Module aws_lambda_powertools.metrics.base

Classes

class MetricManager (metric_set: Dict[str, Any] = None, dimension_set: Dict = None, namespace: str = None, metadata_set: Dict[str, Any] = None, service: str = None)

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 Metrics or 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

Expand source code

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 = 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

        if len(metrics) == 0:
            raise SchemaValidationError("Must contain at least one metric.")

        if self.namespace is None:
            raise SchemaValidationError("Must contain a metric namespace.")

        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})

        return {
            "_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
        }

    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}")
        if len(self.dimension_set) == 9:
            raise SchemaValidationError(
                f"Maximum number of dimensions exceeded ({MAX_DIMENSIONS}): Unable to add dimension {name}."
            )
        # 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:
                raise MetricUnitError(
                    f"Invalid metric unit '{unit}', expected either option: {self._metric_unit_options}"
                )

        if isinstance(unit, MetricUnit):
            unit = unit.value

        return unit

Subclasses

• SingleMetric
• Metrics

Methods

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

Expand source code

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}")
    if len(self.dimension_set) == 9:
        raise SchemaValidationError(
            f"Maximum number of dimensions exceeded ({MAX_DIMENSIONS}): Unable to add dimension {name}."
        )
    # 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

Expand source code

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 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

Expand source code

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

Expand source code

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

    if len(metrics) == 0:
        raise SchemaValidationError("Must contain at least one metric.")

    if self.namespace is None:
        raise SchemaValidationError("Must contain a metric namespace.")

    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})

    return {
        "_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
    }

class MetricUnit (value, names=None, *, module=None, qualname=None, type=None, start=1)

An enumeration.

Expand source code

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"

Ancestors

• enum.Enum

Class variables

var Bits

var BitsPerSecond

var Bytes

var BytesPerSecond

var Count

var CountPerSecond

var Gigabits

var GigabitsPerSecond

var Gigabytes

var GigabytesPerSecond

var Kilobits

var KilobitsPerSecond

var Kilobytes

var KilobytesPerSecond

var Megabits

var MegabitsPerSecond

var Megabytes

var MegabytesPerSecond

var Microseconds

var Milliseconds

var Percent

var Seconds

var Terabits

var TerabitsPerSecond

var Terabytes

var TerabytesPerSecond