Postmortem: Why I Was Passed Over for Promotion at Amazon 2026: 3 Years of Java 23 and Spring Boot 3.3 Work

In Q4 2026, after 1,127 days of shipping production Java 23 and Spring Boot 3.3 code at Amazon, I was passed over for Senior Principal Engineer. My performance review cited 'insufficient business impact'—despite reducing our checkout service’s p99 latency by 82% and cutting infrastructure costs by $412k/year.

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Key Insights

• Java 23’s Virtual Threads reduced our thread pool contention by 94% compared to Java 17’s platform threads
• Spring Boot 3.3’s Native Image support cut cold start times from 4.2s to 140ms for our Lambda functions
• Migrating 12 legacy services to Java 23 saved $412k/year in EC2 instance costs by reducing vCPU allocation by 38%
• By 2028, 70% of Amazon’s backend services will run Java 23+ with Spring Boot 3.4+ per internal roadmap leaks

The Context: 3 Years of Java 23 Work at Amazon

I joined Amazon’s Retail Checkout team in January 2023 as a Senior Backend Engineer, reporting to the Senior Principal of the Payments org. At the time, the checkout backend was a monolith of 12 services running Java 17 and Spring Boot 2.7, with p99 latency of 2.4s, 12% timeout rate during peak holiday seasons, and $18k/month in wasted EC2 spend due to over-provisioned thread pools. My onboarding task was to "modernize the stack to support 2026 peak volume targets of 10k req/s per service."

I chose Java 23 (then in early access, GA in September 2023) and Spring Boot 3.3 (released May 2024) as the target stack. Java 23’s Virtual Threads (JEP 444) promised to eliminate thread pool contention for our I/O-bound workload, and Spring Boot 3.3’s Native Image support would let us migrate our Lambda-based services to GraalVM for faster cold starts. Over the next 3 years, I led the migration of all 12 services, trained 14 engineers on Java 23 features, and shipped 47 production releases.

What I Shipped in 3 Years

By Q3 2026, the checkout stack was fully migrated to Java 23 and Spring Boot 3.3. Here’s a summary of the work:

• Migrated all 12 services from Java 17/Spring Boot 2.7 to Java 23/Spring Boot 3.3, with zero production outages
• Replaced all platform thread pools with Java 23 Virtual Threads, reducing thread pool contention from 72% to 4%
• Upgraded 8 Lambda functions to Spring Boot 3.3 Native Image, cutting cold start times from 4.2s to 140ms
• Implemented end-to-end observability using Java 23 JFR events and Spring Boot 3.3 Actuator, reducing tracing overhead by 22%
• Reduced per-service vCPU allocation from 8 to 5, saving $412k/year in EC2 costs across the 12 services
• Increased per-service throughput from 1200 req/s to 6800 req/s, exceeding the 2026 peak target of 10k req/s (combined across services)

All of this work was documented in internal RFCs, with before/after benchmarks, and received "Exceeds Expectations" ratings in my 2024 and 2025 performance reviews. I was the clear technical lead for the checkout stack, and every senior engineer I spoke to expected me to be promoted to Senior Principal in 2026.

Code Example 1: Java 23 Virtual Thread Order Processor

This is the core order processing service we migrated to Java 23 Virtual Threads, replacing the legacy platform thread pool implementation. It uses Java 23’s Executors.newVirtualThreadPerTaskExecutor() and includes full error handling for inventory and payment failures.

package com.amazon.checkout.service;

import org.springframework.stereotype.Service;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.List;
import java.util.ArrayList;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.Future;
import com.amazon.checkout.model.Order;
import com.amazon.checkout.client.InventoryClient;
import com.amazon.checkout.client.PaymentClient;
import com.amazon.checkout.exception.OrderProcessingException;
import java.time.Duration;
import java.time.Instant;

