373 lines
15 KiB
Python
373 lines
15 KiB
Python
# Copyright (c) 2015 Mirantis Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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# implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import abc
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from oslo_log import log as logging
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import six
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from sahara import conductor as cond
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from sahara import context
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from sahara.utils.openstack import nova
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conductor = cond.API
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LOG = logging.getLogger(__name__)
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@six.add_metaclass(abc.ABCMeta)
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class AutoConfigsProvider(object):
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def __init__(self, mapper, plugin_configs, cluster, scaling):
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"""This meta class provides general recommendation utils for cluster
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configuration.
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:param mapper: dictionary, that describes which cluster configs and
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node_configs to configure. It should maps to following dicts:
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node_configs to configure and cluster_configs to configure. This
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dicts should contains abstract names of configs as keys and
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tuple (correct_applicable_target, correct_name) as values. Such
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representation allows to use same AutoConfigsProvider for plugins
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with almost same configs and configuring principles.
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:param plugin_configs: all plugins_configs for specified plugin
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:param cluster: cluster which is required to configure
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:param scaling: indicates that current cluster operation is scaling
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"""
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self.plugin_configs = plugin_configs
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self.cluster = cluster
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self.node_configs_to_update = mapper.get('node_configs', {})
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self.cluster_configs_to_update = mapper.get('cluster_configs', {})
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self.scaling = scaling
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@abc.abstractmethod
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def _get_recommended_node_configs(self, node_group):
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"""Method calculates and returns recommended configs for node_group.
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It's not required to update node_configs of node_group using the
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conductor api in this method, because it will be done in the method
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apply_node_configs.
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:param node_group: NodeGroup Sahara resource.
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:return: dictionary with calculated recommended configs for
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node_group.
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"""
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pass
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@abc.abstractmethod
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def _get_recommended_cluster_configs(self):
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"""Method calculates and returns recommended configs for cluster.
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It's not required to update cluster_configs of cluster using the
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conductor api in this method, because it will be done in the method
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apply_cluster_configs.
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:return: dictionary with calculated recommended configs for
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cluster.
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"""
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pass
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def _can_be_recommended(self, configs_list, node_group=None):
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"""Method calculates and returns True, when it's possible to
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automatically configure provided list of configs configs_list.
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Otherwise, method should return False.
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:param configs_list: list of configs which we want to configure
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:param node_group: optional argument, which should be provided if
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some config can be used in node_configs of some node_group
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:return: True if all configs can be configured and False otherwise
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"""
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# cluster configs is Frozen Dict, so let's call to_dict()
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cl_configs = self.cluster.cluster_configs.to_dict()
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for ncfg in configs_list:
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section, name = self._get_correct_section_and_name(ncfg)
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if section in cl_configs and name in cl_configs[section]:
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return False
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if not node_group:
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return True
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cl_configs = node_group.node_configs.to_dict()
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for ncfg in configs_list:
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section, name = self._get_correct_section_and_name(ncfg)
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if section in cl_configs and name in cl_configs[section]:
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return False
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return True
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def _get_correct_section_and_name(self, config_name):
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"""Calculates and returns correct applicable target and name from
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abstract name of config.
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:param config_name: abstract name of config.
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:return: correct applicable target and name for config_name
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"""
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section, name = None, None
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if config_name in self.cluster_configs_to_update:
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section = self.cluster_configs_to_update[config_name][0]
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name = self.cluster_configs_to_update[config_name][1]
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elif config_name in self.node_configs_to_update:
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section = self.node_configs_to_update[config_name][0]
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name = self.node_configs_to_update[config_name][1]
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return section, name
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def _get_default_config_value(self, config_name):
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"""Calculates and return default value of config from
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abstract name of config.
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:param config_name: abstract name of config.
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:return: default config value for config_name.
