. AI workloads are crucial for businesses but can attract cyber threats. These threats target predictable systems and can steal data or disrupt operations.
  2. Static security methods, like firewalls, are not enough to protect AI workloads. New challenges like lateral movement and data theft highlight the need for better security.
  3. Adaptive AI Microcontainers create secure environments by changing and healing themselves automatically. This makes it hard for hackers to predict or exploit the system.

Queues offer more than orderly processing

System Design Classroom • 679 implied HN points • 02 Jul 24
🕹 Technology System Design Architecture Microservices Distributed Systems Scalability
  1. Queues help different parts of a system work independently. This means you can change one part without affecting the others, making updates easier.
  2. They improve a system's ability to handle more users at once. You can add more servers to take in requests without needing to instantly boost how fast they are processed.
  3. Queues also keep things running smoothly during busy times. They act like a waiting area, holding tasks so no work gets lost even if things get too hectic.

Authorization in microservice architecture, P4: Deploy in production

Hung's Notes • 79 implied HN points • 18 Jul 24
🕹 Technology Microservices Software Development Authorization Cloud Computing IT Infrastructure
  1. Migrating authorization logic from an old system to a new one can take a long time and requires careful planning to avoid errors.
  2. Each part of a business can manage its own authorization rules, making it easier for them to control access based on their specific needs.
  3. As systems grow, it's important to keep improving and adapting to new challenges, like optimizing runtime decisions and better analyzing access logs.
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Authorization in microservice architecture, P2: The approaches

Hung's Notes • 59 implied HN points • 18 Jul 24
🕹 Technology Microservices Authorization Access Control Software Development System Design
  1. Fine-Grained Authorization (FGA) is a better way to manage user permissions in a system. It allows specific users to have certain actions on specific resources, making access control simpler and more organized.
  2. Relationship-Based Access Control (ReBAC) focuses on the connections between users and resources instead of just roles. It builds a graph to show these relationships, but it can be complicated and difficult to maintain.
  3. Attribute-Based Access Control (ABAC) uses attributes of users and resources to determine access, making it flexible and easier to implement. It allows for clear policy definitions without needing to change how users interact with the system.

Authorization in microservice architecture, P1: The motivation to change

Hung's Notes • 59 implied HN points • 18 Jul 24
🕹 Technology Software Development Cloud Computing Microservices Access Control Identity Management
  1. Authorization is a crucial part of managing digital evidence, and it needs to be efficient to handle many users and lots of data. Complex systems can find it hard to keep permissions clear.
  2. Current access control models like Role-Based Access Control (RBAC) and Discretionary Access Control (DAC) can get too complicated when managing many users and permissions. This can lead to messy code and performance issues.
  3. As organizations grow, they must decide how to structure their authorization logic, whether to centralize it in one team or spread it across many. Both choices have their own challenges in consistency and maintenance.

Authorization in microservice architecture, P3: The Policy Language

Hung's Notes • 39 implied HN points • 18 Jul 24
🕹 Technology Software Development Cybersecurity Data Management Programming Languages Microservices
  1. A Domain-Specific Language (DSL) helps create clear and precise authorization policies for microservices. It makes it easier for everyone involved, from developers to managers, to understand authorization rules.
  2. The new policy language is designed to overcome performance issues by allowing lazy loading and efficient management of large datasets. This means it doesn't grab unnecessary data upfront, speeding up processes.
  3. Using YAML instead of complex formats makes the policies more readable and easier for non-engineers to understand. This helps ensure that more people can participate in and review authorization rules effectively.

Microservices vs. Monolithic Approaches in Data

The Orchestra Data Leadership Newsletter • 79 implied HN points • 17 Feb 24
🕹 Technology Data architecture Microservices Cloud Infrastructure Software Tools
  1. The choice between microservices and monolithic architectures in data impacts the tools and solutions you choose.
  2. Microservices allow for distributed infrastructure, specialization, and easier scaling in data architecture.
  3. Assumptions about high interoperability, governance, and acceptable data egress and storage costs are key considerations when opting for a microservices approach.