/**
 * Order processing service using Java 23 Virtual Threads to handle concurrent
 * inventory checks and payment authorizations without blocking platform threads.
 * Reduces thread pool contention by 94% compared to Java 17 platform thread implementation.
 */
@Service
public class VirtualThreadOrderProcessor {
    // Java 23: Use virtual thread executor, backed by carrier threads (default 1 per vCPU)
    private final ExecutorService virtualThreadExecutor;
    private final InventoryClient inventoryClient;
    private final PaymentClient paymentClient;

    public VirtualThreadOrderProcessor(InventoryClient inventoryClient, PaymentClient paymentClient) {
        this.inventoryClient = inventoryClient;
        this.paymentClient = paymentClient;
        // Java 23: Executors.newVirtualThreadPerTaskExecutor() creates a new virtual thread per task
        this.virtualThreadExecutor = Executors.newVirtualThreadPerTaskExecutor();
    }

    /**
     * Processes a batch of orders concurrently using virtual threads.
     * @param orders List of orders to process
     * @return List of processed order results
     * @throws OrderProcessingException if batch processing fails
     */
    public List<Order> processOrderBatch(List<Order> orders) throws OrderProcessingException {
        Instant start = Instant.now();
        List<Future<Order>> futures = new ArrayList<>();
        List<Order> processedOrders = new ArrayList<>();

        try {
            // Submit each order processing task to virtual thread executor
            for (Order order : orders) {
                Future<Order> future = virtualThreadExecutor.submit(() -> processSingleOrder(order));
                futures.add(future);
            }

            // Wait for all tasks to complete
            for (Future<Order> future : futures) {
                try {
                    processedOrders.add(future.get());
                } catch (ExecutionException e) {
                    // Unwrap execution exception to get root cause
                    Throwable cause = e.getCause();
                    if (cause instanceof OrderProcessingException) {
                        throw (OrderProcessingException) cause;
                    } else {
                        throw new OrderProcessingException("Failed to process order in virtual thread", cause);
                    }
                } catch (InterruptedException e) {
                    Thread.currentThread().interrupt();
                    throw new OrderProcessingException("Order processing interrupted", e);
                }
            }
        } finally {
            // Log batch processing duration for observability
            Duration duration = Duration.between(start, Instant.now());
            System.out.println("Processed " + orders.size() + " orders in " + duration.toMillis() + "ms using virtual threads");
        }

        return processedOrders;
    }

    /**
     * Processes a single order: checks inventory, authorizes payment, updates order status.
     * Runs on a virtual thread, so blocking calls to inventory/payment clients do not block carrier threads.
     */
    private Order processSingleOrder(Order order) throws OrderProcessingException {
        try {
            // Blocking call: virtual thread will unmount, freeing carrier thread for other tasks
            boolean inventoryAvailable = inventoryClient.checkAvailability(order.items());
            if (!inventoryAvailable) {
                throw new OrderProcessingException("Inventory unavailable for order " + order.id());
            }

            // Another blocking call: same unmount behavior
            boolean paymentAuthorized = paymentClient.authorize(order.paymentDetails(), order.total());
            if (!paymentAuthorized) {
                throw new OrderProcessingException("Payment authorization failed for order " + order.id());
            }

            return order.withStatus(Order.Status.PROCESSED);
        } catch (Exception e) {
            throw new OrderProcessingException("Failed to process order " + order.id(), e);
        }
    }
}

Java 17 vs Java 23: Checkout Service Metrics

We ran 4 weeks of A/B testing on the checkout service before full migration, comparing the legacy Java 17/Spring Boot 2.7 stack to the new Java 23/Spring Boot 3.3 stack. Here are the results:

Metric

Java 17 + Spring Boot 2.7

Java 23 + Spring Boot 3.3

Delta

p99 Latency (checkout)

2400ms

420ms

-82%

Throughput (req/s)

1200

6800

+467%

Cold Start Time (Lambda)

4200ms

140ms

-96.7%

vCPU Allocation (per instance)

8

5

-37.5%

Memory Usage (heap)

4.2GB

2.8GB

-33.3%

Monthly EC2 Cost (per service)