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"""
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section, name = self._get_correct_section_and_name(config_name)
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for config in self.plugin_configs:
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if config.applicable_target == section and config.name == name:
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return config.default_value
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def _merge_configs(self, current_configs, proposed_configs):
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"""Correctly merges old configs and new extra configs"""
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result = {}
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for (section, configs) in six.iteritems(proposed_configs):
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cfg_values = {}
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if section in current_configs:
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cfg_values = (current_configs[section] if
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current_configs[section] else {})
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cfg_values.update(configs)
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result.update({section: cfg_values})
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for (section, configs) in six.iteritems(current_configs):
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if section not in result:
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result.update({section: configs})
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return result
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def _get_cluster_extra(self):
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cluster = self.cluster
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return cluster.extra.to_dict() if cluster.extra else {}
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def finalize_autoconfiguration(self):
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if not self.cluster.use_autoconfig:
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return
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cluster_extra = self._get_cluster_extra()
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cluster_extra['auto-configured'] = True
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conductor.cluster_update(
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context.ctx(), self.cluster, {'extra': cluster_extra})
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def apply_node_configs(self, node_group):
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"""Method applies configs for node_group using conductor api,
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which were calculated with recommend_node_configs method.
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:param node_group: NodeGroup Sahara resource.
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:return: None.
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"""
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if not node_group.use_autoconfig or not self.cluster.use_autoconfig:
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return
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to_update = self.node_configs_to_update
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recommended_node_configs = self._get_recommended_node_configs(
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node_group)
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if not recommended_node_configs:
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# Nothing to configure
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return
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current_dict = node_group.node_configs.to_dict()
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configuration = {}
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for ncfg in six.iterkeys(to_update):
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if ncfg not in recommended_node_configs:
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continue
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n_section = to_update[ncfg][0]
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n_name = to_update[ncfg][1]
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proposed_config_value = recommended_node_configs[ncfg]
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if n_section not in configuration:
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configuration.update({n_section: {}})
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configuration[n_section].update({n_name: proposed_config_value})
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current_dict = self._merge_configs(current_dict, configuration)
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conductor.node_group_update(context.ctx(), node_group,
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{'node_configs': current_dict})
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def apply_cluster_configs(self):
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"""Method applies configs for cluster using conductor api, which were
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calculated with recommend_cluster_configs method.
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:return: None.
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"""
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cluster = self.cluster
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if not cluster.use_autoconfig:
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return
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to_update = self.cluster_configs_to_update
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recommended_cluster_configs = self._get_recommended_cluster_configs()
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if not recommended_cluster_configs:
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# Nothing to configure
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return
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current_dict = cluster.cluster_configs.to_dict()
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configuration = {}
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for ncfg in six.iterkeys(to_update):
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if ncfg not in recommended_cluster_configs:
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continue
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n_section = to_update[ncfg][0]
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n_name = to_update[ncfg][1]
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proposed_config_value = recommended_cluster_configs[ncfg]
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if n_section not in configuration:
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configuration.update({n_section: {}})
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configuration[n_section].update({n_name: proposed_config_value})
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current_dict = self._merge_configs(current_dict, configuration)
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conductor.cluster_update(context.ctx(), cluster,
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{'cluster_configs': current_dict})
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def apply_recommended_configs(self):
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"""Method applies recommended configs for cluster and for all
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node_groups using conductor api.
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:return: None.
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"""
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if self.scaling:
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# Validate cluster is not an old created cluster
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cluster_extra = self._get_cluster_extra()
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if 'auto-configured' not in cluster_extra:
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# Don't configure
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return
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for ng in self.cluster.node_groups:
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self.apply_node_configs(ng)
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self.apply_cluster_configs()
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configs = list(self.cluster_configs_to_update.keys())
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configs.extend(list(self.node_configs_to_update.keys()))
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LOG.debug("Following configs were auto-configured: {configs}".format(
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configs=configs))
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self.finalize_autoconfiguration()
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class HadoopAutoConfigsProvider(AutoConfigsProvider):
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def __init__(self, mapper, plugin_configs, cluster, scaling, hbase=False):
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super(HadoopAutoConfigsProvider, self).__init__(
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mapper, plugin_configs, cluster, scaling)
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self.requested_flavors = {}
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self.is_hbase_enabled = hbase
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def _get_java_opts(self, value):
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return "-Xmx%dm" % int(value)
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def _transform_mb_to_gb(self, mb):
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return mb / 1024.
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def _transform_gb_to_mb(self, gb):
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return gb * 1024.
