The initial architecture seemed sound on paper. A frontend built with Qwik to achieve unparalleled Time to Interactive (TTI) and stellar Core Web Vitals. A backend of microservices deployed on Knative, promising the cost-efficiency of scale-to-zero. Tying it all together, a Tyk API Gateway to handle routing, authentication, and rate-limiting. The goal was a stack that was both incredibly fast for the end-user and operationally lean.
The reality, after the first integration tests, was a jarring disconnect. The Qwik application shell loaded instantly, as promised. Users could see the UI, click on disabled buttons, and interact with the non-data-driven parts of the application in milliseconds. But the moment the first critical data fetch was initiated to a service that had scaled to zero, the experience collapsed. A loading spinner would appear and stay for anywhere between 5 to 12 seconds. This wasn’t a minor lag; it was a fundamental failure of the user experience. The promise of an “instant” application was broken by the very technology chosen for its efficiency. The Knative cold start, a known and accepted trade-off in the serverless world, became an unacceptable bottleneck when paired with a frontend framework designed to eliminate waiting.
Our first naive implementation was a direct proxy. A request from the Qwik app would hit Tyk, which would forward it to the Knative service endpoint.
Here’s the baseline Knative service, a simple Go application providing some product data.
// main.go
package main
import (
"encoding/json"
"log"
"net/http"
"os"
"time"
)
type Product struct {
ID string `json:"id"`
Name string `json:"name"`
SKU string `json:"sku"`
}
func productsHandler(w http.ResponseWriter, r *http.Request) {
// Simulate some work
time.Sleep(150 * time.Millisecond)
products := []Product{
{ID: "prod_1", Name: "Quantum Widget", SKU: "QW-1001"},
{ID: "prod_2", Name: "Hyperflux Capacitor", SKU: "HC-2023"},
}
w.Header().Set("Content-Type", "application/json")
w.WriteHeader(http.StatusOK)
if err := json.NewEncoder(w).Encode(products); err != nil {
log.Printf("Error encoding products: %v", err)
}
}
func main() {
port := os.Getenv("PORT")
if port == "" {
port = "8080"
}
http.HandleFunc("/", productsHandler)
log.Printf("Server starting on port %s", port)
if err := http.ListenAndServe(":"+port, nil); err != nil {
log.Fatal(err)
}
}The corresponding Knative service definition was standard, configured for aggressive scaling down to save costs.
# service.yaml
apiVersion: serving.knative.dev/v1
kind: Service
metadata:
name: product-service
spec:
template:
metadata:
annotations:
# Aggressively scale down to demonstrate the cold start problem
autoscaling.knative.dev/scale-down-delay: "10s"
spec:
containers:
- image: gcr.io/my-project/product-service:latest
ports:
- containerPort: 8080
env:
- name: PORT
value: "8080"The Tyk API definition was a simple pass-through proxy.
{
"name": "Product Service - Naive Proxy",
"api_id": "product-service-naive",
"org_id": "default",
"use_keyless": true,
"auth": {
"auth_header_name": "Authorization"
},
"version_data": {
"not_versioned": true,
"versions": {
"Default": {
"name": "Default",
"use_extended_paths": true
}
}
},
"proxy": {
"listen_path": "/products/",
"target_url": "http://product-service.default.svc.cluster.local",
"strip_listen_path": true
}
}And finally, the Qwik component making the call.
// src/routes/products/index.tsx
import { component$ } from '@builder.io/qwik';
import { routeLoader$ } from '@builder.io/qwik-city';
export const useProducts = routeLoader$(async () => {
// This fetch call is the source of the blocking delay
const response = await fetch('https://api.mydomain.com/products/');
if (!response.ok) {
throw new Error('Failed to fetch products');
}
const data = await response.json();
return data as { id: string; name: string }[];
});
export default component$(() => {
const products = useProducts();
return (
<div>
<h1>Products</h1>
<ul>
{products.value.map((p) => (
<li key={p.id}>{p.name}</li>
))}
</ul>
</div>
);
});A quick test with curl after letting the service scale to zero revealed the problem numerically:
$ time curl https://api.mydomain.com/products/
[{"id":"prod_1","name":"Quantum Widget","sku":"QW-1001"},{"id":"prod_2","name":"Hyperflux Capacitor","sku":"HC-2023"}]
real 0m8.452s
user 0m0.012s
sys 0m0.009sOver 8 seconds. In a real-world project, this is an immediate showstopper. The first instinct is often to tweak Knative’s configuration. A common mistake is to solve the problem by eliminating the feature: setting minScale: '1'. This keeps one pod running at all times, effectively turning Knative into a standard Kubernetes deployment and negating its primary cost benefit. This approach was rejected as it sidestepped the problem rather than solving it. We needed a solution that embraced the scale-to-zero nature of the architecture.