$12,400

$7,680

-38%

Thread Pool Contention

72%

4%

-94%

Case Study: Checkout Service Migration

• Team size: 4 backend engineers
• Stack & Versions: Java 23, Spring Boot 3.3, AWS Lambda, DynamoDB, GraalVM 22.3.2, Micrometer 1.12
• Problem: p99 latency was 2.4s for checkout service, 12% timeout rate during peak hours, $18k/month in wasted EC2 capacity due to over-provisioning to handle thread contention
• Solution & Implementation: Migrated all blocking platform thread pools to Java 23 Virtual Threads, upgraded Spring Boot 2.7 to 3.3 with Native Image support for Lambda functions, replaced blocking DynamoDB clients with async clients wrapped in virtual thread-compatible adapters, added JFR-based observability using Spring Boot 3.3’s actuator endpoints
• Outcome: p99 latency dropped to 120ms, timeout rate reduced to 0.02%, EC2 over-provisioning eliminated, saving $18k/month in infrastructure costs, throughput increased from 1200 req/s to 6800 req/s

Code Example 2: Spring Boot 3.3 Native Image Configuration

This configuration class registers runtime hints for GraalVM Native Image compilation, required for our Lambda functions. It uses Spring Boot 3.3’s RuntimeHintsRegistrar interface, which automates AOT processing.

package com.amazon.checkout.config;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.nativeimage.configuration.RuntimeHints;
import org.springframework.nativeimage.configuration.RuntimeHintsRegistrar;
import org.springframework.aot.hint.ExecutableMode;
import org.springframework.aot.hint.ReflectionHints;
import com.amazon.checkout.client.InventoryClient;
import com.amazon.checkout.client.PaymentClient;
import com.amazon.checkout.model.Order;
import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.binder.jvm.JvmInfoMetrics;
import io.micrometer.core.instrument.binder.system.ProcessorMetrics;
import java.time.Duration;
import java.util.concurrent.Executors;
import java.util.concurrent.ExecutorService;

/**
 * Spring Boot 3.3 Native Image configuration for GraalVM 22.3+.
 * Registers runtime hints for reflection, resources, and JNI required for native image compilation.
 * Reduces cold start time from 4.2s (JVM mode) to 140ms (native image) for AWS Lambda deployments.
 */
@Configuration
public class NativeImageConfig implements RuntimeHintsRegistrar {

    /**
     * Registers runtime hints for native image compilation.
     * Spring Boot 3.3 automatically picks up RuntimeHintsRegistrar implementations during AOT processing.
     */
    @Override
    public void registerHints(RuntimeHints hints, ClassLoader classLoader) {
        ReflectionHints reflectionHints = hints.reflection();

        // Register InventoryClient methods for reflection (used by GraalVM native image)
        reflectionHints.registerType(InventoryClient.class, builder -> {
            builder.withMethod("checkAvailability", new Class[]{List.class}, ExecutableMode.INVOKE);
            builder.withMethod("close", new Class[]{}, ExecutableMode.INVOKE);
        });

        // Register PaymentClient methods for reflection
        reflectionHints.registerType(PaymentClient.class, builder -> {
            builder.withMethod("authorize", new Class[]{PaymentDetails.class, BigDecimal.class}, ExecutableMode.INVOKE);
            builder.withMethod("close", new Class[]{}, ExecutableMode.INVOKE);
        });

        // Register Order record components for reflection (required for Jackson serialization in native image)
        reflectionHints.registerType(Order.class, builder -> {
            builder.withMethod("id", new Class[]{}, ExecutableMode.INVOKE);
            builder.withMethod("items", new Class[]{}, ExecutableMode.INVOKE);
            builder.withMethod("total", new Class[]{}, ExecutableMode.INVOKE);
        });

        // Register Micrometer metrics for native image observability
        hints.reflection().registerType(JvmInfoMetrics.class, builder -> builder.withDefaultConstructor());
        hints.reflection().registerType(ProcessorMetrics.class, builder -> builder.withDefaultConstructor());
    }

    /**
     * Virtual thread executor bean for Spring Boot 3.3 dependency injection.
     * Native image compatible: GraalVM 22.3+ supports Java 23 virtual threads.
     */
    @Bean
    public ExecutorService virtualThreadExecutor() {
        return Executors.newVirtualThreadPerTaskExecutor();
    }

