Commerce Cache Design: Handling Traffic Spikes Across Local, Distributed, and Database Layers

Ahead of a major event, what should you prepare in your database, and what should you check first when it goes live? Practice your pre-, during, and post-event response sequence using training Aurora logs of Reader CPU spikes, lock waits, deadlocks, and failovers, along with diagnostic SQL.

What should you prepare in PostgreSQL ahead of a large-scale event, and what should you check first when it goes live? Practice the response sequence before, during, and after the event with hands-on experiments in educational Aurora PostgreSQL that trigger lock waits, deadlocks, VACUUM delays, failovers, and Reader CPU spikes—along with diagnostic SQL and playbooks for seven event types.

Do you paste in the sales table every week, check shipping inquiries, and rewrite reports? Become the operator of the virtual shopping mall Moa Life and assign tasks to Claude in Korean. This 30-lesson hands-on course covers everything from connecting data to Google Sheets calculations, dashboards, artifact reports, customer and shipping verification, and reusing your own custom skills. You’ll learn not just to receive results, but also to cross-check the numbers, correct incorrect conditions, and run the same process again next week using the same standards. You won’t need to write code, SQL, or formulas yourself. The course is based on Mac Claude Desktop and Google Sheets; paid accounts and subscription fees are not included. Korean AI male voice is used.

A hands-on project for office and operations staff who need to check weekly transaction totals and errors. Compare the 44 rows in the provided sample summary sheet against two CSV files, then use Google Sheets and Claude to preserve the source data, flag duplicates and errors for review, compile weekly totals, and verify charts. Includes cartoon-style guidance, actual app screens, male AI voice-over and subtitles, and step-by-step prompts.

Replace synchronous coupon issuance and product registration APIs that buckle under sale-launch traffic with a queue, outbox, idempotent consumer, DLQ, and Temporal—and reproduce the before-and-after metrics (latency, failure rate, and DB commits) under the same load on my computer using Docker.

Reproduce firsthand the problems that arise in a microservice flow from order API → payment → inventory → Kafka → consumer → batch: traces breaking, growing to thousands of spans and silently being dropped, or disappearing from dashboards. Then fix them in symptom → diagnosis → resolution order, completing traces and dashboards that work end to end.

I build a DBA agent right on my laptop in Docker that measures MySQL and PostgreSQL every morning, creates a diagnostic report with A/B/C/N letter grades, analyzes problematic queries with EXPLAIN and drafts improvement requests, and reviews plans and costs for scaling connections and Readers up or down before a sale.

[Final Goal] Build an operational agent that handles the full cycle: fault injection → alert → initial AI analysis → runbook and remediation plan → human-approved action → postmortem draft. Run the virtual commerce platform 'Loop Market' with Docker and practice using Prometheus, Grafana, Loki, and Claude.

Build a development pipeline with Claude Code’s headless mode, where a single-line issue comment triggers a PR ready for review. Codify team rules in CLAUDE.md, skills, and hooks, and add three human approval gates, a review bot, a false-positive ledger, and a cost cap.

Final goal: Turn one of my repetitive tasks into a Claude skill → have a colleague install and use it → report the time saved in numbers. Without writing code, I’ll use materials from “Loop Market,” a fictional sports and outdoor marketplace, to run and review 12 role-specific skills myself, then complete one based on my own work for the capstone.

If you run your database in Docker, you’ll learn how to reproduce 30 scenarios on your own computer that can wipe your data or make it unreadable—from `down -v` and `prune` to folder names, password environment variables, 10-second shutdowns, memory limits, image tags, and volume-copy backups. You’ll read the database logs to find the cause, then learn how to restore your data using point-in-time recovery.

Before the Monday 9 a.m. meeting, delegate performance tracking for three ad channels, coupon reconciliation, review complaints, and inventory checks to Cowork, while people just review the reconciliation sheet. Using data from a virtual shopping mall, create about 40 results files organized by owner and five skills, and set up a scheduled run for every Monday at 8 a.m.

Turn half a day of month-end closing into 30 minutes of uploading files and checking results. Give Claude Cowork 16 sample channel files in Smart Store, Coupang, and Cafe24 formats, and build an integrated order sheet → settlement reconciliation (orders ↔ settlements ↔ bank statements, with zero unexplained discrepancies) → returns P&L → inventory and purchase orders → pending customer service list → month-end closing skill. Give instructions in Korean—no coding or formulas—and verify everything against the original files. Learn from 28 real-world cases and actual Claude execution logs.

If you’re told to “run a load test” before an event, use k6 to ramp up requests per second step by step while monitoring database metrics—CPU, active threads, connection pool, lock waits, buffer pool, and replication lag—all on one screen. Identify what breaks first, then set numerical thresholds for system limits and when to stop. Reproduce everything locally with Docker.

See firsthand where the database gets bottlenecked by first-come, first-served coupons and inventory deductions, using lock waits, hot rows, history list length, and connection counts. From conditional UPDATEs, unique keys, and SKIP LOCKED to counter sharding and Redis-based decisions, this hands-on course by a practicing DBA team lead uses Docker-based scenarios to demonstrate the impact of design changes.

We answer 46 interview questions on indexes, execution plans, transactions, locks, replication, and failure handling from the interviewer’s perspective. Compare bad answers with good ones, and each question includes a hands-on verification exercise using MySQL 8.4 so you can answer follow-up questions by saying, “I checked it myself.”

When the order API stalls but CPU usage is under 1%, when adding a single column brings the service to a halt, or when a newly paid order doesn’t appear. This comprehensive 104-lesson course recreates real-world production database incidents in a MySQL 8.4 primary-replica lab environment and gives you step-by-step runbooks for everything from five-minute diagnostics to zero-downtime schema changes, large-scale operations, recovery, and event preparation.

Analyze Excel, CSV, and database files with Claude Code using natural language—no coding required—and check the AI’s numbers against the originals to catch errors yourself. Learn through actual execution logs, from the data dictionary and source fingerprints to metric definitions, aggregation, cross-checking through two independent paths, a one-page report with numbered evidence, and skills for next month.

We measure from MySQL’s side—not application logs—how many statements and rows a single line of JPA code generates, and what execution plans it uses. This practical course, taught by a team lead currently working as a DBA, reproduces and fixes N+1 queries, pagination with collection fetch joins, IDENTITY batch inserts, bulk operations, readOnly, and OSIV across 61 hands-on tests—and proves the results with numbers.

I’ll learn SQLP practical tuning by reproducing the execution plans from the course on my own PC using Oracle 26ai Free. I’ll evaluate 30 hands-on practice problems I created myself using measured figures (blocks read and execution counts), then write out the answers as well.