Time Series Query
Time series query for the Google Stackdriver Monitoring API (V3).
class google.cloud.monitoring_v3.query.Query(client, project, metric_type='compute.googleapis.com/instance/cpu/utilization', end_time=None, days=0, hours=0, minutes=0)
Bases: object
Query object for retrieving metric data.
Parameters
client (
google.cloud.monitoring_v3.gapic. metric_service_client.MetricServiceClient
) – The client to use.project (str) – The project ID or number.
metric_type (str) – The metric type name. The default value is
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.end_time (
datetime.datetime
) – (Optional) 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
, andminutes
, and subtracting the resulting duration from the end time.It is also allowed to omit the end time and duration here, in which case
select_interval()
must be called before the query is executed.days (int) – The number of days in the time interval.
hours (int) – The number of hours in the time interval.
minutes (int) – The number of minutes in the time interval.
Raises
ValueError
ifend_time
is specified butdays
,hours
, andminutes
are all zero. If you really want to specify a point in time, useselect_interval()
.
_deepcopy_(memo)
Create a deepcopy of the query object.
The client attribute is copied by reference only.
Parameters
memo (dict) – the memo dict to avoid excess copying in case the object is referenced from its member.
Return type
Query
Returns
The new query object.
align(per_series_aligner, seconds=0, minutes=0, hours=0)
Copy the query and add temporal alignment.
If per_series_aligner
is not Aligner.ALIGN_NONE
, each time
series will contain data points only on the period boundaries.
Example:
from google.cloud import monitoring
query = query.align(
monitoring.Aggregation.Aligner.ALIGN_MEAN, minutes=5)
It is also possible to specify the aligner as a literal string:
query = query.align('ALIGN_MEAN', minutes=5)
Parameters
per_series_aligner (str or
Aligner
) – The approach to be used to align individual time series. For example:Aligner.ALIGN_MEAN
. SeeAligner
and the descriptions of the supported aligners.seconds (int) – The number of seconds in the alignment period.
minutes (int) – The number of minutes in the alignment period.
hours (int) – The number of hours in the alignment period.
Return type
Query
Returns
The new query object.
as_dataframe(label=None, labels=None)
Return all the selected time series as a pandas
dataframe.
NOTE: Use of this method requires that you have 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'])
Parameters
label (str) – (Optional) 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"
.labels (list of strings, or *[None*](https://python.readthedocs.io/en/latest/library/constants.html#None)) – 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
andlabels
is an error.
Return type
pandas.DataFrame
Returns
A dataframe where each column represents one time series.
property filter()
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.
iter(headers_only=False, page_size=None)
Yield all time series objects selected by the query.
The generator returned iterates over
TimeSeries
objects
containing points ordered from oldest to newest.
Note that the Query
object itself is an iterable, such that
the following are equivalent:
for timeseries in query:
...
for timeseries in query.iter():
...
Parameters
Raises
ValueError
if the query time interval has not been specified.
property metric_type()
The metric type name.
reduce(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:
from google.cloud import monitoring
query = query.reduce(monitoring.Aggregation.Reducer.REDUCE_MEAN,
'resource.project_id', 'resource.zone')
Parameters
cross_series_reducer (str or
Reducer
) – The approach to be used to combine time series. For example:Reducer.REDUCE_MEAN
. SeeReducer
and the descriptions of the supported reducers.group_by_fields (strs) – 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.
Return type
Query
Returns
The new query object.
select_group(group_id)
Copy the query and add filtering by group.
Example:
query = query.select_group('1234567')
Parameters
group_id (str) – The ID of a group to filter by.
Return type
Query
Returns
The new query object.
select_interval(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.
Parameters
end_time (
datetime.datetime
) – The end time (inclusive) of the time interval for which results should be returned, as a datetime object.start_time (
datetime.datetime
) – (Optional) 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.
Return type
Query
Returns
The new query object.
select_metrics(*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 "_notequal"
to the keyword, you can inequality:
<label>_notequal=<value>
generates:
metric.label.<label> != <value>
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>")
If the label’s value type is INT64
, a similar notation can be
used to express inequalities:
<label>_less=<value>
generates:
metric.label.<label> < <value>
<label>_lessequal=<value>
generates:
metric.label.<label> <= <value>
<label>_greater=<value>
generates:
metric.label.<label> > <value>
<label>_greaterequal=<value>
generates:
metric.label.<label> >= <value>
Parameters
args (tuple) – 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.
kwargs – Label filters to include in the conjunction as described above.
Return type
Query
Returns
The new query object.
select_projects(*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')
Parameters
args (tuple) – Project IDs limiting the resources to be included in the query.
Return type
Query
Returns
The new query object.
select_resources(*args, **kwargs)
Copy the query and add filtering by resource labels.
See more documentation at: https://cloud.google.com/monitoring/api/v3/filters#comparisons.
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=...)
.
Parameters
args (tuple) – 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.
kwargs – Label filters to include in the conjunction as described above.
Return type
Query
Returns
The new query object.