#
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import uuid
from typing import Any, Optional, List
from ._helpers import order_results, construct_iso8601
from ._generated.models import (
Column as InternalColumn,
BatchQueryRequest as InternalLogQueryRequest,
)
[docs]class LogsQueryResultTable(object):
"""Contains the columns and rows for one table in a query response.
All required parameters must be populated in order to send to Azure.
:param name: Required. The name of the table.
:type name: str
:param columns: Required. The list of columns in this table.
:type columns: list[~azure.monitor.query.LogsQueryResultColumn]
:keyword rows: Required. The resulting rows from this query.
:paramtype rows: list[list[str]]
"""
def __init__(self, name, columns, rows):
# type: (str, List[LogsQueryResultColumn], List[List[str]]) -> None
self.name = name
self.columns = columns
self.rows = rows
@classmethod
def _from_generated(cls, generated):
return cls(
name=generated.name,
columns=[LogsQueryResultColumn(name=col.name, type=col.type) for col in generated.columns],
rows=generated.rows
)
[docs]class LogsQueryResultColumn(InternalColumn):
"""A column in a table.
:keyword name: The name of this column.
:paramtype name: str
:keyword type: The data type of this column.
:paramtype type: str
"""
_attribute_map = {
"name": {"key": "name", "type": "str"},
"type": {"key": "type", "type": "str"},
}
def __init__(self, **kwargs):
# type: (Any) -> None
super(LogsQueryResultColumn, self).__init__(**kwargs)
self.name = kwargs.get("name", None)
self.type = kwargs.get("type", None)
[docs]class LogsQueryResults(object):
"""Contains the tables, columns & rows resulting from a query.
:keyword tables: The list of tables, columns and rows.
:paramtype tables: list[~azure.monitor.query.LogsQueryResultTable]
:keyword statistics: Any object.
:paramtype statistics: object
:keyword render: Any object.
:paramtype render: object
"""
def __init__(self, **kwargs):
# type: (Any) -> None
self.tables = kwargs.get("tables", None)
self.statistics = kwargs.get("statistics", None)
self.render = kwargs.get("render", None)
@classmethod
def _from_generated(cls, generated):
if not generated:
return cls()
tables = None
if generated.tables is not None:
tables = [
LogsQueryResultTable._from_generated( # pylint: disable=protected-access
table
) for table in generated.tables
]
return cls(
tables=tables,
statistics=generated.statistics,
render=generated.render
)
[docs]class MetricsResult(object):
"""The response to a metrics query.
All required parameters must be populated in order to send to Azure.
:keyword cost: The integer value representing the cost of the query, for data case.
:paramtype cost: int
:keyword timespan: Required. The timespan for which the data was retrieved. Its value consists of
two datetimes concatenated, separated by '/'. This may be adjusted in the future and returned
back from what was originally requested.
:paramtype timespan: str
:keyword interval: The interval (window size) for which the metric data was returned in. This
may be adjusted in the future and returned back from what was originally requested. This is
not present if a metadata request was made.
:paramtype interval: ~datetime.timedelta
:keyword namespace: The namespace of the metrics been queried.
:paramtype namespace: str
:keyword resourceregion: The region of the resource been queried for metrics.
:paramtype resourceregion: str
:keyword metrics: Required. the value of the collection.
:paramtype metrics: list[~monitor_query_client.models.Metric]
"""
def __init__(self, **kwargs):
# type: (Any) -> None
self.cost = kwargs.get("cost", None)
self.timespan = kwargs["timespan"]
self.interval = kwargs.get("interval", None)
self.namespace = kwargs.get("namespace", None)
self.resourceregion = kwargs.get("resourceregion", None)
self.metrics = kwargs["metrics"]
@classmethod
def _from_generated(cls, generated):
if not generated:
return cls()
return cls(
cost=generated.cost,
timespan=generated.timespan,
interval=generated.interval,
namespace=generated.namespace,
resourceregion=generated.resourceregion,
metrics=[Metric._from_generated(m) for m in generated.value] # pylint: disable=protected-access
)
[docs]class LogsQueryRequest(InternalLogQueryRequest):
"""A single request in a batch.
Variables are only populated by the server, and will be ignored when sending a request.
:param workspace_id: Workspace Id to be included in the query.
