import json
from datetime import datetime, timedelta
from typing import Any, Dict, Iterable, List, Optional, SupportsFloat, Tuple
from dateutil import parser
from dateutil.tz import tzutc
from moto.core.base_backend import BaseBackend
from moto.core.common_models import BackendDict, BaseModel, CloudWatchMetricProvider
from moto.core.utils import (
iso_8601_datetime_with_nanoseconds,
iso_8601_datetime_without_milliseconds,
utcnow,
)
from moto.moto_api._internal import mock_random
from ..utilities.tagging_service import TaggingService
from .exceptions import (
InvalidFormat,
InvalidParameterCombination,
InvalidParameterValue,
ResourceNotFound,
ResourceNotFoundException,
ValidationError,
)
from .metric_data_expression_parser import parse_expression
from .utils import make_arn_for_alarm, make_arn_for_dashboard
_EMPTY_LIST: Any = tuple()
class Dimension(object):
def __init__(self, name: Optional[str], value: Optional[str]):
self.name = name
self.value = value
def __eq__(self, item: Any) -> bool:
if isinstance(item, Dimension):
return self.name == item.name and (
self.value is None or item.value is None or self.value == item.value
)
return False
def __lt__(self, other: "Dimension") -> bool:
return self.name < other.name and self.value < other.name # type: ignore[operator]
class Metric(object):
def __init__(self, metric_name: str, namespace: str, dimensions: List[Dimension]):
self.metric_name = metric_name
self.namespace = namespace
self.dimensions = dimensions
class MetricStat(object):
def __init__(self, metric: Metric, period: str, stat: str, unit: str):
self.metric = metric
self.period = period
self.stat = stat
self.unit = unit
class MetricDataQuery(object):
def __init__(
self,
query_id: str,
label: str,
period: str,
return_data: str,
expression: Optional[str] = None,
metric_stat: Optional[MetricStat] = None,
):
self.id = query_id
self.label = label
self.period = period
self.return_data = return_data
self.expression = expression
self.metric_stat = metric_stat
def daterange(
start: datetime,
stop: datetime,
step: timedelta = timedelta(days=1),
inclusive: bool = False,
) -> Iterable[datetime]:
"""
This method will iterate from `start` to `stop` datetimes with a timedelta step of `step`
(supports iteration forwards or backwards in time)
:param start: start datetime
:param stop: end datetime
:param step: step size as a timedelta
:param inclusive: if True, last item returned will be as step closest to `end` (or `end` if no remainder).
"""
# inclusive=False to behave like range by default
total_step_secs = step.total_seconds()
assert total_step_secs != 0
if total_step_secs > 0:
while start < stop:
yield start
start = start + step
else:
while stop < start:
yield start
start = start + step
if inclusive and start == stop:
yield start
class FakeAlarm(BaseModel):
def __init__(
self,
account_id: str,
region_name: str,
name: str,
namespace: str,
metric_name: str,
metric_data_queries: Optional[List[MetricDataQuery]],
comparison_operator: str,
evaluation_periods: int,
datapoints_to_alarm: Optional[int],
period: int,
threshold: float,
statistic: str,
extended_statistic: Optional[str],
description: str,
dimensions: List[Dict[str, str]],
alarm_actions: List[str],
ok_actions: Optional[List[str]],
insufficient_data_actions: Optional[List[str]],
unit: Optional[str],
actions_enabled: bool,
treat_missing_data: Optional[str],
evaluate_low_sample_count_percentile: Optional[str],
threshold_metric_id: Optional[str],
rule: Optional[str],
):
self.region_name = region_name
self.name = name
self.alarm_arn = make_arn_for_alarm(region_name, account_id, name)
self.namespace = namespace
self.metric_name = metric_name
self.metric_data_queries = metric_data_queries or []
self.comparison_operator = comparison_operator
self.evaluation_periods = evaluation_periods
self.datapoints_to_alarm = datapoints_to_alarm
self.period = period
self.threshold = threshold
self.statistic = statistic
self.extended_statistic = extended_statistic
self.description = description
self.dimensions = [
Dimension(dimension["name"], dimension["value"]) for dimension in dimensions
]
self.actions_enabled = True if actions_enabled is None else actions_enabled
self.alarm_actions = alarm_actions
self.ok_actions = ok_actions or []
