GetRayJob - E-MapReduce - Alibaba Cloud Documentation Center

Retrieves Ray Job information.

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Test

RAM authorization

The table below describes the authorization required to call this API. You can define it in a Resource Access Management (RAM) policy. The table's columns are detailed below:

Action: The actions can be used in the Action element of RAM permission policy statements to grant permissions to perform the operation.
API: The API that you can call to perform the action.
Access level: The predefined level of access granted for each API. Valid values: create, list, get, update, and delete.
Resource type: The type of the resource that supports authorization to perform the action. It indicates if the action supports resource-level permission. The specified resource must be compatible with the action. Otherwise, the policy will be ineffective.
- For APIs with resource-level permissions, required resource types are marked with an asterisk (*). Specify the corresponding Alibaba Cloud Resource Name (ARN) in the Resource element of the policy.
- For APIs without resource-level permissions, it is shown as All Resources. Use an asterisk (*) in the Resource element of the policy.
Condition key: The condition keys defined by the service. The key allows for granular control, applying to either actions alone or actions associated with specific resources. In addition to service-specific condition keys, Alibaba Cloud provides a set of common condition keys applicable across all RAM-supported services.
Dependent action: The dependent actions required to run the action. To complete the action, the RAM user or the RAM role must have the permissions to perform all dependent actions.

Action

Access level

Resource type

Condition key

Dependent action

emr-serverless-spark:GetRayJob

get

*All Resource

*

None

Request syntax

GET /api/v1/workspaces/{workspaceId}/rayJob/{submissionId} HTTP/1.1

Path Parameters

Parameter	Type	Required	Description	Example
workspaceId	string	No	The workspace ID.	w-d2d82aa09155
submissionId	string	No	The ID of the Ray Job.	rj-xxxxxxxxxx

Request parameters

Parameter

Type

Required

Description

Example

No parameters required.

Response elements

Element	Type	Description	Example
	object	Schema of Response
requestId	string	The request ID.	DD6B1B2A-5837-5237-ABE4-FF0C8944
submissionId	string	The ID of the Ray Job.	rj-xxxxxxxxxx
name	string	The name of the Ray cluster.	myRayCluster
displayReleaseVersion	string	The Ray DPI engine version.	err-1.2.0 (Ray 2.55.1, Python 3.12)
tags	array	The tags.
	Tag	The tags.
status	string	The task status. Valid values: Submitted: The task is submitted. Pending: The cluster is being created. Running: The task is running. Succeeded: The task succeeded. Failed: The task failed. Cancelling: The task is being canceled. Cancelled: The task is canceled. Timeout: The task timed out and was canceled.	Running
message	string	The execution information.	Job finished successfully.
entrypoint	string	The startup command.	python main.py
submitTime	integer	The task submission time. This value is a UNIX timestamp in milliseconds.	1750327082303
startTime	integer	The start time. This value is a UNIX timestamp in milliseconds.	1750327083303
endTime	integer	The task end time. This value is a UNIX timestamp in milliseconds.	1762949372000
duration	integer	The task duration, in seconds.	2459764
networkServiceName	string	The network connectivity name.	vpc
runtimeEnvJson	string	The Ray runtime environment JSON string.	{"pip":["requests==2.26.0","pendulum==2.1.2"],"env_vars":{"KEY":"VALUE"}}
workingDir	string	The URL of the task code working directory.	oss://mybucket/hello.zip
submissionMode	string	The job submission mode.	HTTPMode
metadataJson	string	The task metadata JSON string.	{"owner": "alice"}
entrypointNumCpus	string	The number of CPUs requested by the entrypoint task.	1
entrypointNumGpus	string	The number of GPUs requested by the entrypoint task.	0
entrypointMemory	string	The memory size requested by the entrypoint task.	4Gi
entrypointResources	string	The custom resource request JSON string for the entrypoint task.	{"fpu": 1}
shutdownAfterJobFinishes	boolean	Specifies whether to automatically destroy the temporary cluster after the task is completed. Default value: true.	true
ttlSecondsAfterFinished	integer	The number of seconds to wait before destroying the cluster. This parameter takes effect only when shutdownAfterJobFinishes is set to true.	60
activeDeadlineSeconds	integer	The timeout period.	3600
volumeIds	array	The list of managed file IDs.
	string	The managed file ID.	vol-123456789
backoffLimit	integer	The number of failure retries. Currently fixed at 0.	2
headSpec	object	The parameters of the Ray cluster head node.
replica	integer	The number of nodes.	1
enableAutoScaling	boolean	Indicates whether automatic scaling is enabled for worker nodes.	true
idleTimeoutSeconds	integer	The idle timeout for worker nodes when automatic scaling is enabled.	60
queueName	string	The queue name.	root_queue
cpu	string	The number of CPU cores.	2
memory	string	The memory size, in GiB.	8Gi
gpuSpec	string	The GPU type.	ecs.gn6i-c4g1.xlarge
displayReleaseVersion	string	The Ray DPI engine version.	err-1.3.0 (Ray 2.55.1, Python 3.12)
workerSpecs	array<object>	The Ray cluster worker node information.
	object	The Ray cluster worker node information struct.
groupName	string	The worker node group name.	WorkerGroup1
replica	integer	The number of worker nodes.	1
minReplica	integer	The minimum number of workers.	1
maxReplica	integer	The maximum number of workers.	10
queueName	string	The queue name.	root_queue
cpu	string	The number of CPU cores.	2
memory	string	The memory size, in GiB.	8Gi
gpuSpec	string	The GPU type.	ecs.gn6i-c4g1.xlarge
displayReleaseVersion	string	The DPI engine version.	ray-1.2.0 (Ray 2.55.1, Python 3.12)
extraParam	string	The extra parameters in JSON format.	{"userDefinedFiles": "oss://mybucket/artifact/config.json,oss://mybucket/artifact/config2.json", "userRequirementsFile": "oss://mybucket/requirements.txt"}
creatorName	string	The nickname of the creator.	Alice
dashboardUrl	string	The dashboard URL of the Ray cluster. When the Ray cluster is in the Running state, this is the Runtime UI. After the cluster enters the Deleted state, this is the History UI. History UI is supported only in err-1.2.0 and later versions.	https://emr-ray-gateway-cn-hangzhou.aliyuncs.com/workspace/w-xxxxxxxx/raycluster/ray-xxxxxx/dashboard?token=xxxxxx
logBucketName	string	The name of the bucket that stores logs.	ss-ray-cn-hangzhou
logPath	string	The path where logs are stored.	w-xxxxxxx/ray/logs/xxxxxx/
cuHours	number	The consumed CU resources. This value is returned 10 minutes after the cluster is released.	1899
guHours	object	The consumed GPU hours. This field is currently empty.
gpuSpec	string	The GPU type.	ecs.gn6i-c4g1.xlarge
gpuHours	number	The consumed GPU hours.	2.6
dashboardUrlExtra	array	The extra dashboard UI URLs. This field is currently empty.
	string	The extra dashboard UI URLs. This field is currently empty.	-
clusterState	string	The status of the corresponding Ray cluster. Valid values: Deleted: Deleted. Submitted: Submitted but creation has not started. Pending: Being created. Running: Running.	Running
taskBizId	string	The ID of the data development node.	TSK-682e0112f6f24d9f9305b92174846985