:type workspace_id: str
:param query: The Analytics query. Learn more about the `Analytics query syntax
<https://azure.microsoft.com/documentation/articles/app-insights-analytics-reference/>`_.
:type query: str
:param ~datetime.timedelta duration: The duration for which to query the data. This can also be accompanied
with either start_time or end_time. If start_time or end_time is not provided, the current time is
taken as the end time.
:keyword datetime start_time: The start time from which to query the data. This should be accompanied
with either end_time or duration.
:keyword datetime end_time: The end time till which to query the data. This should be accompanied
with either start_time or duration.
:keyword additional_workspaces: A list of workspaces that are included in the query.
These can be qualified workspace names, workspsce Ids or Azure resource Ids.
:paramtype additional_workspaces: list[str]
:keyword request_id: The error details.
:paramtype request_id: str
:keyword int server_timeout: the server timeout. The default timeout is 3 minutes,
and the maximum timeout is 10 minutes.
:keyword bool include_statistics: To get information about query statistics.
:keyword bool include_render: In the query language, it is possible to specify different render options.
By default, the API does not return information regarding the type of visualization to show.
:keyword headers: Dictionary of :code:`<string>`.
:paramtype headers: dict[str, str]
"""
def __init__(self, query, workspace_id, duration=None, **kwargs): #pylint: disable=super-init-not-called
# type: (str, str, Optional[str], Any) -> None
include_statistics = kwargs.pop("include_statistics", False)
include_render = kwargs.pop("include_render", False)
server_timeout = kwargs.pop("server_timeout", None)
prefer = ""
if server_timeout:
prefer += "wait=" + str(server_timeout)
if include_statistics:
if len(prefer) > 0:
prefer += " "
prefer += "include-statistics=true"
if include_render:
if len(prefer) > 0:
prefer += " "
prefer += "include-render=true"
headers = kwargs.get("headers", None)
try:
headers['Prefer'] = prefer
except TypeError:
headers = {'Prefer': prefer}
start = kwargs.pop('start_time', None)
end = kwargs.pop('end_time', None)
timespan = construct_iso8601(start, end, duration)
additional_workspaces = kwargs.pop("additional_workspaces", None)
self.id = kwargs.get("request_id", str(uuid.uuid4()))
self.body = {
"query": query, "timespan": timespan, "workspaces": additional_workspaces
}
self.headers = headers
self.workspace = workspace_id
class LogsQueryResult(object):
"""The LogsQueryResult.
:param id:
:type id: str
:param status:
:type status: int
:param body: Contains the tables, columns & rows resulting from a query.
:type body: ~azure.monitor.query.LogsQueryResults
"""
def __init__(
self,
**kwargs
):
self.id = kwargs.get('id', None)
self.status = kwargs.get('status', None)
self.body = kwargs.get('body', None)
@classmethod
def _from_generated(cls, generated):
if not generated:
return cls()
return cls(
id=generated.id,
status=generated.status,
body=LogsQueryResults._from_generated(generated.body) # pylint: disable=protected-access
)
[docs]class LogsBatchResults(object):
"""Response to a batch.
:keyword responses: An array of responses corresponding to each individual request in a batch.
:paramtype responses: list[azure.monitor.query.LogsQueryResult]
:keyword error: Error response for a batch request.
:paramtype error: ~azure.monitor.query.LogsBatchResultError
"""
def __init__(self, **kwargs):
# type: (Any) -> None
self.responses = kwargs.get("responses", None)
self.error = kwargs.get("error", None)
@classmethod
def _from_generated(cls, generated, request_order):
if not generated:
return cls()
return cls(
responses=order_results(request_order, [
LogsQueryResult._from_generated(rsp) for rsp in generated.responses # pylint: disable=protected-access
])
)
[docs]class LogsBatchResultError(object):
"""Error response for a batch request.
:param message: The error message describing the cause of the error.
:type message: str
:param code: The error code.
:type code: str
:param details: The details of the error.
:type inner_error: list[~azure.monitor.query.ErrorDetails]
"""
def __init__(self, **kwargs):
# type: (Any) -> None
self.message = kwargs.get("message", None)
self.code = kwargs.get("code", None)
self.details = kwargs.get("details", None)
@classmethod
def _from_generated(cls, generated):
if not generated:
return cls()
return cls(
message=generated.inner_error.message,
code=generated.code,
details=generated.inner_error.details
)
[docs]class MetricNamespace(object):
"""Metric namespace class specifies the metadata for a metric namespace.
