Prerequisite: Read Part 11 — Deconstructing the Ecosystem by Domain for service boundaries.

Vetting Go Engineers in Vietnam: 5 Production Migration Scenarios

Answer-first: Vetting senior Go engineers for a Magento re-architecture project requires evaluating distributed systems migration competency rather than basic greenfield syntax or algorithmic trivia. Technical interview scorecards must stress-test five concrete production scenarios: 1) Distributed Saga Rollbacks during gateway failures, 2) Debezium CDC Event Deduplication, 3) Zero-Downtime Dual-Write Identity Mapping (magento_id_map), 4) Redis Distributed Locking against Flash Sale Overselling, and 5) Zero-Allocation Memory Pooling under 10,000 concurrent goroutines.

A developer who can write a clean Go REST API from scratch is not necessarily qualified to dismantle a live, high-volume Magento monolith.

Migration engineering is significantly harder than greenfield development: it requires operating inside messy legacy constraints, synchronizing databases asynchronously, and designing for graceful degradation during multi-month cutovers.

1. Technical Vetting Funnel Topology

flowchart TD
    Candidate["Candidate Pipeline (Senior Go Vietnam)"] --> Stage1["1. Architecture Screening (45 Min)<br/>DDD boundaries & Strangler Fig concepts"]
    
    Stage1 --> Stage2["2. Live Coding: Concurrency & Goroutine Safety (60 Min)<br/>Worker pools, context cancellation, sync.Pool"]
    
    Stage2 --> Stage3["3. System Design: 5 Migration Scenarios (90 Min)<br/>Debezium CDC, Saga rollback, distributed locks"]
    
    Stage3 --> Stage4["4. Cultural & Asynchronous Communication Check (30 Min)<br/>Loom review, RFC writing, English precision"]
    
    Stage4 --> Decision{"Hiring Decision"}
    Decision -->|"Pass All 5 Gates"| Offer["Extend Senior / Lead Offer ($3,500 - $5,500/mo)"]
    Decision -->|"Fails Distributed Scenarios"| Reject["Reject Candidate"]

2. Five Production Migration Interview Scenarios

flowchart LR
    Scenario1["Scenario 1:<br/>Saga Rollback Failure"] --> Test1["Can they design compensating transactions with idempotency?"]
    Scenario2["Scenario 2:<br/>Debezium Binlog Outage"] --> Test2["Do they understand Kafka offsets and outbox deduplication?"]
    Scenario3["Scenario 3:<br/>Flash Sale Lock Spikes"] --> Test3["Can they implement Redlock with lease expiration?"]
    Scenario4["Scenario 4:<br/>Goroutine Memory Leak"] --> Test4["Do they master context propagation and sync.Pool allocation?"]
    Scenario5["Scenario 5:<br/>EAV UUID Mapping"] --> Test5["Can they handle dual-write primary key translation?"]

Scenario Breakdown & Evaluation Criteria

1. Scenario 1: Distributed Saga Compensation: Ask the candidate to write an order coordinator where Step 1 (Inventory Reserve) succeeds, Step 2 (Payment Authorize) fails, and the network crashes while attempting the compensation step. Green Signal: Candidate implements persistent outbox retries with exponential backoff and idempotency keys.
2. Scenario 2: CDC Event Ordering & Deduplication: Ask how they prevent duplicate order event processing when Debezium crashes and replays 5 minutes of binlog events from its last checkpoint. Green Signal: Candidate utilizes Redis bloom filters or database unique constraints rather than relying on Kafka partition ordering alone.
3. Scenario 3: Flash Sale Distributed Locking: Ask them to prevent 2,000 concurrent goroutines from overselling 10 remaining items in stock. Green Signal: Candidate rejects raw MySQL SELECT FOR UPDATE and implements Redis atomic decrements (DECRBY) or Redlock with explicit TTLs.
4. Scenario 4: High-Concurrency Goroutine Leak Prevention: Provide code containing an unbuffered channel and an un-cancelled context causing thousands of goroutines to leak during HTTP timeouts. Green Signal: Candidate identifies leak within 3 minutes and instruments pprof goroutine stack traces.
5. Scenario 5: Legacy Integer to UUIDv7 Translation: Ask how they bridge Magento’s 32-bit integer auto-increments with microservice UUIDv7 identifiers during a 6-month dual-write window. Green Signal: Candidate designs an immutable bidirectional lookup table with local LRU caching.

3. Production Code Challenge: Goroutine Worker Pool with Context Cancellation

A standard technical screening task evaluating concurrency control, graceful termination, and backpressure:

package main

import (
	"context"
	"fmt"
	"sync"
	"time"
)

type Job struct {
	ID    int
	SKU   string
	Price float64
}

func WorkerPool(ctx context.Context, numWorkers int, jobs <-chan Job) <-chan string {
	results := make(chan string, numWorkers*2)
	var wg sync.WaitGroup

	for i := 0; i < numWorkers; i++ {
		wg.Add(1)
		go func(workerID int) {
			defer wg.Done()
			for {
				select {
				case <-ctx.Done():
					fmt.Printf("[Worker %d] Context cancelled, shutting down gracefully.\n", workerID)
					return
				case job, ok := <-jobs:
					if !ok {
						return
					}
					// Process item simulation
					res := fmt.Sprintf("Worker %d processed SKU %s at $%.2f", workerID, job.SKU, job.Price)
					select {
					case results <- res:
					case <-ctx.Done():
						return
					}
				}
			}
		}(i)
	}

	go func() {
		wg.Wait()
		close(results)
	}()

	return results
}

4. Candidate Scoring Matrix: Greenfield vs Migration Engineers

❓ Frequently Asked Questions (FAQ)

🔗 Next Step: Continue to Part 13 — Magento Migration Cost: Vietnam vs US/EU Team (2026 Model).