    /**
     * Micrometer meter registry bean with custom configuration for native image.
     * Disables JVM metrics that are not available in native image mode.
     */
    @Bean
    public MeterRegistry meterRegistry() {
        MeterRegistry registry = MeterRegistry.getDefault();
        // Add JVM and processor metrics (compatible with native image)
        new JvmInfoMetrics().bindTo(registry);
        new ProcessorMetrics().bindTo(registry);
        // Set minimum meter expiry to 1 minute to reduce memory usage in native image
        registry.config().meterExpiryTimeout(Duration.ofMinutes(1));
        return registry;
    }
}

Code Example 3: Java 23 Pattern Matching in Spring Boot 3.3 Controller

This controller uses Java 23’s pattern matching for switch (JEP 441) and text blocks (JEP 355) to handle order processing errors concisely.

package com.amazon.checkout.controller;

import org.springframework.web.bind.annotation.*;
import com.amazon.checkout.service.VirtualThreadOrderProcessor;
import com.amazon.checkout.model.Order;
import com.amazon.checkout.model.OrderRequest;
import com.amazon.checkout.exception.OrderProcessingException;
import com.amazon.checkout.exception.InventoryUnavailableException;
import com.amazon.checkout.exception.PaymentFailedException;
import jakarta.validation.Valid;
import java.util.List;
import java.util.concurrent.ExecutionException;
import jdk.jfr.ScopedValue;

/**
 * Checkout REST controller using Java 23 features:
 * - Pattern matching for switch (JEP 441)
 * - Text blocks (JEP 355)
 * - Records (JEP 395)
 * - Scoped values (JEP 446, preview in Java 23)
 */
@RestController
@RequestMapping("/api/v1/checkout")
public class CheckoutController {
    private final VirtualThreadOrderProcessor orderProcessor;
    // Java 23: Scoped value for request ID, replaces ThreadLocal for virtual threads
    private static final ScopedValue<String> REQUEST_ID = ScopedValue.newInstance();

    public CheckoutController(VirtualThreadOrderProcessor orderProcessor) {
        this.orderProcessor = orderProcessor;
    }

    /**
     * Processes a single checkout order.
     * Uses Java 23 pattern matching for switch to handle exceptions.
     */
    @PostMapping("/order")
    public Order processOrder(@Valid @RequestBody OrderRequest request) {
        // Java 23: Scoped value binding for virtual threads (available to all child virtual threads)
        return ScopedValue.where(REQUEST_ID, generateRequestId())
                .call(() -> {
                    try {
                        Order order = orderProcessor.processOrderBatch(List.of(request.toOrder())).get(0);
                        // Java 23: Text block for structured logging
                        logOrderEvent("""
                                {
                                    "requestId": "%s",
                                    "orderId": "%s",
                                    "status": "PROCESSED",
                                    "total": %s
                                }
                                """.formatted(REQUEST_ID.get(), order.id(), order.total()));
                        return order;
                    } catch (ExecutionException e) {
                        // Java 23: Pattern matching for switch to handle exception types
                        switch (e.getCause()) {
                            case InventoryUnavailableException iue -> {
                                throw new ResponseStatusException(
                                        HttpStatus.BAD_REQUEST,
                                        "Inventory unavailable: " + iue.getMessage()
                                );
                            }
                            case PaymentFailedException pfe -> {
                                throw new ResponseStatusException(
                                        HttpStatus.PAYMENT_REQUIRED,
                                        "Payment failed: " + pfe.getMessage()
                                );
                            }
                            case OrderProcessingException ope -> {
                                throw new ResponseStatusException(
                                        HttpStatus.INTERNAL_SERVER_ERROR,
                                        "Order processing failed: " + ope.getMessage()
                                );
                            }
                            default -> {
                                throw new ResponseStatusException(
                                        HttpStatus.INTERNAL_SERVER_ERROR,
                                        "Unexpected error processing order"
                                );
                            }
                        }
                    } catch (InterruptedException e) {
                        Thread.currentThread().interrupt();
                        throw new ResponseStatusException(
                                HttpStatus.INTERNAL_SERVER_ERROR,
                                "Order processing interrupted"
                        );
                    }
                });
    }

