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””” Kubernetes

No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501

The version of the OpenAPI document: release-1.31 Generated by: https://openapi-generator.tech

””“

import pprint import re # noqa: F401

import six

from kubernetes.client.configuration import Configuration

class V1PodAffinity(object): “”“NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech

Do not edit the class manually.
          """
          
          """
          Attributes:
          openapi_types (dict): The key is attribute name
          and the value is attribute type.
          attribute_map (dict): The key is attribute name
          and the value is json key in definition.
          """
          openapi_types = {
          'preferred_during_scheduling_ignored_during_execution': 'list[V1WeightedPodAffinityTerm]',
          'required_during_scheduling_ignored_during_execution': 'list[V1PodAffinityTerm]'
          }
          attribute_map = {
          'preferred_during_scheduling_ignored_during_execution': 'preferredDuringSchedulingIgnoredDuringExecution',
          'required_during_scheduling_ignored_during_execution': 'requiredDuringSchedulingIgnoredDuringExecution'
          }
          def init(self, preferred_during_scheduling_ignored_during_execution=None, required_during_scheduling_ignored_during_execution=None, local_vars_configuration=None):  # noqa: E501
          """V1PodAffinity - a model defined in OpenAPI"""  # noqa: E501
          if local_vars_configuration is None:
          local_vars_configuration = Configuration()
          self.local_vars_configuration = local_vars_configuration
          self._preferred_during_scheduling_ignored_during_execution = None
          self._required_during_scheduling_ignored_during_execution = None
          self.discriminator = None
          if preferred_during_scheduling_ignored_during_execution is not None:
          self.preferred_during_scheduling_ignored_during_execution = preferred_during_scheduling_ignored_during_execution
          if required_during_scheduling_ignored_during_execution is not None:
          self.required_during_scheduling_ignored_during_execution = required_during_scheduling_ignored_during_execution
          @property
          def preferred_during_scheduling_ignored_during_execution(self):
          """Gets the preferred_during_scheduling_ignored_during_execution of this V1PodAffinity.  # noqa: E501
          The scheduler will prefer to schedule pods to nodes that satisfy the affinity expressions specified by this field, but it may choose a node that violates one or more of the expressions. The node that is most preferred is the one with the greatest sum of weights, i.e. for each node that meets all of the scheduling requirements (resource request, requiredDuringScheduling affinity expressions, etc.), compute a sum by iterating through the elements of this field and adding "weight" to the sum if the node has pods which matches the corresponding podAffinityTerm; the node(s) with the highest sum are the most preferred.  # noqa: E501
          :return: The preferred_during_scheduling_ignored_during_execution of this V1PodAffinity.  # noqa: E501
          :rtype: list[V1WeightedPodAffinityTerm]
          """
          return self._preferred_during_scheduling_ignored_during_execution
          @preferred_during_scheduling_ignored_during_execution.setter
          def preferred_during_scheduling_ignored_during_execution(self, preferred_during_scheduling_ignored_during_execution):
          """Sets the preferred_during_scheduling_ignored_during_execution of this V1PodAffinity.
          The scheduler will prefer to schedule pods to nodes that satisfy the affinity expressions specified by this field, but it may choose a node that violates one or more of the expressions. The node that is most preferred is the one with the greatest sum of weights, i.e. for each node that meets all of the scheduling requirements (resource request, requiredDuringScheduling affinity expressions, etc.), compute a sum by iterating through the elements of this field and adding "weight" to the sum if the node has pods which matches the corresponding podAffinityTerm; the node(s) with the highest sum are the most preferred.  # noqa: E501
          :param preferred_during_scheduling_ignored_during_execution: The preferred_during_scheduling_ignored_during_execution of this V1PodAffinity.  # noqa: E501
          :type: list[V1WeightedPodAffinityTerm]
          """
          self._preferred_during_scheduling_ignored_during_execution = preferred_during_scheduling_ignored_during_execution
          @property
          def required_during_scheduling_ignored_during_execution(self):
          """Gets the required_during_scheduling_ignored_during_execution of this V1PodAffinity.  # noqa: E501
          If the affinity requirements specified by this field are not met at scheduling time, the pod will not be scheduled onto the node. If the affinity requirements specified by this field cease to be met at some point during pod execution (e.g. due to a pod label update), the system may or may not try to eventually evict the pod from its node. When there are multiple elements, the lists of nodes corresponding to each podAffinityTerm are intersected, i.e. all terms must be satisfied.  # noqa: E501
          :return: The required_during_scheduling_ignored_during_execution of this V1PodAffinity.  # noqa: E501
          :rtype: list[V1PodAffinityTerm]
          """
          return self._required_during_scheduling_ignored_during_execution
          @required_during_scheduling_ignored_during_execution.setter
          def required_during_scheduling_ignored_during_execution(self, required_during_scheduling_ignored_during_execution):
          """Sets the required_during_scheduling_ignored_during_execution of this V1PodAffinity.
          If the affinity requirements specified by this field are not met at scheduling time, the pod will not be scheduled onto the node. If the affinity requirements specified by this field cease to be met at some point during pod execution (e.g. due to a pod label update), the system may or may not try to eventually evict the pod from its node. When there are multiple elements, the lists of nodes corresponding to each podAffinityTerm are intersected, i.e. all terms must be satisfied.  # noqa: E501
          :param required_during_scheduling_ignored_during_execution: The required_during_scheduling_ignored_during_execution of this V1PodAffinity.  # noqa: E501
          :type: list[V1PodAffinityTerm]
          """
          self._required_during_scheduling_ignored_during_execution = required_during_scheduling_ignored_during_execution
          def to_dict(self):
          """Returns the model properties as a dict"""
          result = {}
          for attr, _ in six.iteritems(self.openapi_types):
          value = getattr(self, attr)
          if isinstance(value, list):
          result[attr] = list(map(
          lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
          value
          ))
          elif hasattr(value, "to_dict"):
          result[attr] = value.to_dict()
          elif isinstance(value, dict):
          result[attr] = dict(map(
          lambda item: (item[0], item[1].to_dict())
          if hasattr(item[1], "to_dict") else item,
          value.items()
          ))
          else:
          result[attr] = value
          return result
          def to_str(self):
          """Returns the string representation of the model"""
          return pprint.pformat(self.to_dict())
          def repr(self):
          """For print and pprint”””
          return self.to_str()
          def eq(self, other):
          “”“Returns true if both objects are equal”“”
          if not isinstance(other, V1PodAffinity):
          return False
          return self.to_dict() == other.to_dict()
          def ne(self, other):
          “”“Returns true if both objects are not equal”“”
          if not isinstance(other, V1PodAffinity):
          return True
          return self.to_dict() != other.to_dict()

