afbe1fb481
Styling overview page. Extracting performance charts to separate template, so it is reusable elsewhere. Change-Id: Iea55815f7680b8fb15b06670281f3a7105f00d8e
319 lines
11 KiB
Python
319 lines
11 KiB
Python
# -*- coding: utf8 -*-
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# 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, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import copy
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from datetime import datetime # noqa
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from datetime import timedelta # noqa
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from django.utils.http import urlencode
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from django.utils import timezone
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from django.utils.translation import ugettext_lazy as _
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from horizon import exceptions
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from openstack_dashboard.api import ceilometer
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import pytz
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SETTINGS = {
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'settings': {
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'renderer': 'StaticAxes',
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'xMin': None,
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'xMax': None,
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'higlight_last_point': True,
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'auto_size': False,
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'auto_resize': False,
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'axes_x': False,
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'axes_y': True,
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'axes_y_label': False,
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'bar_chart_settings': {
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'orientation': 'vertical',
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'used_label_placement': 'left',
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'width': 30,
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'color_scale_domain': [0, 80, 80, 100],
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'color_scale_range': [
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'#0000FF',
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'#0000FF',
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'#FF0000',
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'#FF0000'
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],
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'average_color_scale_domain': [0, 100],
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'average_color_scale_range': ['#0000FF', '#0000FF']
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}
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},
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'stats': {
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'average': None,
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'used': None,
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'tooltip_average': None,
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}
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}
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LABELS = {
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'hardware.cpu.load.1min': _("CPU load 1 min average"),
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'hardware.system_stats.cpu.util': _("CPU utilization"),
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'hardware.system_stats.io.outgoing.blocks': _("IO raw sent"),
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'hardware.system_stats.io.incoming.blocks': _("IO raw received"),
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'hardware.network.ip.outgoing.datagrams': _("IP out requests"),
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'hardware.network.ip.incoming.datagrams': _("IP in requests"),
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'hardware.memory.swap.util': _("Swap utilization"),
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}
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# TODO(lsmola) this should probably live in Horizon common
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def query_data(request,
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date_from,
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date_to,
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group_by,
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meter,
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period=None,
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query=None,
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additional_query=None):
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if not period:
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period = _calc_period(date_from, date_to, 50)
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if additional_query is None:
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additional_query = []
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if date_from:
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additional_query += [{'field': 'timestamp',
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'op': 'ge',
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'value': date_from}]
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if date_to:
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additional_query += [{'field': 'timestamp',
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'op': 'le',
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'value': date_to}]
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# TODO(lsmola) replace this by logic implemented in I1 in bugs
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# 1226479 and 1226482, this is just a quick fix for RC1
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try:
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meter_list = [m for m in ceilometer.meter_list(request)
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if m.name == meter]
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unit = meter_list[0].unit
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except Exception:
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unit = ""
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ceilometer_usage = ceilometer.CeilometerUsage(request)
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try:
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if group_by:
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resources = ceilometer_usage.resource_aggregates_with_statistics(
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query, [meter], period=period, stats_attr=None,
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additional_query=additional_query)
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else:
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resources = ceilometer_usage.resources_with_statistics(
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query, [meter], period=period, stats_attr=None,
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additional_query=additional_query)
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except Exception:
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resources = []
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exceptions.handle(request,
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_('Unable to retrieve statistics.'))
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return resources, unit
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# TODO(lsmola) push this function to Horizon common then delete this
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def _calc_period(date_from, date_to, number_of_samples=400):
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if date_from and date_to:
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if date_to < date_from:
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# TODO(lsmola) propagate the Value error through Horizon
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# handler to the client with verbose message.
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raise ValueError("Date to must be bigger than date "
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"from.")
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# get the time delta in seconds
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delta = date_to - date_from
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delta_in_seconds = delta.days * 24 * 3600 + delta.seconds
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period = delta_in_seconds / number_of_samples
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else:
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# If some date is missing, just set static window to one day.
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period = 3600 * 24
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return period
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# TODO(lsmola) push this function to Horizon common then delete this
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def _calc_date_args(date_from, date_to, date_options):
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# TODO(lsmola) all timestamps should probably work with
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# current timezone. And also show the current timezone in chart.
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if date_options == "other":
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try:
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if date_from:
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date_from = pytz.utc.localize(
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datetime.strptime(date_from, "%Y-%m-%d"))
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else:
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# TODO(lsmola) there should be probably the date
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# of the first sample as default, so it correctly
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# counts the time window. Though I need ordering
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# and limit of samples to obtain that.
