6 API Architectural designs You Must Know
1. REST 🌐
Representational State Transfer - REST is like a classic library where you request specific books and receive them as they are. It's simple and widely used for web APIs, like ordering a la carte from a menu 🍽️.
2. GraphQL 🚀
GraphQL is like a customizable buffet 🍴 where you ask for exactly what you want and get a tailored plate. It allows clients to request only the data they need, reducing over-fetching.
3. SOAP 🧼
SOAP (Simple Object Access Protocol) is like sending a letter 💌 with detailed instructions, complete with a table of contents. It's more structured but can be heavier than REST or GraphQL.
4. gRPC 🚄:
gRPC is like a high-speed train 🚄 for communication between services. It uses Protocol Buffers for efficient data exchange and supports streaming and bidirectional communication.
5. WebSockets 🌐💬
WebSockets are like real-time phone calls ☎️ for the web. They enable two-way communication, perfect for chat apps and live updates.
6. MQTT 📡
MQTT (Message Queuing Telemetry Transport) is like a radio broadcast 📻, designed for low-bandwidth, high-latency, or unreliable networks. Ideal for IoT devices and sensor data.
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How do we transform a system to be Cloud Native?
The diagram below shows the action spectrum and adoption roadmap. You can use it as a blueprint for adopting cloud-native in your organization.
For a company to adopt cloud native architecture, there are 6 aspects in the spectrum:
1. Application definition development
2. Orchestration and management
3. Runtime
4. Provisioning
5. Observability
6. Serverless
Most companies start from Step 1 containerization and gradually adopt CI/CD, service orchestration. This microservice architecture significantly increases the number of instances to manage, so systematic testing and monitoring are required to increase plant observability.
In fact, a lot of companies stop at Step 4 without moving to service mesh and cloud-native networking due to the complexity and the required DevOps talent.
Over to you: Where does your system stand in the adoption roadmap?
Reference: Cloud & DevOps: Continuous Transformation by MIT
Redrawn by ByteByteGo
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LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
paper page: https://t.co/dLLAVajQhq
Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications. In this paper, we introduce LMSYS-Chat-1M, a large-scale dataset containing one million real-world conversations with 25 state-of-the-art LLMs. This dataset is collected from 210K unique IP addresses in the wild on our Vicuna demo and Chatbot Arena website. We offer an overview of the dataset's content, including its curation process, basic statistics, and topic distribution, highlighting its diversity, originality, and scale. We demonstrate its versatility through four use cases: developing content moderation models that perform similarly to GPT-4, building a safety benchmark, training instruction-following models that perform similarly to Vicuna, and creating challenging benchmark questions. We believe that this dataset will serve as a valuable resource for understanding and advancing LLM capabilities.