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<!doctype html>

<head>

<title>aws_lambda_powertools.metrics.base API documentation</title>

</head>

<body>

<main>

<h1 class="title">Module <code>aws_lambda_powertools.metrics.base</code></h1>

</header>

<span>Expand source code</span>

</summary>

<pre><code class="python">import datetime

import json

import logging

import numbers

import os

import pathlib

from collections import defaultdict

from enum import Enum

from typing import Any, Dict, List, Union

import fastjsonschema

from ..shared import constants

from ..shared.functions import resolve_env_var_choice

from .exceptions import MetricUnitError, MetricValueError, SchemaValidationError

logger = logging.getLogger(__name__)

_schema_path = pathlib.Path(__file__).parent / "./schema.json"

with _schema_path.open() as f:

CLOUDWATCH_EMF_SCHEMA = json.load(f)

MAX_METRICS = 100

class MetricUnit(Enum):

Seconds = "Seconds"

Microseconds = "Microseconds"

Milliseconds = "Milliseconds"

Bytes = "Bytes"

Kilobytes = "Kilobytes"

Megabytes = "Megabytes"

Gigabytes = "Gigabytes"

Terabytes = "Terabytes"

Bits = "Bits"

Kilobits = "Kilobits"

Megabits = "Megabits"

Gigabits = "Gigabits"

Terabits = "Terabits"

Percent = "Percent"

Count = "Count"

BytesPerSecond = "Bytes/Second"

KilobytesPerSecond = "Kilobytes/Second"

MegabytesPerSecond = "Megabytes/Second"

GigabytesPerSecond = "Gigabytes/Second"

TerabytesPerSecond = "Terabytes/Second"

BitsPerSecond = "Bits/Second"

KilobitsPerSecond = "Kilobits/Second"

MegabitsPerSecond = "Megabits/Second"

GigabitsPerSecond = "Gigabits/Second"

TerabitsPerSecond = "Terabits/Second"

CountPerSecond = "Count/Second"

class MetricManager:

"""Base class for metric functionality (namespace, metric, dimension, serialization)

MetricManager creates metrics asynchronously thanks to CloudWatch Embedded Metric Format (EMF).

CloudWatch EMF can create up to 100 metrics per EMF object

and metrics, dimensions, and namespace created via MetricManager

will adhere to the schema, will be serialized and validated against EMF Schema.

**Use `aws_lambda_powertools.metrics.metrics.Metrics` or

`aws_lambda_powertools.metrics.metric.single_metric` to create EMF metrics.**

Environment variables

---------------------

POWERTOOLS_METRICS_NAMESPACE : str

metric namespace to be set for all metrics

POWERTOOLS_SERVICE_NAME : str

service name used for default dimension

Raises

------

MetricUnitError

When metric metric isn't supported by CloudWatch

MetricValueError

When metric value isn't a number

SchemaValidationError

When metric object fails EMF schema validation

"""

def __init__(

self,

metric_set: Dict[str, Any] = None,

dimension_set: Dict = None,

namespace: str = None,

metadata_set: Dict[str, Any] = None,

service: str = None,

self.metric_set = metric_set if metric_set is not None else {}

self.dimension_set = dimension_set if dimension_set is not None else {}

self.namespace = resolve_env_var_choice(choice=namespace, env=os.getenv(constants.METRICS_NAMESPACE_ENV))

self.service = resolve_env_var_choice(choice=service, env=os.getenv(constants.SERVICE_NAME_ENV))

self._metric_units = [unit.value for unit in MetricUnit]

self._metric_unit_options = list(MetricUnit.__members__)

self.metadata_set = self.metadata_set if metadata_set is not None else {}

def add_metric(self, name: str, unit: Union[MetricUnit, str], value: float):

"""Adds given metric

Example

-------

**Add given metric using MetricUnit enum**

metric.add_metric(name="BookingConfirmation", unit=MetricUnit.Count, value=1)

