响应 Amazon EMR 集群实例集调整大小超时事件 - Amazon EMR
概述建议自动化示例
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响应 Amazon EMR 集群实例集调整大小超时事件

概述

Amazon EMR 集群在对实例集集群执行调整大小操作时会发出事件。超时到期后，当 Amazon EMR 停止为实例集预置竞价型或按需容量时，就会发出预置超时事件。用户可以将超时持续时间配置为实例集调整大小规范的一部分。在对相同实例集连续调整大小的情况下，当前调整大小操作的超时到期时，Amazon EMR 会发出 Spot provisioning timeout - continuing resize 或 On-Demand provisioning timeout - continuing resize 事件。然后，它开始为队列的下一次调整大小操作预置容量。

响应实例集调整大小超时事件

我们建议您通过以下方式之一来响应预置超时事件：

  • 重访调整大小规范，然后重试调整大小操作。由于容量频繁变化，只要 Amazon EC2 容量可用，您的集群就会成功调整大小。我们建议客户为需要更严格 SLA 的任务配置较低的超时持续时间值。

  • 或者，您可以：

  • 对于配置超时 – 继续调整事件大小，您还可以等待调整大小操作的处理。Amazon EMR 将继续按顺序处理针对实例触发的调整大小操作，同时遵守配置的调整大小规范。

您还可以设置对该事件的规则或自动响应，如下一节所述。

自动从预置超时事件中恢复

您可以使用 Spot Provisioning timeout 事件代码构建自动化以响应 Amazon EMR 事件。例如，以下 Amazon Lambda 函数关闭了具有使用竞价型实例作为任务节点的实例集的 EMR 集群，然后创建一个新的 EMR 集群，其实例集包含的实例类型比原始请求更加多样化。在此示例中，为任务节点发出的 Spot Provisioning timeout 事件将触发 Lambda 函数的执行。

例 响应 Spot Provisioning timeout 事件的示例函数
// Lambda code with Python 3.10 and handler is lambda_function.lambda_handler
// Note: related IAM role requires permission to use Amazon EMR
 
import json
import boto3
import datetime
from datetime import timezone
 
SPOT_PROVISIONING_TIMEOUT_EXCEPTION_DETAIL_TYPE = "EMR Instance Fleet Resize"
SPOT_PROVISIONING_TIMEOUT_EXCEPTION_EVENT_CODE = (
    "Spot Provisioning timeout"
)
 
CLIENT = boto3.client("emr", region_name="us-east-1")
 
# checks if the incoming event is 'EMR Instance Fleet Resize' with eventCode 'Spot provisioning timeout'
def is_spot_provisioning_timeout_event(event):
    if not event["detail"]:
        return False
    else:
        return (
            event["detail-type"] == SPOT_PROVISIONING_TIMEOUT_EXCEPTION_DETAIL_TYPE
            and event["detail"]["eventCode"]
            == SPOT_PROVISIONING_TIMEOUT_EXCEPTION_EVENT_CODE
        )
 
 
# checks if the cluster is eligible for termination
def is_cluster_eligible_for_termination(event, describeClusterResponse):
    # instanceFleetType could be CORE, MASTER OR TASK
    instanceFleetType = event["detail"]["instanceFleetType"]
 
    # Check if instance fleet receiving Spot provisioning timeout event is TASK
    if (instanceFleetType == "TASK"):
        return True
    else:
        return False
 
 
# create a new cluster by choosing different InstanceType.
def create_cluster(event):
    # instanceFleetType cloud be CORE, MASTER OR TASK
    instanceFleetType = event["detail"]["instanceFleetType"]
 
    # the following two lines assumes that the customer that created the cluster already knows which instance types they use in original request
    instanceTypesFromOriginalRequestMaster = "m5.xlarge"
    instanceTypesFromOriginalRequestCore = "m5.xlarge"
   
    # select new instance types to include in the new createCluster request
    instanceTypesForTask = [
        "m5.xlarge",
        "m5.2xlarge",
        "m5.4xlarge",
        "m5.8xlarge",
        "m5.12xlarge"
    ]
    
    print("Starting to create cluster...")
    instances = {
        "InstanceFleets": [
            {
                "InstanceFleetType":"MASTER",
                "TargetOnDemandCapacity":1,
                "TargetSpotCapacity":0,
                "InstanceTypeConfigs":[
                    {
                        'InstanceType': instanceTypesFromOriginalRequestMaster,
                        "WeightedCapacity":1,
                    }
                ]
            },
            {
                "InstanceFleetType":"CORE",
                "TargetOnDemandCapacity":1,
                "TargetSpotCapacity":0,
                "InstanceTypeConfigs":[
                    {
                        'InstanceType': instanceTypesFromOriginalRequestCore,
                        "WeightedCapacity":1,
                    }
                ]
            },
            {
                "InstanceFleetType":"TASK",
                "TargetOnDemandCapacity":0,
                "TargetSpotCapacity":100,
                "LaunchSpecifications":{},
                "InstanceTypeConfigs":[
                    {
                        'InstanceType': instanceTypesForTask[0],
                        "WeightedCapacity":1,
                    },
                    {
                        'InstanceType': instanceTypesForTask[1],
                        "WeightedCapacity":2,
                    },
                    {
                        'InstanceType': instanceTypesForTask[2],
                        "WeightedCapacity":4,
                    },
                    {
                        'InstanceType': instanceTypesForTask[3],
                        "WeightedCapacity":8,
                    },
                    {
                        'InstanceType': instanceTypesForTask[4],
                        "WeightedCapacity":12,
                    }
                ],
                "ResizeSpecifications": {
                    "SpotResizeSpecification": {
                        "TimeoutDurationMinutes": 30
                    }
                }
            }
        ]
    }
    response = CLIENT.run_job_flow(
        Name="Test Cluster",
        Instances=instances,
        VisibleToAllUsers=True,
        JobFlowRole="EMR_EC2_DefaultRole",
        ServiceRole="EMR_DefaultRole",
        ReleaseLabel="emr-6.10.0",
    )
 
    return response["JobFlowId"]
 
 
# terminated the cluster using clusterId received in an event
def terminate_cluster(event):
    print("Trying to terminate cluster, clusterId: " + event["detail"]["clusterId"])
    response = CLIENT.terminate_job_flows(JobFlowIds=[event["detail"]["clusterId"]])
    print(f"Terminate cluster response: {response}")
 
 
def describe_cluster(event):
    response = CLIENT.describe_cluster(ClusterId=event["detail"]["clusterId"])
    return response
 
 
def lambda_handler(event, context):
    if is_spot_provisioning_timeout_event(event):
        print(
            "Received spot provisioning timeout event for instanceFleet, clusterId: "
            + event["detail"]["clusterId"]
        )
 
        describeClusterResponse = describe_cluster(event)
 
        shouldTerminateCluster = is_cluster_eligible_for_termination(
            event, describeClusterResponse
        )
        if shouldTerminateCluster:
            terminate_cluster(event)
 
            clusterId = create_cluster(event)
            print("Created a new cluster, clusterId: " + clusterId)
        else:
            print(
                "Cluster is not eligible for termination, clusterId: "
                + event["detail"]["clusterId"]
            )
 
    else:
        print("Received event is not spot provisioning timeout event, skipping")