# -*- coding: utf-8 -*- #
# Copyright 2015 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Update cluster command."""

from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals

from googlecloudsdk.api_lib.dataproc import dataproc as dp
from googlecloudsdk.api_lib.dataproc import exceptions
from googlecloudsdk.api_lib.dataproc import util
from googlecloudsdk.calliope import arg_parsers
from googlecloudsdk.calliope import base
from googlecloudsdk.command_lib.dataproc import flags
from googlecloudsdk.command_lib.util.args import labels_util
from googlecloudsdk.core import log
from googlecloudsdk.core.util import times
import six


class Update(base.UpdateCommand):
  """Update labels and/or the number of worker nodes in a cluster.

  Update the number of worker nodes and/or the labels in a cluster.

  ## EXAMPLES

  To resize a cluster, run:

    $ {command} my_cluster --region=us-central1 --num-workers=5

  To change the number preemptible workers in a cluster, run:

    $ {command} my_cluster --region=us-central1 --num-preemptible-workers=5

  To add the label 'customer=acme' to a cluster, run:

    $ {command} my_cluster --region=us-central1 --update-labels=customer=acme

  To update the label 'customer=ackme' to 'customer=acme', run:

    $ {command} my_cluster --region=us-central1 --update-labels=customer=acme

  To remove the label whose key is 'customer', run:

    $ {command} my_cluster --region=us-central1 --remove-labels=customer

  """

  @classmethod
  def Args(cls, parser):
    dataproc = dp.Dataproc(cls.ReleaseTrack())
    base.ASYNC_FLAG.AddToParser(parser)
    # Allow the user to specify new labels as well as update/remove existing
    labels_util.AddUpdateLabelsFlags(parser)
    # Updates can take hours if a lot of data needs to be moved on HDFS
    flags.AddTimeoutFlag(parser, default='3h')
    flags.AddClusterResourceArg(parser, 'update', dataproc.api_version)
    parser.add_argument(
        '--num-workers',
        type=int,
        help='The new number of worker nodes in the cluster.')
    parser.add_argument(
        '--num-preemptible-workers',
        type=int,
        help='The new number of preemptible worker nodes in the cluster.')

    parser.add_argument(
        '--graceful-decommission-timeout',
        type=arg_parsers.Duration(lower_bound='0s', upper_bound='1d'),
        help="""
              The graceful decommission timeout for decommissioning Node Managers
              in the cluster, used when removing nodes. Graceful decommissioning
              allows removing nodes from the cluster without interrupting jobs in
              progress. Timeout specifies how long to wait for jobs in progress to
              finish before forcefully removing nodes (and potentially
              interrupting jobs). Timeout defaults to 0 if not set (for forceful
              decommission), and the maximum allowed timeout is 1 day.
              See $ gcloud topic datetimes for information on duration formats.
              """)

    idle_delete_group = parser.add_mutually_exclusive_group()
    idle_delete_group.add_argument(
        '--max-idle',
        type=arg_parsers.Duration(),
        help="""\
        The duration before cluster is auto-deleted after last job finished,
        such as "2h" or "1d".
        See $ gcloud topic datetimes for information on duration formats.
        """)
    idle_delete_group.add_argument(
        '--no-max-idle',
        action='store_true',
        help="""\
        Cancels the cluster auto-deletion by cluster idle duration (configured
         by --max-idle flag)
        """)

    auto_delete_group = parser.add_mutually_exclusive_group()
    auto_delete_group.add_argument(
        '--max-age',
        type=arg_parsers.Duration(),
        help="""\
        The lifespan of the cluster before it is auto-deleted, such as
        "2h" or "1d".
        See $ gcloud topic datetimes for information on duration formats.
        """)
    auto_delete_group.add_argument(
        '--expiration-time',
        type=arg_parsers.Datetime.Parse,
        help="""\
        The time when cluster will be auto-deleted, such as
        "2017-08-29T18:52:51.142Z". See $ gcloud topic datetimes for
        information on time formats.
        """)
    auto_delete_group.add_argument(
        '--no-max-age',
        action='store_true',
        help="""\
        Cancels the cluster auto-deletion by maximum cluster age (configured by
         --max-age or --expiration-time flags)
        """)

