Responding to Amazon EMR cluster instance fleet resize timeout events
Overview
Amazon EMR clusters emit events while executing the resize operation for instance fleet clusters.
The provisioning timeout events are emitted when Amazon EMR stops provisioning Spot or
On-demand capacity for the fleet after the timeout expires. The timeout duration can
be configured by the user as part of the resize specifications for the instance fleets. In scenarios of
consecutive resizes for the same instance fleet, Amazon EMR emits the Spot
provisioning timeout - continuing resize
or On-Demand provisioning
timeout - continuing resize
events when timeout for the current resize
operation expires. It then starts provisioning capacity for the fleet’s next resize
operation.
Responding to instance fleet resize timeout events
We recommend that you respond to a provisioning timeout event in one of the following ways:
-
Revisit the resize specifications and retry the resize operation. As capacity shifts frequently, your clusters will successfully resize as soon as Amazon EC2 capacity becomes available. We recommend customers to configure lower values for the timeout duration for the jobs that require stricter SLAs.
-
Alternatively, you can either:
-
Launch a new cluster with diversified instance types based on the best practices for instance and Availability Zone flexibility or
-
Launch a cluster with On-demand capacity
-
-
For the provisioning timeout - continuing resize event, you can additionally wait for resize operations to be processed. Amazon EMR will continue to sequentially process the resize operations triggered for the fleet, respecting the configured resize specifications.
You can also set up rules or automated responses to this event as described in the next section.
Automated recovery from a provisioning timeout event
You can build automation in response to Amazon EMR events with the Spot
Provisioning timeout
event code. For example, the following AWS Lambda
function shuts down an EMR cluster with an instance fleet that uses Spot instances
for Task nodes, and then creates a new EMR cluster with an instance fleet that
contains more diversified instance types than the original request. In this example,
the Spot Provisioning timeout
event emitted for task nodes will trigger
the execution of the Lambda function.
Example function to respond to Spot Provisioning timeout
event
// 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")