SShortSingh.
Back to feed

Why Database Transactions Must Wrap Entire Operations, Not Individual Queries

0
·1 views

A software engineering lab demonstrates how omitting database transactions can leave systems in a partially completed, inconsistent state after a failure. In the example, a payment flow inserts a payment record and marks an order as paid before crashing, but never records the wallet transaction — resulting in corrupted data rather than a clean rollback. The core lesson is that a database transaction acts as a boundary: all related writes inside it either commit or roll back together, while statements executed outside any transaction cannot be automatically undone. A safer implementation wraps all three related writes — payment insert, order status update, and wallet transaction insert — inside a single transaction, ensuring the database reverts to its original state if any step fails. The lab also applies the same principle to an outbox pattern, where an invoice update and an outbox event insert are committed atomically to guarantee consistency between business state and event intent.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

AI Agents Can Now Act on Your Systems — But Who Controls What They Do?

AI agents have evolved beyond generating text responses and can now read databases, call APIs, modify code, send messages, and trigger real-world actions across tools like GitHub, Slack, AWS, and Jira. This shift introduces a critical security gap: the same system deciding what action to take is also deciding whether that action should be permitted, which is a weak security boundary. The Model Context Protocol (MCP), whose latest specification was updated on July 28, 2026, standardizes how agents interact with external tools but does not inherently make those interactions safe or authorized. Experts argue that robust agentic AI architecture must separate decision-making from authorization, incorporating policy checks, user confirmation, and audit logs before any tool is executed. Securing AI agent permissions and runtime behavior is rapidly emerging as one of the most pressing engineering challenges of the agentic-AI era.

0
ProgrammingDEV Community ·

Ex-Helicopter Pilot Breaks Down Azure Cloud Resume Challenge Into 7 Free Steps

A cloud and DevSecOps professional, who transitioned into tech after years of flying helicopters commercially, has published a simplified guide to the Azure Cloud Resume Challenge. The challenge, created by Forrest Brazeal in 2020, asks candidates to build a resume as a fully functional cloud application, covering static hosting, serverless APIs, a database, and a CI/CD pipeline. The author warns that most existing walkthroughs quietly lead beginners into roughly $35 per month in costs, particularly when adding HTTPS on a custom domain. To address this, he restructured the original 16-step project into seven manageable chunks, flagging a free alternative at every stage where a paid option exists. He recommends the challenge to anyone entering cloud roles without a degree, noting that hiring managers already recognise what the project entails and how difficult it is to complete.

0
ProgrammingDEV Community ·

Big AI Labs Accused of Using Safety Rules to Block Smaller Rivals

Major AI companies including OpenAI, Anthropic, and Google DeepMind are facing allegations that their public support for strict AI safety regulations is partly a strategy to entrench their market dominance. Analysts argue that expensive compliance requirements, such as mandatory third-party audits, would be financially prohibitive for smaller developers and open-source projects, consolidating the industry among a few wealthy players. PitchBook senior analyst Harrison Rolfes notes that large labs are skilled at centering safety in public discourse while pursuing deeper business interests. Third-party evaluation firms like Apollo Research and METR could become industry gatekeepers, drawing comparisons to the Big Four accounting firms in finance. Notably, OpenAI reportedly explored whether coordinating an industry-wide slowdown in AI development could raise antitrust concerns, underscoring the tension between safety goals and competitive dynamics.

0
ProgrammingDEV Community ·

How Prometheus Scrapes Metrics: A Practical Guide with a Pure Python App

A tutorial series on observability tools revisits a previously configured Prometheus, Loki, and Grafana stack — this time adding a real data source. The article explains Prometheus's pull-based scraping model, where the server periodically fetches metrics from an application's HTTP endpoint, contrasting it with the push model used by tools like StatsD. Key advantages of pull-based collection include centralized failure detection, easy manual testing via curl or a browser, and central control over collection intervals. To demonstrate the concept hands-on, the guide walks through building a minimal Python HTTP server — using no external frameworks — that exposes a /metrics endpoint in Prometheus's plain-text exposition format. The example app tracks total request counts and process uptime, illustrating that no special client library is needed to instrument an application for Prometheus.