Explanation

This Python code defines a model named V1PodAffinity representing a Kubernetes pod affinity. Pod affinity describes the scheduling preferences and constraints for where a pod can be placed.

The model is auto-generated by OpenAPI Generator, which ensures compatibility with Kubernetes API specifications.
It has two main attributes:
preferred_during_scheduling_ignored_during_execution: Represents a list of V1WeightedPodAffinityTerm objects. These terms define “soft” affinity preferences, where the scheduler will try to fulfill them but is not obligated to.
required_during_scheduling_ignored_during_execution: Represents a list of V1PodAffinityTerm objects. These terms define “hard” affinity constraints, where the scheduler must meet them for a pod to be scheduled.

Key Functionality:

Constructor: The __init__ method initializes the object, optionally taking values for the preferred_during_scheduling_ignored_during_execution and required_during_scheduling_ignored_during_execution attributes.
Property Accessors: @property decorators provide getter and setter methods for the two main attributes, ensuring that the values are validated and managed consistently.
Serialization: The to_dict, to_str, and __repr__ methods provide ways to serialize the object into a dictionary, string representation, and a printable form for debugging.
Comparison: The __eq__ and __ne__ methods define how to compare two V1PodAffinity objects based on their attributes.

Use Case:

The V1PodAffinity model allows you to control the scheduling preferences and constraints for your pods. For example, you can use:

preferred_during_scheduling_ignored_during_execution to encourage pods to be placed on nodes where other pods with specific labels already exist.
required_during_scheduling_ignored_during_execution to ensure pods are only placed on nodes where other pods with specific labels already exist.

This model is crucial for controlling pod placement within your Kubernetes clusters, influencing resource allocation and potentially enhancing pod communication or data locality.

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