The next logical step was to devise a pattern that could shield the user from this initial latency. The core idea shifted from making the backend faster (which has physical limits in a cold start scenario) to making the frontend tolerate the backend’s latency gracefully. This required an intermediary to manage the asynchronous interaction. The Tyk gateway was the perfect place for this logic.
We designed an asynchronous request-poll pattern, orchestrated entirely by Tyk.
sequenceDiagram
participant Client (Qwik)
participant Gateway (Tyk)
participant Cache (Redis)
participant Backend (Knative)
Client->>Gateway: POST /products/request
Note right of Gateway: This is the initial request
Gateway-->>Client: 202 Accepted
Location: /products/poll/{jobId}
par Background Task
Gateway->>Backend: GET /
Note left of Backend: Knative Cold Start (5-10s)
Backend-->>Gateway: 200 OK (Product Data)
Gateway->>Cache: SET {jobId} "Product Data"
end
loop Polling
Client->>Gateway: GET /products/poll/{jobId}
Gateway->>Cache: GET {jobId}
alt Data not ready
Cache-->>Gateway: (nil)
Gateway-->>Client: 202 Accepted
else Data is ready
Cache-->>Gateway: "Product Data"
Gateway-->>Client: 200 OK (Product Data)
end
end
This architecture required a custom middleware for Tyk. While Tyk supports various languages, we chose Go for performance and to align with our backend stack. The middleware needs to be intelligent enough to differentiate between an initial request and a polling request.
Here is the complete, production-grade Go plugin for Tyk that implements this logic. It uses Redis for state management.
// tyk-async-middleware.go
package main
import (
"context"
"fmt"
"io"
"log"
"net/http"
"strings"
"time"
"github.com/TykTechnologies/tyk/coprocess"
"github.com/go-redis/redis/v8"
"github.com/google/uuid"
)
var (
redisClient *redis.Client
ctx = context.Background()
)
// Init is called by Tyk when the plugin is loaded.
// It's the ideal place for initialization code like connecting to a database.
func init() {
log.Println("Initializing async middleware: connecting to Redis")
redisClient = redis.NewClient(&redis.Options{
Addr: "redis-master:6379", // Use the Kubernetes service name for Redis
DB: 0,
})
// Ping Redis to ensure the connection is alive.
// In a real project, you'd want more robust connection handling and retries.
_, err := redisClient.Ping(ctx).Result()
if err != nil {
log.Fatalf("Failed to connect to Redis: %v", err)
}
log.Println("Async middleware: Redis connection successful")
}
// AsyncRequestMiddleware is the main middleware handler function.
func AsyncRequestMiddleware(rw http.ResponseWriter, r *http.Request) {
// The coprocess.Object is Tyk's way of passing request metadata.
// We don't use it directly here but it's part of the required function signature.
_ = coprocess.Object{}
// Differentiate between the initial request and a polling request based on the path.
if strings.HasPrefix(r.URL.Path, "/poll/") {
handlePollRequest(rw, r)
} else {
handleInitialRequest(rw, r)
}
}
// handleInitialRequest triggers the backend process and returns a polling location.
func handleInitialRequest(rw http.ResponseWriter, r *http.Request) {
jobID := uuid.New().String()
log.Printf("Handling initial request, generated JobID: %s", jobID)
// We need the original request details to forward to the backend.
// This is a simplified representation; a real implementation would need to copy headers, body, etc.
backendURL := "http://product-service.default.svc.cluster.local" + r.URL.Path
// Launch a goroutine to handle the long-running backend request.
// This makes the initial response to the client non-blocking.
go func() {
// Create a new context for the background task with a timeout.
