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Browse files- aworld/metrics/README.md +80 -0
- aworld/metrics/__init__.py +9 -0
- aworld/metrics/context_manager.py +186 -0
- aworld/metrics/metric.py +287 -0
- aworld/metrics/template.py +43 -0
aworld/metrics/README.md
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# Metrics Module
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## Overview
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The `aworld.core.metrics` module provides a unified interface for collecting and exporting metrics. It supports various types of metrics (e.g., counters, histograms) and allows exporting data to different monitoring systems (e.g., Prometheus).
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## Key Features
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- **Metric Types**:
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- `Counter`: A cumulative metric that represents a single numerical value that only ever increases.
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- `UpDownCounter`: A cumulative metric that can increase or decrease.
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- `Gauge`: A metric that represents a single numerical value that can arbitrarily go up and down.
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- `Histogram`: A metric that represents the distribution of a set of values.
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- **Adapters**:
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- `PrometheusAdapter`: Exports metrics to Prometheus.
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- `ConsoleAdapter`: Prints metrics to the console (for debugging purposes).
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## Usage Example
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```python
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import random
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import time
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from aworld.core.metrics.metric import set_metric_provider, MetricType
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from aworld.core.metrics.prometheus.prometheus_adapter import PrometheusConsoleMetricExporter,
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PrometheusMetricProvider
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from aworld.core.metrics.context_manager import MetricContext, ApiMetricTracker
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from aworld.core.metrics.template import MetricTemplate
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# Set OpenTelemetry as the metric provider
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# set_metric_provider(OpentelemetryMetricProvider())
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# Set Prometheus as the metric provider
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set_metric_provider(PrometheusMetricProvider(PrometheusConsoleMetricExporter(out_interval_secs=2)))
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# Define metric templates
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my_counter = MetricTemplate(
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type=MetricType.COUNTER,
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name="my_counter",
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description="My custom counter",
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unit="1"
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)
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my_gauge = MetricTemplate(
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type=MetricType.GAUGE,
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name="my_gauge"
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)
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my_histogram = MetricTemplate(
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type=MetricType.HISTOGRAM,
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name="my_histogram",
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buckets=[2, 4, 6, 8, 10]
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)
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# Track API metrics using decorator
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@ApiMetricTracker()
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def test_api():
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time.sleep(random.uniform(0, 1))
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# Track custom code block using context manager
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def test_custom_code():
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with ApiMetricTracker("test_custom_code"):
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time.sleep(random.uniform(0, 1))
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# Main loop to generate and record metrics
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while 1:
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MetricContext.count(my_counter, random.randint(1, 10))
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MetricContext.gauge_set(my_gauge, random.randint(1, 10))
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MetricContext.histogram_record(my_histogram, random.randint(1, 10))
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test_api()
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test_custom_code()
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time.sleep(random.random())
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```
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## Notes
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- Before using metrics, you must set a metric provider ( set_metric_provider ).
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- Different metric types serve different purposes; choose the appropriate type based on your needs.
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- For production environments, it is recommended to use Prometheus as the exporter.
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aworld/metrics/__init__.py
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# coding: utf-8
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# Copyright (c) 2025 inclusionAI.
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import os
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from aworld.metrics.context_manager import MetricContext
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# MetricContext.configure(provider="otlp",
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# backend="logfire",
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# write_token=os.getenv("LOGFIRE_WRITE_TOKEN")
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# )
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aworld/metrics/context_manager.py
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import time
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import asyncio
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from typing import Callable
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from functools import wraps
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from aworld.metrics.metric import get_metric_provider, MetricType, BaseMetric
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from aworld.metrics.template import MetricTemplate, MetricTemplates
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_GLOBAL_METIRCS = {}
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class MetricContext:
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_initialized = False
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@classmethod
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def configure(cls,
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provider: str,
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backend: str,
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base_url: str = None,
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write_token: str = None,
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**kwargs):
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"""
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Configure the metric provider.
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Args:
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provider: The provider of the metric provider.
