InfoQ Homepage API Content on InfoQ
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Service Mesh Ultimate Guide 2021 - Second Edition: Next Generation Microservices Development
Get up to speed on the adoption of service mesh. Learn how to deploy service mesh solutions in heterogeneous infrastructures and application/service connectivity.
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Adoption of Cloud Native Architecture, Part 3: Service Orchestration and Service Mesh
This part 3 article in Cloud Native Architecture Adoption series, explores service interaction in a microservices based architecture, typical challenges we experience in distributed systems without proper governance, and how patterns like service orchestration and service mesh can help address those challenges.
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Bootstrapping the Authentication Layer and Server with Auth0.js and Hasura
When you're trying to prototype an MVP for your app and want to start iterating quickly, the upfront cost of setting up authentication can be a massive roadblock. The authentication layer requires significant work, and you must always be on the lookout for security vulnerabilities.
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Consistency, Coupling, and Complexity at the Edge
Successful use of a microservices architecture requires maintaining a clear separation of concerns in the various layers and by employing design principles best suited to each layer. While RESTful API design has become the standard for microservices, it can cause problems at the UI layer. Alternatives such as the Backend-for-Frontend pattern using GraphQL can provide better separation of concerns.
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Turning Microservices Inside-Out
Turning microservices inside-out means moving past a single, request/response API to designing microservices with an inbound API for queries and commands, an outbound APIs to emit events, and a meta API to describe them both. A database can be supplemented with Apache Kafka via a connecting tissue such as Debezium.
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Benefits of Loosely Coupled Deep Learning Serving
As deep networks are becoming more specialized and resource-hungry, serving such networks on acceleration hardware in tight-budget environments is also becoming difficult. Instead of using API frameworks, loosely coupled components can be preferred as an alternative. They bring high controllability, easy adaptability, transparent observability, and cost-effectiveness when serving deep networks.
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Why Design Systems Need APIs - Q&A with Louis Chenais, Chief Evangelist at Specify
Design systems exist for their business value: to help organizations reach brand consistency across many platforms like the web, Android, or iOS. Design APIs strive to connect the consumers and the contributors of a design system through the tools they use on a daily basis.
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GraphQL Reference Guide: Building Flexible and Understandable APIs
This online guide aims to answer pertinent questions for software architects and tech leaders, such as: Why would you use GraphQL? Why should you pay attention to GraphQL now? How can GraphQL help with data modelling in the Enterprise?
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A Seven-Step Guide to API-First Integration
For a successful digital transformation project, following an API-first approach is more effective and future proof than using an integration-first approach.
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Real Time APIs in the Context of Apache Kafka
Events offer a Goldilocks-style approach in which real-time APIs can be used as the foundation for applications which is flexible yet performant; loosely-coupled yet efficient. Apache Kafka offers a scalable event streaming platform with which you can build applications around the powerful concept of events.
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Four Case Studies for Implementing Real-Time APIs
API calls now make up 83% of all web traffic. Competitive advantage is no longer won by simply having APIs; the key to gaining ground is based on the performance and the reliability of those APIs. This article presents a series of four case studies of how real time APIs were implemented.
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Load Testing APIs and Websites with Gatling: It’s Never Too Late to Get Started
Conducting load tests against APIs and websites can both validate performance after a long stretch of development and get useful feedback from an app in order to increase its scaling capabilities and performance. Engineers should avoid creating “the cathedral” of load testing and end up with little time to improve performance overall. Write the simplest possible test and iterate from there.