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Updated DB_Helper by adding firebase methods.

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Batuhan Berk Başoğlu 2020-10-05 16:53:40 -04:00
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# Copyright 2016 Google Inc. All rights reserved.
#
# 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.
"""Time series query for the `Google Monitoring API (V3)`_.
.. _Google Monitoring API (V3):
https://cloud.google.com/monitoring/api/ref_v3/rest/v3/\
projects.timeSeries/list
"""
import copy
import datetime
import itertools
import six
from gcloud.monitoring._dataframe import _build_dataframe
from gcloud.monitoring.timeseries import TimeSeries
_UTCNOW = datetime.datetime.utcnow # To be replaced by tests.
class Aligner(object):
"""Allowed values for the `supported aligners`_."""
ALIGN_NONE = 'ALIGN_NONE'
ALIGN_DELTA = 'ALIGN_DELTA'
ALIGN_RATE = 'ALIGN_RATE'
ALIGN_INTERPOLATE = 'ALIGN_INTERPOLATE'
ALIGN_NEXT_OLDER = 'ALIGN_NEXT_OLDER'
ALIGN_MIN = 'ALIGN_MIN'
ALIGN_MAX = 'ALIGN_MAX'
ALIGN_MEAN = 'ALIGN_MEAN'
ALIGN_COUNT = 'ALIGN_COUNT'
ALIGN_SUM = 'ALIGN_SUM'
ALIGN_STDDEV = 'ALIGN_STDDEV'
ALIGN_COUNT_TRUE = 'ALIGN_COUNT_TRUE'
ALIGN_FRACTION_TRUE = 'ALIGN_FRACTION_TRUE'
class Reducer(object):
"""Allowed values for the `supported reducers`_."""
REDUCE_NONE = 'REDUCE_NONE'
REDUCE_MEAN = 'REDUCE_MEAN'
REDUCE_MIN = 'REDUCE_MIN'
REDUCE_MAX = 'REDUCE_MAX'
REDUCE_SUM = 'REDUCE_SUM'
REDUCE_STDDEV = 'REDUCE_STDDEV'
REDUCE_COUNT = 'REDUCE_COUNT'
REDUCE_COUNT_TRUE = 'REDUCE_COUNT_TRUE'
REDUCE_FRACTION_TRUE = 'REDUCE_FRACTION_TRUE'
REDUCE_PERCENTILE_99 = 'REDUCE_PERCENTILE_99'
REDUCE_PERCENTILE_95 = 'REDUCE_PERCENTILE_95'
REDUCE_PERCENTILE_50 = 'REDUCE_PERCENTILE_50'
REDUCE_PERCENTILE_05 = 'REDUCE_PERCENTILE_05'
class Query(object):
"""Query object for retrieving metric data.
The preferred way to construct a query object is using the
:meth:`~gcloud.monitoring.client.Client.query` method
of the :class:`~gcloud.monitoring.client.Client` class.
:type client: :class:`gcloud.monitoring.client.Client`
:param client: The client to use.
:type metric_type: string
:param metric_type: The metric type name. The default value is
:data:`Query.DEFAULT_METRIC_TYPE
<gcloud.monitoring.query.Query.DEFAULT_METRIC_TYPE>`,
but please note that this default value is provided only for
demonstration purposes and is subject to change. See the
`supported metrics`_.
:type end_time: :class:`datetime.datetime` or None
:param end_time: The end time (inclusive) of the time interval
for which results should be returned, as a datetime object.
The default is the start of the current minute.
The start time (exclusive) is determined by combining the
values of ``days``, ``hours``, and ``minutes``, and
subtracting the resulting duration from the end time.
It is also allowed to omit the end time and duration here,
in which case
:meth:`~gcloud.monitoring.query.Query.select_interval`
must be called before the query is executed.
:type days: integer
:param days: The number of days in the time interval.
:type hours: integer
:param hours: The number of hours in the time interval.
