|
| 1 | +from __future__ import annotations |
| 2 | + |
| 3 | +import json |
| 4 | +import logging |
| 5 | +import numbers |
| 6 | +import os |
| 7 | +import time |
| 8 | +import warnings |
| 9 | +from typing import Any, List, Optional |
| 10 | + |
| 11 | +from aws_lambda_powertools.metrics.provider import MetricsBase |
| 12 | +from aws_lambda_powertools.metrics.provider.base.exceptions import MetricValueError, SchemaValidationError |
| 13 | + |
| 14 | +logger = logging.getLogger(__name__) |
| 15 | + |
| 16 | +# Check if using datadog layer |
| 17 | +try: |
| 18 | + from datadog_lambda.metric import lambda_metric # type: ignore |
| 19 | +except ImportError: |
| 20 | + lambda_metric = None |
| 21 | + |
| 22 | +DEFAULT_NAMESPACE = "default" |
| 23 | + |
| 24 | + |
| 25 | +class DatadogProvider: |
| 26 | + """ |
| 27 | + Class for datadog provider. This Class should only be used inside DatadogMetrics |
| 28 | + all datadog metric data will be stored as |
| 29 | + { |
| 30 | + "m": metric_name, |
| 31 | + "v": value, |
| 32 | + "e": timestamp |
| 33 | + "t": List["tag:value","tag2:value2"] |
| 34 | + } |
| 35 | + see https://github.com/Datadog/datadog-lambda-python/blob/main/datadog_lambda/metric.py#L77 |
| 36 | +
|
| 37 | + Examples |
| 38 | + -------- |
| 39 | +
|
| 40 | + """ |
| 41 | + |
| 42 | + def __init__(self, namespace: str = DEFAULT_NAMESPACE, flush_to_log: bool = False): |
| 43 | + """ |
| 44 | +
|
| 45 | + Parameters |
| 46 | + ---------- |
| 47 | + namespace: str |
| 48 | + For datadog, namespace will be appended in front of the metrics name in metrics exported. |
| 49 | + (namespace.metrics_name) |
| 50 | + flush_to_log: bool |
| 51 | + Flush datadog metrics to log (collect with log forwarder) rather than using datadog extension |
| 52 | + """ |
| 53 | + self.metrics: List = [] |
| 54 | + self.namespace: str = namespace |
| 55 | + # either is true then flush to log |
| 56 | + self.flush_to_log = (os.environ.get("DD_FLUSH_TO_LOG", "").lower() == "true") or flush_to_log |
| 57 | + super().__init__() |
| 58 | + |
| 59 | + # adding name,value,timestamp,tags |
| 60 | + def add_metric( |
| 61 | + self, |
| 62 | + name: str, |
| 63 | + value: float, |
| 64 | + timestamp: Optional[int] = None, |
| 65 | + tags: Optional[List] = None, |
| 66 | + **kwargs: Any, |
| 67 | + ) -> None: |
| 68 | + """ |
| 69 | + The add_metrics function that will be used by metrics class. |
| 70 | +
|
| 71 | + Parameters |
| 72 | + ---------- |
| 73 | + name: str |
| 74 | + Name/Key for the metrics |
| 75 | + value: float |
| 76 | + Value for the metrics |
| 77 | + timestamp: int |
| 78 | + Timestamp in int for the metrics, default = time.time() |
| 79 | + tags: List[str] |
| 80 | + In format like List["tag:value","tag2:value2"] |
| 81 | + args: Any |
| 82 | + extra args will be dropped for compatibility |
| 83 | + kwargs: Any |
| 84 | + extra kwargs will be converted into tags, e.g., add_metrics(sales=sam) -> tags=['sales:sam'] |
| 85 | +
|
| 86 | + Examples |
| 87 | + -------- |
| 88 | + >>> provider = DatadogProvider() |
| 89 | + >>> |
| 90 | + >>> provider.add_metric( |
| 91 | + >>> name='coffee_house.order_value', |
| 92 | + >>> value=12.45, |
| 93 | + >>> tags=['product:latte', 'order:online'], |
| 94 | + >>> sales='sam' |