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backend: The backend of the metric provider.
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base_url: The base url of the metric provider.
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write_token: The write token of the metric provider.
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export_console: Whether to export the metrics to console.
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**kwargs: The other parameters of the metric provider.
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"""
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if cls._initialized:
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cls.shutdown()
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if provider == "prometheus":
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from aworld.metrics.prometheus.prometheus_adapter import configure_prometheus_provider
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configure_prometheus_provider(
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backend, base_url, write_token, **kwargs)
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elif provider == "otlp":
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from aworld.metrics.opentelemetry.opentelemetry_adapter import configure_otlp_provider
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configure_otlp_provider(backend, base_url, write_token, **kwargs)
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cls._initialized = True
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@classmethod
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def metric_initialized(cls):
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return cls._initialized
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@staticmethod
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def get_or_create_metric(template: MetricTemplate):
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if template.name in _GLOBAL_METIRCS:
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return _GLOBAL_METIRCS[template.name]
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metric = None
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if template.type == MetricType.COUNTER:
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metric = get_metric_provider().create_counter(template.name, template.description, template.unit,
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template.labels)
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elif template.type == MetricType.UPDOWNCOUNTER:
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metric = get_metric_provider().create_un_down_counter(template.name, template.description, template.unit,
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template.labels)
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elif template.type == MetricType.GAUGE:
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metric = get_metric_provider().create_gauge(template.name, template.description, template.unit,
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template.labels)
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elif template.type == MetricType.HISTOGRAM:
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metric = get_metric_provider().create_histogram(template.name, template.description, template.unit,
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template.buckets, template.labels)
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_GLOBAL_METIRCS[template.name] = metric
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return metric
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@classmethod
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def _validate_type(cls, metric: BaseMetric, type: str):
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if type != metric._type:
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raise ValueError(f"metric type {metric._type} is not {type}")
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@classmethod
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def count(cls, template: MetricTemplate, value: int, labels: dict = None):
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"""
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Increment a counter metric.
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"""
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metric = cls.get_or_create_metric(template)
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cls._validate_type(metric, MetricType.COUNTER)
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metric.add(value, labels)
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@classmethod
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def inc(cls, template: MetricTemplate, value: int, labels: dict = None):
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"""
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Increment a updowncounter metric.
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"""
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metric = cls.get_or_create_metric(template)
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cls._validate_type(metric, MetricType.UPDOWNCOUNTER)
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metric.inc(value, labels)
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@classmethod
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def dec(cls, template: MetricTemplate, value: int, labels: dict = None):
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"""
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Decrement a updowncounter metric.
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"""
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metric = cls.get_or_create_metric(template)
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cls._validate_type(metric, MetricType.UPDOWNCOUNTER)
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metric.dec(value, labels)
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@classmethod
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def gauge_set(cls, template: MetricTemplate, value: int, labels: dict = None):
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"""
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Set a value to a gauge metric.
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"""
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metric = cls.get_or_create_metric(template)
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cls._validate_type(metric, MetricType.GAUGE)
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metric.set(value, labels)
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@classmethod
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def histogram_record(cls, template: MetricTemplate, value: int, labels: dict = None):
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"""
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Set a value to a histogram metric.
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"""
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metric = cls.get_or_create_metric(template)
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cls._validate_type(metric, MetricType.HISTOGRAM)
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metric.record(value, labels)
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@classmethod
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def shutdown(cls):
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"""
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Shutdown the metric provider.
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"""
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provider = get_metric_provider()
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if provider:
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provider.shutdown()
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cls._initialized = False
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class ApiMetricTracker:
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"""
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Decorator to track API metrics.
