#
# Copyright 2013 Intel Corp.
# Copyright 2014 Red Hat, Inc
#
# Authors: Yunhong Jiang <yunhong.jiang@intel.com>
#          Eoghan Glynn <eglynn@redhat.com>
#
# 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.

import fnmatch
import itertools
import operator
import os

from oslo.config import cfg
import yaml

from ceilometer.openstack.common.gettextutils import _
from ceilometer.openstack.common import log
from ceilometer import publisher
from ceilometer import transformer as xformer


OPTS = [
    cfg.StrOpt('pipeline_cfg_file',
               default="pipeline.yaml",
               help="Configuration file for pipeline definition."
               ),
]

cfg.CONF.register_opts(OPTS)

LOG = log.getLogger(__name__)


class PipelineException(Exception):
    def __init__(self, message, pipeline_cfg):
        self.msg = message
        self.pipeline_cfg = pipeline_cfg

    def __str__(self):
        return 'Pipeline %s: %s' % (self.pipeline_cfg, self.msg)


class PublishContext(object):

    def __init__(self, context, pipelines=None):
        pipelines = pipelines or []
        self.pipelines = set(pipelines)
        self.context = context

    def add_pipelines(self, pipelines):
        self.pipelines.update(pipelines)

    def __enter__(self):
        def p(samples):
            for p in self.pipelines:
                p.publish_samples(self.context,
                                  samples)
        return p

    def __exit__(self, exc_type, exc_value, traceback):
        for p in self.pipelines:
            p.flush(self.context)


class Source(object):
    """Represents a source of samples, in effect a set of pollsters
    and/or notification handlers emitting samples for a set of matching
    meters.

    Each source encapsulates meter name matching, polling interval
    determination, optional resource enumeration or discovery, and
    mapping to one or more sinks for publication.

    """

    def __init__(self, cfg):
        self.cfg = cfg

        try:
            self.name = cfg['name']
            try:
                self.interval = int(cfg['interval'])
            except ValueError:
                raise PipelineException("Invalid interval value", cfg)
            # Support 'counters' for backward compatibility
            self.meters = cfg.get('meters', cfg.get('counters'))
            self.sinks = cfg.get('sinks')
        except KeyError as err:
            raise PipelineException(
                "Required field %s not specified" % err.args[0], cfg)

        if self.interval <= 0:
            raise PipelineException("Interval value should > 0", cfg)

        self.resources = cfg.get('resources') or []
        if not isinstance(self.resources, list):
            raise PipelineException("Resources should be a list", cfg)

        self.discovery = cfg.get('discovery') or []
        if not isinstance(self.discovery, list):
            raise PipelineException("Discovery should be a list", cfg)

        self._check_meters()

    def __str__(self):
        return self.name

    def _check_meters(self):
        """Meter rules checking

        At least one meaningful meter exist
        Included type and excluded type meter can't co-exist at
        the same pipeline
        Included type meter and wildcard can't co-exist at same pipeline

        """
        meters = self.meters
        if not meters:
            raise PipelineException("No meter specified", self.cfg)

        if [x for x in meters if x[0] not in '!*'] and \
           [x for x in meters if x[0] == '!']:
            raise PipelineException(
                "Both included and excluded meters specified",
                cfg)

        if '*' in meters and [x for x in meters if x[0] not in '!*']:
            raise PipelineException(
                "Included meters specified with wildcard",
                self.cfg)

    # (yjiang5) To support meters like instance:m1.tiny,
    # which include variable part at the end starting with ':'.
    # Hope we will not add such meters in future.
    @staticmethod
    def _variable_meter_name(name):
        m = name.partition(':')
        if m[1] == ':':
            return m[1].join((m[0], '*'))
        else:
            return name

    def support_meter(self, meter_name):
        meter_name = self._variable_meter_name(meter_name)

        # Special case: if we only have negation, we suppose the default is
        # allow
        default = all(meter.startswith('!') for meter in self.meters)

        # Support wildcard like storage.* and !disk.*
        # Start with negation, we consider that the order is deny, allow
        if any(fnmatch.fnmatch(meter_name, meter[1:])
               for meter in self.meters
               if meter[0] == '!'):
            return False

        if any(fnmatch.fnmatch(meter_name, meter)
               for meter in self.meters
               if meter[0] != '!'):
            return True

        return default

    def check_sinks(self, sinks):
        if not self.sinks:
            raise PipelineException(
                "No sink defined in source %s" % self,
                self.cfg)
        for sink in self.sinks:
            if sink not in sinks:
                raise PipelineException(
                    "Dangling sink %s from source %s" % (sink, self),
                    self.cfg)


class Sink(object):
    """Represents a sink for the transformation and publication of
    samples emitted from a related source.

    Each sink config is concerned *only* with the transformation rules
    and publication conduits for samples.

    In effect, a sink describes a chain of handlers. The chain starts
    with zero or more transformers and ends with one or more publishers.

    The first transformer in the chain is passed samples from the
    corresponding source, takes some action such as deriving rate of
    change, performing unit conversion, or aggregating, before passing
    the modified sample to next step.