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pass
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if date_to:
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date_to = pytz.utc.localize(
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datetime.strptime(date_to, "%Y-%m-%d"))
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# It return beginning of the day, I want the and of
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# the day, so i will add one day without a second.
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date_to = (date_to + timedelta(days=1) -
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timedelta(seconds=1))
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else:
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date_to = timezone.now()
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except Exception:
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raise ValueError("The dates haven't been "
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"recognized")
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else:
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try:
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date_from = timezone.now() - timedelta(days=float(date_options))
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date_to = timezone.now()
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except Exception:
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raise ValueError("The time delta must be an "
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"integer representing days.")
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return date_from, date_to
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def url_part(meter_name, barchart):
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d = {'meter': meter_name}
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if barchart:
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d['barchart'] = True
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return urlencode(d)
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def get_meter_name(meter):
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return meter.replace('.', '_')
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def get_meters(meters):
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return [(m, get_meter_name(m)) for m in meters]
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def get_barchart_stats(series, unit):
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values = [point['y'] for point in series[0]['data']]
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average = sum(values) / len(values)
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used = values[-1]
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first_date = series[0]['data'][0]['x']
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last_date = series[0]['data'][-1]['x']
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tooltip_average = _('Average %(average)s %(unit)s<br> From: '
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'%(first_date)s, to: %(last_date)s') % (
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dict(average=average, unit=unit,
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first_date=first_date,
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last_date=last_date))
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return average, used, tooltip_average
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def create_json_output(series, barchart, unit, date_from, date_to):
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start_datetime = end_datetime = ''
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if date_from:
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start_datetime = date_from.strftime("%Y-%m-%dT%H:%M:%S")
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if date_to:
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end_datetime = date_to.strftime("%Y-%m-%dT%H:%M:%S")
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settings = copy.deepcopy(SETTINGS)
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settings['settings']['xMin'] = start_datetime
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settings['settings']['xMax'] = end_datetime
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if series and barchart:
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average, used, tooltip_average = get_barchart_stats(series, unit)
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settings['settings']['yMin'] = 0
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settings['settings']['yMax'] = 100
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settings['stats']['average'] = average
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settings['stats']['used'] = used
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settings['stats']['tooltip_average'] = tooltip_average
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else:
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del settings['settings']['bar_chart_settings']
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del settings['stats']
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json_output = {'series': series}
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json_output = dict(json_output.items() + settings.items())
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return json_output
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def _series_for_meter(aggregates,
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meter_id,
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meter_name,
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stats_name,
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unit):
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"""Construct datapoint series for a meter from resource aggregates."""
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series = []
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for resource in aggregates:
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if resource.get_meter(meter_name):
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name = LABELS.get(meter_id, meter_name)
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point = {'unit': unit,
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'name': unicode(name),
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'meter': meter_id,
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'data': []}
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for statistic in resource.get_meter(meter_name):
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date = statistic.duration_end[:19]
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value = float(getattr(statistic, stats_name))
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point['data'].append({'x': date, 'y': value})
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series.append(point)
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return series
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def get_nodes_stats(request, uuid, meter, date_options=None, date_from=None,
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date_to=None, stats_attr=None, barchart=None,
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group_by=None):
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unit = ''
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series = []
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if uuid:
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if group_by == "image_id":
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query = {}
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image_query = [{"field": "metadata.%s" % group_by,
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"op": "eq",
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"value": uuid}]
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query[uuid] = image_query
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else:
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query = [{'field': 'resource_id',
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'op': 'eq',
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'value': uuid}]
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else:
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# query will be aggregated across all resources
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group_by = "all"
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query = {}
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query['all'] = []
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# Disk and Network I/O: data from 2 meters in one chart
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if meter == 'disk-io':
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meters = get_meters([
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'hardware.system_stats.io.outgoing.blocks',
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'hardware.system_stats.io.incoming.blocks'
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])
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elif meter == 'network-io':
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meters = get_meters([
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'hardware.network.ip.outgoing.datagrams',
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'hardware.network.ip.incoming.datagrams'
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])
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else:
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meters = get_meters([meter])
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date_from, date_to = _calc_date_args(
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date_from,
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date_to,
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date_options)
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for meter_id, meter_name in meters:
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resources, unit = query_data(
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request=request,
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date_from=date_from,
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date_to=date_to,
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group_by=group_by,
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meter=meter_id,
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query=query)
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serie = _series_for_meter(
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resources,
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meter_id,
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meter_name,
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stats_attr,
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unit)
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series += serie
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json_output = create_json_output(
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series,
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barchart,
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unit,
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date_from,
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date_to)
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return json_output
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