:keyword id: The ID of the metricNamespace.
:paramtype id: str
:keyword type: The type of the namespace.
:paramtype type: str
:keyword name: The name of the namespace.
:paramtype name: str
:keyword metric_namespace_name: The fully qualified namespace name.
:paramtype properties: str
"""
def __init__(
self,
**kwargs
):
self.id = kwargs.get('id', None)
self.type = kwargs.get('type', None)
self.name = kwargs.get('name', None)
self.metric_namespace_name = kwargs.get('metric_namespace_name', None)
@classmethod
def _from_generated(cls, generated):
if not generated:
return cls()
metric_namespace_name = None
if generated.properties:
metric_namespace_name = generated.properties.metric_namespace_name
return cls(
id=generated.id,
type=generated.type,
name=generated.name,
metric_namespace_name=metric_namespace_name
)
[docs]class MetricDefinition(object):
"""Metric definition class specifies the metadata for a metric.
:keyword is_dimension_required: Flag to indicate whether the dimension is required.
:paramtype is_dimension_required: bool
:keyword resource_id: the resource identifier of the resource that emitted the metric.
:paramtype resource_id: str
:keyword namespace: the namespace the metric belongs to.
:paramtype namespace: str
:keyword name: the name and the display name of the metric, i.e. it is a localizable string.
:paramtype name: str
:keyword unit: the unit of the metric. Possible values include: "Count", "Bytes", "Seconds",
"CountPerSecond", "BytesPerSecond", "Percent", "MilliSeconds", "ByteSeconds", "Unspecified",
"Cores", "MilliCores", "NanoCores", "BitsPerSecond".
:paramtype unit: str or ~monitor_query_client.models.Unit
:keyword primary_aggregation_type: the primary aggregation type value defining how to use the
values for display. Possible values include: "None", "Average", "Count", "Minimum", "Maximum",
"Total".
:paramtype primary_aggregation_type: str or ~monitor_query_client.models.AggregationType
:keyword supported_aggregation_types: the collection of what aggregation types are supported.
:paramtype supported_aggregation_types: list[str or ~monitor_query_client.models.AggregationType]
:keyword metric_availabilities: the collection of what aggregation intervals are available to be
queried.
:paramtype metric_availabilities: list[~monitor_query_client.models.MetricAvailability]
:keyword id: the resource identifier of the metric definition.
:paramtype id: str
:keyword dimensions: the name and the display name of the dimension, i.e. it is a localizable
string.
:paramtype dimensions: list[str]
"""
def __init__(
self,
**kwargs
):
# type: (Any) -> None
self.is_dimension_required = kwargs.get('is_dimension_required', None) # type: Optional[bool]
self.resource_id = kwargs.get('resource_id', None) # type: Optional[str]
self.namespace = kwargs.get('namespace', None) # type: Optional[str]
self.name = kwargs.get('name', None) # type: Optional[str]
self.unit = kwargs.get('unit', None) # type: Optional[str]
self.primary_aggregation_type = kwargs.get('primary_aggregation_type', None) # type: Optional[str]
self.supported_aggregation_types = kwargs.get('supported_aggregation_types', None) # type: Optional[str]
self.metric_availabilities = kwargs.get('metric_availabilities', None) # type: List[MetricAvailability]
self.id = kwargs.get('id', None) # type: Optional[str]
self.dimensions = kwargs.get('dimensions', None) # type: Optional[List[str]]
@classmethod
def _from_generated(cls, generated):
if not generated:
return cls()
dimensions = None
if generated.dimensions is not None:
dimensions = [d.value for d in generated.dimensions]
return cls(
is_dimension_required=generated.is_dimension_required,
resource_id=generated.resource_id,
namespace=generated.namespace,
name=generated.name.value,
unit=generated.unit,
primary_aggregation_type=generated.primary_aggregation_type,
supported_aggregation_types=generated.supported_aggregation_types,
metric_availabilities=[
MetricAvailability._from_generated( # pylint: disable=protected-access
val
) for val in generated.metric_availabilities
],
id=generated.id,
dimensions=dimensions
)
[docs]class MetricValue(object):
"""Represents a metric value.
All required parameters must be populated in order to send to Azure.
:keyword time_stamp: Required. the timestamp for the metric value in ISO 8601 format.
