Official ITPUB account sharing technical insights, community news, and exciting events.
The recent plunge in software stocks reveals that large‑model AI is eroding the core value of traditional SaaS, forcing a shift from GUI‑driven products to language‑based interfaces, prompting firms to focus on token efficiency, plugin architectures, and outcome‑based pricing to survive.
The article argues that the SaaS model is collapsing as AI agents can replace traditional front‑end, back‑end, and middleware layers, driving a shift toward a simple Agent + Database architecture, reviving CLI tools, and redefining the role of GUIs in the era of large language models.
OpenAI’s engineering team expanded a single‑primary PostgreSQL cluster with nearly 50 read‑only replicas, migrated write‑heavy workloads to Azure Cosmos DB, and applied extensive optimizations to reliably serve the global traffic of ChatGPT and the OpenAI API for 800 million users at multi‑million queries per second.
This guide explains how to extend a Flink CDC pipeline so that, in addition to real‑time data replication, DDL ALTER statements from MySQL are captured, split from the data stream, and applied to Doris using side‑outputs and a custom JDBC sink.
An interviewee’s instinct to simply add more RocketMQ consumers for a backlog of 100 million messages sounds plausible but fails to address root causes; the article breaks down why this quick fix is insufficient and outlines a multi‑stage, systematic approach—from emergency mitigation to root‑cause analysis and long‑term prevention—to handle massive message queues effectively.
macOS, iOS and tvOS all run on the open‑source BSD‑based Darwin system, whose history traces back to NeXTSTEP and explains why many macOS‑related tools carry the Darwin label despite most macOS components remaining proprietary.
This article explains how to use Flink CDC together with the CdcTools utility to automatically capture newly created MySQL tables and synchronize both their schema and data to a Doris database in real time, covering the required code, side‑output handling, async execution, and a special delete‑sign field.
This article breaks down the interview‑level system design of a WeChat‑style step leaderboard that must support over a billion users, handling massive write spikes, low‑latency friend ranking queries, storage scaling, and relationship complexity with a three‑part architecture using MQ, Redis, and MySQL.
A well‑defined naming convention for data‑warehouse tables reduces chaos, improves maintainability, speeds up queries, and cuts cross‑team collaboration costs, turning raw data into a strategic asset for modern enterprises.
The article explains how using IN and NOT IN in SQL can dramatically hurt performance and produce incorrect results, especially with large tables or NULL values, and demonstrates safer alternatives such as EXISTS, NOT EXISTS, and JOIN with concrete code examples.