| 95 | + >>> ) |
| 96 | + """ |
| 97 | + if not isinstance(value, numbers.Real): |
| 98 | + raise MetricValueError(f"{value} is not a valid number") |
| 99 | + if tags is None: |
| 100 | + tags = [] |
| 101 | + if not timestamp: |
| 102 | + timestamp = int(time.time()) |
| 103 | + for k, w in kwargs.items(): |
| 104 | + tags.append(f"{k}:{w}") |
| 105 | + self.metrics.append({"m": name, "v": value, "e": timestamp, "t": tags}) |
| 106 | + |
| 107 | + def serialize(self) -> List: |
| 108 | + output_list: List = [] |
| 109 | + |
| 110 | + for single_metric in self.metrics: |
| 111 | + if self.namespace != DEFAULT_NAMESPACE: |
| 112 | + metric_name = f"{self.namespace}.{single_metric['m']}" |
| 113 | + else: |
| 114 | + metric_name = single_metric["m"] |
| 115 | + output_list.append( |
| 116 | + { |
| 117 | + "m": metric_name, |
| 118 | + "v": single_metric["v"], |
| 119 | + "e": single_metric["e"], |
| 120 | + "t": single_metric["t"], |
| 121 | + }, |
| 122 | + ) |
| 123 | + |
| 124 | + return output_list |
| 125 | + |
| 126 | + # flush serialized data to output |
| 127 | + def flush(self, metrics: List): |
| 128 | + """ |
| 129 | +
|
| 130 | + Parameters |
| 131 | + ---------- |
| 132 | + metrics: List[Dict] |
| 133 | + [{ |
| 134 | + "m": metric_name, |
| 135 | + "v": value, |
| 136 | + "e": timestamp |
| 137 | + "t": List["tag:value","tag2:value2"] |
| 138 | + }] |
| 139 | +
|
| 140 | + Raises |
| 141 | + ------- |
| 142 | + SchemaValidationError |
| 143 | + When metric object fails EMF schema validation |
| 144 | + """ |
| 145 | + if len(metrics) == 0: |
| 146 | + raise SchemaValidationError("Must contain at least one metric.") |
| 147 | + # submit through datadog extension |
| 148 | + if lambda_metric and self.flush_to_log is False: |
| 149 | + # use lambda_metric function from datadog package, submit metrics to datadog |
| 150 | + for metric_item in metrics: |
| 151 | + lambda_metric( |
| 152 | + metric_name=metric_item["m"], |
| 153 | + value=metric_item["v"], |
| 154 | + timestamp=metric_item["e"], |
| 155 | + tags=metric_item["t"], |
| 156 | + ) |
| 157 | + else: |
| 158 | + # dd module not found: flush to log, this format can be recognized via datadog log forwarder |
| 159 | + # https://github.com/Datadog/datadog-lambda-python/blob/main/datadog_lambda/metric.py#L77 |
| 160 | + for metric_item in metrics: |
| 161 | + print(json.dumps(metric_item, separators=(",", ":"))) |
| 162 | + |
| 163 | + def clear_metrics(self): |
| 164 | + self.metrics = [] |
| 165 | + |
| 166 | + |
| 167 | +class DatadogMetrics(MetricsBase): |
| 168 | + """ |
| 169 | + Class for datadog metrics |
| 170 | +
|
| 171 | + Parameters |
| 172 | + ---------- |
| 173 | + provider: DatadogProvider |
| 174 | + The datadog provider which will be used to process metrics data |
| 175 | +
|
| 176 | + Example |
| 177 | + ------- |
| 178 | + **Creates a few metrics and publish at the end of a function execution** |
| 179 | +
|
| 180 | + >>> from aws_lambda_powertools.metrics.provider import DatadogMetrics, DatadogProvider |
| 181 | + >>> |
| 182 | + >>> dd_provider = DatadogProvider(namespace="Serverlesspresso") |
| 183 | + >>> metrics = DatadogMetrics(provider=dd_provider) |
| 184 | + >>> |
| 185 | + >>> @metrics.log_metrics(capture_cold_start_metric=True, raise_on_empty_metrics=False) |