self.insufficient_data_actions = insufficient_data_actions or []
self.unit = unit
self.configuration_updated_timestamp = iso_8601_datetime_with_nanoseconds()
self.treat_missing_data = treat_missing_data
self.evaluate_low_sample_count_percentile = evaluate_low_sample_count_percentile
self.threshold_metric_id = threshold_metric_id
self.history: List[Any] = []
self.state_reason = "Unchecked: Initial alarm creation"
self.state_reason_data = "{}"
self.state_value = "OK"
self.state_updated_timestamp = iso_8601_datetime_with_nanoseconds()
# only used for composite alarms
self.rule = rule
def update_state(self, reason: str, reason_data: str, state_value: str) -> None:
# History type, that then decides what the rest of the items are, can be one of ConfigurationUpdate | StateUpdate | Action
self.history.append(
(
"StateUpdate",
self.state_reason,
self.state_reason_data,
self.state_value,
self.state_updated_timestamp,
)
)
self.state_reason = reason
self.state_reason_data = reason_data
self.state_value = state_value
self.state_updated_timestamp = iso_8601_datetime_with_nanoseconds()
def are_dimensions_same(
metric_dimensions: List[Dimension], dimensions: List[Dimension]
) -> bool:
if len(metric_dimensions) != len(dimensions):
return False
for dimension in metric_dimensions:
for new_dimension in dimensions:
if (
dimension.name != new_dimension.name
or dimension.value != new_dimension.value
):
return False
return True
class MetricDatumBase(BaseModel):
"""
Base class for Metrics Datum (represents value or statistics set by put-metric-data)
"""
def __init__(
self,
namespace: str,
name: str,
dimensions: List[Dict[str, str]],
timestamp: datetime,
unit: Any = None,
):
self.namespace = namespace
self.name = name
self.timestamp = timestamp or utcnow().replace(tzinfo=tzutc())
self.dimensions = [
Dimension(dimension["Name"], dimension["Value"]) for dimension in dimensions
]
self.unit = unit
def filter(
self,
namespace: Optional[str],
name: Optional[str],
dimensions: List[Dict[str, str]],
already_present_metrics: Optional[List["MetricDatumBase"]] = None,
) -> bool:
if namespace and namespace != self.namespace:
return False
if name and name != self.name:
return False
for metric in already_present_metrics or []:
if (
(
self.dimensions
and are_dimensions_same(metric.dimensions, self.dimensions)
)
and self.name == metric.name
and self.namespace == metric.namespace
): # should be considered as already present only when name, namespace and dimensions all three are same
return False
if dimensions and any(
Dimension(d["Name"], d.get("Value")) not in self.dimensions
for d in dimensions
):
return False
return True
class MetricDatum(MetricDatumBase):
"""
Single Metric value, represents the "value" (or a single value from the list "values") used in put-metric-data
"""
def __init__(
self,
namespace: str,
name: str,
value: float,
dimensions: List[Dict[str, str]],
timestamp: datetime,
unit: Any = None,
):
super().__init__(namespace, name, dimensions, timestamp, unit)
self.value = value
class MetricAggregatedDatum(MetricDatumBase):
"""
Metric Statistics, represents "statistics-values" used in put-metric-data
"""
def __init__(
self,
namespace: str,
name: str,
min_stat: float,
max_stat: float,
sample_count: float,
sum_stat: float,
dimensions: List[Dict[str, str]],
timestamp: datetime,
unit: Any = None,
):
super().__init__(namespace, name, dimensions, timestamp, unit)
self.min = min_stat
self.max = max_stat
self.sample_count = sample_count
self.sum = sum_stat
class Dashboard(BaseModel):
def __init__(self, account_id: str, region_name: str, name: str, body: str):
# Guaranteed to be unique for now as the name is also the key of a dictionary where they are stored
self.arn = make_arn_for_dashboard(account_id, region_name, name)
self.name = name
self.body = body
self.last_modified = datetime.now()
@property
def last_modified_iso(self) -> str:
return self.last_modified.isoformat()
@property
def size(self) -> int:
return len(self)
def __len__(self) -> int:
return len(self.body)
def __repr__(self) -> str:
return f"<CloudWatchDashboard {self.name}>"
class Statistics:
"""
Helper class to calculate statics for a list of metrics (MetricDatum, or MetricAggregatedDatum)
"""
