Reaktion auf Ereignisse mit unzureichender Instance-Kapazität im Amazon-EMR-Cluster

// 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")