**Add given metric using plain string as value unit**

metric.add_metric(name="BookingConfirmation", unit="Count", value=1)

Parameters

----------

name : str

Metric name

unit : Union[MetricUnit, str]

`aws_lambda_powertools.helper.models.MetricUnit`

value : float

Metric value

Raises

------

MetricUnitError

When metric unit is not supported by CloudWatch

"""

if not isinstance(value, numbers.Number):

raise MetricValueError(f"{value} is not a valid number")

unit = self.__extract_metric_unit_value(unit=unit)

metric: Dict = self.metric_set.get(name, defaultdict(list))

metric["Unit"] = unit

metric["Value"].append(float(value))

logger.debug(f"Adding metric: {name} with {metric}")

self.metric_set[name] = metric

if len(self.metric_set) == MAX_METRICS:

logger.debug(f"Exceeded maximum of {MAX_METRICS} metrics - Publishing existing metric set")

metrics = self.serialize_metric_set()

print(json.dumps(metrics))

# clear metric set only as opposed to metrics and dimensions set

# since we could have more than 100 metrics

self.metric_set.clear()

def serialize_metric_set(self, metrics: Dict = None, dimensions: Dict = None, metadata: Dict = None) -> Dict:

"""Serializes metric and dimensions set

Parameters

----------

metrics : Dict, optional

Dictionary of metrics to serialize, by default None

dimensions : Dict, optional

Dictionary of dimensions to serialize, by default None

metadata: Dict, optional

Dictionary of metadata to serialize, by default None

Example

-------

**Serialize metrics into EMF format**

metrics = MetricManager()

# ...add metrics, dimensions, namespace

ret = metrics.serialize_metric_set()

Returns

-------

Dict

Serialized metrics following EMF specification

Raises

------

SchemaValidationError

Raised when serialization fail schema validation

"""

if metrics is None: # pragma: no cover

metrics = self.metric_set

if dimensions is None: # pragma: no cover

dimensions = self.dimension_set

if metadata is None: # pragma: no cover

metadata = self.metadata_set

if self.service and not self.dimension_set.get("service"):

self.dimension_set["service"] = self.service

logger.debug({"details": "Serializing metrics", "metrics": metrics, "dimensions": dimensions})

metric_names_and_units: List[Dict[str, str]] = [] # [ { "Name": "metric_name", "Unit": "Count" } ]

metric_names_and_values: Dict[str, float] = {} # { "metric_name": 1.0 }

for metric_name in metrics:

metric: dict = metrics[metric_name]

metric_value: int = metric.get("Value", 0)

metric_unit: str = metric.get("Unit", "")

metric_names_and_units.append({"Name": metric_name, "Unit": metric_unit})

metric_names_and_values.update({metric_name: metric_value})

embedded_metrics_object = {

"_aws": {

"Timestamp": int(datetime.datetime.now().timestamp() * 1000), # epoch

"CloudWatchMetrics": [

{

"Namespace": self.namespace, # "test_namespace"

"Dimensions": [list(dimensions.keys())], # [ "service" ]

"Metrics": metric_names_and_units,

}

**dimensions, # "service": "test_service"

**metadata, # "username": "test"

**metric_names_and_values, # "single_metric": 1.0

}

try:

logger.debug("Validating serialized metrics against CloudWatch EMF schema")

fastjsonschema.validate(definition=CLOUDWATCH_EMF_SCHEMA, data=embedded_metrics_object)

except fastjsonschema.JsonSchemaException as e:

message = f"Invalid format. Error: {e.message}, Invalid item: {e.name}" # noqa: B306, E501

raise SchemaValidationError(message)

return embedded_metrics_object

def add_dimension(self, name: str, value: str):

"""Adds given dimension to all metrics

Example

-------

**Add a metric dimensions**

metric.add_dimension(name="operation", value="confirm_booking")

Parameters

----------

name : str

Dimension name

value : str

Dimension value

"""

logger.debug(f"Adding dimension: {name}:{value}")

# Cast value to str according to EMF spec

# Majority of values are expected to be string already, so

# checking before casting improves performance in most cases

self.dimension_set[name] = value if isinstance(value, str) else str(value)

def add_metadata(self, key: str, value: Any):

"""Adds high cardinal metadata for metrics object

This will not be available during metrics visualization.