// This prevents goroutines from running indefinitely if the backend is unresponsive.
bgCtx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
defer cancel()
log.Printf("JobID: %s, starting background fetch to: %s", jobID, backendURL)
req, err := http.NewRequestWithContext(bgCtx, http.MethodGet, backendURL, nil)
if err != nil {
log.Printf("JobID: %s, ERROR creating backend request: %v", jobID, err)
// Store an error state in Redis so the client knows the job failed.
redisClient.Set(ctx, jobID, `{"error":"internal server error"}`, 1*time.Minute)
return
}
// A production implementation should propagate specific headers (e.g., auth, tracing).
// req.Header.Set("Authorization", r.Header.Get("Authorization"))
client := &http.Client{}
resp, err := client.Do(req)
if err != nil {
log.Printf("JobID: %s, ERROR executing backend request: %v", jobID, err)
redisClient.Set(ctx, jobID, `{"error":"backend unavailable"}`, 1*time.Minute)
return
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
log.Printf("JobID: %s, ERROR reading backend response body: %v", jobID, err)
redisClient.Set(ctx, jobID, `{"error":"failed to read backend response"}`, 1*time.Minute)
return
}
if resp.StatusCode >= 400 {
log.Printf("JobID: %s, backend returned non-success status: %d", jobID, resp.StatusCode)
// Store the entire response to allow the client to inspect the error.
// A real system might have more structured error handling.
errorPayload := fmt.Sprintf(`{"error":"backend error", "statusCode": %d, "body": "%s"}`, resp.StatusCode, string(body))
redisClient.Set(ctx, jobID, errorPayload, 1*time.Minute)
return
}
// Store the successful result in Redis with an expiry.
// The expiry prevents Redis from filling up with old job results.
err = redisClient.Set(ctx, jobID, string(body), 5*time.Minute).Err()
if err != nil {
log.Printf("JobID: %s, ERROR caching result in Redis: %v", jobID, err)
} else {
log.Printf("JobID: %s, successfully cached result in Redis", jobID)
}
}()
// Immediately respond to the client.
pollPath := fmt.Sprintf("/products/poll/%s", jobID)
rw.Header().Set("Location", pollPath)
rw.WriteHeader(http.StatusAccepted)
log.Printf("JobID: %s, responded 202 Accepted to client, poll at %s", jobID, pollPath)
}
// handlePollRequest checks Redis for the result of a job.
func handlePollRequest(rw http.ResponseWriter, r *http.Request) {
parts := strings.Split(r.URL.Path, "/")
if len(parts) < 3 {
http.Error(rw, "Invalid poll URL", http.StatusBadRequest)
return
}
jobID := parts[2]
log.Printf("Handling poll request for JobID: %s", jobID)
// Check Redis for the job result.
result, err := redisClient.Get(ctx, jobID).Result()
if err == redis.Nil {
// Key does not exist, meaning the job is not yet complete.
log.Printf("JobID: %s, result not found in cache. Responding 202.", jobID)
rw.WriteHeader(http.StatusAccepted)
return
} else if err != nil {
// A real error occurred talking to Redis.
log.Printf("JobID: %s, ERROR querying Redis: %v", jobID, err)
http.Error(rw, "Internal Server Error", http.StatusInternalServerError)
return
}
// We found the result in Redis.
log.Printf("JobID: %s, result found in cache. Responding 200.", jobID)
rw.Header().Set("Content-Type", "application/json")
rw.WriteHeader(http.StatusOK)
rw.Write([]byte(result))
}
func main() {} // Required for Go plugins but not used by Tyk.To integrate this with Tyk, the API definition needs to be updated to enable the custom Go middleware.
{
"name": "Product Service - Async Proxy",
"api_id": "product-service-async",
"org_id": "default",
"use_keyless": true,
"custom_middleware": {
"pre": [
{
"name": "AsyncRequestMiddleware",
"path": "/path/to/tyk-async-middleware.so",
"require_session": false
}
],
"driver": "goplugin"
},
"version_data": {
"not_versioned": true,
"versions": {
"Default": {
"name": "Default",
"use_extended_paths": true
}
}
},
"proxy": {
"listen_path": "/products/"
// The target_url is removed because the middleware now handles the upstream call.
// We set a "dummy" target to satisfy Tyk's validation.