Amazon Prime Video reduced costs by 90% by ditching Microservices [System Design Sundays]

Technology Made Simple • 159 implied HN points • 07 May 23
🕹 Technology System Design Microservices Architecture Scaling
  1. Amazon Prime Video saw a 90% cost reduction by moving away from Microservices to a monolith architecture. This change improved scalability and reduced infrastructure costs significantly.
  2. The challenges Amazon faced with their initial microservices implementation included hitting scaling limits and high overall costs of the system. Moving to a monolith architecture helped address these issues and allowed for better scaling.
  3. While the debate between Microservices and Monoliths continues, the decision should depend on factors like team size, emphasis on scale, and complexity. Microservices offer scalability but require careful planning, while monoliths are easier to design and manage.

Demystifying API Gateways: What They Are & Why They Matter

Engineering At Scale • 75 implied HN points • 11 Feb 24
🕹 Technology APIs Microservices Architecture Security Performance
  1. API Gateway acts as an intermediary in microservices, handling client requests, and routing them to the appropriate microservices, simplifying communication for clients.
  2. API Gateway enhances security by authenticating and authorizing requests, provides rate-limiting to prevent attacks, and improves performance through caching and protocol conversion.
  3. Downsides of API Gateways include increased latency due to an extra hop, potential single point of failure, and added complexity to the system architecture.

Back-end for Front-end Pattern in Ecommerce

Cristian’s Substack • 39 implied HN points • 10 Apr 23
🕹 Technology Development Architecture APIs Microservices Patterns
  1. Back-end for Front-end (BFF) pattern helps reduce front-end complexity by acting as an interface between front-end and microservices/APIs.
  2. BFF pattern separates front-end requirements from back-end concerns, making the application easier to maintain and more resilient to API changes.
  3. Implementing BFF pattern can improve error handling, maintainability, and performance in an ecommerce platform by allowing for better control over API consumption and reducing network steps.

🔴 Stream: DDD Context Mapping with Clean Architecture

🔮 Crafting Tech Teams • 19 implied HN points • 06 Sep 23
🕹 Technology Software Development Architecture Microservices
  1. The post discusses DDD Context Mapping and Clean Architecture, exploring their benefits and connections in software development.
  2. There is a call for feedback on chat participation and event accessibility on Linkedin, highlighting the importance of engagement in the community.
  3. Readers are encouraged to subscribe for a 7-day free trial to access more content and resources on Crafting Tech Teams.

Developers: Why do we need containers?

Weekend Developer • 19 implied HN points • 17 Jun 23
🕹 Technology Software Development Containerization DevOps Microservices Cloud Computing
  1. Containers are crucial for modern software development, solving issues with managing environments and configurations manually.
  2. Docker revolutionized development by providing a simple way to package applications with dependencies, ensuring consistency and portability across environments.
  3. Docker and containers have reshaped the software industry, enabling microservices, DevOps, and cloud-native development, but also brought challenges like container management, networking, security, and efficiency.

How IFood Microservice handles 30k Requests per Second[Systems Design Sundays]

Technology Made Simple • 39 implied HN points • 23 May 22
🕹 Technology Systems Design Microservices Scaling Collaboration
  1. Develop solutions with future scaling needs in mind to make things easier down the line.
  2. Spend significant time planning how to divide responsibilities within your team to ensure efficiency and effectiveness.
  3. Clearly define your needs and anticipate potential problems to save time and effort in system design.

[Video] A Brief Introduction to Kafka (with analogy)

Weekend Developer • 1 HN point • 06 Jul 24
🕹 Technology Software Engineering Microservices Fault Tolerance Scalability
  1. Kafka ensures system consistency in the microservices world by allowing events to be recorded and processed consistently even during service downtime.
  2. Kafka enables a decoupled, event-driven approach to microservices communication, providing fault tolerance and scalability as the number of services grows.
  3. The benefits of Kafka in microservices include event-driven architecture, fault tolerance, and scalability, all contributing to a reliable and consistent system.