Instead, this will be searchable through logs.

If you're looking to add metadata to filter metrics, then

use add_dimensions method.

Example

-------

**Add metrics metadata**

metric.add_metadata(key="booking_id", value="booking_id")

Parameters

----------

key : str

Metadata key

value : any

Metadata value

"""

logger.debug(f"Adding metadata: {key}:{value}")

# Cast key to str according to EMF spec

# Majority of keys are expected to be string already, so

# checking before casting improves performance in most cases

if isinstance(key, str):

self.metadata_set[key] = value

else:

self.metadata_set[str(key)] = value

def __extract_metric_unit_value(self, unit: Union[str, MetricUnit]) -> str:

"""Return metric value from metric unit whether that's str or MetricUnit enum

Parameters

----------

unit : Union[str, MetricUnit]

Metric unit

Returns

-------

str

Metric unit value (e.g. "Seconds", "Count/Second")

Raises

------

MetricUnitError

When metric unit is not supported by CloudWatch

"""

if isinstance(unit, str):

if unit in self._metric_unit_options:

unit = MetricUnit[unit].value

if unit not in self._metric_units: # str correta

raise MetricUnitError(

f"Invalid metric unit '{unit}', expected either option: {self._metric_unit_options}"

)

if isinstance(unit, MetricUnit):

unit = unit.value

return unit</code></pre>

</details>

</section>

</section>

</section>

</section>

<h2 class="section-title" id="header-classes">Classes</h2>

<dl>

<span>class <span class="ident">MetricManager</span></span>

<span>(</span><span>metric_set=None, dimension_set=None, namespace=None, metadata_set=None, service=None)</span>

</code></dt>

<dd>

<section class="desc"><p>Base class for metric functionality (namespace, metric, dimension, serialization)</p>

<p>MetricManager creates metrics asynchronously thanks to CloudWatch Embedded Metric Format (EMF).

CloudWatch EMF can create up to 100 metrics per EMF object

and metrics, dimensions, and namespace created via MetricManager

will adhere to the schema, will be serialized and validated against EMF Schema.</p>

<p><strong>Use <a title="aws_lambda_powertools.metrics.metrics.Metrics" href="metrics.html#aws_lambda_powertools.metrics.metrics.Metrics"><code>Metrics</code></a> or

<a title="aws_lambda_powertools.metrics.metric.single_metric" href="metric.html#aws_lambda_powertools.metrics.metric.single_metric"><code>single_metric()</code></a> to create EMF metrics.</strong></p>

<h2 id="environment-variables">Environment variables</h2>

<dl>

<dt><strong><code>POWERTOOLS_METRICS_NAMESPACE</code></strong> :&ensp;<code>str</code></dt>

<dd>metric namespace to be set for all metrics</dd>

<dt><strong><code>POWERTOOLS_SERVICE_NAME</code></strong> :&ensp;<code>str</code></dt>

<dd>service name used for default dimension</dd>

</dl>

<h2 id="raises">Raises</h2>

<dl>

<dt><code>MetricUnitError</code></dt>

<dd>When metric metric isn't supported by CloudWatch</dd>

<dt><code>MetricValueError</code></dt>

<dd>When metric value isn't a number</dd>

<dt><code>SchemaValidationError</code></dt>

<dd>When metric object fails EMF schema validation</dd>

</dl></section>

<span>Expand source code</span>

</summary>

<pre><code class="python">class MetricManager:

"""Base class for metric functionality (namespace, metric, dimension, serialization)

MetricManager creates metrics asynchronously thanks to CloudWatch Embedded Metric Format (EMF).