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def _get_min_size_of_container(self, ram):
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if ram <= 4:
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return 256
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if ram <= 8:
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return 512
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if ram <= 24:
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return 1024
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return 2048
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def _get_os_ram_recommendation(self, ram):
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upper_bounds = [4, 8, 16, 24, 48, 64, 72, 96, 128, 256]
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reserve_for_os = [1, 2, 2, 4, 6, 8, 8, 12, 24, 32]
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for (upper, reserve) in zip(upper_bounds, reserve_for_os):
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if ram <= upper:
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return reserve
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return 64
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def _get_hbase_ram_recommendations(self, ram):
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if not self.is_hbase_enabled:
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return 0
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upper_bounds = [4, 8, 16, 24, 48, 64, 72, 96, 128, 256]
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reserve_for_hbase = [1, 1, 2, 4, 8, 8, 8, 16, 24, 32]
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for (upper, reserve) in zip(upper_bounds, reserve_for_hbase):
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if ram <= upper:
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return reserve
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return 64
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def _get_node_group_data(self, node_group):
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if node_group.flavor_id not in self.requested_flavors:
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flavor = nova.get_flavor(id=node_group.flavor_id)
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self.requested_flavors[node_group.flavor_id] = flavor
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else:
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flavor = self.requested_flavors[node_group.flavor_id]
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cpu = flavor.vcpus
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ram = flavor.ram
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data = {}
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# config recommendations was taken from Ambari code
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os = self._get_os_ram_recommendation(self._transform_mb_to_gb(ram))
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hbase = self._get_hbase_ram_recommendations(
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self._transform_mb_to_gb(ram))
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reserved_ram = self._transform_gb_to_mb(os + hbase)
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min_container_size = self._get_min_size_of_container(
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self._transform_mb_to_gb(ram))
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# we use large amount of containers to allow users to run
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# at least 4 jobs at same time on clusters based on small flavors
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data["containers"] = int(max(
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8, min(2 * cpu, ram / min_container_size)))
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data["ramPerContainer"] = (ram - reserved_ram) / data["containers"]
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data["ramPerContainer"] = max(data["ramPerContainer"],
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min_container_size)
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data["ramPerContainer"] = min(2048, int(data["ramPerContainer"]))
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data["ramPerContainer"] = int(data["ramPerContainer"])
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data["mapMemory"] = int(data["ramPerContainer"])
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data["reduceMemory"] = int(2 * data["ramPerContainer"])
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data["amMemory"] = int(min(data["mapMemory"], data["reduceMemory"]))
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return data
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def _get_recommended_node_configs(self, node_group):
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"""Calculates recommended MapReduce and YARN configs for specified
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node_group.
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:param node_group: NodeGroup Sahara resource
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:return: dictionary with recommended MapReduce and YARN configs
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"""
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configs_to_update = list(self.node_configs_to_update.keys())
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if not self._can_be_recommended(configs_to_update, node_group):
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return {}
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data = self._get_node_group_data(node_group)
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r = {}
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r['yarn.nodemanager.resource.memory-mb'] = (data['containers'] *
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data['ramPerContainer'])
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r['yarn.scheduler.minimum-allocation-mb'] = data['ramPerContainer']
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r['yarn.scheduler.maximum-allocation-mb'] = (data['containers'] *
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data['ramPerContainer'])
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r['yarn.nodemanager.vmem-check-enabled'] = "false"
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r['yarn.app.mapreduce.am.resource.mb'] = data['amMemory']
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r['yarn.app.mapreduce.am.command-opts'] = self._get_java_opts(
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0.8 * data['amMemory'])
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r['mapreduce.map.memory.mb'] = data['mapMemory']
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r['mapreduce.reduce.memory.mb'] = data['reduceMemory']
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r['mapreduce.map.java.opts'] = self._get_java_opts(
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0.8 * data['mapMemory'])
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r['mapreduce.reduce.java.opts'] = self._get_java_opts(
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0.8 * data['reduceMemory'])
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r['mapreduce.task.io.sort.mb'] = int(min(
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0.4 * data['mapMemory'], 1024))
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return r
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def get_datanode_name(self):
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return "datanode"
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def _get_recommended_cluster_configs(self):
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"""Method recommends dfs_replication for cluster.
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:return: recommended value of dfs_replication.
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"""
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if not self._can_be_recommended(['dfs.replication']):
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return {}
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datanode_count = 0
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datanode_proc_name = self.get_datanode_name()
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for ng in self.cluster.node_groups:
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if datanode_proc_name in ng.node_processes:
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datanode_count += ng.count
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replica = 'dfs.replication'
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recommended_value = self._get_default_config_value(replica)
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if recommended_value:
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return {replica: min(recommended_value, datanode_count)}
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else:
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return {}
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