Engineering Team Scrapped Cloudflare Path for Internal Prometheus Metrics Over VPC

·1 views

A software team running two DigitalOcean droplets in the same VPC initially routed internal Prometheus metrics scrapes through Cloudflare, a WAF, and OAuth token minting — adding unnecessary complexity to a private network task. The setup worked but forced every 30-second scrape to traverse multiple TLS handshakes and public infrastructure just to move roughly 80 KB of data between adjacent servers. About a month after deployment, the team noticed both droplets shared the same private VPC subnet in DigitalOcean's nyc3 region, meaning a direct private-IP connection had always been available. They reconfigured Prometheus to scrape the API directly over the VPC using the private interface, removing Cloudflare from the path entirely while retaining OAuth enforcement at the application layer. The incident highlights how the architecturally obvious solution — routing internal traffic the same way as public traffic — can introduce avoidable overhead when simpler private networking options already exist.

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)

Developer Launches SeedSQL to Auto-Generate Relational SQL Test Data with Key Integrity

A developer has built SeedSQL, a free web tool designed to simplify the creation of realistic mock data for complex relational database schemas. The tool addresses common pain points such as foreign key mismatches, format errors, and the time-consuming task of writing manual seed scripts. SeedSQL automatically maps parent-child table dependencies, ensuring referential integrity across linked tables like users, orders, and payments. Users can export generated data as SQL insert scripts or CSV files compatible with PostgreSQL and MySQL. The free tier supports up to 1,000 rows and includes localization features for country-specific data formats.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

AI Agents Can Help Automate Shopify, But Production Failures Are Costly

A developer and e-commerce consultant shares hard-won lessons from deploying AI agents on Shopify, warning that real-world results often fall short of the marketing promise. One client spent $5,000 in LLM credits on a product description agent that repeatedly hallucinated features unrelated to their store. In a separate case, an inventory automation agent misread Shopify's API and accidentally zeroed out stock across entire product categories. Customer service bots built with frameworks like CrewAI have also been observed entering endless clarification loops, burning API costs while failing to resolve basic refund requests. The author concludes that AI works best for repetitive, low-stakes tasks like drafting bulk product descriptions, but requires strict guardrails, human review, and robust error-handling before it can be trusted in production.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Five Common Data Leakage Traps That Make ML Models Look Better Than They Are

Data leakage is one of the most frequent reasons machine learning results appear stronger than they actually are, yet few practitioners check for it systematically. The five main culprits include duplicate or near-duplicate rows shared across training and test sets, temporal look-ahead leakage from random splits on time-series data, and features that inadvertently encode the target variable. A real-world demonstration using the EEG Eye State dataset showed a model's AUC score collapsing from 0.971 to 0.546 simply by switching from a random split to a time-respecting one. To address this, the author developed two open-source Python tools — LeakHound, which detects all five leak types and reports honest versus inflated scores, and safesplit, which prevents leakage by enabling group-aware, time-aware, and sequence-aware data splitting.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why AI Agent Reports Are Not Proof: The Case for Independent Artifact Verification

A software engineering post argues that session reports generated by AI agents are self-descriptions, not independently verifiable evidence. The author contends that approving a report — even with multiple reviewers — only means one observation has been read multiple times, not independently confirmed. The post cites a real example where a test suite was reported stable and approved twice, yet a fresh rerun against the actual files on disk revealed two failures in sixteen runs. The recommended practice is to check the exact worktree, read the actual diff, run the declared command, and preserve its output for others to inspect. The core principle is that a report serves as a map to the artifact, not a substitute for re-running and directly observing it.

0 comments Read more at DEV Community