:type minutes: integer
:param minutes: The number of minutes in the time interval.
:raises: :exc:`ValueError` if ``end_time`` is specified but
``days``, ``hours``, and ``minutes`` are all zero.
If you really want to specify a point in time, use
:meth:`~gcloud.monitoring.query.Query.select_interval`.
.. _supported metrics: https://cloud.google.com/monitoring/api/metrics
"""
DEFAULT_METRIC_TYPE = 'compute.googleapis.com/instance/cpu/utilization'
def __init__(self, client,
metric_type=DEFAULT_METRIC_TYPE,
end_time=None, days=0, hours=0, minutes=0):
start_time = None
if days or hours or minutes:
if end_time is None:
end_time = _UTCNOW().replace(second=0, microsecond=0)
start_time = end_time - datetime.timedelta(days=days,
hours=hours,
minutes=minutes)
elif end_time is not None:
raise ValueError('Non-zero duration required for time interval.')
self._client = client
self._end_time = end_time
self._start_time = start_time
self._filter = _Filter(metric_type)
self._per_series_aligner = None
self._alignment_period_seconds = None
self._cross_series_reducer = None
self._group_by_fields = ()
def __iter__(self):
return self.iter()
@property
def metric_type(self):
"""The metric type name."""
return self._filter.metric_type
@property
def filter(self):
"""The filter string.
This is constructed from the metric type, the resource type, and
selectors for the group ID, monitored projects, resource labels,
and metric labels.
"""
return str(self._filter)
def select_interval(self, end_time, start_time=None):
"""Copy the query and set the query time interval.
Example::
import datetime
now = datetime.datetime.utcnow()
query = query.select_interval(
end_time=now,
start_time=now - datetime.timedelta(minutes=5))
As a convenience, you can alternatively specify the end time and
an interval duration when you create the query initially.
:type end_time: :class:`datetime.datetime`
:param end_time: The end time (inclusive) of the time interval
for which results should be returned, as a datetime object.
:type start_time: :class:`datetime.datetime` or None
:param start_time: The start time (exclusive) of the time interval
for which results should be returned, as a datetime object.
If not specified, the interval is a point in time.
:rtype: :class:`Query`
:returns: The new query object.
"""
new_query = self.copy()
new_query._end_time = end_time
new_query._start_time = start_time
return new_query
def select_group(self, group_id):
"""Copy the query and add filtering by group.
Example::
query = query.select_group('1234567')
:type group_id: string
:param group_id: The ID of a group to filter by.
:rtype: :class:`Query`
:returns: The new query object.
"""
new_query = self.copy()
new_query._filter.group_id = group_id
return new_query
def select_projects(self, *args):
"""Copy the query and add filtering by monitored projects.
This is only useful if the target project represents a Stackdriver
account containing the specified monitored projects.
Examples::
query = query.select_projects('project-1')
query = query.select_projects('project-1', 'project-2')
:type args: tuple
:param args: Project IDs limiting the resources to be included
in the query.
:rtype: :class:`Query`
:returns: The new query object.
"""
new_query = self.copy()
new_query._filter.projects = args
return new_query
def select_resources(self, *args, **kwargs):
"""Copy the query and add filtering by resource labels.
Examples::
query = query.select_resources(zone='us-central1-a')
query = query.select_resources(zone_prefix='europe-')
query = query.select_resources(resource_type='gce_instance')
A keyword argument ``<label>=<value>`` ordinarily generates a filter
expression of the form::
resource.label.<label> = "<value>"
However, by adding ``"_prefix"`` or ``"_suffix"`` to the keyword,
you can specify a partial match.
``<label>_prefix=<value>`` generates::
resource.label.<label> = starts_with("<value>")
``<label>_suffix=<value>`` generates::
resource.label.<label> = ends_with("<value>")
As a special case, ``"resource_type"`` is treated as a special
pseudo-label corresponding to the filter object ``resource.type``.