def __init__(self, stats: List[str], dt: datetime, unit: Optional[str] = None):
self.timestamp: str = iso_8601_datetime_without_milliseconds(dt or utcnow())
self.metric_data: List[MetricDatumBase] = []
self.stats = stats
self.unit = unit
def get_statistics_for_type(self, stat: str) -> Optional[SupportsFloat]:
"""Calculates the statistic for the metric_data provided
:param stat: the statistic that should be returned, case-sensitive (Sum, Average, Minium, Maximum, SampleCount)
:return: the statistic of the current 'metric_data' in this class, or 0
"""
if stat == "Sum":
return self.sum
if stat == "Average":
return self.average
if stat == "Minimum":
return self.minimum
if stat == "Maximum":
return self.maximum
if stat == "SampleCount":
return self.sample_count
return None
@property
def metric_single_values_list(self) -> List[float]:
"""
:return: list of all values for the MetricDatum instances of the metric_data list
"""
return [m.value for m in self.metric_data or [] if isinstance(m, MetricDatum)]
@property
def metric_aggregated_list(self) -> List[MetricAggregatedDatum]:
"""
:return: list of all MetricAggregatedDatum instances from the metric_data list
"""
return [
s for s in self.metric_data or [] if isinstance(s, MetricAggregatedDatum)
]
@property
def sample_count(self) -> Optional[SupportsFloat]:
if "SampleCount" not in self.stats:
return None
return self.calc_sample_count()
@property
def sum(self) -> Optional[SupportsFloat]:
if "Sum" not in self.stats:
return None
return self.calc_sum()
@property
def minimum(self) -> Optional[SupportsFloat]:
if "Minimum" not in self.stats:
return None
if not self.metric_single_values_list and not self.metric_aggregated_list:
return None
metrics = self.metric_single_values_list + [
s.min for s in self.metric_aggregated_list
]
return min(metrics)
@property
def maximum(self) -> Optional[SupportsFloat]:
if "Maximum" not in self.stats:
return None
if not self.metric_single_values_list and not self.metric_aggregated_list:
return None
metrics = self.metric_single_values_list + [
s.max for s in self.metric_aggregated_list
]
return max(metrics)
@property
def average(self) -> Optional[SupportsFloat]:
if "Average" not in self.stats:
return None
sample_count = self.calc_sample_count()
if not sample_count:
return None
return self.calc_sum() / sample_count
def calc_sample_count(self) -> float:
return len(self.metric_single_values_list) + sum(
[s.sample_count for s in self.metric_aggregated_list]
)
def calc_sum(self) -> float:
return sum(self.metric_single_values_list) + sum(
[s.sum for s in self.metric_aggregated_list]
)
class CloudWatchBackend(BaseBackend):
def __init__(self, region_name: str, account_id: str):
super().__init__(region_name, account_id)
self.alarms: Dict[str, FakeAlarm] = {}
self.dashboards: Dict[str, Dashboard] = {}
self.metric_data: List[MetricDatumBase] = []
self.paged_metric_data: Dict[str, List[MetricDatumBase]] = {}
self.tagger = TaggingService()
@property
# Retrieve a list of all OOTB metrics that are provided by metrics providers
# Computed on the fly
def aws_metric_data(self) -> List[MetricDatumBase]:
providers = CloudWatchMetricProvider.__subclasses__()
md = []
for provider in providers:
md.extend(
provider.get_cloudwatch_metrics(
self.account_id, region=self.region_name
)
)
return md
def put_metric_alarm(
self,
name: str,
namespace: str,
metric_name: str,
comparison_operator: str,
evaluation_periods: int,
period: int,
threshold: float,
statistic: str,
description: str,
dimensions: List[Dict[str, str]],
alarm_actions: List[str],
metric_data_queries: Optional[List[MetricDataQuery]] = None,
datapoints_to_alarm: Optional[int] = None,
extended_statistic: Optional[str] = None,
ok_actions: Optional[List[str]] = None,
insufficient_data_actions: Optional[List[str]] = None,
unit: Optional[str] = None,
actions_enabled: bool = True,
treat_missing_data: Optional[str] = None,
evaluate_low_sample_count_percentile: Optional[str] = None,
threshold_metric_id: Optional[str] = None,
rule: Optional[str] = None,
tags: Optional[List[Dict[str, str]]] = None,
) -> FakeAlarm:
if extended_statistic and not extended_statistic.startswith("p"):
raise InvalidParameterValue(
f"The value {extended_statistic} for parameter ExtendedStatistic is not supported."