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"""
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def __init__(self, api_name: str = None, func: Callable = None):
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self.start_time = None
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self.status = "success"
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self.func = func
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self.api_name = api_name
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if self.api_name is None and self.func is not None:
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self.api_name = self.func.__name__
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def _new_tracker(self, func: Callable):
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return self.__class__(func=func)
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def __enter__(self):
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self.start_time = time.time() * 1000
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def __exit__(self, exc_type, value, traceback):
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if exc_type is None:
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self.status = "success"
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else:
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self.status = "failure"
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self._record_metrics(self.api_name, self.start_time, self.status)
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def __call__(self, func: Callable = None) -> Callable:
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if func is None:
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return self
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return self.decorator(func)
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def _record_metrics(self, api_name: str, start_time: float, status: str) -> None:
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"""
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Record metrics for the API.
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"""
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elapsed_time = time.time() * 1000 - start_time
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MetricContext.count(MetricTemplates.REQUEST_COUNT, 1,
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labels={"method": api_name, "status": status})
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MetricContext.histogram_record(MetricTemplates.REQUEST_LATENCY, elapsed_time,
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labels={"method": api_name, "status": status})
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def decorator(self, func):
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"""
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Decorator to track API metrics.
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"""
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@wraps(func)
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async def async_wrapper(*args, **kwargs):
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with self._new_tracker(func):
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return await func(*args, **kwargs)
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@wraps(func)
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def wrapper(*args, **kwargs):
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with self._new_tracker(func):
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return func(*args, **kwargs)
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return async_wrapper if asyncio.iscoroutinefunction(func) else wrapper
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aworld/metrics/metric.py
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|
|
|
1 |
+
from abc import ABC, abstractmethod
|
2 |
+
from typing import Optional, Sequence
|
3 |
+
|
4 |
+
|
5 |
+
class MetricType:
|
6 |
+
"""
|
7 |
+
MetricType is a class for defining the type of a metric.
|
8 |
+
"""
|
9 |
+
COUNTER = "counter"
|
10 |
+
UPDOWNCOUNTER = "updowncounter"
|
11 |
+
GAUGE = "gauge"
|
12 |
+
HISTOGRAM = "histogram"
|
13 |
+
|
14 |
+
|
15 |
+
class MetricProvider(ABC):
|
16 |
+
"""
|
17 |
+
MeterProvider is the entry point of the API.
|
18 |
+
"""
|
19 |
+
|
20 |
+
def __init__(self):
|
21 |
+
# list of exporters
|
22 |
+
self._exporters = []
|
23 |
+
|
24 |
+
@abstractmethod
|
25 |
+
def shutdown(self):
|
26 |
+
"""
|
27 |
+
shutdown the metric provider.
|
28 |
+
"""
|
29 |
+
|
30 |
+
def add_exporter(self, exporter):
|
31 |
+
"""
|
32 |
+
Add an exporter to the list of exporters.
|
33 |
+
"""
|
34 |
+
self._exporters.append(exporter)
|
35 |
+
|
36 |
+
@abstractmethod
|
37 |
+
def create_counter(self, name: str, description: str, unit: str,
|
38 |
+
label_names: Optional[Sequence[str]] = None) -> "Counter":
|
39 |
+
"""
|
40 |
+
Create a counter.
|
41 |
+
|
42 |
+
Args:
|
43 |
+
name: The name of the instrument to be created
|
44 |
+
description: A description for this instrument and what it measures.
|
45 |
+
unit: The unit for observations this instrument reports. For
|
46 |
+
example, ``By`` for bytes. UCUM units are recommended.
|
47 |
+
"""
|
48 |
+
|
49 |
+
@abstractmethod
|
50 |
+
def create_un_down_counter(self, name: str, description: str, unit: str,
|
51 |
+
label_names: Optional[Sequence[str]] = None) -> "UnDownCounter":
|
52 |
+
"""
|
53 |
+
Create a un-down counter.
|
54 |
+
Args:
|
55 |
+
name: The name of the instrument to be created
|
56 |
+
description: A description for this instrument and what it measures.
|
57 |
+
unit: The unit for observations this instrument reports. For
|
58 |
+
example, ``By`` for bytes. UCUM units are recommended.