| 186 | + >>> def lambda_handler(event, context): |
| 187 | + >>> metrics.add_metric(name="item_sold",value=1,tags=['product:latte', 'order:online']) |
| 188 | + """ |
| 189 | + |
| 190 | + # `log_metrics` and `_add_cold_start_metric` are directly inherited from `MetricsBase` |
| 191 | + def __init__(self, provider: DatadogProvider): |
| 192 | + self.provider = provider |
| 193 | + super().__init__() |
| 194 | + |
| 195 | + # drop additional kwargs to keep same experience |
| 196 | + def add_metric( |
| 197 | + self, |
| 198 | + name: str, |
| 199 | + value: float, |
| 200 | + timestamp: Optional[int] = None, |
| 201 | + tags: Optional[List] = None, |
| 202 | + *args, |
| 203 | + **kwargs, |
| 204 | + ): |
| 205 | + """ |
| 206 | + The add_metrics function that will be used by metrics class. |
| 207 | +
|
| 208 | + Parameters |
| 209 | + ---------- |
| 210 | + name: str |
| 211 | + Name/Key for the metrics |
| 212 | + value: float |
| 213 | + Value for the metrics |
| 214 | + timestamp: int |
| 215 | + Timestamp in int for the metrics, default = time.time() |
| 216 | + tags: List[str] |
| 217 | + In format like List["tag:value","tag2:value2"], |
| 218 | + args: Any |
| 219 | + extra args will be dropped |
| 220 | + kwargs: Any |
| 221 | + extra kwargs will be converted into tags, e.g., add_metrics(sales=sam) -> tags=['sales:sam'] |
| 222 | +
|
| 223 | + Examples |
| 224 | + -------- |
| 225 | + >>> from aws_lambda_powertools.metrics.provider import DatadogMetrics, DatadogProvider |
| 226 | + >>> |
| 227 | + >>> metrics = DatadogMetrics(provider=DatadogProvider()) |
| 228 | + >>> metrics.add_metric( |
| 229 | + >>> name='coffee_house.order_value', |
| 230 | + >>> value=12.45, |
| 231 | + >>> tags=['product:latte', 'order:online'] |
| 232 | + >>> ) |
| 233 | + """ |
| 234 | + self.provider.add_metric(name=name, value=value, timestamp=timestamp, tags=tags, **kwargs) |
| 235 | + |
| 236 | + def flush_metrics(self, raise_on_empty_metrics: bool = False) -> None: |
| 237 | + """ |
| 238 | + Manually flushes the metrics. This is normally not necessary, |
| 239 | + unless you're running on other runtimes besides Lambda, where the @log_metrics |
| 240 | + decorator already handles things for you. |
| 241 | +
|
| 242 | + Parameters |
| 243 | + ---------- |
| 244 | + raise_on_empty_metrics: bool |
| 245 | + raise exception if no metrics are emitted, by default False |
| 246 | + """ |
| 247 | + metrics = self.provider.serialize() |
| 248 | + if not metrics and not raise_on_empty_metrics: |
| 249 | + warnings.warn( |
| 250 | + "No application metrics to publish. The cold-start metric may be published if enabled. " |
| 251 | + "If application metrics should never be empty, consider using 'raise_on_empty_metrics'", |
| 252 | + stacklevel=2, |
| 253 | + ) |
| 254 | + else: |
| 255 | + # will raise on empty metrics |
| 256 | + self.provider.flush(metrics) |
| 257 | + self.provider.clear_metrics() |
| 258 | + |
| 259 | + def add_cold_start_metric(self, metric_name: str, function_name: str) -> None: |
| 260 | + logger.debug("Adding cold start metric and function_name tagging") |
| 261 | + self.add_metric(name="ColdStart", value=1, function_name=function_name) |
0 commit comments