)
if (
evaluate_low_sample_count_percentile
and evaluate_low_sample_count_percentile not in ("evaluate", "ignore")
):
raise ValidationError(
f"Option {evaluate_low_sample_count_percentile} is not supported. "
"Supported options for parameter EvaluateLowSampleCountPercentile are evaluate and ignore."
)
alarm = FakeAlarm(
account_id=self.account_id,
region_name=self.region_name,
name=name,
namespace=namespace,
metric_name=metric_name,
metric_data_queries=metric_data_queries,
comparison_operator=comparison_operator,
evaluation_periods=evaluation_periods,
datapoints_to_alarm=datapoints_to_alarm,
period=period,
threshold=threshold,
statistic=statistic,
extended_statistic=extended_statistic,
description=description,
dimensions=dimensions,
alarm_actions=alarm_actions,
ok_actions=ok_actions,
insufficient_data_actions=insufficient_data_actions,
unit=unit,
actions_enabled=actions_enabled,
treat_missing_data=treat_missing_data,
evaluate_low_sample_count_percentile=evaluate_low_sample_count_percentile,
threshold_metric_id=threshold_metric_id,
rule=rule,
)
self.alarms[name] = alarm
if tags:
self.tagger.tag_resource(alarm.alarm_arn, tags)
return alarm
def describe_alarms(self) -> Iterable[FakeAlarm]:
return self.alarms.values()
@staticmethod
def _list_element_starts_with(items: List[str], needle: str) -> bool:
"""True of any of the list elements starts with needle"""
for item in items:
if item.startswith(needle):
return True
return False
def get_alarms_by_action_prefix(self, action_prefix: str) -> Iterable[FakeAlarm]:
return [
alarm
for alarm in self.alarms.values()
if CloudWatchBackend._list_element_starts_with(
alarm.alarm_actions, action_prefix
)
]
def get_alarms_by_alarm_name_prefix(self, name_prefix: str) -> Iterable[FakeAlarm]:
return [
alarm
for alarm in self.alarms.values()
if alarm.name.startswith(name_prefix)
]
def get_alarms_by_alarm_names(self, alarm_names: List[str]) -> Iterable[FakeAlarm]:
return [alarm for alarm in self.alarms.values() if alarm.name in alarm_names]
def get_alarms_by_state_value(self, target_state: str) -> Iterable[FakeAlarm]:
return filter(
lambda alarm: alarm.state_value == target_state, self.alarms.values()
)
def delete_alarms(self, alarm_names: List[str]) -> None:
for alarm_name in alarm_names:
self.alarms.pop(alarm_name, None)
def put_metric_data(
self, namespace: str, metric_data: List[Dict[str, Any]]
) -> None:
for i, metric in enumerate(metric_data):
self._validate_parameters_put_metric_data(metric, i + 1)
for metric_member in metric_data:
# Preserve "datetime" for get_metric_statistics comparisons
timestamp = metric_member.get("Timestamp")
if timestamp is not None and type(timestamp) != datetime:
timestamp = parser.parse(timestamp)
metric_name = metric_member["MetricName"]
dimension = metric_member.get("Dimensions.member", _EMPTY_LIST)
unit = metric_member.get("Unit")
# put_metric_data can include "value" as single value or "values" as a list
if metric_member.get("Values.member"):
values = metric_member["Values.member"]
# value[i] should be added count[i] times (with default count 1)
counts = metric_member.get("Counts.member") or ["1"] * len(values)
for i in range(0, len(values)):
value = values[i]
timestamp = metric_member.get("Timestamp")
if timestamp is not None and type(timestamp) != datetime:
timestamp = parser.parse(timestamp)
# add the value count[i] times
for _ in range(0, int(float(counts[i]))):
self.metric_data.append(
MetricDatum(
namespace=namespace,
name=metric_name,
value=float(value),
dimensions=dimension,
timestamp=timestamp,
unit=unit,
)
)
elif metric_member.get("StatisticValues"):
stats = metric_member["StatisticValues"]
self.metric_data.append(
MetricAggregatedDatum(
namespace=namespace,
name=metric_name,
sum_stat=float(stats["Sum"]),