CloudWatch EMF can create up to 100 metrics per EMF object

and metrics, dimensions, and namespace created via MetricManager

will adhere to the schema, will be serialized and validated against EMF Schema.

**Use `aws_lambda_powertools.metrics.metrics.Metrics` or

`aws_lambda_powertools.metrics.metric.single_metric` to create EMF metrics.**

Environment variables

---------------------

POWERTOOLS_METRICS_NAMESPACE : str

metric namespace to be set for all metrics

POWERTOOLS_SERVICE_NAME : str

service name used for default dimension

Raises

------

MetricUnitError

When metric metric isn't supported by CloudWatch

MetricValueError

When metric value isn't a number

SchemaValidationError

When metric object fails EMF schema validation

"""

def __init__(

self,

metric_set: Dict[str, Any] = None,

dimension_set: Dict = None,

namespace: str = None,

metadata_set: Dict[str, Any] = None,

service: str = None,

self.metric_set = metric_set if metric_set is not None else {}

self.dimension_set = dimension_set if dimension_set is not None else {}

self.namespace = resolve_env_var_choice(choice=namespace, env=os.getenv(constants.METRICS_NAMESPACE_ENV))

self.service = resolve_env_var_choice(choice=service, env=os.getenv(constants.SERVICE_NAME_ENV))

self._metric_units = [unit.value for unit in MetricUnit]

self._metric_unit_options = list(MetricUnit.__members__)

self.metadata_set = self.metadata_set if metadata_set is not None else {}

def add_metric(self, name: str, unit: Union[MetricUnit, str], value: float):

"""Adds given metric

Example

-------

**Add given metric using MetricUnit enum**

metric.add_metric(name="BookingConfirmation", unit=MetricUnit.Count, value=1)

**Add given metric using plain string as value unit**

metric.add_metric(name="BookingConfirmation", unit="Count", value=1)

Parameters

----------

name : str

Metric name

unit : Union[MetricUnit, str]

`aws_lambda_powertools.helper.models.MetricUnit`

value : float

Metric value

Raises

------

MetricUnitError

When metric unit is not supported by CloudWatch

"""

if not isinstance(value, numbers.Number):

raise MetricValueError(f"{value} is not a valid number")

unit = self.__extract_metric_unit_value(unit=unit)

metric: Dict = self.metric_set.get(name, defaultdict(list))

metric["Unit"] = unit

metric["Value"].append(float(value))

logger.debug(f"Adding metric: {name} with {metric}")

self.metric_set[name] = metric

if len(self.metric_set) == MAX_METRICS:

logger.debug(f"Exceeded maximum of {MAX_METRICS} metrics - Publishing existing metric set")

metrics = self.serialize_metric_set()

print(json.dumps(metrics))

# clear metric set only as opposed to metrics and dimensions set

# since we could have more than 100 metrics

self.metric_set.clear()

def serialize_metric_set(self, metrics: Dict = None, dimensions: Dict = None, metadata: Dict = None) -> Dict:

"""Serializes metric and dimensions set

Parameters

----------

metrics : Dict, optional

Dictionary of metrics to serialize, by default None

dimensions : Dict, optional

Dictionary of dimensions to serialize, by default None

metadata: Dict, optional

Dictionary of metadata to serialize, by default None

Example

-------

**Serialize metrics into EMF format**

metrics = MetricManager()

# ...add metrics, dimensions, namespace

ret = metrics.serialize_metric_set()

