Great question! Since Traefik doesn't include built-in caching out of the box, there are several clean, config-first approaches to add caching without spinning up dedicated pods for every microservice. Let's break down the best options that fit your requirements:

1. Use Traefik's Community Cache Middleware (Config-Only, No Extra Pods) #

Traefik has a well-maintained community plugin for caching that you can enable and configure entirely via Kubernetes resources—no additional pods required. This is perfect for your use case since it’s purely config-driven and avoids per-service overhead.

First, enable the plugin in Traefik's static configuration (e.g., traefik.yaml or via Helm values):

experimental:
  plugins:
    cache:
      moduleName: "github.com/traefik/plugin-cache"
      version: "v0.6.1"

Then define a Traefik Middleware to handle caching for your static resources, and attach it to your IngressRoute or standard Ingress:

# Middleware to cache static assets
apiVersion: traefik.containo.us/v1alpha1
kind: Middleware
metadata:
  name: cache-static-assets
spec:
  plugin:
    cache:
      cacheDuration: "1h" # Cache static files for 1 hour
      cacheKey:
        headers:
          - "Host"
          - "Accept-Encoding" # Respect different encodings like gzip
      allowedHTTPMethods:
        - GET
        - HEAD
---
# Example IngressRoute applying the cache middleware only to static paths
apiVersion: traefik.containo.us/v1alpha1
kind: IngressRoute
metadata:
  name: my-service-ingress
spec:
  entryPoints:
    - web
  routes:
    - match: PathPrefix(`/static`)
      kind: Rule
      middlewares:
        - name: cache-static-assets
      services:
        - name: my-microservice
          port: 80
    - match: PathPrefix(`/api`)
      kind: Rule
      services:
        - name: my-microservice
          port: 80

This setup lets you target specific paths (like /static/*) for caching, while leaving dynamic /api requests untouched—exactly what you need to offload static resource handling from your microservices.

2. Build a Custom Operator for Global Shared Caching #

If you need more advanced caching logic (like automated cache invalidation when microservices deploy, or multi-tier caching), a custom Operator is a great config-focused solution. The Operator will manage a shared global cache cluster (e.g., Redis or Varnish) and automatically configure Traefik to use it—no per-service pods required.

Here’s a high-level breakdown of how this would work:

• Create a Custom Resource Definition (CRD) like CachePolicy, where you define which paths to cache, TTLs, and invalidation rules.
• The Operator watches these CachePolicy resources and:
  1. Maintains a shared cache cluster (scaled as needed)
  2. Generates Traefik Middleware resources to route static requests to the cache
  3. Handles cache invalidation (e.g., triggers a cache flush when a microservice’s Deployment is updated)

This approach keeps all cache management centralized via Kubernetes configs, aligns with your preference for config-driven solutions, and avoids the overhead of per-service cache pods.

3. Varnish as a Shared DaemonSet Cache Layer #

For high-performance caching needs, you can deploy Varnish as a DaemonSet (one instance per node) to act as a shared cache layer in front of Traefik. All incoming traffic hits Varnish first—static assets are served directly from cache, while dynamic requests pass through to Traefik and your microservices.

Configure Varnish’s caching rules via a ConfigMap:

# Varnish VCL configuration
backend traefik {
    .host = "traefik-ingress-service.default.svc.cluster.local";
    .port = "80";
}

sub vcl_recv {
    # Cache GET/HEAD requests for static paths
    if (req.url ~ "^/static/" && req.method ~ "^(GET|HEAD)$") {
        return (hash);
    }
    # Pass all other requests to Traefik
    return (pass);
}

sub vcl_backend_response {
    if (bereq.url ~ "^/static/") {
        set beresp.ttl = 1h;
        set beresp.http.Cache-Control = "public, max-age=3600";
    }
}

Deploy Varnish as a DaemonSet, then update your cluster’s ingress traffic to route to Varnish instead of Traefik directly. This setup uses shared, node-local cache instances—no per-service pods—and is managed entirely via Kubernetes configs.

Recommendation #

If your caching needs are straightforward, start with Option 1 (Traefik community plugin)—it’s the fastest, simplest solution with zero extra pods. For advanced use cases like automated invalidation, go with Option 2 (custom Operator) to keep everything config-driven. Option 3 is ideal if you need maximum caching performance at scale.

内容的提问来源于stack exchange，提问作者muffel