min_stat=float(stats["Minimum"]),
max_stat=float(stats["Maximum"]),
sample_count=float(stats["SampleCount"]),
dimensions=dimension,
timestamp=timestamp,
unit=unit,
)
)
else:
# there is only a single value
self.metric_data.append(
MetricDatum(
namespace,
metric_name,
float(metric_member.get("Value", 0)),
dimension,
timestamp,
unit,
)
)
def get_metric_data(
self,
queries: List[Dict[str, Any]],
start_time: datetime,
end_time: datetime,
scan_by: str = "TimestampAscending",
) -> List[Dict[str, Any]]:
start_time = start_time.replace(microsecond=0)
end_time = end_time.replace(microsecond=0)
if start_time > end_time:
raise ValidationError(
"The parameter EndTime must be greater than StartTime."
)
if start_time == end_time:
raise ValidationError(
"The parameter StartTime must not equal parameter EndTime."
)
period_data = [
md for md in self.get_all_metrics() if start_time <= md.timestamp < end_time
]
results = []
results_to_return = []
metric_stat_queries = [q for q in queries if "MetricStat" in q]
metric_math_expression_queries = [
q
for q in queries
if "Expression" in q and not q["Expression"].startswith("SELECT")
]
metric_insights_expression_queries = [
q
for q in queries
if "Expression" in q and q["Expression"].startswith("SELECT")
]
for query in metric_stat_queries:
period_start_time = start_time
metric_stat = query["MetricStat"]
query_ns = metric_stat["Metric"]["Namespace"]
query_name = metric_stat["Metric"]["MetricName"]
delta = timedelta(seconds=int(metric_stat["Period"]))
dimensions = [
Dimension(name=d["Name"], value=d["Value"])
for d in metric_stat["Metric"].get("Dimensions", [])
]
unit = metric_stat.get("Unit")
result_vals: List[SupportsFloat] = []
timestamps: List[str] = []
stat = metric_stat["Stat"]
while period_start_time <= end_time:
period_end_time = period_start_time + delta
period_md = [
period_md
for period_md in period_data
if period_start_time <= period_md.timestamp < period_end_time
]
query_period_data = [
md
for md in period_md
if md.namespace == query_ns and md.name == query_name
]
if dimensions:
query_period_data = [
md
for md in period_md
if sorted(md.dimensions) == sorted(dimensions)
and md.name == query_name
]
# Filter based on unit value
if unit:
query_period_data = [
md for md in query_period_data if md.unit == unit
]
if len(query_period_data) > 0:
stats = Statistics([stat], period_start_time)
stats.metric_data = query_period_data
result_vals.append(stats.get_statistics_for_type(stat)) # type: ignore[arg-type]
timestamps.append(stats.timestamp)
period_start_time += delta
if scan_by == "TimestampDescending" and len(timestamps) > 0:
timestamps.reverse()
result_vals.reverse()
label = query.get("Label") or f"{query_name} {stat}"
results.append(
{
"id": query["Id"],
"label": label,
"vals": result_vals,
"timestamps": timestamps,
}
)
if query.get("ReturnData", "true") == "true":
results_to_return.append(
{
"id": query["Id"],
"label": label,
"vals": result_vals,
"timestamps": timestamps,
}
)
# Metric Math expression Queries run on top of the results of other queries
for query in metric_math_expression_queries:
label = query.get("Label") or query["Id"]
result_vals, timestamps = parse_expression(query["Expression"], results)
results_to_return.append(
{
"id": query["Id"],
"label": label,
"vals": result_vals,
"timestamps": timestamps,
}
)
# Metric Insights Expression Queries act on all results, and are essentially SQL queries
for query in metric_insights_expression_queries:
period_start_time = start_time
delta = timedelta(seconds=int(query["Period"]))
result_vals: List[SupportsFloat] = [] # type: ignore[no-redef]
timestamps: List[str] = [] # type: ignore[no-redef]
while period_start_time <= end_time:
period_end_time = period_start_time + delta
period_md = [
period_md
