GCS fault tolerance in KubeRay#

Global Control Service (GCS) manages cluster-level metadata. By default, the GCS lacks fault tolerance as it stores all data in-memory, and a failure can cause the entire Ray cluster to fail. To make the GCS fault tolerant, you must have a high-availability Redis. This way, in the event of a GCS restart, it retrieves all the data from the Redis instance and resumes its regular functions.

Fate-sharing without GCS fault tolerance

Without GCS fault tolerance, the Ray cluster, the GCS process, and the Ray head Pod are fate-sharing. If the GCS process dies, the Ray head Pod dies as well after RAY_gcs_rpc_server_reconnect_timeout_s seconds. If the Ray head Pod is restarted according to the Pod’s restartPolicy, worker Pods attempt to reconnect to the new head Pod. The worker Pods are terminated by the new head Pod; without GCS fault tolerance enabled, the cluster state is lost, and the worker Pods are perceived as “unknown workers” by the new head Pod. This is adequate for most Ray applications; however, it is not ideal for Ray Serve, especially if high availability is crucial for your use cases. Hence, we recommend enabling GCS fault tolerance on the RayService custom resource to ensure high availability. See Ray Serve end-to-end fault tolerance documentation for more information.

Use cases#

Ray Serve: The recommended configuration is enabling GCS fault tolerance on the RayService custom resource to ensure high availability. See Ray Serve end-to-end fault tolerance documentation for more information.
Other workloads: GCS fault tolerance isn’t recommended and the compatibility isn’t guaranteed.

Prerequisites#

Ray 2.0.0+
KubeRay 0.6.0+
Redis: single shard Redis Cluster or Redis Sentinel, one or multiple replicas

Quickstart#

Step 1: Create a Kubernetes cluster with Kind#

kind create cluster --image=kindest/node:v1.26.0

Step 2: Install the KubeRay operator#

Follow this document to install the latest stable KubeRay operator via Helm repository.

Step 3: Install a RayCluster with GCS FT enabled#

curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.2.2/ray-operator/config/samples/ray-cluster.external-redis.yaml
kubectl apply -f ray-cluster.external-redis.yaml

Step 4: Verify the Kubernetes cluster status#

# Step 4.1: List all Pods in the `default` namespace.
# The expected output should be 4 Pods: 1 head, 1 worker, 1 KubeRay, and 1 Redis.
kubectl get pods

# Step 4.2: List all ConfigMaps in the `default` namespace.
kubectl get configmaps

# [Example output]
# NAME                  DATA   AGE
# ray-example           2      5m45s
# redis-config          1      5m45s
# ...

The ray-cluster.external-redis.yaml file defines Kubernetes resources for RayCluster, Redis, and ConfigMaps. There are two ConfigMaps in this example: ray-example and redis-config. The ray-example ConfigMap houses two Python scripts: detached_actor.py and increment_counter.py.

detached_actor.py is a Python script that creates a detached actor with the name, counter_actor.

import ray

@ray.remote(num_cpus=1)
class Counter:
    def __init__(self):
        self.value = 0

    def increment(self):
        self.value += 1
        return self.value

ray.init(namespace="default_namespace")
Counter.options(name="counter_actor", lifetime="detached").remote()

increment_counter.py is a Python script that increments the counter.

import ray

ray.init(namespace="default_namespace")
counter = ray.get_actor("counter_actor")
print(ray.get(counter.increment.remote()))

Step 5: Create a detached actor#

# Step 4.1: Create a detached actor with the name, `counter_actor`.
export HEAD_POD=$(kubectl get pods --selector=ray.io/node-type=head -o custom-columns=POD:metadata.name --no-headers)
kubectl exec -it $HEAD_POD -- python3 /home/ray/samples/detached_actor.py

# Step 4.2: Increment the counter.
kubectl exec -it $HEAD_POD -- python3 /home/ray/samples/increment_counter.py

# 2023-09-07 16:01:41,925 INFO worker.py:1313 -- Using address 127.0.0.1:6379 set in the environment variable RAY_ADDRESS
# 2023-09-07 16:01:41,926 INFO worker.py:1431 -- Connecting to existing Ray cluster at address: 10.244.0.17:6379...
# 2023-09-07 16:01:42,000 INFO worker.py:1612 -- Connected to Ray cluster. View the dashboard at http://10.244.0.17:8265
# 1

Step 6: Check the data in Redis#

# Step 6.1: Check the RayCluster's UID.
kubectl get rayclusters.ray.io raycluster-external-redis -o=jsonpath='{.metadata.uid}'
# [Example output]: 864b004c-6305-42e3-ac46-adfa8eb6f752

# Step 6.2: Check the head Pod's environment variable `RAY_external_storage_namespace`.
kubectl get pods $HEAD_POD -o=jsonpath='{.spec.containers[0].env}' | jq
# [Example output]:
# [
#   {
#     "name": "RAY_external_storage_namespace",
#     "value": "864b004c-6305-42e3-ac46-adfa8eb6f752"
#   },
#   ...
# ]

# Step 6.3: Log into the Redis Pod.
export REDIS_POD=$(kubectl get pods --selector=app=redis -o custom-columns=POD:metadata.name --no-headers)
# The password `5241590000000000` is defined in the `redis-config` ConfigMap.
kubectl exec -it $REDIS_POD -- redis-cli -a "5241590000000000"

# Step 6.4: Check the keys in Redis.
# Note: the schema changed in Ray 2.38.0. Previously we use a single HASH table,
# now we use multiple HASH tables with a common prefix.

