Scalability

Table of Contents Introduction Latency vs. Throughput: Core Trade‑offs Key Building Blocks of an LLM Inference Pipeline 3.1 Hardware Layer 3.2 Model Optimizations 3.3 Serving & Orchestration Batching Strategies for Real‑Time Traffic Asynchronous & Streaming Inference Scalable Architecture Patterns 6.1 Horizontal Scaling with Stateless Workers 6.2 Edge‑First Deployment Observability, Monitoring, and Auto‑Scaling Practical Code Walkthroughs 8.1 Quantized Inference with 🤗 BitsAndBytes 8.2 FastAPI + Triton Async Client 8.3 Dynamic Batching with NVIDIA Triton Real‑World Case Study: Conversational AI at Scale Best‑Practice Checklist Conclusion Resources Introduction Large language models (LLMs) have moved from research prototypes to production‑grade services powering chatbots, code assistants, search augmentation, and real‑time translation. While model size and capability have exploded, user experience hinges on latency—the time between a request and the model’s first token. At the same time, many applications demand high throughput, processing thousands of concurrent queries per second (QPS). ...

Table of Contents Introduction Getting Started with PostgreSQL Core Concepts Every Developer Should Know Data Modeling for Scale Indexing Strategies Scaling Reads: Replication & Read‑Replicas Scaling Writes: Partitioning & Sharding Connection Pooling & Session Management High Availability & Failover Monitoring & Observability Deploying PostgreSQL in the Cloud Vector Search with pgvector Integrating Vector Search into Applications Performance Tuning for Vector Workloads Security & Compliance Best‑Practice Checklist Conclusion Resources Introduction PostgreSQL has evolved from a reliable relational database to a full‑featured data platform capable of powering everything from simple CRUD APIs to massive, globally distributed systems. In the last few years, two trends have reshaped how developers think about PostgreSQL: ...

Introduction Retrieval‑Augmented Generation (RAG) has emerged as a powerful paradigm for building language‑model applications that combine the creative flexibility of generative AI with the factual grounding of external knowledge sources. In a RAG pipeline, a vector database (or “vector store”) holds dense embeddings of documents, code snippets, product catalogs, or any other textual artefacts. When a user query arrives, the system performs a similarity search, retrieves the most relevant pieces of information, and feeds them into a large language model (LLM) to produce a context‑aware response. ...

Introduction In today’s data‑driven world, businesses need to react to events as they happen. Whether it’s a fraud detection engine, a recommendation system, or a monitoring dashboard, the ability to ingest, process, and act on streams of data in real time is a competitive differentiator. Apache Kafka has emerged as the de‑facto backbone for building such pipelines because it combines high throughput, durable storage, and horizontal scalability in a single, simple abstraction: the distributed log. ...

Introduction In the era of cloud‑native computing, event‑driven microservices have emerged as a powerful paradigm for building scalable, resilient, and loosely coupled systems. By reacting to immutable events rather than invoking synchronous APIs, teams can achieve higher throughput, better fault isolation, and more natural support for asynchronous workflows such as order processing, IoT telemetry, and real‑time analytics. This guide walks you through the fundamentals, design patterns, implementation strategies, and operational concerns of event‑driven microservices architecture (EDMA). Whether you are a seasoned architect or a developer stepping into distributed systems, the article provides a comprehensive roadmap to design, build, and run production‑grade event‑driven services. ...