for period_md in period_data
if period_start_time <= period_md.timestamp < period_end_time
]
# https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/cloudwatch-metrics-insights-querylanguage.html
# We should filter even further, but Moto currently does not support Metrics Insights Queries
# Let's just add all metric data found within this period
if len(period_md) > 0:
stats = Statistics(["Sum"], period_start_time)
stats.metric_data = period_md
result_vals.append(stats.get_statistics_for_type("Sum")) # type: ignore[arg-type]
timestamps.append(stats.timestamp)
period_start_time += delta
if scan_by == "TimestampDescending" and len(timestamps) > 0:
timestamps.reverse()
result_vals.reverse()
results_to_return.append(
{
"id": query["Id"],
"label": (query.get("Label") or query["Id"]),
"vals": result_vals,
"timestamps": timestamps,
}
)
return results_to_return
def get_metric_statistics(
self,
namespace: str,
metric_name: str,
start_time: datetime,
end_time: datetime,
period: int,
stats: List[str],
dimensions: List[Dict[str, str]],
unit: Optional[str] = None,
) -> List[Statistics]:
start_time = start_time.replace(microsecond=0)
end_time = end_time.replace(microsecond=0)
if start_time >= end_time:
raise InvalidParameterValue(
"The parameter StartTime must be less than the parameter EndTime."
)
period_delta = timedelta(seconds=period)
filtered_data = [
md
for md in self.get_all_metrics()
if md.namespace == namespace
and md.name == metric_name
and start_time <= md.timestamp < end_time
]
if unit:
filtered_data = [md for md in filtered_data if md.unit == unit]
if dimensions:
filtered_data = [
md for md in filtered_data if md.filter(None, None, dimensions)
]
# earliest to oldest
filtered_data = sorted(filtered_data, key=lambda x: x.timestamp)
if not filtered_data:
return []
idx = 0
data: List[Statistics] = list()
for dt in daterange(
filtered_data[0].timestamp,
filtered_data[-1].timestamp + period_delta,
period_delta,
):
s = Statistics(stats, dt)
while idx < len(filtered_data) and filtered_data[idx].timestamp < (
dt + period_delta
):
s.metric_data.append(filtered_data[idx])
s.unit = filtered_data[idx].unit
idx += 1
if not s.metric_data:
continue
data.append(s)
return data
def get_all_metrics(self) -> List[MetricDatumBase]:
return self.metric_data + self.aws_metric_data
def put_dashboard(self, name: str, body: str) -> None:
self.dashboards[name] = Dashboard(self.account_id, self.region_name, name, body)
def list_dashboards(self, prefix: str = "") -> Iterable[Dashboard]:
for key, value in self.dashboards.items():
if key.startswith(prefix):
yield value
def delete_dashboards(self, dashboards: List[str]) -> Optional[str]:
to_delete = set(dashboards)
all_dashboards = set(self.dashboards.keys())
left_over = to_delete - all_dashboards
if len(left_over) > 0:
# Some dashboards are not found
db_list = ", ".join(left_over)
return f"The specified dashboard does not exist. [{db_list}]"
for dashboard in to_delete:
del self.dashboards[dashboard]
return None
def get_dashboard(self, dashboard: str) -> Optional[Dashboard]:
return self.dashboards.get(dashboard)
def set_alarm_state(
self, alarm_name: str, reason: str, reason_data: str, state_value: str
) -> None:
try:
if reason_data is not None:
json.loads(reason_data)
except ValueError:
raise InvalidFormat("Unknown")
if alarm_name not in self.alarms:
raise ResourceNotFound
if state_value not in ("OK", "ALARM", "INSUFFICIENT_DATA"):
raise ValidationError(
"1 validation error detected: "
f"Value '{state_value}' at 'stateValue' failed to satisfy constraint: "
"Member must satisfy enum value set: [INSUFFICIENT_DATA, ALARM, OK]"
)
self.alarms[alarm_name].update_state(reason, reason_data, state_value)
def list_metrics(
self,
next_token: Optional[str],
