SShortSingh.
Back to feed

How to Safely Test a MySQL 5.7 to 8.0 Upgrade on Azure Before Going Live

0
·1 views

Upgrading a production MySQL database carries significant risk, making it critical to test the process in an isolated environment first. The recommended approach involves restoring a recent production backup to a separate Azure MySQL server, leaving the live database completely untouched. Developers should use MySQL Shell to run the built-in Upgrade Checker tool, which flags known compatibility issues that must be resolved before proceeding. After fixing those issues, the test server is upgraded to MySQL 8.0 and the application is validated across queries, APIs, migrations, and other functions. Only once the test upgrade is confirmed successful should teams proceed with upgrading the production database.

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 ·

Developer's paid AI feature was free for anyone while invisible to paying users

A developer at APEX Versus, an AI-powered comparison site, discovered two simultaneous bugs in its paid 'Deep Analysis' feature that produced the exact opposite of intended access control. The server-side API had no authentication or credit checks, allowing anyone to access the premium feature for free via direct requests, while a reversed visibility condition in the frontend hid the feature from users who had actually paid for it. The root cause was a silent, empty catch block masking a failed client-side credit deduction that had been quietly rejected by updated Firestore security rules for weeks. The developer fixed the issues by moving all credit checks and deductions server-side within a Firestore transaction, correcting the inverted UI logic, and adding the missing button that had never been wired up. The incident highlights a critical distinction between client-side and server-side validation, especially for revenue-sensitive features.

0
ProgrammingDEV Community ·

Web data growth outpaces bots: 402 million terabytes generated daily in 2026

By 2026, humans and machines together generate approximately 402.74 million terabytes of data every 24 hours, equivalent to nearly 3.8 petabytes per second. No existing bot, crawler, or supercomputer has the bandwidth, energy, or storage capacity to process this volume of information in real time. As a result, major tech crawlers from companies like Google and OpenAI now apply priority algorithms, frequently indexing only high-authority sites while independent and niche content can remain invisible for months. This selective crawling means corporate AI systems decide what portion of the internet is deemed relevant, effectively burying non-mainstream content from automated summaries people rely on. The article argues that local, on-demand fetch tools offer a more reliable and secure alternative to depending on overwhelmed third-party crawlers for retrieving and processing web content.

0
ProgrammingDEV Community ·

AI Pipeline Uses Barcodes and LLM Re-ranking to Identify Comic Books for Resale

A developer has built an open-source tool called intent-longbox that helps comic shop staff photograph back issues and generate accurate Shopify product listings. The pipeline decodes UPC barcodes to extract series, issue number, cover variant, and print run data before any AI model is involved, reducing reliance on error-prone vision-only identification. An LLM then re-ranks candidates but cannot alter the deterministic barcode output, and no listing goes live without a human confirming the result. The system is currently in pilot at a shop called Gotham City Limit, with similarity-based image retrieval not yet built, making damaged or pre-1990 barcodes the weakest point in the workflow. To preserve a tamper-proof audit trail, the database enforces append-only rules at the trigger level rather than relying solely on developer discipline.

0
ProgrammingDEV Community ·

What Password Managers Really Track — And What Their Privacy Policies Promise

Password managers store far more than encrypted credentials, collecting behavioral data such as login timestamps, device details, IP addresses, and browser extension usage. Privacy policies across major providers vary significantly in how they handle data retention, third-party sharing, and government compliance requests. Services like 1Password and Bitwarden are noted for minimizing third-party analytics, while LastPass drew criticism for retaining vault metadata even after account deletion. Some policies contain vague language around data sharing with affiliates or law enforcement, which experts say obscures meaningful privacy risks. Users are advised to scrutinize key policy details — including deletion timelines, zero-knowledge architecture claims, and third-party data flows — before choosing a password manager.

How to Safely Test a MySQL 5.7 to 8.0 Upgrade on Azure Before Going Live · ShortSingh