    The subsequent transformers, if any, handle the data similarly.

    At the end of the chain, publishers publish the data. The exact
    publishing method depends on publisher type, for example, pushing
    into data storage via the message bus providing guaranteed delivery,
    or for loss-tolerant samples UDP may be used.

    If no transformers are included in the chain, the publishers are
    passed samples directly from the sink which are published unchanged.

    """

    def __init__(self, cfg, transformer_manager):
        self.cfg = cfg

        try:
            self.name = cfg['name']
            # It's legal to have no transformer specified
            self.transformer_cfg = cfg['transformers'] or []
        except KeyError as err:
            raise PipelineException(
                "Required field %s not specified" % err.args[0], cfg)

        if not cfg.get('publishers'):
            raise PipelineException("No publisher specified", cfg)

        self.publishers = []
        for p in cfg['publishers']:
            if '://' not in p:
                # Support old format without URL
                p = p + "://"
            try:
                self.publishers.append(publisher.get_publisher(p))
            except Exception:
                LOG.exception(_("Unable to load publisher %s"), p)

        self.transformers = self._setup_transformers(cfg, transformer_manager)

    def __str__(self):
        return self.name

    def _setup_transformers(self, cfg, transformer_manager):
        transformer_cfg = cfg['transformers'] or []
        transformers = []
        for transformer in transformer_cfg:
            parameter = transformer['parameters'] or {}
            try:
                ext = transformer_manager.get_ext(transformer['name'])
            except KeyError:
                raise PipelineException(
                    "No transformer named %s loaded" % transformer['name'],
                    cfg)
            transformers.append(ext.plugin(**parameter))
            LOG.info(_(
                "Pipeline %(pipeline)s: Setup transformer instance %(name)s "
                "with parameter %(param)s") % ({'pipeline': self,
                                                'name': transformer['name'],
                                                'param': parameter}))

        return transformers

    def _transform_sample(self, start, ctxt, sample):
        try:
            for transformer in self.transformers[start:]:
                sample = transformer.handle_sample(ctxt, sample)
                if not sample:
                    LOG.debug(_(
                        "Pipeline %(pipeline)s: Sample dropped by "
                        "transformer %(trans)s") % ({'pipeline': self,
                                                     'trans': transformer}))
                    return
            return sample
        except Exception as err:
            LOG.warning(_("Pipeline %(pipeline)s: "
                          "Exit after error from transformer "
                          "%(trans)s for %(smp)s") % ({'pipeline': self,
                                                       'trans': transformer,
                                                       'smp': sample}))
            LOG.exception(err)

    def _publish_samples(self, start, ctxt, samples):
        """Push samples into pipeline for publishing.

        :param start: The first transformer that the sample will be injected.
                      This is mainly for flush() invocation that transformer
                      may emit samples.
        :param ctxt: Execution context from the manager or service.
        :param samples: Sample list.

        """

        transformed_samples = []
        for sample in samples:
            LOG.debug(_(
                "Pipeline %(pipeline)s: Transform sample "
                "%(smp)s from %(trans)s transformer") % ({'pipeline': self,
                                                          'smp': sample,
                                                          'trans': start}))
            sample = self._transform_sample(start, ctxt, sample)
            if sample:
                transformed_samples.append(sample)

        if transformed_samples:
            for p in self.publishers:
                try:
                    p.publish_samples(ctxt, transformed_samples)
                except Exception:
                    LOG.exception(_(
                        "Pipeline %(pipeline)s: Continue after error "
                        "from publisher %(pub)s") % ({'pipeline': self,
                                                      'pub': p}))

    def publish_samples(self, ctxt, samples):
        for meter_name, samples in itertools.groupby(
                sorted(samples, key=operator.attrgetter('name')),
                operator.attrgetter('name')):
            self._publish_samples(0, ctxt, samples)

    def flush(self, ctxt):
        """Flush data after all samples have been injected to pipeline."""

        for (i, transformer) in enumerate(self.transformers):
            try:
                self._publish_samples(i + 1, ctxt,
                                      list(transformer.flush(ctxt)))
            except Exception as err:
                LOG.warning(_(
                    "Pipeline %(pipeline)s: Error flushing "
                    "transformer %(trans)s") % ({'pipeline': self,
                                                 'trans': transformer}))
                LOG.exception(err)


class Pipeline(object):
    """Represents a coupling between a sink and a corresponding source.
    """

    def __init__(self, source, sink):
        self.source = source
        self.sink = sink
        self.name = str(self)

    def __str__(self):
        return (self.source.name if self.source.name == self.sink.name
                else '%s:%s' % (self.source.name, self.sink.name))

    def get_interval(self):
        return self.source.interval

    @property
    def resources(self):
        return self.source.resources

    @property
    def discovery(self):
        return self.source.discovery

    def support_meter(self, meter_name):
        return self.source.support_meter(meter_name)

    @property
    def publishers(self):
        return self.sink.publishers

    def publish_sample(self, ctxt, sample):
        self.publish_samples(ctxt, [sample])

    def publish_samples(self, ctxt, samples):
        supported = [s for s in samples if self.source.support_meter(s.name)]
        self.sink.publish_samples(ctxt, supported)

    def flush(self, ctxt):
        self.sink.flush(ctxt)


class PipelineManager(object):
    """Pipeline Manager

    Pipeline manager sets up pipelines according to config file

    Usually only one pipeline manager exists in the system.