Returns

-------

Dict

Serialized metrics following EMF specification

Raises

------

SchemaValidationError

Raised when serialization fail schema validation

"""

if metrics is None: # pragma: no cover

metrics = self.metric_set

if dimensions is None: # pragma: no cover

dimensions = self.dimension_set

if metadata is None: # pragma: no cover

metadata = self.metadata_set

if self.service and not self.dimension_set.get("service"):

self.dimension_set["service"] = self.service

logger.debug({"details": "Serializing metrics", "metrics": metrics, "dimensions": dimensions})

metric_names_and_units: List[Dict[str, str]] = [] # [ { "Name": "metric_name", "Unit": "Count" } ]

metric_names_and_values: Dict[str, float] = {} # { "metric_name": 1.0 }

for metric_name in metrics:

metric: dict = metrics[metric_name]

metric_value: int = metric.get("Value", 0)

metric_unit: str = metric.get("Unit", "")

metric_names_and_units.append({"Name": metric_name, "Unit": metric_unit})

metric_names_and_values.update({metric_name: metric_value})

embedded_metrics_object = {

"_aws": {

"Timestamp": int(datetime.datetime.now().timestamp() * 1000), # epoch

"CloudWatchMetrics": [

{

"Namespace": self.namespace, # "test_namespace"

"Dimensions": [list(dimensions.keys())], # [ "service" ]

"Metrics": metric_names_and_units,

}

**dimensions, # "service": "test_service"

**metadata, # "username": "test"

**metric_names_and_values, # "single_metric": 1.0

}

try:

logger.debug("Validating serialized metrics against CloudWatch EMF schema")

fastjsonschema.validate(definition=CLOUDWATCH_EMF_SCHEMA, data=embedded_metrics_object)

except fastjsonschema.JsonSchemaException as e:

message = f"Invalid format. Error: {e.message}, Invalid item: {e.name}" # noqa: B306, E501

raise SchemaValidationError(message)

return embedded_metrics_object

def add_dimension(self, name: str, value: str):

"""Adds given dimension to all metrics

Example

-------

**Add a metric dimensions**

metric.add_dimension(name="operation", value="confirm_booking")

Parameters

----------

name : str

Dimension name

value : str

Dimension value

"""

logger.debug(f"Adding dimension: {name}:{value}")

# Cast value to str according to EMF spec

# Majority of values are expected to be string already, so

# checking before casting improves performance in most cases

self.dimension_set[name] = value if isinstance(value, str) else str(value)

def add_metadata(self, key: str, value: Any):

"""Adds high cardinal metadata for metrics object

This will not be available during metrics visualization.

Instead, this will be searchable through logs.

If you're looking to add metadata to filter metrics, then

use add_dimensions method.

Example

-------

**Add metrics metadata**

metric.add_metadata(key="booking_id", value="booking_id")

Parameters

----------

key : str

Metadata key

value : any

Metadata value

"""

logger.debug(f"Adding metadata: {key}:{value}")

# Cast key to str according to EMF spec

# Majority of keys are expected to be string already, so

# checking before casting improves performance in most cases

if isinstance(key, str):

self.metadata_set[key] = value

else:

self.metadata_set[str(key)] = value

def __extract_metric_unit_value(self, unit: Union[str, MetricUnit]) -> str:

"""Return metric value from metric unit whether that's str or MetricUnit enum

Parameters

----------

unit : Union[str, MetricUnit]

Metric unit

Returns

-------

str

Metric unit value (e.g. "Seconds", "Count/Second")

Raises

------

MetricUnitError

When metric unit is not supported by CloudWatch

"""

if isinstance(unit, str):

if unit in self._metric_unit_options:

unit = MetricUnit[unit].value

if unit not in self._metric_units: # str correta

raise MetricUnitError(

f"Invalid metric unit '{unit}', expected either option: {self._metric_unit_options}"

)

if isinstance(unit, MetricUnit):

unit = unit.value

return unit</code></pre>

</details>

<h3>Subclasses</h3>

<li><a title="aws_lambda_powertools.metrics.metric.SingleMetric" href="metric.html#aws_lambda_powertools.metrics.metric.SingleMetric">SingleMetric</a></li>

<li><a title="aws_lambda_powertools.metrics.metrics.Metrics" href="metrics.html#aws_lambda_powertools.metrics.metrics.Metrics">Metrics</a></li>

</ul>

<h3>Methods</h3>

<dl>

<span>def <span class="ident">add_dimension</span></span>(<span>self, name, value)</span>