namespace: str,
metric_name: str,
dimensions: List[Dict[str, str]],
) -> Tuple[Optional[str], List[MetricDatumBase]]:
if next_token:
if next_token not in self.paged_metric_data:
raise InvalidParameterValue("Request parameter NextToken is invalid")
else:
metrics = self.paged_metric_data[next_token]
del self.paged_metric_data[next_token] # Cant reuse same token twice
return self._get_paginated(metrics)
else:
metrics = self.get_filtered_metrics(metric_name, namespace, dimensions)
return self._get_paginated(metrics)
def get_filtered_metrics(
self, metric_name: str, namespace: str, dimensions: List[Dict[str, str]]
) -> List[MetricDatumBase]:
metrics = self.get_all_metrics()
new_metrics: List[MetricDatumBase] = []
for md in metrics:
if md.filter(
namespace=namespace,
name=metric_name,
dimensions=dimensions,
already_present_metrics=new_metrics,
):
new_metrics.append(md)
return new_metrics
def list_tags_for_resource(self, arn: str) -> Dict[str, str]:
return self.tagger.get_tag_dict_for_resource(arn)
def tag_resource(self, arn: str, tags: List[Dict[str, str]]) -> None:
# From boto3:
# Currently, the only CloudWatch resources that can be tagged are alarms and Contributor Insights rules.
all_arns = [alarm.alarm_arn for alarm in self.describe_alarms()]
if arn not in all_arns:
raise ResourceNotFoundException
self.tagger.tag_resource(arn, tags)
def untag_resource(self, arn: str, tag_keys: List[str]) -> None:
if arn not in self.tagger.tags.keys():
raise ResourceNotFoundException
self.tagger.untag_resource_using_names(arn, tag_keys)
def _get_paginated(
self, metrics: List[MetricDatumBase]
) -> Tuple[Optional[str], List[MetricDatumBase]]:
if len(metrics) > 500:
next_token = str(mock_random.uuid4())
self.paged_metric_data[next_token] = metrics[500:]
return next_token, metrics[0:500]
else:
return None, metrics
def _validate_parameters_put_metric_data(
self, metric: Dict[str, Any], query_num: int
) -> None:
"""Runs some basic validation of the Metric Query
:param metric: represents one metric query
:param query_num: the query number (starting from 1)
:returns: nothing if the validation passes, else an exception is thrown
:raises: InvalidParameterValue
:raises: InvalidParameterCombination
"""
# basic validation of input
if metric.get("Value") == "NaN":
# single value
raise InvalidParameterValue(
f"The value NaN for parameter MetricData.member.{query_num}.Value is invalid."
)
if metric.get("Values.member"):
# list of values
if "Value" in metric:
raise InvalidParameterValue(
f"The parameters MetricData.member.{query_num}.Value and MetricData.member.{query_num}.Values are mutually exclusive and you have specified both."
)
if metric.get("Counts.member"):
if len(metric["Counts.member"]) != len(metric["Values.member"]):
raise InvalidParameterValue(
f"The parameters MetricData.member.{query_num}.Values and MetricData.member.{query_num}.Counts must be of the same size."
)
for value in metric["Values.member"]:
if value.lower() == "nan":
raise InvalidParameterValue(
f"The value {value} for parameter MetricData.member.{query_num}.Values is invalid."
)
if metric.get("StatisticValues"):
if metric.get("Value"):
raise InvalidParameterCombination(
f"The parameters MetricData.member.{query_num}.Value and MetricData.member.{query_num}.StatisticValues are mutually exclusive and you have specified both."
)
# aggregated (statistic) for values, must contain sum, maximum, minimum and sample count
statistic_values = metric["StatisticValues"]
expected = ["Sum", "Maximum", "Minimum", "SampleCount"]
for stat in expected:
if stat not in statistic_values:
raise InvalidParameterValue(
f'Missing required parameter in MetricData[{query_num}].StatisticValues: "{stat}"'
)
cloudwatch_backends = BackendDict(CloudWatchBackend, "cloudwatch")