For example, ``resource_type=<value>`` generates::
resource.type = "<value>"
See the `defined resource types`_.
.. note::
The label ``"instance_name"`` is a metric label,
not a resource label. You would filter on it using
``select_metrics(instance_name=...)``.
:type args: tuple
:param args: Raw filter expression strings to include in the
conjunction. If just one is provided and no keyword arguments
are provided, it can be a disjunction.
:type kwargs: dict
:param kwargs: Label filters to include in the conjunction as
described above.
:rtype: :class:`Query`
:returns: The new query object.
.. _defined resource types:
https://cloud.google.com/monitoring/api/v3/monitored-resources
"""
new_query = self.copy()
new_query._filter.select_resources(*args, **kwargs)
return new_query
def select_metrics(self, *args, **kwargs):
"""Copy the query and add filtering by metric labels.
Examples::
query = query.select_metrics(instance_name='myinstance')
query = query.select_metrics(instance_name_prefix='mycluster-')
A keyword argument ``<label>=<value>`` ordinarily generates a filter
expression of the form::
metric.label.<label> = "<value>"
However, by adding ``"_prefix"`` or ``"_suffix"`` to the keyword,
you can specify a partial match.
``<label>_prefix=<value>`` generates::
metric.label.<label> = starts_with("<value>")
``<label>_suffix=<value>`` generates::
metric.label.<label> = ends_with("<value>")
:type args: tuple
:param args: Raw filter expression strings to include in the
conjunction. If just one is provided and no keyword arguments
are provided, it can be a disjunction.
:type kwargs: dict
:param kwargs: Label filters to include in the conjunction as
described above.
:rtype: :class:`Query`
:returns: The new query object.
"""
new_query = self.copy()
new_query._filter.select_metrics(*args, **kwargs)
return new_query
def align(self, per_series_aligner, seconds=0, minutes=0, hours=0):
"""Copy the query and add temporal alignment.
If ``per_series_aligner`` is not :data:`Aligner.ALIGN_NONE`, each time
series will contain data points only on the period boundaries.
Example::
query = query.align(Aligner.ALIGN_MEAN, minutes=5)
It is also possible to specify the aligner as a literal string::
query = query.align('ALIGN_MEAN', minutes=5)
:type per_series_aligner: string
:param per_series_aligner: The approach to be used to align
individual time series. For example: :data:`Aligner.ALIGN_MEAN`.
See :class:`Aligner` and the descriptions of the `supported
aligners`_.
:type seconds: integer
:param seconds: The number of seconds in the alignment period.
:type minutes: integer
:param minutes: The number of minutes in the alignment period.
:type hours: integer
:param hours: The number of hours in the alignment period.
:rtype: :class:`Query`
:returns: The new query object.
.. _supported aligners:
https://cloud.google.com/monitoring/api/ref_v3/rest/v3/\
projects.timeSeries/list#Aligner
"""
new_query = self.copy()
new_query._per_series_aligner = per_series_aligner
new_query._alignment_period_seconds = seconds + 60 * (minutes +
60 * hours)
return new_query
def reduce(self, cross_series_reducer, *group_by_fields):
"""Copy the query and add cross-series reduction.
Cross-series reduction combines time series by aggregating their
data points.
For example, you could request an aggregated time series for each
combination of project and zone as follows::
query = query.reduce(Reducer.REDUCE_MEAN,
'resource.project_id', 'resource.zone')
:type cross_series_reducer: string
:param cross_series_reducer:
The approach to be used to combine time series. For example:
:data:`Reducer.REDUCE_MEAN`. See :class:`Reducer` and the
descriptions of the `supported reducers`_.
:type group_by_fields: strings
:param group_by_fields:
Fields to be preserved by the reduction. For example, specifying
just ``"resource.zone"`` will result in one time series per zone.
The default is to aggregate all of the time series into just one.