</code></dt>

<dd>

<section class="desc"><p>Adds given dimension to all metrics</p>

<h2 id="example">Example</h2>

<p><strong>Add a metric dimensions</strong></p>

<pre><code>metric.add_dimension(name="operation", value="confirm_booking")

</code></pre>

<h2 id="parameters">Parameters</h2>

<dl>

<dd>Dimension name</dd>

<dt><strong><code>value</code></strong> :&ensp;<code>str</code></dt>

<dd>Dimension value</dd>

</dl></section>

<span>Expand source code</span>

</summary>

<pre><code class="python">def add_dimension(self, name: str, value: str):

"""Adds given dimension to all metrics

Example

-------

**Add a metric dimensions**

metric.add_dimension(name="operation", value="confirm_booking")

Parameters

----------

name : str

Dimension name

value : str

Dimension value

"""

logger.debug(f"Adding dimension: {name}:{value}")

# Cast value to str according to EMF spec

# Majority of values are expected to be string already, so

# checking before casting improves performance in most cases

self.dimension_set[name] = value if isinstance(value, str) else str(value)</code></pre>

</details>

</dd>

<span>def <span class="ident">add_metadata</span></span>(<span>self, key, value)</span>

</code></dt>

<dd>

<section class="desc"><p>Adds high cardinal metadata for metrics object</p>

<p>This will not be available during metrics visualization.

Instead, this will be searchable through logs.</p>

<p>If you're looking to add metadata to filter metrics, then

use add_dimensions method.</p>

<h2 id="example">Example</h2>

<p><strong>Add metrics metadata</strong></p>

<pre><code>metric.add_metadata(key="booking_id", value="booking_id")

</code></pre>

<h2 id="parameters">Parameters</h2>

<dl>

<dd>Metadata key</dd>

<dt><strong><code>value</code></strong> :&ensp;<code>any</code></dt>

<dd>Metadata value</dd>

</dl></section>

<span>Expand source code</span>

</summary>

<pre><code class="python">def add_metadata(self, key: str, value: Any):

"""Adds high cardinal metadata for metrics object

This will not be available during metrics visualization.

Instead, this will be searchable through logs.

If you're looking to add metadata to filter metrics, then

use add_dimensions method.

Example

-------

**Add metrics metadata**

metric.add_metadata(key="booking_id", value="booking_id")

Parameters

----------

key : str

Metadata key

value : any

Metadata value

"""

logger.debug(f"Adding metadata: {key}:{value}")

# Cast key to str according to EMF spec

# Majority of keys are expected to be string already, so

# checking before casting improves performance in most cases

if isinstance(key, str):

self.metadata_set[key] = value

else:

self.metadata_set[str(key)] = value</code></pre>

</details>

</dd>

<span>def <span class="ident">add_metric</span></span>(<span>self, name, unit, value)</span>

</code></dt>

<dd>

<section class="desc"><p>Adds given metric</p>

<h2 id="example">Example</h2>

<p><strong>Add given metric using MetricUnit enum</strong></p>

<pre><code>metric.add_metric(name="BookingConfirmation", unit=MetricUnit.Count, value=1)

</code></pre>

<p><strong>Add given metric using plain string as value unit</strong></p>

<pre><code>metric.add_metric(name="BookingConfirmation", unit="Count", value=1)

</code></pre>

<h2 id="parameters">Parameters</h2>

<dl>

<dd>Metric name</dd>

<dt><strong><code>unit</code></strong> :&ensp;<code>Union</code>[<code>MetricUnit</code>, <code>str</code>]</dt>

<dd><code>aws_lambda_powertools.helper.models.MetricUnit</code></dd>

<dt><strong><code>value</code></strong> :&ensp;<code>float</code></dt>

<dd>Metric value</dd>

</dl>

<h2 id="raises">Raises</h2>

<dl>

<dt><code>MetricUnitError</code></dt>

<dd>When metric unit is not supported by CloudWatch</dd>

</dl></section>

<span>Expand source code</span>

</summary>

<pre><code class="python">def add_metric(self, name: str, unit: Union[MetricUnit, str], value: float):

