A slightly rustic name 🤠A tech veteran navigating the internet wave Hardcore tech: fixing all bugs and tough challenges
The article details how an e‑commerce platform re‑architected its order database by separating recent data into a hot store and historical data into a cold store, describing the motivations, design principles, write and read routing, downstream impacts, consistency safeguards, risks, and observed performance improvements.
The article details a full‑stack solution for real‑time underground personnel monitoring in coal mines, covering why identity recognition is needed, the YOLOv8‑based detection pipeline, OSNet‑based ReID, edge‑box hardware choices, deployment costs, and practical pitfalls learned over two years of field work.
At 10 am a monitoring alarm revealed that the day's commission settlement jumped to ¥600 K—seven times the usual amount—prompting an immediate shutdown of withdrawals, a rapid root‑cause investigation that uncovered a missing else‑if condition, and a nine‑hour data rollback followed by the launch of a real‑time business monitoring platform.
This article details a real‑time risk control system for e‑commerce and finance built on Apache Flink 2.2.1 and CEP, featuring a dynamic Aviator rule engine, three Kafka event streams, multi‑channel output to Redis, MySQL and Kafka, a Spring Boot‑React management UI, and step‑by‑step deployment instructions.
This guide explains how FSCrawler works with Apache Tika and Elasticsearch to automatically crawl, parse, and index diverse unstructured documents, then secure the searchable knowledge base with API‑based access control and OIDC SSO for enterprise use.
This article walks through setting up the foundational environment for a real‑time data visualization pipeline using RocketMQ, Flink, Elasticsearch and Kibana, covering code configuration, index verification, Kibana dashboard creation, and practical production tips.
The article outlines practical architecture strategies for small‑to‑medium enterprises, progressing from simple monolithic designs for a few thousand daily users to micro‑service and data‑sharding solutions for millions, emphasizing realistic scaling, avoiding premature complexity, and focusing on team capabilities and cost‑effective cloud services.
This article walks through setting up a real‑time dashboard stack using RocketMQ, Flink, Elasticsearch and Kibana, covering environment preparation, order data simulation, Flink job creation with source, transformation and Elasticsearch upsert sink, and visualisation considerations.