:rtype: :class:`Query`
:returns: The new query object.
.. _supported reducers:
https://cloud.google.com/monitoring/api/ref_v3/rest/v3/\
projects.timeSeries/list#Reducer
"""
new_query = self.copy()
new_query._cross_series_reducer = cross_series_reducer
new_query._group_by_fields = group_by_fields
return new_query
def iter(self, headers_only=False, page_size=None):
"""Yield all time series objects selected by the query.
Note that the :class:`Query` object itself is an iterable, such that
the following are equivalent::
for timeseries in query:
...
for timeseries in query.iter():
...
:type headers_only: boolean
:param headers_only:
Whether to omit the point data from the time series objects.
:type page_size: integer or None
:param page_size:
An optional positive number specifying the maximum number of
points to return per page. This can be used to control how far
the iterator reads ahead.
:rtype: iterator over :class:`~gcloud.monitoring.timeseries.TimeSeries`
:returns: Time series objects, containing points ordered from oldest
to newest.
:raises: :exc:`ValueError` if the query time interval has not been
specified.
"""
# The following use of groupby() relies on equality comparison
# of time series as (named) tuples.
for timeseries, fragments in itertools.groupby(
self._iter_fragments(headers_only, page_size),
lambda fragment: fragment.header()):
points = list(itertools.chain.from_iterable(
fragment.points for fragment in fragments))
points.reverse() # Order from oldest to newest.
yield timeseries.header(points=points)
def _iter_fragments(self, headers_only=False, page_size=None):
"""Yield all time series fragments selected by the query.
There may be multiple fragments per time series. These will be
contiguous.
The parameters and return value are as for :meth:`Query.iter`.
"""
if self._end_time is None:
raise ValueError('Query time interval not specified.')
path = '/projects/{project}/timeSeries/'.format(
project=self._client.project)
page_token = None
while True:
params = list(self._build_query_params(
headers_only=headers_only,
page_size=page_size,
page_token=page_token,
))
response = self._client.connection.api_request(
method='GET',
path=path,
query_params=params,
)
for info in response.get('timeSeries', ()):
yield TimeSeries._from_dict(info)
page_token = response.get('nextPageToken')
if not page_token:
break
def _build_query_params(self, headers_only=False,
page_size=None, page_token=None):
"""Yield key-value pairs for the URL query string.
We use a series of key-value pairs instead of a ``dict`` to allow for
repeated fields.
:type headers_only: boolean
:param headers_only:
Whether to omit the point data from the
:class:`~gcloud.monitoring.timeseries.TimeSeries` objects.
:type page_size: integer or None
:param page_size: A limit on the number of points to return per page.
:type page_token: string or None
:param page_token: A token to continue the retrieval.
:rtype: iterator over tuples
:returns:
Key-value pairs suitable for passing to ``urlencode``.
"""
yield 'filter', self.filter
yield 'interval.endTime', _format_timestamp(self._end_time)
if self._start_time is not None:
yield 'interval.startTime', _format_timestamp(self._start_time)
if self._per_series_aligner is not None:
yield 'aggregation.perSeriesAligner', self._per_series_aligner
if self._alignment_period_seconds is not None:
alignment_period = '{period}s'.format(
period=self._alignment_period_seconds)
yield 'aggregation.alignmentPeriod', alignment_period
if self._cross_series_reducer is not None:
yield ('aggregation.crossSeriesReducer',
self._cross_series_reducer)
for field in self._group_by_fields:
yield 'aggregation.groupByFields', field
if headers_only:
yield 'view', 'HEADERS'
if page_size is not None:
yield 'pageSize', page_size
if page_token is not None:
yield 'pageToken', page_token
def as_dataframe(self, label=None, labels=None):
"""Return all the selected time series as a :mod:`pandas` dataframe.
.. note::
Use of this method requires that you have :mod:`pandas` installed.