"""Adds given metric

Example

-------

**Add given metric using MetricUnit enum**

metric.add_metric(name="BookingConfirmation", unit=MetricUnit.Count, value=1)

**Add given metric using plain string as value unit**

metric.add_metric(name="BookingConfirmation", unit="Count", value=1)

Parameters

----------

name : str

Metric name

unit : Union[MetricUnit, str]

`aws_lambda_powertools.helper.models.MetricUnit`

value : float

Metric value

Raises

------

MetricUnitError

When metric unit is not supported by CloudWatch

"""

if not isinstance(value, numbers.Number):

raise MetricValueError(f"{value} is not a valid number")

unit = self.__extract_metric_unit_value(unit=unit)

metric: Dict = self.metric_set.get(name, defaultdict(list))

metric["Unit"] = unit

metric["Value"].append(float(value))

logger.debug(f"Adding metric: {name} with {metric}")

self.metric_set[name] = metric

if len(self.metric_set) == MAX_METRICS:

logger.debug(f"Exceeded maximum of {MAX_METRICS} metrics - Publishing existing metric set")

metrics = self.serialize_metric_set()

print(json.dumps(metrics))

# clear metric set only as opposed to metrics and dimensions set

# since we could have more than 100 metrics

self.metric_set.clear()</code></pre>

</details>

</dd>

<span>def <span class="ident">serialize_metric_set</span></span>(<span>self, metrics=None, dimensions=None, metadata=None)</span>

</code></dt>

<dd>

<section class="desc"><p>Serializes metric and dimensions set</p>

<h2 id="parameters">Parameters</h2>

<dl>

<dt><strong><code>metrics</code></strong> :&ensp;<code>Dict</code>, optional</dt>

<dd>Dictionary of metrics to serialize, by default None</dd>

<dt><strong><code>dimensions</code></strong> :&ensp;<code>Dict</code>, optional</dt>

<dd>Dictionary of dimensions to serialize, by default None</dd>

<dt><strong><code>metadata</code></strong> :&ensp;<code>Dict</code>, optional</dt>

<dd>Dictionary of metadata to serialize, by default None</dd>

</dl>

<h2 id="example">Example</h2>

<p><strong>Serialize metrics into EMF format</strong></p>

<pre><code>metrics = MetricManager()

# ...add metrics, dimensions, namespace

ret = metrics.serialize_metric_set()

</code></pre>

<h2 id="returns">Returns</h2>

<dl>

<dd>Serialized metrics following EMF specification</dd>

</dl>

<h2 id="raises">Raises</h2>

<dl>

<dt><code>SchemaValidationError</code></dt>

<dd>Raised when serialization fail schema validation</dd>

</dl></section>

<span>Expand source code</span>

</summary>

<pre><code class="python">def serialize_metric_set(self, metrics: Dict = None, dimensions: Dict = None, metadata: Dict = None) -> Dict:

"""Serializes metric and dimensions set

Parameters

----------

metrics : Dict, optional

Dictionary of metrics to serialize, by default None

dimensions : Dict, optional

Dictionary of dimensions to serialize, by default None

metadata: Dict, optional

Dictionary of metadata to serialize, by default None

Example

-------

**Serialize metrics into EMF format**

metrics = MetricManager()

# ...add metrics, dimensions, namespace

ret = metrics.serialize_metric_set()