"target_url": "http://localhost:9999"
}
}With the gateway logic in place, the final piece was updating the Qwik frontend to handle this new asynchronous flow. This is where Qwik’s reactivity model shines. We can create a resource that polls and updates the UI automatically as the state changes, without blocking rendering.
// src/routes/products/index.tsx (updated for polling)
import { component$, useSignal, useVisibleTask$ } from '@builder.io/qwik';
interface Product {
id: string;
name: string;
}
// A simple polling function with exponential backoff and a timeout
async function pollForData(pollUrl: string): Promise<any> {
const MAX_POLL_TIME_MS = 20000; // 20 seconds timeout
const INITIAL_DELAY_MS = 500;
let delay = INITIAL_DELAY_MS;
const startTime = Date.now();
return new Promise(async (resolve, reject) => {
const poll = async () => {
if (Date.now() - startTime > MAX_POLL_TIME_MS) {
return reject(new Error('Polling timed out.'));
}
try {
const res = await fetch(pollUrl);
if (res.status === 200) {
const data = await res.json();
return resolve(data);
} else if (res.status === 202) {
setTimeout(poll, delay);
delay = Math.min(delay * 1.5, 3000); // Exponential backoff up to 3 seconds
} else {
// Handle server-side errors propagated through the cache
return reject(new Error(`Polling failed with status: ${res.status}`));
}
} catch (error) {
return reject(error);
}
};
poll();
});
}
export default component$(() => {
const products = useSignal<Product[]>([]);
const error = useSignal<string | null>(null);
const isLoading = useSignal<boolean>(true);
// useVisibleTask$ runs on the client when the component becomes visible.
// This is perfect for initiating our async data fetching flow.
useVisibleTask$(async () => {
try {
const initialResponse = await fetch('https://api.mydomain.com/products/request');
if (initialResponse.status === 202) {
const pollUrl = initialResponse.headers.get('Location');
if (!pollUrl) {
throw new Error('No Location header provided for polling.');
}
// The API base path needs to be prepended correctly.
const fullPollUrl = `https://api.mydomain.com${pollUrl}`;
const data = await pollForData(fullPollUrl);
products.value = data;
} else if (initialResponse.ok) {
// Handle the case where the service was already warm
products.value = await initialResponse.json();
} else {
throw new Error(`Initial request failed with status: ${initialResponse.status}`);
}
} catch (e: any) {
console.error('Failed to get products:', e);
error.value = e.message || 'An unknown error occurred.';
} finally {
isLoading.value = false;
}
});
return (
<div>
<h1>Products</h1>
{isLoading.value && <p>Loading product list...</p>}
{error.value && <p style="color: red;">Error: {error.value}</p>}
{!isLoading.value && !error.value && (
<ul>
{products.value.map((p) => (
<li key={p.id}>{p.name}</li>
))}
</ul>
)}
</div>
);
});The result was a night-and-day difference in perceived performance. The user lands on the page, the UI is instantly interactive, and a subtle “Loading product list…” message appears. In the background, Tyk and Knative are doing their work. A few seconds later, the product list seamlessly populates the view. The cold start still happens, but it now occurs in a non-blocking way that doesn’t degrade the user experience. We preserved the cost benefits of Knative’s scale-to-zero while fulfilling the performance promise of Qwik.
This pattern, however, introduces its own set of trade-offs and complexities. The number of requests to the API gateway increases due to polling, which can have cost implications and requires careful rate-limiting. The system’s state is now distributed between the client, the gateway, and the Redis cache, making debugging more challenging. A robust implementation needs to handle failure scenarios diligently: What happens if the backend job fails? How are errors communicated back to the client? What if Redis is down? The timeout logic on both the client-side poll and the background goroutine in the Tyk middleware is not just a nice-to-have; it’s critical to prevent resource exhaustion and provide a predictable user experience. Furthermore, this pattern is best suited for idempotent GET requests for initial page data. It is not appropriate for state-changing mutations (POST, PUT, DELETE) where the user expects immediate, synchronous feedback. Future iterations could explore replacing client-side polling with a more efficient mechanism like Server-Sent Events (SSE) or WebSockets, where Tyk could push a notification to the client once the job is complete, though this would introduce even more statefulness to the gateway layer.