Examples::
# Generate a dataframe with a multi-level column header including
# the resource type and all available resource and metric labels.
# This can be useful for seeing what labels are available.
dataframe = query.as_dataframe()
# Generate a dataframe using a particular label for the column
# names.
dataframe = query.as_dataframe(label='instance_name')
# Generate a dataframe with a multi-level column header.
dataframe = query.as_dataframe(labels=['zone', 'instance_name'])
# Generate a dataframe with a multi-level column header, assuming
# the metric is issued by more than one type of resource.
dataframe = query.as_dataframe(
labels=['resource_type', 'instance_id'])
:type label: string or None
:param label: The label name to use for the dataframe header.
This can be the name of a resource label or metric label
(e.g., ``"instance_name"``), or the string ``"resource_type"``.
:type labels: list of strings, or None
:param labels: A list or tuple of label names to use for the dataframe
header. If more than one label name is provided, the resulting
dataframe will have a multi-level column header. Providing values
for both ``label`` and ``labels`` is an error.
:rtype: :class:`pandas.DataFrame`
:returns: A dataframe where each column represents one time series.
"""
return _build_dataframe(self, label, labels) # pragma: NO COVER
def copy(self):
"""Copy the query object.
:rtype: :class:`Query`
:returns: The new query object.
"""
# Using copy.deepcopy() would be appropriate, except that we want
# to copy self._client only as a reference.
new_query = copy.copy(self)
new_query._filter = copy.copy(self._filter)
return new_query
class _Filter(object):
"""Helper for assembling a filter string."""
def __init__(self, metric_type):
self.metric_type = metric_type
self.group_id = None
self.projects = ()
self.resource_label_filter = None
self.metric_label_filter = None
def select_resources(self, *args, **kwargs):
"""Select by resource labels.
See :meth:`Query.select_resources`.
"""
self.resource_label_filter = _build_label_filter('resource',
*args, **kwargs)
def select_metrics(self, *args, **kwargs):
"""Select by metric labels.
See :meth:`Query.select_metrics`.
"""
self.metric_label_filter = _build_label_filter('metric',
*args, **kwargs)
def __str__(self):
filters = ['metric.type = "{type}"'.format(type=self.metric_type)]
if self.group_id is not None:
filters.append('group.id = "{id}"'.format(id=self.group_id))
if self.projects:
filters.append(
' OR '.join('project = "{project}"'.format(project=project)
for project in self.projects))
if self.resource_label_filter:
filters.append(self.resource_label_filter)
if self.metric_label_filter:
filters.append(self.metric_label_filter)
# Parentheses are never actually required, because OR binds more
# tightly than AND in the Monitoring API's filter syntax.
return ' AND '.join(filters)
def _build_label_filter(category, *args, **kwargs):
"""Construct a filter string to filter on metric or resource labels."""
terms = list(args)
for key, value in six.iteritems(kwargs):
if value is None:
continue
suffix = None
if key.endswith('_prefix') or key.endswith('_suffix'):
key, suffix = key.rsplit('_', 1)
if category == 'resource' and key == 'resource_type':
key = 'resource.type'
else:
key = '.'.join((category, 'label', key))
if suffix == 'prefix':
term = '{key} = starts_with("{value}")'
elif suffix == 'suffix':
term = '{key} = ends_with("{value}")'
else:
term = '{key} = "{value}"'
terms.append(term.format(key=key, value=value))
return ' AND '.join(sorted(terms))
def _format_timestamp(timestamp):
"""Convert a datetime object to a string as required by the API.
:type timestamp: :class:`datetime.datetime`
:param timestamp: A datetime object.
:rtype: string
:returns: The formatted timestamp. For example:
``"2016-02-17T19:18:01.763000Z"``
"""
if timestamp.tzinfo is not None:
# Convert to UTC and remove the time zone info.
timestamp = timestamp.replace(tzinfo=None) - timestamp.utcoffset()
return timestamp.isoformat() + 'Z'