Returns

-------

Dict

Serialized metrics following EMF specification

Raises

------

SchemaValidationError

Raised when serialization fail schema validation

"""

if metrics is None: # pragma: no cover

metrics = self.metric_set

if dimensions is None: # pragma: no cover

dimensions = self.dimension_set

if metadata is None: # pragma: no cover

metadata = self.metadata_set

if self.service and not self.dimension_set.get("service"):

self.dimension_set["service"] = self.service

logger.debug({"details": "Serializing metrics", "metrics": metrics, "dimensions": dimensions})

metric_names_and_units: List[Dict[str, str]] = [] # [ { "Name": "metric_name", "Unit": "Count" } ]

metric_names_and_values: Dict[str, float] = {} # { "metric_name": 1.0 }

for metric_name in metrics:

metric: dict = metrics[metric_name]

metric_value: int = metric.get("Value", 0)

metric_unit: str = metric.get("Unit", "")

metric_names_and_units.append({"Name": metric_name, "Unit": metric_unit})

metric_names_and_values.update({metric_name: metric_value})

embedded_metrics_object = {

"_aws": {

"Timestamp": int(datetime.datetime.now().timestamp() * 1000), # epoch

"CloudWatchMetrics": [

{

"Namespace": self.namespace, # "test_namespace"

"Dimensions": [list(dimensions.keys())], # [ "service" ]

"Metrics": metric_names_and_units,

}

**dimensions, # "service": "test_service"

**metadata, # "username": "test"

**metric_names_and_values, # "single_metric": 1.0

}

try:

logger.debug("Validating serialized metrics against CloudWatch EMF schema")

fastjsonschema.validate(definition=CLOUDWATCH_EMF_SCHEMA, data=embedded_metrics_object)

except fastjsonschema.JsonSchemaException as e:

message = f"Invalid format. Error: {e.message}, Invalid item: {e.name}" # noqa: B306, E501

raise SchemaValidationError(message)

return embedded_metrics_object</code></pre>

</details>

</dd>

</dl>

</dd>

<span>class <span class="ident">MetricUnit</span></span>

<span>(</span><span>value, names=None, *, module=None, qualname=None, type=None, start=1)</span>

</code></dt>

<dd>

<section class="desc"><p>An enumeration.</p></section>

<span>Expand source code</span>

</summary>

<pre><code class="python">class MetricUnit(Enum):

Seconds = "Seconds"

Microseconds = "Microseconds"

Milliseconds = "Milliseconds"

Bytes = "Bytes"

Kilobytes = "Kilobytes"

Megabytes = "Megabytes"

Gigabytes = "Gigabytes"

Terabytes = "Terabytes"

Bits = "Bits"

Kilobits = "Kilobits"

Megabits = "Megabits"

Gigabits = "Gigabits"

Terabits = "Terabits"

Percent = "Percent"

Count = "Count"

BytesPerSecond = "Bytes/Second"

KilobytesPerSecond = "Kilobytes/Second"

MegabytesPerSecond = "Megabytes/Second"

GigabytesPerSecond = "Gigabytes/Second"

TerabytesPerSecond = "Terabytes/Second"

BitsPerSecond = "Bits/Second"

KilobitsPerSecond = "Kilobits/Second"

MegabitsPerSecond = "Megabits/Second"

GigabitsPerSecond = "Gigabits/Second"

TerabitsPerSecond = "Terabits/Second"

CountPerSecond = "Count/Second"</code></pre>

</details>

<h3>Ancestors</h3>

</ul>

<h3>Class variables</h3>

<dl>

<dd>

</dd>

<dt id="aws_lambda_powertools.metrics.base.MetricUnit.BitsPerSecond"><code class="name">var <span class="ident">BitsPerSecond</span></code></dt>

<dd>

</dd>

<dt id="aws_lambda_powertools.metrics.base.MetricUnit.Bytes"><code class="name">var <span class="ident">Bytes</span></code></dt>

<dd>

</dd>

<dt id="aws_lambda_powertools.metrics.base.MetricUnit.BytesPerSecond"><code class="name">var <span class="ident">BytesPerSecond</span></code></dt>

<dd>

</dd>

<dt id="aws_lambda_powertools.metrics.base.MetricUnit.Count"><code class="name">var <span class="ident">Count</span></code></dt>

<dd>

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