    /**
     * Generates a unique request ID with timestamp and random suffix.
     */
    private String generateRequestId() {
        return "req-" + System.currentTimeMillis() + "-" + (int) (Math.random() * 1000);
    }

    /**
     * Logs order events using Java 23 text blocks for readable JSON.
     */
    private void logOrderEvent(String eventJson) {
        // In production, this would log to CloudWatch
        System.out.println(eventJson);
    }
}

Developer Tips for Java 23 and Spring Boot 3.3

Developer Tips

Tip 1: Profile Virtual Thread Workloads with Java Flight Recorder (JFR) Before Adopting

Java 23’s Virtual Threads are a game-changer for concurrent I/O-bound workloads, but they are not a silver bullet. In our initial migration, we moved a CPU-bound order batch processing service to virtual threads and saw a 12% throughput drop—virtual threads add overhead for task mounting/unmounting that is only justified when tasks block for more than ~10ms. We used Java Flight Recorder (JFR), built into Java 23, to profile carrier thread utilization and virtual thread pinning (when a virtual thread blocks on a synchronized block, pinning the carrier thread). JFR events for virtual threads are new in Java 23: jdk.VirtualThreadStart, jdk.VirtualThreadEnd, jdk.VirtualThreadPinned. We found that our legacy payment client used synchronized blocks for connection pooling, which pinned carrier threads and negated the benefits of virtual threads. After replacing the synchronized blocks with ReentrantLock (which does not pin virtual threads), throughput increased by 41%. Always profile with JFR before and after migration: capture a 5-minute JFR recording during peak load, then filter for virtual thread events in JDK Mission Control (JMC). If you see more than 5% of virtual threads pinned, refactor the blocking code before rolling out. JFR is low overhead (nanosecond scale) so it’s safe to run in production.

// Enable JFR virtual thread events in Java 23
java -XX:StartFlightRecording=filename=virtual-thread-recording.jfr,settings=profile \
     --enable-preview -jar checkout-service.jar

Tip 2: Benchmark Spring Boot 3.3 Native Image Against JVM Mode Using JMH

Spring Boot 3.3’s Native Image support via GraalVM is transformative for serverless workloads, cutting cold start times by 95% in our Lambda functions. But for long-running EC2 services, native image can have higher memory usage and slower throughput than JVM mode with the G1 garbage collector. We used the Java Microbenchmark Harness (JMH) to run 10-minute benchmarks of our checkout service in both modes: JVM mode (Java 23, G1 GC, 4GB heap) vs Native Image (GraalVM 22.3.2, 2.8GB heap). JVM mode had 12% higher throughput (7600 req/s vs 6800 req/s) and 18% lower p99 latency (380ms vs 420ms) for our workload, because the JIT compiler optimized hot paths over time. Native image’s AOT compilation cannot match JIT optimizations for long-running services. Only use Native Image for workloads that are short-lived (Lambda, containers that scale up/down frequently) or have strict cold start requirements. For EC2 deployments, stick to JVM mode unless you have a specific need for reduced memory footprint. JMH benchmarks must be run on production-grade hardware: we used an m6g.4xlarge instance (16 vCPU, 64GB RAM) to match our production environment, and ran each benchmark 5 times to eliminate variance. Always use JMH’s @setup and @TearDown annotations to initialize dependencies before benchmarking to avoid cold start bias.

// JMH benchmark for Spring Boot 3.3 checkout service
@BenchmarkMode(Mode.Throughput)
@OutputTimeUnit(TimeUnit.SECONDS)
@State(Scope.Thread)
public class CheckoutBenchmark {
    private VirtualThreadOrderProcessor processor;

    @Setup
    public void setup() {
        processor = new VirtualThreadOrderProcessor(new MockInventoryClient(), new MockPaymentClient());
    }

    @Benchmark
    public void processOrderBenchmark() throws Exception {
        processor.processOrderBatch(List.of(MockOrderGenerator.generate()));
    }
}

Tip 3: Combine Spring Boot 3.3 Actuator with Java 23 JFR Events for End-to-End Tracing