KEYS *
# [Example output]:
# 1) "RAY864b004c-6305-42e3-ac46-adfa8eb6f752@INTERNAL_CONFIG"
# 2) "RAY864b004c-6305-42e3-ac46-adfa8eb6f752@KV"
# 3) "RAY864b004c-6305-42e3-ac46-adfa8eb6f752@NODE"
# [Example output Before Ray 2.38.0]:
# 2) "864b004c-6305-42e3-ac46-adfa8eb6f752"
#

# Step 6.5: Check the value of the key.
HGETALL RAY864b004c-6305-42e3-ac46-adfa8eb6f752@NODE
# Before Ray 2.38.0:
# HGETALL 864b004c-6305-42e3-ac46-adfa8eb6f752

In ray-cluster.external-redis.yaml, the ray.io/external-storage-namespace annotation isn’t set for the RayCluster. Therefore, KubeRay automatically injects the environment variable RAY_external_storage_namespace to all Ray Pods managed by the RayCluster with the RayCluster’s UID as the external storage namespace by default. See this section to learn more about the annotation.

Step 7: Kill the GCS process in the head Pod#

# Step 7.1: Check the `RAY_gcs_rpc_server_reconnect_timeout_s` environment variable in both the head Pod and worker Pod.
kubectl get pods $HEAD_POD -o=jsonpath='{.spec.containers[0].env}' | jq
# [Expected result]:
# No `RAY_gcs_rpc_server_reconnect_timeout_s` environment variable is set. Hence, the Ray head uses its default value of `60`.
kubectl get pods $YOUR_WORKER_POD -o=jsonpath='{.spec.containers[0].env}' | jq
# [Expected result]:
# KubeRay injects the `RAY_gcs_rpc_server_reconnect_timeout_s` environment variable with the value `600` to the worker Pod.
# [
#   {
#     "name": "RAY_gcs_rpc_server_reconnect_timeout_s",
#     "value": "600"
#   },
#   ...
# ]

# Step 7.2: Kill the GCS process in the head Pod.
kubectl exec -it $HEAD_POD -- pkill gcs_server

# Step 7.3: The head Pod fails and restarts after `RAY_gcs_rpc_server_reconnect_timeout_s` (60) seconds.
# In addition, the worker Pod isn't terminated by the new head after reconnecting because GCS fault
# tolerance is enabled.
kubectl get pods -l=ray.io/is-ray-node=yes
# [Example output]:
# NAME                                                 READY   STATUS    RESTARTS      AGE
# raycluster-external-redis-head-xxxxx                 1/1     Running   1 (64s ago)   xxm
# raycluster-external-redis-worker-small-group-yyyyy   1/1     Running   0             xxm

In ray-cluster.external-redis.yaml, the RAY_gcs_rpc_server_reconnect_timeout_s environment variable isn’t set in the specifications for either the head Pod or the worker Pod within the RayCluster. Therefore, KubeRay automatically injects the RAY_gcs_rpc_server_reconnect_timeout_s environment variable with the value 600 to the worker Pod and uses the default value 60 for the head Pod. The timeout value for worker Pods must be longer than the timeout value for the head Pod so that the worker Pods don’t terminate before the head Pod restarts from a failure.

Step 8: Access the detached actor again#

kubectl exec -it $HEAD_POD -- python3 /home/ray/samples/increment_counter.py
# 2023-09-07 17:31:17,793 INFO worker.py:1313 -- Using address 127.0.0.1:6379 set in the environment variable RAY_ADDRESS
# 2023-09-07 17:31:17,793 INFO worker.py:1431 -- Connecting to existing Ray cluster at address: 10.244.0.21:6379...
# 2023-09-07 17:31:17,875 INFO worker.py:1612 -- Connected to Ray cluster. View the dashboard at http://10.244.0.21:8265
# 2

The detached actor is always on the worker Pod in this example.

The head Pod’s rayStartParams is set to num-cpus: "0". Hence, no tasks or actors will be scheduled on the head Pod.

With GCS fault tolerance enabled, you can still access the detached actor after the GCS process is dead and restarted. Note that the fault tolerance doesn’t persist the actor’s state. The reason why the result is 2 instead of 1 is that the detached actor is on the worker Pod which is always running. On the other hand, if the head Pod hosts the detached actor, the increment_counter.py script yields a result of 1 in this step.