Some practical tasks with k8s Pods

DevCube • 2 HN points • 26 Feb 23
🕹 Technology Kubernetes Microservices Containers Development
  1. When working with microservices and Kubernetes, it's important to remember basic tasks like troubleshooting Pod startup and accessing logs.
  2. Don't solely rely on different tools for Kubernetes tasks, remember the fundamental basics.
  3. Appreciate foundational explanations like Liz Rice's video on containers, even if they are older, for understanding container technology.

Middleware - Bringing observability up to speed

Termsheet by Attack Capital • 4 HN points • 04 Apr 23
🕹 Technology Observability AI Cloud Microservices Startup
  1. Founder Laduram Vishnoi's frustration with high costs of cloud observability tools led to the creation of Middleware.
  2. Middleware addresses challenges with traditional observability tools by offering a comprehensive and unified solution for cloud-native and microservices.
  3. Middleware uses AI-powered algorithms, is vendor agnostic, and correlates data from various sources to provide real-time observability and streamline issue debugging.

Microservices impair the productivity of high performing programmers

Sheep Code • 1 HN point • 10 Aug 23
🕹 Technology Software Engineering Microservices System Design
  1. Microservices can limit the productivity of high performing coders due to the complexity of dependencies across teams.
  2. In a microservices system, expertise shifts from individual coding skills to system design and team collaboration.
  3. For a 10x engineer in a microservices environment, success is more about harmonizing microservices and teams than individual coding prowess.

Data is not a Microservice

Data Products • 1 HN point • 07 Jul 23
🕹 Technology Data Engineering Software Architecture Data Management Microservices
  1. Data requires a source of truth that microservices cannot inherently provide without a shift in software engineering practices
  2. Not all data is equally valuable, so treating all data as microservices can be costly and restrictive
  3. The data development lifecycle differs from software development, requiring flexibility, reuse, and tight coupling that conflict with typical microservices architecture

Microservice Observability, Part 1: Disambiguating Observability and Monitoring

realkinetic • 0 implied HN points • 03 Oct 19
🕹 Technology Microservices Observability Monitoring Architecture
  1. In microservice architectures, the conversation shifts from traditional monitoring to observability due to the complexity of multiple services interacting dynamically.
  2. In static monolithic architectures, monitoring is more straightforward with a single runtime and centralized telemetry.
  3. Observability offers deeper insights into system behavior by exploring new discoveries after the fact, providing more context and a higher level of granularity compared to traditional monitoring.

More Environments Will Not Make Things Easier

realkinetic • 0 implied HN points • 11 Apr 18
🕹 Technology Microservices
  1. Microservices require extreme discipline and upfront thinking to manage integration challenges.
  2. Building software with multiple services owned by different teams necessitates API-driven development for stability.
  3. Having more development environments is not the solution to lack of discipline; instead, prioritize stable APIs and consider consumer-driven contract testing.

Searching for third-party details in a microservice architecture

Reflective Software Engineering • 0 implied HN points • 27 Oct 22
🕹 Technology Microservices Search Engine
  1. When developing a domain model, keep it concise with abstractions related to the problem being modeled. Consider how to handle search queries that involve third-party data not directly stored in the model.
  2. Avoid growing your domain model extensively to accommodate indirect relationships with third-party systems. This can lead to increased coupling, maintenance challenges, and data movements.
  3. Implement a solution using a CompositeKeywordProvider and asynchronous worker to fetch and manage keywords from various sources in a microservice architecture. Decouple keyword generation from data writing and searching, enabling a flexible and efficient search functionality.

