kilmlogger 0.2.2

The Python Logger for all KiLM's Project

KiLM Logger library

The KiLM Logger library provides convenient log to for metrics, monitoring, and structure log.

Installation

pip install kilmlogger

Usage

Use the KiLM Logger's Default Configuration

import os

from kilmlogger import KilmLoggerConfiguration


LOG_PATH = os.getenv("LOG_PATH")
APP_NAME = os.getenv("APP_NAME")

# Logging's level that will be sent to scribe
SCRIBE_LOGGING_LEVEL

# Logging's level that will show only console
CONSOLE_LOGGING_LEVEL

# Accepted levels that will actually sent metrics to
ACCEPTED_LEVELS = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]


# Setup your file to store logging records
def get_logging_file_path() -> str:
    log_file_name: str = f"{APP_NAME}.log"
    log_path: str = os.path.join(LOG_PATH, APP_NAME)
    if not os.path.exists(log_path):
        os.makedirs(log_path)
    return os.path.join(log_path, log_file_name)


# Setup logging level, file, scribe level,...
def use_default_logging():
    kilm_logger_config: KilmLoggerConfiguration = KilmLoggerConfiguration(
        logging_filename=get_logging_file_path(),
        logging_level=SCRIBE_LOGGING_LEVEL,
        console_logging_level=CONSOLE_LOGGING_LEVEL,
        accepted_levels=ACCEPTED_LEVELS,
        # use_async=True,
    )
    kilm_logger_config.use_default_configuration()

Send metrics to Scribe

import functools
import kilmlogger as logging

from enum import Enum
from typing import Awaitable
from datetime import datetime as dt


logger = logging.get_logger("kilmlogger")

# Scribe category
SCRIBE_LOGGING_CATEGORY


class MetricCommandEnum(int, Enum):
    METRIC_COMMAND = 1111
    ...



class MetricSubCommandEnum(int, Enum):
    METRIC_SUB_COMMAND = 0
    ...


class MetricResultEnum(int, Enum):
    METRIC_SUCCESS = 0
    METRIC_FAILURE = 1
    ...


def async_send_metric(func: Awaitable):
    @functools.wraps(func)
    async def wrapper(*args, **kwargs):
        command: MetricCommandEnum = kwargs.get("command")
        category: str = kwargs.get("category", SCRIBE_LOGGING_CATEGORY)
        sub_command: MetricSubCommandEnum = kwargs.get("sub_command")
        try:
            start_time: float = int(dt.now().timestamp() * 1000)
            result: MetricResultEnum = MetricResultEnum.SUCCESS
            return await func(*args, **kwargs)
        except Exception as e:
            result = MetricResultEnum.FAILURE
            logger.error(
                f"Error when executing function {func.__name__}",
            )
            raise e
        finally:
            logger.debug(
                f"Finish executing {command.name}",
                start_time=start_time,
                category=category,
                command=command,
                sub_command=sub_command,
                result=result,
                metric_only=True,
            )

    return wrapper


def async_metric_sender(
    command: MetricCommandEnum,
    category: str = SCRIBE_LOGGING_CATEGORY,
    sub_command: MetricSubCommandEnum = MetricSubCommandEnum.DEFAULT,
):
    def decorator(func: Awaitable):
        @functools.wraps(func)
        async def wrapper(*args, **kwargs):
            try:
                start_time: float = int(dt.utcnow().timestamp() * 1000)
                result: MetricResultEnum = MetricResultEnum.SUCCESS
                return await func(*args, **kwargs)
            except BaseApplicationException as e:
                raise e
            except Exception as e:
                result = MetricResultEnum.FAILURE
                logger.error(
                    f"Error when executing function {func.__name__}",
                )
                raise e
            finally:
                logger.debug(
                    f"Finish executing {command.name}",
                    start_time=start_time,
                    category=category,
                    command=command,
                    sub_command=sub_command,
                    result=result,
                    metric_only=True,
                )

        return wrapper

    return decorator


def sync_metric_sender(
    command: MetricCommandEnum,
    category: str = SCRIBE_LOGGING_CATEGORY,
    sub_command: MetricSubCommandEnum = MetricSubCommandEnum.DEFAULT,
):
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            try:
                start_time: float = int(dt.utcnow().timestamp() * 1000)
                result: MetricResultEnum = MetricResultEnum.SUCCESS
                return func(*args, **kwargs)
            except BaseApplicationException as e:
                raise e
            except Exception as e:
                result = MetricResultEnum.FAILURE
                logger.error(
                    f"Error when executing function {func.__name__}",
                    correlation_id="",
                )
                raise e
            finally:
                logger.debug(
                    f"Finish executing {command.name}",
                    start_time=start_time,
                    category=category,
                    command=command,
                    sub_command=sub_command,
                    result=result,
                    metric_only=True,
                    correlation_id="",
                )

        return wrapper

    return decorator

Examples:

import asyncio
import kilmlogger as logging

from httpx import AsyncClient, Response, Timeout, TimeoutException

from app.utils.metric import async_send_metric


logger = logging.get_logger("kilmlogger")


@async_send_metric
async def make_request(
    *,
    client: AsyncClient,
    url: str,
    method: str = "GET",
    data: dict | None = None,
    json: dict | None = None,
    params: dict | None = None,
    headers: dict | None = None,
    timeout: float | None = 5.0,
    connect_timeout: float | None = 5.0,
    **kwargs,
) -> Response:
    try:
        return await client.request(
            method=method,
            url=url,
            data=data,
            json=json,
            params=params,
            headers=headers,
            timeout=Timeout(timeout=timeout, connect=connect_timeout),
        )
    except TimeoutException as e:
        logger.error(f"Timeout when request {url} with error: {type(e)}")
        raise e
    except Exception as e:
        logger.error(f"Error when request {url} with error: {e}")
        raise e


asyncio.run(
    make_request(
        ...
        command=MetricCommandEnum.RETRIEVE_DOC,
    )
)

This example use decorator as a middle layer that will wrap the method make_request() to sent the metrics to the Scribe Log

We also need some environments config:

ENVIRONMENT=staging
SCRIBE_HOST=kiki-scribelog-forward-grpc-headless-svc.kiki-infras
SCRIBE_PORT=9080
# DP Cate Name
DP_CATEGORY=KILM_EVENT_LOG
# To push log to Central Log (Data Platform Cate)
# The order needs to be the same as the DP Cate order
DP_LOG=app_name,app_mode,event_category,correlation_id,message,metrics,extra_data

or ArgoCD env:

...
  - name: ENVIRONMENT
    value: staging
  - name: SCRIBE_HOST
    value: "kiki-scribelog-forward-grpc-headless-svc.kiki-infras"
  - name: SCRIBE_PORT
    value: "9080"
  - name: DP_CATEGORY
    value: "KILM_EVENT_LOG"
  - name: DP_LOG
    value: "app_name,app_mode,event_category,correlation_id,message,metrics,extra_data"

Requirements

Python 3.12 or higher.