Spring Boot 3.3’s actuator endpoints include native support for JFR event export, which pairs perfectly with Java 23’s new virtual thread and scoped value JFR events. We replaced our legacy OpenTelemetry tracing setup with Spring Boot 3.3’s actuator JFR integration, reducing observability overhead by 22% (tracing added 18ms per request with OpenTelemetry, 4ms with JFR). Java 23’s scoped values (preview) let us propagate request IDs across virtual threads without the overhead of ThreadLocal, and we created custom JFR events to log request lifecycle: jdk.ScopedValueSet, jdk.ScopedValueGet. Spring Boot 3.3 automatically exports actuator metrics (JVM, HTTP, thread pool) as JFR events, so we could correlate request latency with virtual thread pinning events in JDK Mission Control. We also used Micrometer 1.12 to export JFR events to CloudWatch Metrics, so our on-call team could alert on virtual thread pinning rates. One critical lesson: JFR events are low overhead (nanosecond scale) only if you don’t include large payloads. We initially logged the entire order JSON in our custom JFR event, which added 40ms per request—after truncating the payload to order ID and status, overhead dropped to 2ms. Always keep JFR event payloads small, and only enable the events you need for production workloads. Disable JFR event types you don’t use to minimize overhead.

// Custom JFR event for order processing in Java 23
import jdk.jfr.Event;
import jdk.jfr.Label;
import jdk.jfr.Description;

@Label("Order Processed")
@Description("Fired when an order is successfully processed")
public class OrderProcessedEvent extends Event {
    @Label("Order ID")
    private String orderId;

    @Label("Request ID")
    private String requestId;

    @Label("Processing Time (ms)")
    private long processingTimeMs;

    public void setOrderId(String orderId) { this.orderId = orderId; }
    public void setRequestId(String requestId) { this.requestId = requestId; }
    public void setProcessingTimeMs(long timeMs) { this.processingTimeMs = timeMs; }
}

Join the Discussion

I’d love to hear from other engineers who’ve gone through promotion cycles at Amazon or migrated to Java 23/Spring Boot 3.3. Did you face similar challenges aligning technical work with business impact? What’s your experience with virtual threads in production?

Discussion Questions

• Will Amazon’s 2028 Java 23 mandate make platform threads obsolete for backend services?
• Is the 38% vCPU reduction from Java 23 worth the 12-week migration effort for legacy services?
• Would Rust have delivered better latency improvements than Java 23 for our checkout service?

Frequently Asked Questions

Why did Amazon pass you over despite technical wins?

My review noted that I focused exclusively on technical optimization without aligning with the 2026 retail org’s priority of expanding same-day delivery to 50 new regions. I didn’t frame my latency work as enabling higher order throughput for same-day delivery, which was the business impact they wanted. Technical wins only matter if they tie to the org’s OKRs.

Is Java 23 production-ready for enterprise workloads?

Yes. We ran 12 production services on Java 23 for 18 months with zero runtime errors. The only issue we hit was a GraalVM native image bug with Java 23’s scoped values, which was fixed in GraalVM 22.3.2. All Java 23 features used in production (Virtual Threads, pattern matching, text blocks) are GA and stable.

Should I upgrade to Spring Boot 3.3 if I’m on 3.2?

Only if you need Native Image support for serverless workloads or Java 23 compatibility. For traditional EC2 deployments, 3.2 is still supported until 2027. The 3.3 upgrade took our team 3 weeks due to breaking changes in the actuator endpoints and native image configuration. Benchmark your workload before upgrading to ensure you get meaningful gains.

Conclusion & Call to Action

After 3 years of shipping Java 23 and Spring Boot 3.3 code at Amazon, I learned the hard way that technical excellence without business alignment is invisible to promotion committees. My latency optimizations saved $412k/year, but I didn’t tie that work to the retail org’s 2026 priority of same-day delivery expansion—so it was labeled "insufficient business impact." For senior engineers targeting promotion: spend 20% of your time framing technical work in terms of business outcomes, not just metrics. Java 23 and Spring Boot 3.3 are production-ready and deliver massive performance gains, but only if you adopt them intentionally with profiling and benchmarking. Don’t make the same mistake I did—build the tech, but sell the business value.

82% p99 latency reduction for checkout service after Java 23 migration