    # Can only specify one of --autoscaling-policy or --disable-autoscaling
    autoscaling_group = parser.add_mutually_exclusive_group()
    flags.AddAutoscalingPolicyResourceArgForCluster(
        autoscaling_group, api_version='v1')
    autoscaling_group.add_argument(
        '--disable-autoscaling',
        action='store_true',
        help="""\
        Disable autoscaling, if it is enabled. This is an alias for passing the
        empty string to --autoscaling-policy'.
        """)

  def Run(self, args):
    dataproc = dp.Dataproc(self.ReleaseTrack())

    cluster_ref = args.CONCEPTS.cluster.Parse()

    cluster_config = dataproc.messages.ClusterConfig()
    changed_fields = []

    has_changes = False

    if args.num_workers is not None:
      worker_config = dataproc.messages.InstanceGroupConfig(
          numInstances=args.num_workers)
      cluster_config.workerConfig = worker_config
      changed_fields.append('config.worker_config.num_instances')
      has_changes = True

    if args.num_preemptible_workers is not None:
      worker_config = dataproc.messages.InstanceGroupConfig(
          numInstances=args.num_preemptible_workers)
      cluster_config.secondaryWorkerConfig = worker_config
      changed_fields.append(
          'config.secondary_worker_config.num_instances')
      has_changes = True

    if args.autoscaling_policy:
      cluster_config.autoscalingConfig = dataproc.messages.AutoscalingConfig(
          policyUri=args.CONCEPTS.autoscaling_policy.Parse().RelativeName())
      changed_fields.append('config.autoscaling_config.policy_uri')
      has_changes = True
    elif args.autoscaling_policy == '' or args.disable_autoscaling:  # pylint: disable=g-explicit-bool-comparison
      # Disabling autoscaling. Don't need to explicitly set
      # cluster_config.autoscaling_config to None.
      changed_fields.append('config.autoscaling_config.policy_uri')
      has_changes = True

    lifecycle_config = dataproc.messages.LifecycleConfig()
    changed_config = False
    if args.max_age is not None:
      lifecycle_config.autoDeleteTtl = six.text_type(args.max_age) + 's'
      changed_fields.append('config.lifecycle_config.auto_delete_ttl')
      changed_config = True
    if args.expiration_time is not None:
      lifecycle_config.autoDeleteTime = times.FormatDateTime(
          args.expiration_time)
      changed_fields.append('config.lifecycle_config.auto_delete_time')
      changed_config = True
    if args.max_idle is not None:
      lifecycle_config.idleDeleteTtl = six.text_type(args.max_idle) + 's'
      changed_fields.append('config.lifecycle_config.idle_delete_ttl')
      changed_config = True
    if args.no_max_age:
      lifecycle_config.autoDeleteTtl = None
      changed_fields.append('config.lifecycle_config.auto_delete_ttl')
      changed_config = True
    if args.no_max_idle:
      lifecycle_config.idleDeleteTtl = None
      changed_fields.append('config.lifecycle_config.idle_delete_ttl')
      changed_config = True
    if changed_config:
      cluster_config.lifecycleConfig = lifecycle_config
      has_changes = True

    # Put in a thunk so we only make this call if needed
    def _GetCurrentLabels():
      # We need to fetch cluster first so we know what the labels look like. The
      # labels_util will fill out the proto for us with all the updates and
      # removals, but first we need to provide the current state of the labels
      get_cluster_request = (
          dataproc.messages.DataprocProjectsRegionsClustersGetRequest(
              projectId=cluster_ref.projectId,
              region=cluster_ref.region,
              clusterName=cluster_ref.clusterName))
      current_cluster = dataproc.client.projects_regions_clusters.Get(
          get_cluster_request)
      return current_cluster.labels
    labels_update = labels_util.ProcessUpdateArgsLazy(
        args, dataproc.messages.Cluster.LabelsValue,
        orig_labels_thunk=_GetCurrentLabels)
    if labels_update.needs_update:
      has_changes = True
      changed_fields.append('labels')
    labels = labels_update.GetOrNone()

    if not has_changes:
      raise exceptions.ArgumentError(
          'Must specify at least one cluster parameter to update.')

    cluster = dataproc.messages.Cluster(
        config=cluster_config,
        clusterName=cluster_ref.clusterName,
        labels=labels,
        projectId=cluster_ref.projectId)

    request = dataproc.messages.DataprocProjectsRegionsClustersPatchRequest(
        clusterName=cluster_ref.clusterName,
        region=cluster_ref.region,
        projectId=cluster_ref.projectId,
        cluster=cluster,
        updateMask=','.join(changed_fields),
        requestId=util.GetUniqueId())

    if args.graceful_decommission_timeout is not None:
      request.gracefulDecommissionTimeout = (
          six.text_type(args.graceful_decommission_timeout) + 's')

    operation = dataproc.client.projects_regions_clusters.Patch(request)

    if args.async_:
      log.status.write(
          'Updating [{0}] with operation [{1}].'.format(
              cluster_ref, operation.name))
      return

    util.WaitForOperation(
        dataproc,
        operation,
        message='Waiting for cluster update operation',
        timeout_s=args.timeout)

    request = dataproc.messages.DataprocProjectsRegionsClustersGetRequest(
        projectId=cluster_ref.projectId,
        region=cluster_ref.region,
        clusterName=cluster_ref.clusterName)
    cluster = dataproc.client.projects_regions_clusters.Get(request)
    log.UpdatedResource(cluster_ref)
    return cluster