:paramtype time_stamp: ~datetime.datetime
:keyword average: the average value in the time range.
:paramtype average: float
:keyword minimum: the least value in the time range.
:paramtype minimum: float
:keyword maximum: the greatest value in the time range.
:paramtype maximum: float
:keyword total: the sum of all of the values in the time range.
:paramtype total: float
:keyword count: the number of samples in the time range. Can be used to determine the number of
values that contributed to the average value.
:paramtype count: float
"""
def __init__(
self,
**kwargs
):
# type: (Any) -> None
self.time_stamp = kwargs['time_stamp']
self.average = kwargs.get('average', None)
self.minimum = kwargs.get('minimum', None)
self.maximum = kwargs.get('maximum', None)
self.total = kwargs.get('total', None)
self.count = kwargs.get('count', None)
@classmethod
def _from_generated(cls, generated):
if not generated:
return cls()
return cls(
time_stamp=generated.time_stamp,
average=generated.average,
minimum=generated.minimum,
maximum=generated.maximum,
total=generated.total,
count=generated.count
)
[docs]class Metric(object):
"""The result data of a query.
All required parameters must be populated in order to send to Azure.
:keyword id: Required. the metric Id.
:paramtype id: str
:keyword type: Required. the resource type of the metric resource.
:paramtype type: str
:keyword name: Required. the name of the metric.
:paramtype name: str
:keyword unit: Required. the unit of the metric. Possible values include: "Count", "Bytes",
"Seconds", "CountPerSecond", "BytesPerSecond", "Percent", "MilliSeconds", "ByteSeconds",
"Unspecified", "Cores", "MilliCores", "NanoCores", "BitsPerSecond".
:paramtype unit: str
:keyword timeseries: Required. the time series returned when a data query is performed.
:paramtype timeseries: list[~monitor_query_client.models.TimeSeriesElement]
"""
def __init__(
self,
**kwargs
):
# type: (Any) -> None
self.id = kwargs['id']
self.type = kwargs['type']
self.name = kwargs['name']
self.unit = kwargs['unit']
self.timeseries = kwargs['timeseries']
@classmethod
def _from_generated(cls, generated):
if not generated:
return cls()
return cls(
id=generated.id,
type=generated.type,
name=generated.name.value,
unit=generated.unit,
timeseries=[
TimeSeriesElement._from_generated(t) for t in generated.timeseries # pylint: disable=protected-access
]
)
[docs]class TimeSeriesElement(object):
"""A time series result type. The discriminator value is always TimeSeries in this case.
:keyword metadata_values: the metadata values returned if $filter was specified in the call.
:paramtype metadata_values: list[~monitor_query_client.models.MetadataValue]
:keyword data: An array of data points representing the metric values. This is only returned if
a result type of data is specified.
:paramtype data: list[~monitor_query_client.models.MetricValue]
"""
_attribute_map = {
'metadata_values': {'key': 'metadata_values', 'type': '[MetadataValue]'},
'data': {'key': 'data', 'type': '[MetricValue]'},
}
def __init__(
self,
**kwargs
):
# type: (Any) -> None
self.metadata_values = kwargs.get('metadatavalues', None)
self.data = kwargs.get('data', None)
@classmethod
def _from_generated(cls, generated):
if not generated:
return cls()
return cls(
metadata_values=[
MetricsMetadataValue._from_generated( # pylint: disable=protected-access
mval
) for mval in generated.metadatavalues
],
data=[MetricValue._from_generated(val) for val in generated.data] # pylint: disable=protected-access
)
[docs]class MetricAvailability(object):
"""Metric availability specifies the time grain (aggregation interval or frequency)
and the retention period for that time grain.
:keyword time_grain: the time grain specifies the aggregation interval for the metric. Expressed
as a duration 'PT1M', 'P1D', etc.
:paramtype time_grain: ~datetime.timedelta
:keyword retention: the retention period for the metric at the specified timegrain. Expressed as
a duration 'PT1M', 'P1D', etc.
:paramtype retention: ~datetime.timedelta
"""
def __init__(
self,
**kwargs
):
# type: (Any) -> None
self.time_grain = kwargs.get('time_grain', None)
self.retention = kwargs.get('retention', None)
@classmethod
def _from_generated(cls, generated):
if not generated:
return cls()
return cls(
time_grain=generated.time_grain,
retention=generated.retention
)