Merge pull request #401 from rabbitmq/production-example

An example of a production cluster configuration.

docs/examples/production-ready/README.md

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+# Production Example
+
+This is an example of a good starting point for a production RabbitMQ deployment. It deploys a 3-node cluster with enough resources to handle reasonable traffic.
+
+Please keep in mind that:
+
+1. It may not be suitable for YOUR production deployment. Please go through the [Production Checklist](https://www.rabbitmq.com/production-checklist.html) to learn more about production deployment considerations.
+
+2. While it is important to correctly deploy RabbitMQ cluster for production deployment, it is even more important to correctly use RabbitMQ from your applications. [Production Checklist](https://www.rabbitmq.com/production-checklist.html) covers some of the common issues such as connection churn and polling cunsumers. Please also consider using [Quorum Queues](https://www.rabbitmq.com/quorum-queues.html) since they provide better data safety.
+
+You can deploy this example like this:
+
+```shell
+kubectl apply -f rabbitmq.yaml
+```
+
+Please keep in mind that you need a multi-zone Kubernetes cluster with 12 CPUs, 30Gi RAM, 1.5Ti disk space available as well as a `storageClass` called `ssd` to deploy this example as-is. Of course you can adjust these values to your environment if needed.
+
+An SSD storage class can be defined using [the example](ssd-gke.yaml) (which is GKE-specific and needs to be adjusted for other environments). Read more about the expected disk perforamnce [in Google Cloud Documentation](https://cloud.google.com/compute/docs/disks/performance#ssd_persistent_disk).

docs/examples/production-ready/rabbitmq.yaml

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+apiVersion: rabbitmq.com/v1beta1
+kind: RabbitmqCluster
+metadata:
+  name: production-ready
+spec:
+  replicas: 3
+  resources:
+    requests:
+      cpu: 4
+      memory: 10Gi
+    limits:
+      cpu: 4
+      memory: 10Gi
+  rabbitmq:
+    additionalConfig: |
+      cluster_partition_handling = ignore
+      vm_memory_high_watermark_paging_ratio = 0.99
+      disk_free_limit.relative = 1.0
+  persistence:
+    storageClassName: ssd
+    storage: "500Gi"
+  override:
+    statefulSet:
+      spec:
+        template:
+          spec:
+            containers: []
+            topologySpreadConstraints:
+            - maxSkew: 1
+              topologyKey: "topology.kubernetes.io/zone"
+              whenUnsatisfiable: DoNotSchedule
+              labelSelector:
+                matchLabels:
+                  app.kubernetes.io/component: rabbitmq

ssd-gke.yaml

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+# This example is GKE specific, other Kubernetes deployments will have a different `storageClass` for SSD disks.
+#
+# https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/ssd-pd
+# https://cloud.google.com/compute/docs/disks/performance#ssd_persistent_disk
+apiVersion: storage.k8s.io/v1
+kind: StorageClass
+metadata:
+  name: ssd
+provisioner: kubernetes.io/gce-pd
+parameters:
+  type: pd-ssd
+