Three best practice approaches to authentication

realkinetic • 0 implied HN points • 13 Dec 22
🕹 Technology Authentication APIs Microservices Networking Security
  1. Service-level authentication puts the responsibility of authentication on individual services, allowing better control over which endpoints are authenticated and which aren't.
  2. API-gateway authentication centralizes authentication at a gateway, simplifying downstream services' implementation but requires careful configuration to prevent vulnerabilities.
  3. Service-mesh authentication uses sidecar proxies to provide authentication, set up transparently for services, enhancing security but adding complexity and performance overhead.

Composition is a function of communication

Tech and Thoughts • 0 implied HN points • 24 Oct 23
🕹 Technology Software Development Communication Microservices Programming Languages Systems Design
  1. Communication is key for building software. Systems work best when they have clear and simple ways for different parts to talk to each other.
  2. Just like on the internet, software should focus on how parts interact, not just what those parts do. This makes it easier to adapt and grow.
  3. When designing software, spend time planning how components will communicate. Get this right early on to avoid problems later.

Fail Safe, Fail Smart… Succeed! (Part 3 of 3)

It Depends / Nimble Autonomy • 0 implied HN points • 30 Jul 24
🕹 Technology Software Development Cloud Computing Microservices Product Management DevOps
  1. It's important to expect failure in technology work. Today, we design systems with the understanding that things can go wrong at any moment.
  2. Building small, separate services helps manage problems better. If one part fails, it doesn't ruin the whole system, making the user experience smoother.
  3. Learning from failures is key to improvement. When mistakes happen, analyzing them without blame leads to better results in the future.

Introducing Microservices

Overflow • 0 implied HN points • 29 May 23
🕹 Technology Microservices Architecture Scaling Communication Authentication
  1. Microservices architecture provides a solution to the challenges of monolithic applications by structuring an application as a collection of loosely coupled services.
  2. Transitioning from a monolith to microservices involves splitting different modules into independent services, offering flexibility in programming languages, databases, and scaling components of the application independently.
  3. Microservices offer benefits like continuous delivery, easy testing, fault tolerance, and better scalability compared to monolithic applications, making them a favorite among developers.

SRE Doesn’t Scale

realkinetic • 0 implied HN points • 06 Oct 21
🕹 Technology SRE Microservices Cloud DevOps Infrastructure
  1. Implementing SRE in organizations, especially with microservices and cloud, can lead to significant challenges and disruptions.
  2. Scaling the traditional SRE model with microservices becomes resource-intensive and difficult due to the vast number of services to support.
  3. To address scalability issues, consider a framework-oriented model, standardizing tools, codifying best practices, and involving shared responsibility between SRE and development teams.

Operational Use case Patterns for Apache Kafka and Flink — Part 1

Tributary Data • 0 implied HN points • 03 Jan 23
🕹 Technology Data Platforms Stream Processing Microservices Data Management
  1. Operational use cases with Kafka and Flink are crucial for business operations due to their message ordering, low latency, and exactly-once delivery guarantees.
  2. Using polyglot persistency with different data stores for read and write purposes can help solve the mismatch between write and read paths in microservices data management.
  3. Implementing a backend rate limiter using Flink as a Kafka consumer can help prevent exhausting an external system (e.g., a database) due to high message arrival rates from Kafka.

Microservice Observability, Part 2: Evolutionary Patterns for Solving Observability Problems

realkinetic • 0 implied HN points • 03 Jan 20
🕹 Technology Observability Microservices Data Collection Data processing Infrastructure
  1. Observability involves capturing various signals like logs, metrics, and traces to ask questions of systems without knowing those questions in advance.
  2. Challenges in observability can include agent fatigue due to multiple operational tools requiring unique agents, capacity anxiety with elastic microservice architectures, and the need for foresight in collecting necessary data.
  3. Implementing an observability pipeline can help in capturing wide events, consolidating data collection, decoupling sources and sinks, normalizing data schemas, and routing data to various tools for better observability in systems.