响应 Amazon EMR 集群实例容量不足事件 - Amazon EMR
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响应 Amazon EMR 集群实例容量不足事件

概述

当所选可用区没有足够的容量来满足您的集群启动或调整大小请求时,Amazon EMR 集群会返回事件代码 EC2 provisioning - Insufficient Instance Capacity。如果 Amazon EMR 反复遇到容量不足异常,并且无法满足您的集群启动或集群调整大小操作的预置请求,则对于实例组和实例集该事件都会定期触发。

本页介绍在 EMR 集群发生此类事件时,如何最好地响应此类事件。

对容量不足事件的建议响应

我们建议您通过以下方式之一来应对容量不足事件:

  • 等待容量恢复。容量经常变化,因此容量不足的异常可以自行恢复。只要 Amazon EC2 容量可用,您的集群就会开始或完成大小调整。

  • 或者,您可以终止集群,修改实例类型配置,然后使用更新的集群配置请求创建新集群。有关更多信息,请参阅实例和可用区灵活性的最佳实践

您还可以设置对容量不足事件的规则或自动响应,如下一节所述。

从容量不足事件中自动恢复

您可以构建自动化以响应 Amazon EMR 事件,例如带有事件代码 EC2 provisioning - Insufficient Instance Capacity 的事件。例如,以下 Amazon Lambda 函数终止具有使用按需型实例的实例组的 EMR 集群,然后创建一个新的 EMR 集群,其实例组包含的实例类型与原始请求不同。

以下条件会触发自动流程的发生:

  • 主节点或核心节点的容量不足事件已持续超过 20 分钟。

  • 集群未处于就绪等待状态。有关 EMR 集群状态的更多信息,请参阅 了解集群的生命周期

注意

在为容量不足异常建立自动化流程时,应考虑容量不足事件是可以恢复的。容量经常发生变化,只要 Amazon EC2 容量可用,您的集群就会恢复调整大小或开始操作。

例 响应容量不足事件的功能
// 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 INSUFFICIENT_CAPACITY_EXCEPTION_DETAIL_TYPE = "EMR Instance Group Provisioning" INSUFFICIENT_CAPACITY_EXCEPTION_EVENT_CODE = ( "EC2 provisioning - Insufficient Instance Capacity" ) ALLOWED_INSTANCE_TYPES_TO_USE = [ "m5.xlarge", "c5.xlarge", "m5.4xlarge", "m5.2xlarge", "t3.xlarge", ] CLUSTER_START_ACCEPTABLE_STATES = ["WAITING", "RUNNING"] CLUSTER_START_SLA = 20 CLIENT = boto3.client("emr", region_name="us-east-1") # checks if the incoming event is 'EMR Instance Fleet Provisioning' with eventCode 'EC2 provisioning - Insufficient Instance Capacity' def is_insufficient_capacity_event(event): if not event["detail"]: return False else: return ( event["detail-type"] == INSUFFICIENT_CAPACITY_EXCEPTION_DETAIL_TYPE and event["detail"]["eventCode"] == INSUFFICIENT_CAPACITY_EXCEPTION_EVENT_CODE ) # checks if the cluster is eligible for termination def is_cluster_eligible_for_termination(event, describeClusterResponse): # instanceGroupType could be CORE, MASTER OR TASK instanceGroupType = event["detail"]["instanceGroupType"] clusterCreationTime = describeClusterResponse["Cluster"]["Status"]["Timeline"][ "CreationDateTime" ] clusterState = describeClusterResponse["Cluster"]["Status"]["State"] now = datetime.datetime.now() now = now.replace(tzinfo=timezone.utc) isClusterStartSlaBreached = clusterCreationTime < now - datetime.timedelta( minutes=CLUSTER_START_SLA ) # Check if instance group receiving Insufficient capacity exception is CORE or PRIMARY (MASTER), # and it's been more than 20 minutes since cluster was created but the cluster state and the cluster state is not updated to RUNNING or WAITING if ( (instanceGroupType == "CORE" or instanceGroupType == "MASTER") and isClusterStartSlaBreached and clusterState not in CLUSTER_START_ACCEPTABLE_STATES ): return True else: return False # Choose item from the list except the exempt value def choice_excluding(exempt): for i in ALLOWED_INSTANCE_TYPES_TO_USE: if i != exempt: return i # Create a new cluster by choosing different InstanceType. def create_cluster(event): # instanceGroupType cloud be CORE, MASTER OR TASK instanceGroupType = event["detail"]["instanceGroupType"] # 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 instanceTypeForMaster = ( instanceTypesFromOriginalRequestMaster if instanceGroupType != "MASTER" else choice_excluding(instanceTypesFromOriginalRequestMaster) ) instanceTypeForCore = ( instanceTypesFromOriginalRequestCore if instanceGroupType != "CORE" else choice_excluding(instanceTypesFromOriginalRequestCore) ) print("Starting to create cluster...") instances = { "InstanceGroups": [ { "InstanceRole": "MASTER", "InstanceCount": 1, "InstanceType": instanceTypeForMaster, "Market": "ON_DEMAND", "Name": "Master", }, { "InstanceRole": "CORE", "InstanceCount": 1, "InstanceType": instanceTypeForCore, "Market": "ON_DEMAND", "Name": "Core", }, ] } 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_insufficient_capacity_event(event): print( "Received insufficient capacity event for instanceGroup, 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 insufficient capacity event, skipping")