|
59 |
+
"""
|
60 |
+
|
61 |
+
@abstractmethod
|
62 |
+
def create_gauge(self, name: str, description: str, unit: str,
|
63 |
+
label_names: Optional[Sequence[str]] = None) -> "Gauge":
|
64 |
+
"""
|
65 |
+
Create a gauge.
|
66 |
+
Args:
|
67 |
+
name: The name of the instrument to be created
|
68 |
+
description: A description for this instrument and what it measures.
|
69 |
+
unit: The unit for observations this instrument reports. For
|
70 |
+
example, ``By`` for bytes. UCUM units are recommended.
|
71 |
+
"""
|
72 |
+
|
73 |
+
@abstractmethod
|
74 |
+
def create_histogram(self,
|
75 |
+
name: str,
|
76 |
+
description: str,
|
77 |
+
unit: str,
|
78 |
+
buckets: Optional[Sequence[float]] = None,
|
79 |
+
label_names: Optional[Sequence[str]] = None) -> "Histogram":
|
80 |
+
"""
|
81 |
+
Create a histogram.
|
82 |
+
Args:
|
83 |
+
name: The name of the instrument to be created
|
84 |
+
description: A description for this instrument and what it measures.
|
85 |
+
unit: The unit for observations this instrument reports. For
|
86 |
+
example, ``By`` for bytes. UCUM units are recommended.
|
87 |
+
"""
|
88 |
+
|
89 |
+
|
90 |
+
class BaseMetric(ABC):
|
91 |
+
"""
|
92 |
+
Metric is the base class for all metrics.
|
93 |
+
Args:
|
94 |
+
name: The name of the metric.
|
95 |
+
description: The description of the metric.
|
96 |
+
unit: The unit of the metric.
|
97 |
+
provider: The provider of the metric.
|
98 |
+
"""
|
99 |
+
|
100 |
+
def __init__(self,
|
101 |
+
name: str,
|
102 |
+
description: str,
|
103 |
+
unit: str,
|
104 |
+
provider: MetricProvider,
|
105 |
+
label_names: Optional[Sequence[str]] = None):
|
106 |
+
self._name = name
|
107 |
+
self._description = description
|
108 |
+
self._unit = unit
|
109 |
+
self._provider = provider
|
110 |
+
self._label_names = label_names
|
111 |
+
self._type = None
|
112 |
+
|
113 |
+
|
114 |
+
class Counter(BaseMetric):
|
115 |
+
"""
|
116 |
+
Counter is a subclass of BaseMetric, representing a counter metric.
|
117 |
+
A counter is a cumulative metric that represents a single numerical value that only ever goes up.
|
118 |
+
"""
|
119 |
+
|
120 |
+
def __init__(self,
|
121 |
+
name: str,
|
122 |
+
description: str,
|
123 |
+
unit: str,
|
124 |
+
provider: MetricProvider,
|
125 |
+
label_names: Optional[Sequence[str]] = None):
|
126 |
+
"""
|
127 |
+
Initialize the Counter.
|
128 |
+
Args:
|
129 |
+
name: The name of the metric.
|
130 |
+
description: The description of the metric.
|
131 |
+
unit: The unit of the metric.
|
132 |
+
provider: The provider of the metric.
|
133 |
+
"""
|
134 |
+
super().__init__(name, description, unit, provider, label_names)
|
135 |
+
self._type = MetricType.COUNTER
|
136 |
+
|
137 |
+
@abstractmethod
|
138 |
+
def add(self, value: int, labels: dict = None) -> None:
|
139 |
+
"""
|
140 |
+
Add a value to the counter.
|
141 |
+
Args:
|
142 |
+
value: The value to add to the counter.
|
143 |
+
labels: The labels to associate with the value.
|
144 |
+
"""
|
145 |
+
|
146 |
+
|
147 |
+
class UpDownCounter(BaseMetric):
|
148 |
+
"""
|
149 |
+
UpDownCounter is a subclass of BaseMetric, representing an un-down counter metric.
|
150 |
+
An un-down counter is a cumulative metric that represents a single numerical value that only ever goes up.
