In the Age of AI, I Built a Retry Queue by Hand — and It Felt Amazing

Some weekends, you just want to build something uselessly beautiful.

For me, that meant writing a lightweight retry proxy — a small layer that would sit between my service and a flaky third-party API.

Nothing enterprise-grade. No Kafka, no frameworks, no resilience libraries.

Just Java, a terminal, and curiosity.

It wasn't meant to be useful.

It was meant to remind me what useful code feels like.

🔗When All You Want Is "Try Again Later"

The idea was simple:

Forward API requests downstream.
If they fail, queue them for retry.
Process retries in the background until success or timeout.

A tiny open-source-style playground — the kind you'd hack together between cups of coffee.

🔗The Old Reflex: "I'll Build My Own Linked List"

My muscle memory kicked in:

class RetryNode {
    Request request;
    RetryNode next;
}

I could've chained these up, managed head/tail pointers, and built my own in-memory queue.

But halfway through sketching it out, I stopped.

I wasn't trying to prove I could build data structures.

I wanted to prove I could still use the language well.

Java already gives us decades of concurrency-safe, memory-efficient primitives.

I didn't need to out-engineer them — I just needed to rediscover them.

So I reached for an old friend I'd long ignored:

Deque<Request> retryQueue = new ArrayDeque<>();

🔗Designing the Retry Queue (and Why It Works)

Once the core idea took shape, I stepped back to sketch how requests would actually move through the system.

That's when this simple sequence diagram brought everything together:

Retry Queue Sequence Diagram

At a glance, it shows exactly what matters:

Clear boundaries — each participant has one role:

Client → sends requests.
RetryProxy → forwards and decides when to retry.
Worker → executes retries in the background.
ThirdPartyAPI → the sometimes unreliable downstream dependency.

Intent made visible — every arrow is readable:

A failure calls addLast(req) — the most natural way to say "try again later."
A worker thread polls with pollFirst() — meaning "oldest first."
Successful requests complete quietly, just like they should.

Graceful failure flow — the alt branches tell the real story of resilience:

[failure] → enqueue
[retry fails] → re-enqueue
[retry succeeds] → done

No frameworks, no annotations — just clean, visible behavior.

🔗The Java Shape Behind the Diagram

Here's how that design translated directly into code:

class RetryProxy {
    private final Deque<Request> retryQueue = new ArrayDeque<>();
    private final ScheduledExecutorService scheduler =
        Executors.newScheduledThreadPool(2);

    void send(Request req) {
        try {
            forward(req);
        } catch (IOException e) {
            System.out.println("Failed: " + req.id + " → queued for retry");
            retryQueue.addLast(req); // enqueue on failure
        }
    }

    void start() {
        scheduler.scheduleAtFixedRate(this::processQueue, 0, 2, TimeUnit.SECONDS);
    }

    private void processQueue() {
        Request req = retryQueue.pollFirst();
        if (req == null) return;

        try {
            forward(req);
        } catch (IOException e) {
            req.incrementRetries();
            if (req.shouldRetry()) retryQueue.addLast(req); // re-enqueue
        }
    }

    private void forward(Request req) throws IOException {
        // Simulate network call to ThirdPartyAPI
    }
}

🔗Seeing Behavior, Not Just Code

Watching that retry loop come alive felt like watching a small ecosystem breathe:

a request fails,
the proxy quietly queues it,
the worker gives it another shot,
and eventually, everything stabilizes.

It's not complex, but it's alive — and you can reason about every transition. That's the difference between code that "runs" and code that tells a story.

🔗The Temptation to Overbuild

There's something seductive about low-level control. Every seasoned engineer has that voice in their head whispering:

You can make this faster… tighter… smarter.

But I've learned that every time I rewrite something the JDK already does, I'm not expressing mastery — I'm expressing distrust.

Java's ArrayDeque already solves this problem elegantly:

It's lock-free,
cache-friendly,
and amortized constant time for add/remove operations.

It's been optimized by people who think about micro-architectural memory access patterns for fun.

All I had to do was use it with intention.

This project wasn't about proving depth — it was about practicing restraint.

Re-implementing a queue in 2025 doesn't make you clever; using Deque well does.

Java's standard library already encapsulates the right trade-offs:

backed by a resizable array rather than pointers,
no synchronization overhead like Stack or Vector

When the language offers something beautifully simple, sometimes the smartest thing you can do is just say thank you and use it.

🔗Inside Java's Deque: What Makes It Elegant

The more I used Deque, the more I appreciated how well it captures what good APIs should be — simple at the surface, powerful in composition.

At its core, Deque (Double-Ended Queue) is exactly what it sounds like:

a linear collection supporting insertion and removal at both ends.

That one design choice unlocks a world of patterns.

What I love is how expressive the method names are.

They don't hide complexity behind abstractions — they describe behavior.

You can almost read your concurrency logic aloud and know what's happening.

retryQueue.addLast(req);        // enqueue at end
retryQueue.removeFirst();       // oldest first
retryQueue.addFirst(urgentReq); // bump priority

🔗Deque Implementations: Choosing the Right One

Not all Deques are created equal — and understanding their trade-offs is what separates a clean solution from a latent performance bug.

Deque Implementation Decision Tree

The decision tree above captures the key questions:

Is it single-threaded?

Yes → Use ArrayDeque — Fast, cache-friendly, minimal GC pressure

No — Do you need blocking semantics?

Yes → Use LinkedBlockingDeque — Capacity + blocking put/take
No → Use ConcurrentLinkedDeque — Lock-free, great under contention

🔗Practical Use Cases of Deque

Once you start noticing it, Deque is everywhere. It's one of those invisible workhorses that quietly keeps modern Java frameworks humming — from messaging systems to caches to Spring internals.

It's not the hero of the architecture, but it's part of the infrastructure of elegance.

🔗Retry Buffers and Back Pressure Loops

Your classic use case — queue up failed requests, re-enqueue on failure, process oldest first — is the same principle that powers many internal request pipelines.

🔗Apache Kafka

Kafka's network client layer maintains a Deque per connection to track outstanding requests:

InFlightRequests uses a Map<NodeId, Deque<NetworkClient.InFlightRequest>> to represent pending network operations for each broker connection.

🔗Annotation Traversal in Spring Framework

Spring itself leans on Deque in multiple modules — not publicly exposed, but deeply embedded in core utilities.

Annotation scanning:

AnnotationTypeMappings uses ArrayDeque and Deque for managing annotation traversal order during metadata resolution.

🔗Why It Matters — Even When AI Can Write the Code

We've reached a point where AI coding tools don't just autocomplete lines — they generate entire working implementations.

Ask Claude, Cursor, or Copilot to "build a retry mechanism," and you'll get a runnable solution in seconds.

But the real question isn't can it build it — it's will it build it right?

Because here's the truth: if you don't know what a Deque is, and if you don't understand why it's better for ordered, reversible, or bounded workflows — then you can't tell the AI to use it.

You'll get a perfectly valid queue, or maybe a synchronized List, and it'll even pass tests. But it won't reflect intent. It won't express the system's rhythm — that subtle, almost architectural decision between order and flow.

AI can write a retry queue.

Only you can shape it into a resilient one.

That's why fundamentals still matter.

Not because we need to hand-craft everything — but because we need the language to direct these tools.

When I asked an AI assistant to generate a retry layer, it produced a BlockingQueue loop — correct, functional, but not expressive.

When I said "use a Deque for retry ordering and prioritization", the design immediately improved.

That difference — between asking for code and asking for the right code — comes only from understanding.