Step 9: Remove the key stored in Redis when deleting RayCluster#

# Step 9.1: Delete the RayCluster custom resource.
kubectl delete raycluster raycluster-external-redis

# Step 9.2: KubeRay operator deletes all Pods in the RayCluster.
# Step 9.3: KubeRay operator creates a Kubernetes Job to delete the Redis key after the head Pod is terminated.

# Step 9.4: Check whether the RayCluster has been deleted.
kubectl get raycluster
# [Expected output]: No resources found in default namespace.

# Step 9.5: Check Redis keys after the Kubernetes Job finishes.
export REDIS_POD=$(kubectl get pods --selector=app=redis -o custom-columns=POD:metadata.name --no-headers)
kubectl exec -it $REDIS_POD -- redis-cli -a "5241590000000000"
KEYS *
# [Expected output]: (empty list or set)

In KubeRay v1.0.0, the KubeRay operator adds a Kubernetes finalizer to the RayCluster with GCS fault tolerance enabled to ensure Redis cleanup. KubeRay only removes this finalizer after the Kubernetes Job successfully cleans up Redis.

In other words, if the Kubernetes Job fails, the RayCluster won’t be deleted. In that case, you should remove the finalizer and cleanup Redis manually.
```
kubectl patch rayclusters.ray.io raycluster-external-redis --type json --patch='[ { "op": "remove", "path": "/metadata/finalizers" } ]'
```

Starting with KubeRay v1.1.0, KubeRay changes the Redis cleanup behavior from a mandatory to a best-effort basis. KubeRay still removes the Kubernetes finalizer from the RayCluster if the Kubernetes Job fails, thereby unblocking the deletion of the RayCluster.

Users can turn off this by setting the feature gate value ENABLE_GCS_FT_REDIS_CLEANUP. Refer to the KubeRay GCS fault tolerance configurations section for more details.

Step 10: Delete the Kubernetes cluster#

kind delete cluster

KubeRay GCS fault tolerance configurations#

The ray-cluster.external-redis.yaml used in the quickstart example contains detailed comments about the configuration options. Read this section in conjunction with the YAML file.

1. Enable GCS fault tolerance#

ray.io/ft-enabled: Add ray.io/ft-enabled: "true" annotation to the RayCluster custom resource to enable GCS fault tolerance.

kind: RayCluster
metadata:
annotations:
    ray.io/ft-enabled: "true" # <- Add this annotation to enable GCS fault tolerance

2. Connect to an external Redis#

redis-password in head’s rayStartParams: Use this option to specify the password for the Redis service, thus allowing the Ray head to connect to it. In the ray-cluster.external-redis.yaml, the RayCluster custom resource uses an environment variable REDIS_PASSWORD to store the password from a Kubernetes secret.

rayStartParams:
  redis-password: $REDIS_PASSWORD
template:
  spec:
    containers:
      - name: ray-head
        env:
          # This environment variable is used in the `rayStartParams` above.
          - name: REDIS_PASSWORD
            valueFrom:
              secretKeyRef:
                name: redis-password-secret
                key: password

RAY_REDIS_ADDRESS environment variable in head’s Pod: Ray reads the RAY_REDIS_ADDRESS environment variable to establish a connection with the Redis server. In the ray-cluster.external-redis.yaml, the RayCluster custom resource uses the redis Kubernetes ClusterIP service name as the connection point to the Redis server. The ClusterIP service is also created by the YAML file.
```
template:
  spec:
    containers:
      - name: ray-head
        env:
          - name: RAY_REDIS_ADDRESS
            value: redis:6379
```

3. Use an external storage namespace#

ray.io/external-storage-namespace annotation (optional): KubeRay uses the value of this annotation to set the environment variable RAY_external_storage_namespace to all Ray Pods managed by the RayCluster. In most cases, you don’t need to set ray.io/external-storage-namespace because KubeRay automatically sets it to the UID of RayCluster. Only modify this annotation if you fully understand the behaviors of the GCS fault tolerance and RayService to avoid this issue. Refer to this section in the earlier quickstart example for more details.
```
kind: RayCluster
metadata:
annotations:
    ray.io/external-storage-namespace: "my-raycluster-storage" # <- Add this annotation to specify a storage namespace
```

4. Turn off Redis cleanup#

ENABLE_GCS_FT_REDIS_CLEANUP: True by default. You can turn this feature off by setting the environment variable in the KubeRay operator’s Helm chart.

Key eviction setup on Redis

If you disable ENABLE_GCS_FT_REDIS_CLEANUP but want Redis to remove GCS metadata automatically, set these two options in your redis.conf or in the command line options of your redis-server command (example):

maxmemory=<your_memory_limit>
maxmemory-policy=allkeys-lru

These two options instruct Redis to delete the least recently used keys when it reaches the maxmemory limit. See Key eviction from Redis for more information.

Note that Redis does this eviction and it doesn’t guarantee that Ray won’t use the deleted keys.

Next steps#

See Ray Serve end-to-end fault tolerance documentation for more information.
See Ray Core GCS fault tolerance documentation for more information.