|
151 |
+
"""
|
152 |
+
|
153 |
+
def __init__(self,
|
154 |
+
name: str,
|
155 |
+
description: str,
|
156 |
+
unit: str,
|
157 |
+
provider: MetricProvider,
|
158 |
+
label_names: Optional[Sequence[str]] = None):
|
159 |
+
"""
|
160 |
+
Initialize the UnDownCounter.
|
161 |
+
Args:
|
162 |
+
name: The name of the metric.
|
163 |
+
description: The description of the metric.
|
164 |
+
unit: The unit of the metric.
|
165 |
+
provider: The provider of the metric.
|
166 |
+
"""
|
167 |
+
super().__init__(name, description, unit, provider, label_names)
|
168 |
+
self._type = MetricType.UPDOWNCOUNTER
|
169 |
+
|
170 |
+
@abstractmethod
|
171 |
+
def inc(self, value: int, labels: dict = None) -> None:
|
172 |
+
"""
|
173 |
+
Add a value to the gauge.
|
174 |
+
Args:
|
175 |
+
value: The value to add to the gauge.
|
176 |
+
labels: The labels to associate with the value.
|
177 |
+
"""
|
178 |
+
|
179 |
+
@abstractmethod
|
180 |
+
def dec(self, value: int, labels: dict = None) -> None:
|
181 |
+
"""
|
182 |
+
Subtract a value from the gauge.
|
183 |
+
Args:
|
184 |
+
value: The value to subtract from the gauge.
|
185 |
+
labels: The labels to associate with the value.
|
186 |
+
"""
|
187 |
+
|
188 |
+
|
189 |
+
class Gauge(BaseMetric):
|
190 |
+
"""
|
191 |
+
Gauge is a subclass of BaseMetric, representing a gauge metric.
|
192 |
+
A gauge is a metric that represents a single numerical value that can arbitrarily go up and down.
|
193 |
+
"""
|
194 |
+
|
195 |
+
def __init__(self,
|
196 |
+
name: str,
|
197 |
+
description: str,
|
198 |
+
unit: str,
|
199 |
+
provider: MetricProvider,
|
200 |
+
label_names: Optional[Sequence[str]] = None):
|
201 |
+
"""
|
202 |
+
Initialize the Gauge.
|
203 |
+
Args:
|
204 |
+
name: The name of the metric.
|
205 |
+
description: The description of the metric.
|
206 |
+
unit: The unit of the metric.
|
207 |
+
provider: The provider of the metric.
|
208 |
+
"""
|
209 |
+
super().__init__(name, description, unit, provider, label_names)
|
210 |
+
self._type = MetricType.GAUGE
|
211 |
+
|
212 |
+
@abstractmethod
|
213 |
+
def set(self, value: int, labels: dict = None) -> None:
|
214 |
+
"""
|
215 |
+
Set the value of the gauge.
|
216 |
+
Args:
|
217 |
+
value: The value to set the gauge to.
|
218 |
+
labels: The labels to associate with the value.
|
219 |
+
"""
|
220 |
+
|
221 |
+
|
222 |
+
class Histogram(BaseMetric):
|
223 |
+
"""
|
224 |
+
Histogram is a subclass of BaseMetric, representing a histogram metric.
|
225 |
+
A histogram is a metric that represents the distribution of a set of values.
|
226 |
+
"""
|
227 |
+
|
228 |
+
def __init__(self,
|
229 |
+
name: str,
|
230 |
+
description: str,
|
231 |
+
unit: str,
|
232 |
+
provider: MetricProvider,
|
233 |
+
buckets: Sequence[float] = None,
|
234 |
+
label_names: Optional[Sequence[str]] = None):
|
235 |
+
"""
|
236 |
+
Initialize the Histogram.
|
237 |
+
Args:
|
238 |
+
name: The name of the metric.
|
239 |
+
description: The description of the metric.
|
240 |
+
unit: The unit of the metric.
|
241 |
+
provider: The provider of the metric.
