#
# This file is part of pretix (Community Edition).
#
# Copyright (C) 2014-2020 Raphael Michel and contributors
# Copyright (C) 2020-2021 rami.io GmbH and contributors
#
# This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General
# Public License as published by the Free Software Foundation in version 3 of the License.
#
# ADDITIONAL TERMS APPLY: Pursuant to Section 7 of the GNU Affero General Public License, additional terms are
# applicable granting you additional permissions and placing additional restrictions on your usage of this software.
# Please refer to the pretix LICENSE file to obtain the full terms applicable to this work. If you did not receive
# this file, see <https://pretix.eu/about/en/license>.
#
# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied
# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU Affero General Public License for more
# details.
#
# You should have received a copy of the GNU Affero General Public License along with this program.  If not, see
# <https://www.gnu.org/licenses/>.
#

# This file is based on an earlier version of pretix which was released under the Apache License 2.0. The full text of
# the Apache License 2.0 can be obtained at <http://www.apache.org/licenses/LICENSE-2.0>.
#
# This file may have since been changed and any changes are released under the terms of AGPLv3 as described above. A
# full history of changes and contributors is available at <https://github.com/pretix/pretix>.
#
# This file contains Apache-licensed contributions copyrighted by: Jonas Große Sundrup
#
# Unless required by applicable law or agreed to in writing, software distributed under the Apache License 2.0 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 json
import math
import time
from collections import defaultdict

from django.apps import apps
from django.conf import settings
from django.db import connection

from pretix.base.models import Event, Invoice, Order, OrderPosition, Organizer
from pretix.celery_app import app

if settings.HAS_REDIS:
    import django_redis
    redis = django_redis.get_redis_connection("redis")

REDIS_KEY = "pretix_metrics"
_INF = float("inf")
_MINUS_INF = float("-inf")


def _float_to_go_string(d):
    # inspired by https://github.com/prometheus/client_python/blob/master/prometheus_client/core.py
    if d == _INF:
        return '+Inf'
    elif d == _MINUS_INF:
        return '-Inf'
    elif math.isnan(d):
        return 'NaN'
    else:
        return repr(float(d))


class Metric(object):
    """
    Base Metrics Object
    """

    def __init__(self, name, helpstring, labelnames=None):
        self.name = name
        self.helpstring = helpstring
        self.labelnames = labelnames or []

    def __repr__(self):
        return self.name + "{" + ",".join(self.labelnames) + "}"

    def _check_label_consistency(self, labels):
        """
        Checks if the given labels provides exactly the labels that are required.
        """

        # test if every required label is provided
        for labelname in self.labelnames:
            if labelname not in labels:
                raise ValueError("Label {0} not specified.".format(labelname))

        # now test if no further labels are required
        if len(labels) != len(self.labelnames):
            raise ValueError("Unknown labels used: {}".format(", ".join(set(labels) - set(self.labelnames))))

    def _construct_metric_identifier(self, metricname, labels=None, labelnames=None):
        """
        Constructs the scrapable metricname usable in the output format.
        """
        if not labels:
            return metricname
        else:
            named_labels = []
            for labelname in (labelnames or self.labelnames):
                named_labels.append('{}="{}"'.format(labelname, labels[labelname]))

            return metricname + "{" + ",".join(named_labels) + "}"

    def _inc_in_redis(self, key, amount, pipeline=None):
        """
        Increments given key in Redis.
        """
        if settings.HAS_REDIS:
            if not pipeline:
                pipeline = redis
            pipeline.hincrbyfloat(REDIS_KEY, key, amount)

    def _set_in_redis(self, key, value, pipeline=None):
        """
        Sets given key in Redis.
        """
        if settings.HAS_REDIS:
            if not pipeline:
                pipeline = redis
            pipeline.hset(REDIS_KEY, key, value)

    def _get_redis_pipeline(self):
        if settings.HAS_REDIS:
            return redis.pipeline()

    def _execute_redis_pipeline(self, pipeline):
        if settings.HAS_REDIS:
            return pipeline.execute()


class Counter(Metric):
    """
    Counter Metric Object
    Counters can only be increased, they can neither be set to a specific value
    nor decreased.
    """

    def inc(self, amount=1, **kwargs):
        """
        Increments Counter by given amount for the labels specified in kwargs.
        """
        if amount < 0:
            raise ValueError("Counter cannot be increased by negative values.")

        self._check_label_consistency(kwargs)

        fullmetric = self._construct_metric_identifier(self.name, kwargs)
        self._inc_in_redis(fullmetric, amount)


class Gauge(Metric):
    """
    Gauge Metric Object
    Gauges can be set to a specific value, increased and decreased.
    """

    def set(self, value=1, **kwargs):
        """
        Sets Gauge to a specific value for the labels specified in kwargs.
        """
        self._check_label_consistency(kwargs)

        fullmetric = self._construct_metric_identifier(self.name, kwargs)
        self._set_in_redis(fullmetric, value)

    def inc(self, amount=1, **kwargs):
        """
        Increments Gauge by given amount for the labels specified in kwargs.
        """
        if amount < 0:
            raise ValueError("Amount must be greater than zero. Otherwise use dec().")