Examples

Success response

JSON format

{
  "requestId": "DD6B1B2A-5837-5237-ABE4-FF0C8944",
  "submissionId": "rj-xxxxxxxxxx",
  "name": "myRayCluster",
  "displayReleaseVersion": "err-1.2.0 (Ray 2.55.1, Python 3.12)",
  "tags": [
    {
      "key": "workflowId",
      "value": "wf-123test"
    }
  ],
  "status": "Running",
  "message": "Job finished successfully.",
  "entrypoint": "python main.py",
  "submitTime": 1750327082303,
  "startTime": 1750327083303,
  "endTime": 1762949372000,
  "duration": 2459764,
  "networkServiceName": "vpc",
  "runtimeEnvJson": "{\"pip\":[\"requests==2.26.0\",\"pendulum==2.1.2\"],\"env_vars\":{\"KEY\":\"VALUE\"}}",
  "workingDir": "oss://mybucket/hello.zip",
  "submissionMode": "HTTPMode",
  "metadataJson": "{\"owner\": \"alice\"}",
  "entrypointNumCpus": "1",
  "entrypointNumGpus": "0",
  "entrypointMemory": "4Gi",
  "entrypointResources": "{\"fpu\": 1}",
  "shutdownAfterJobFinishes": true,
  "ttlSecondsAfterFinished": 60,
  "activeDeadlineSeconds": 3600,
  "volumeIds": [
    "vol-123456789"
  ],
  "backoffLimit": 2,
  "headSpec": {
    "replica": 1,
    "enableAutoScaling": true,
    "idleTimeoutSeconds": 60,
    "queueName": "root_queue",
    "cpu": "2",
    "memory": "8Gi",
    "gpuSpec": "ecs.gn6i-c4g1.xlarge",
    "displayReleaseVersion": "err-1.3.0 (Ray 2.55.1, Python 3.12)"
  },
  "workerSpecs": [
    {
      "groupName": "WorkerGroup1",
      "replica": 1,
      "minReplica": 1,
      "maxReplica": 10,
      "queueName": "root_queue",
      "cpu": "2",
      "memory": "8Gi",
      "gpuSpec": "ecs.gn6i-c4g1.xlarge",
      "displayReleaseVersion": "ray-1.2.0 (Ray 2.55.1, Python 3.12)"
    }
  ],
  "extraParam": "{\"userDefinedFiles\": \"oss://mybucket/artifact/config.json,oss://mybucket/artifact/config2.json\", \"userRequirementsFile\": \"oss://mybucket/requirements.txt\"}",
  "creatorName": "Alice",
  "dashboardUrl": "https://emr-ray-gateway-cn-hangzhou.aliyuncs.com/workspace/w-xxxxxxxx/raycluster/ray-xxxxxx/dashboard?token=xxxxxx",
  "logBucketName": "ss-ray-cn-hangzhou",
  "logPath": "w-xxxxxxx/ray/logs/xxxxxx/",
  "cuHours": 1899,
  "guHours": {
    "gpuSpec": "ecs.gn6i-c4g1.xlarge",
    "gpuHours": 2.6
  },
  "dashboardUrlExtra": [
    "-"
  ],
  "clusterState": "Running",
  "taskBizId": "TSK-682e0112f6f24d9f9305b92174846985"
}

Error codes

See Error Codes for a complete list.

Release notes

See Release Notes for a complete list.