|
242 |
+
buckets: The buckets of the histogram.
|
243 |
+
"""
|
244 |
+
super().__init__(name, description, unit, provider, label_names)
|
245 |
+
self._type = MetricType.HISTOGRAM
|
246 |
+
self._buckets = buckets
|
247 |
+
|
248 |
+
@abstractmethod
|
249 |
+
def record(self, value: int, labels: dict = None) -> None:
|
250 |
+
"""
|
251 |
+
Record a value in the histogram.
|
252 |
+
Args:
|
253 |
+
value: The value to record in the histogram.
|
254 |
+
labels: The labels to associate with the value.
|
255 |
+
"""
|
256 |
+
|
257 |
+
|
258 |
+
class MetricExporter(ABC):
|
259 |
+
"""
|
260 |
+
MetricExporter is the base class for all metric exporters.
|
261 |
+
"""
|
262 |
+
@abstractmethod
|
263 |
+
def shutdown(self):
|
264 |
+
"""
|
265 |
+
Export the metrics.
|
266 |
+
"""
|
267 |
+
|
268 |
+
|
269 |
+
_GLOBAL_METRIC_PROVIDER: Optional[MetricProvider] = None
|
270 |
+
|
271 |
+
|
272 |
+
def set_metric_provider(provider):
|
273 |
+
"""
|
274 |
+
Set the global metric provider.
|
275 |
+
"""
|
276 |
+
global _GLOBAL_METRIC_PROVIDER
|
277 |
+
_GLOBAL_METRIC_PROVIDER = provider
|
278 |
+
|
279 |
+
|
280 |
+
def get_metric_provider():
|
281 |
+
"""
|
282 |
+
Get the global metric provider.
|
283 |
+
"""
|
284 |
+
global _GLOBAL_METRIC_PROVIDER
|
285 |
+
if _GLOBAL_METRIC_PROVIDER is None:
|
286 |
+
raise ValueError("No metric provider has been set.")
|
287 |
+
return _GLOBAL_METRIC_PROVIDER
|
aworld/metrics/template.py
ADDED
@@ -0,0 +1,43 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import Sequence
|
2 |
+
from pydantic import BaseModel, Field, root_validator
|
3 |
+
from typing import Optional
|
4 |
+
|
5 |
+
|
6 |
+
class MetricTemplate(BaseModel):
|
7 |
+
"""
|
8 |
+
MetricTemplate is a class for defining a metric template.
|
9 |
+
"""
|
10 |
+
type: str
|
11 |
+
name: str
|
12 |
+
description: Optional[str] = None
|
13 |
+
unit: Optional[str] = Field(default="1")
|
14 |
+
labels: Optional[list[str]] = None
|
15 |
+
buckets: Optional[Sequence[float]] = None
|
16 |
+
|
17 |
+
@root_validator(pre=True)
|
18 |
+
def set_default_description(cls, values):
|
19 |
+
"""
|
20 |
+
Set the default description if it is not set.
|
21 |
+
"""
|
22 |
+
if 'description' not in values or values['description'] is None:
|
23 |
+
values['description'] = values['name']
|
24 |
+
return values
|
25 |
+
|
26 |
+
|
27 |
+
class MetricTemplates:
|
28 |
+
REQUEST_COUNT = MetricTemplate(**{
|
29 |
+
"type": "counter",
|
30 |
+
"name": "request_count",
|
31 |
+
"description": "The number of requests received",
|
32 |
+
"unit": "1",
|
33 |
+
"labels": ["method", "status"]
|
34 |
+
})
|
35 |
+
|
36 |
+
REQUEST_LATENCY = MetricTemplate(**{
|
37 |
+
"type": "histogram",
|
38 |
+
"name": "request_latency",
|
39 |
+
"description": "The latency of requests",
|
40 |
+
"unit": "ms",
|
41 |
+
"labels": ["method", "status"],
|
42 |
+
# "buckets": [0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 500, 1000]
|
43 |
+
})
|