        self._check_label_consistency(kwargs)

        fullmetric = self._construct_metric_identifier(self.name, kwargs)
        self._inc_in_redis(fullmetric, amount)

    def dec(self, amount=1, **kwargs):
        """
        Decrements Gauge by given amount for the labels specified in kwargs.
        """
        if amount < 0:
            raise ValueError("Amount must be greater than zero. Otherwise use inc().")

        self._check_label_consistency(kwargs)

        fullmetric = self._construct_metric_identifier(self.name, kwargs)
        self._inc_in_redis(fullmetric, amount * -1)


class Histogram(Metric):
    """
    Histogram Metric Object
    """

    def __init__(self, name, helpstring, labelnames=None,
                 buckets=(.005, .01, .025, .05, .075, .1, .25, .5, .75, 1.0, 2.5, 5.0, 7.5, 10.0, 30.0, _INF)):
        if list(buckets) != sorted(buckets):
            # This is probably an error on the part of the user,
            # so raise rather than sorting for them.
            raise ValueError('Buckets not in sorted order')

        if buckets and buckets[-1] != _INF:
            buckets.append(_INF)

        if len(buckets) < 2:
            raise ValueError('Must have at least two buckets')

        self.buckets = buckets
        super().__init__(name, helpstring, labelnames)

    def observe(self, amount, **kwargs):
        """
        Stores a value in the histogram for the labels specified in kwargs.
        """
        if amount < 0:
            raise ValueError("Amount must be greater than zero. Otherwise use inc().")

        self._check_label_consistency(kwargs)

        pipe = self._get_redis_pipeline()

        countmetric = self._construct_metric_identifier(self.name + '_count', kwargs)
        self._inc_in_redis(countmetric, 1, pipeline=pipe)

        summetric = self._construct_metric_identifier(self.name + '_sum', kwargs)
        self._inc_in_redis(summetric, amount, pipeline=pipe)

        kwargs_le = dict(kwargs.items())
        for i, bound in enumerate(self.buckets):
            if amount <= bound:
                kwargs_le['le'] = _float_to_go_string(bound)
                bmetric = self._construct_metric_identifier(self.name + '_bucket', kwargs_le,
                                                            labelnames=self.labelnames + ["le"])
                self._inc_in_redis(bmetric, 1, pipeline=pipe)

        self._execute_redis_pipeline(pipe)


def estimate_count_fast(type):
    """
    See https://wiki.postgresql.org/wiki/Count_estimate
    """
    if 'postgres' in settings.DATABASES['default']['ENGINE']:
        cursor = connection.cursor()
        cursor.execute("select reltuples from pg_class where relname='%s';" % type._meta.db_table)
        row = cursor.fetchone()
        if not row:
            return 0
        return int(row[0])
    else:
        return type.objects.count()


def metric_values():
    """
    Produces the the values to be presented to the monitoring system
    """
    metrics = defaultdict(dict)

    # Metrics from redis
    if settings.HAS_REDIS:
        for key, value in redis.hscan_iter(REDIS_KEY, count=1000):
            dkey = key.decode("utf-8")
            splitted = dkey.split("{", 2)
            value = float(value.decode("utf-8"))
            if len(splitted) == 1:
                metrics[splitted[0]][""] = value
            else:
                metrics[splitted[0]]["{" + splitted[1]] = value

    # Aliases
    aliases = {
        'pretix_view_requests_total': 'pretix_view_duration_seconds_count'
    }
    for a, atarget in aliases.items():
        metrics[a] = metrics[atarget]

    # Throwaway metrics
    exact_tables = [
        Order, OrderPosition, Invoice, Event, Organizer
    ]
    for m in apps.get_models():  # Count all models
        if any(issubclass(m, p) for p in exact_tables):
            metrics['pretix_model_instances']['{model="%s"}' % m._meta] = m.objects.count()
        else:
            metrics['pretix_model_instances']['{model="%s"}' % m._meta] = estimate_count_fast(m)

    if settings.HAS_CELERY:
        channel = app.broker_connection().channel()
        if hasattr(channel, 'client') and channel.client is not None:
            client = channel.client
            for q in settings.CELERY_TASK_QUEUES:
                llen = client.llen(q.name)
                lfirst = client.lindex(q.name, -1)
                metrics['pretix_celery_tasks_queued_count']['{queue="%s"}' % q.name] = llen
                if lfirst:
                    ldata = json.loads(lfirst)
                    dt = time.time() - ldata.get('created', 0)
                    metrics['pretix_celery_tasks_queued_age_seconds']['{queue="%s"}' % q.name] = dt
                else:
                    metrics['pretix_celery_tasks_queued_age_seconds']['{queue="%s"}' % q.name] = 0

    return metrics


"""
Provided metrics
"""
pretix_view_duration_seconds = Histogram("pretix_view_duration_seconds", "Return time of views.",
                                         ["status_code", "method", "url_name"])
pretix_task_runs_total = Counter("pretix_task_runs_total", "Total calls to a celery task",
                                 ["task_name", "status"])
pretix_task_duration_seconds = Histogram("pretix_task_duration_seconds", "Call time of a celery task",
                                         ["task_name"])
pretix_successful_logins = Counter("pretix_logins_successful", "Successful logins", [])
pretix_failed_logins = Counter("pretix_logins_failed", "Failed logins", ["reason"])