    """

    def __init__(self, cfg, transformer_manager):
        """Setup the pipelines according to config.

        The configuration is supported in one of two forms:

        1. Deprecated: the source and sink configuration are conflated
           as a list of consolidated pipelines.

           The pipelines are defined as a list of dictionaries each
           specifying the target samples, the transformers involved,
           and the target publishers, for example:

           [{"name": pipeline_1,
             "interval": interval_time,
             "meters" : ["meter_1", "meter_2"],
             "resources": ["resource_uri1", "resource_uri2"],
             "transformers": [
                              {"name": "Transformer_1",
                               "parameters": {"p1": "value"}},

                              {"name": "Transformer_2",
                               "parameters": {"p1": "value"}},
                              ],
             "publishers": ["publisher_1", "publisher_2"]
            },
            {"name": pipeline_2,
             "interval": interval_time,
             "meters" : ["meter_3"],
             "publishers": ["publisher_3"]
            },
           ]

        2. Decoupled: the source and sink configuration are separately
           specified before being linked together. This allows source-
           specific configuration, such as resource discovery, to be
           kept focused only on the fine-grained source while avoiding
           the necessity for wide duplication of sink-related config.

           The configuration is provided in the form of separate lists
           of dictionaries defining sources and sinks, for example:

           {"sources": [{"name": source_1,
                         "interval": interval_time,
                         "meters" : ["meter_1", "meter_2"],
                         "resources": ["resource_uri1", "resource_uri2"],
                         "sinks" : ["sink_1", "sink_2"]
                        },
                        {"name": source_2,
                         "interval": interval_time,
                         "meters" : ["meter_3"],
                         "sinks" : ["sink_2"]
                        },
                       ],
            "sinks": [{"name": sink_1,
                       "transformers": [
                              {"name": "Transformer_1",
                               "parameters": {"p1": "value"}},

                              {"name": "Transformer_2",
                               "parameters": {"p1": "value"}},
                             ],
                        "publishers": ["publisher_1", "publisher_2"]
                       },
                       {"name": sink_2,
                        "publishers": ["publisher_3"]
                       },
                      ]
           }

        The semantics of the common individual configuration elements
        are identical in the deprecated and decoupled version.

        The interval determines the cadence of sample injection into
        the pipeline where samples are produced under the direct control
        of an agent, i.e. via a polling cycle as opposed to incoming
        notifications.

        Valid meter format is '*', '!meter_name', or 'meter_name'.
        '*' is wildcard symbol means any meters; '!meter_name' means
        "meter_name" will be excluded; 'meter_name' means 'meter_name'
        will be included.

        The 'meter_name" is Sample name field. For meter names with
        variable like "instance:m1.tiny", it's "instance:*".

        Valid meters definition is all "included meter names", all
        "excluded meter names", wildcard and "excluded meter names", or
        only wildcard.

        The resources is list of URI indicating the resources from where
        the meters should be polled. It's optional and it's up to the
        specific pollster to decide how to use it.

        Transformer's name is plugin name in setup.cfg.

        Publisher's name is plugin name in setup.cfg

        """
        self.pipelines = []
        if 'sources' in cfg or 'sinks' in cfg:
            if not ('sources' in cfg and 'sinks' in cfg):
                raise PipelineException("Both sources & sinks are required",
                                        cfg)
            LOG.info(_('detected decoupled pipeline config format'))
            sources = [Source(s) for s in cfg.get('sources', [])]
            sinks = dict((s['name'], Sink(s, transformer_manager))
                         for s in cfg.get('sinks', []))
            for source in sources:
                source.check_sinks(sinks)
                for target in source.sinks:
                    self.pipelines.append(Pipeline(source,
                                                   sinks[target]))
        else:
            LOG.warning(_('detected deprecated pipeline config format'))
            for pipedef in cfg:
                source = Source(pipedef)
                sink = Sink(pipedef, transformer_manager)
                self.pipelines.append(Pipeline(source, sink))

    def publisher(self, context):
        """Build a new Publisher for these manager pipelines.

        :param context: The context.
        """
        return PublishContext(context, self.pipelines)


def setup_pipeline(transformer_manager=None):
    """Setup pipeline manager according to yaml config file."""
    cfg_file = cfg.CONF.pipeline_cfg_file
    if not os.path.exists(cfg_file):
        cfg_file = cfg.CONF.find_file(cfg_file)

    LOG.debug(_("Pipeline config file: %s"), cfg_file)

    with open(cfg_file) as fap:
        data = fap.read()

    pipeline_cfg = yaml.safe_load(data)
    LOG.info(_("Pipeline config: %s"), pipeline_cfg)

    return PipelineManager(pipeline_cfg,
                           transformer_manager or
                           xformer.TransformerExtensionManager(
                               'ceilometer.transformer',
                           ))