Distributed-Systems

Introduction Autonomous agents—ranging from self‑driving cars and delivery drones to industrial robots—generate and consume massive streams of telemetry, sensor data, and control messages. To make real‑time decisions, these agents rely on high‑throughput stream processing pipelines that can ingest, transform, and act upon data within milliseconds. At the same time, the rise of serverless edge platforms (e.g., Cloudflare Workers, AWS Lambda@Edge, Azure Functions on IoT Edge) reshapes how developers deploy compute close to the data source. Edge nodes provide low latency, geographic proximity, and elastic scaling, but they also impose constraints such as limited CPU time, cold‑start latency, and stateless execution models. ...

Table of Contents Introduction Background: Why Latency Matters for LLM Inference Core Challenges in Heterogeneous Multi‑GPU Environments Architectural Foundations 4.1 Model Parallelism 4.2 Pipeline Parallelism 4.3 Tensor Parallelism 4.4 Hybrid Strategies Communication Optimizations 5.1 NVLink & PCIe Topology 5.2 NCCL & Collective Algorithms 5.3 RDMA & GPUDirect 5.4 Compression & Quantization Scheduling, Load Balancing, and Straggler Mitigation Memory Management Techniques 7.1 KV‑Cache Sharding & Offloading 7.2 Activation Checkpointing for Inference Serving Patterns that Reduce Latency 8.1 Dynamic Batching 8.2 Asynchronous Request Pipelines Practical End‑to‑End Example Best‑Practice Checklist Conclusion Resources Introduction Large language models (LLMs) such as GPT‑4, LLaMA‑2, and Claude have moved from research curiosities to production‑grade services. Companies now expose these models through APIs that must deliver sub‑second response times while handling thousands of concurrent users. Achieving low inference latency is especially hard when the model does not fit on a single GPU and must be spread across a heterogeneous multi‑GPU cluster—a mix of different GPU generations, memory capacities, and interconnect topologies. ...

Introduction Semantic search has moved from a research curiosity to a production‑ready capability that powers everything from e‑commerce recommendation engines to enterprise knowledge bases. At its core, semantic search relies on vector embeddings—dense, high‑dimensional representations of text, images, or other modalities that capture meaning in a way that traditional keyword matching cannot. While the algorithms for generating embeddings are now widely available (e.g., OpenAI’s text‑embedding‑ada‑002, Hugging Face’s sentence‑transformers), delivering low‑latency, high‑throughput search over billions of vectors remains a formidable engineering challenge. This challenge is amplified when you try to run the service in a serverless environment—where you have no control over the underlying servers, must contend with cold starts, and need to keep costs predictable. ...

Table of Contents Introduction Why Rate Limiting Matters in Microservices Fundamental Rate‑Limiting Algorithms 3.1 Fixed Window Counter 3.2 Sliding Window Log 3.3 Sliding Window Counter 3.4 Token Bucket 3.5 Leaky Bucket Challenges of Distributed Environments Designing a Distributed Rate Limiter 5.1 Choosing the Right Data Store 5.2 Consistency Models and Trade‑offs 5.3 Sharding & Partitioning Strategies Implementation Walk‑throughs 6.1 Redis‑Based Token Bucket (Go) 6.2 Apache Cassandra Sliding Window Counter (Java) 6.3 gRPC Interceptor for Centralised Enforcement (Node.js) Testing, Metrics, and Observability Best Practices & Anti‑Patterns Case Study: Scaling Rate Limiting for a Global E‑Commerce Platform Conclusion Resources Introduction Modern applications are increasingly built as collections of loosely coupled microservices that communicate over HTTP/REST, gRPC, or message queues. While this architecture brings agility and scalability, it also introduces new operational challenges—one of the most pervasive being rate limiting. Rate limiting protects downstream services from overload, enforces fair usage policies, and helps maintain a predictable quality of service (QoS) for end‑users. ...

Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto pattern for building LLM‑powered applications that need up‑to‑date, factual, or domain‑specific knowledge. At its core, a RAG pipeline consists of three stages: Retrieval – a similarity search over a vector store that returns the most relevant chunks of text. Augmentation – the retrieved passages are combined with the user prompt. Generation – a large language model (LLM) synthesizes a response using the augmented context. While the conceptual flow is simple, production‑grade RAG systems must handle high query volume, low latency, dynamic data updates, and cost constraints. Two architectural levers help meet these demands: ...