Skip to main content

Tremors is a library for logging while collecting metrics.

Project description

Tremors is a library for logging while collecting metrics. Tremors loggers are drop-in replacements for standard loggers. But Tremors loggers have metrics collectors that run when messages are logged. The loggers are also context managers. The library maintains a hierarchy of nested contexts, where all logs and metrics are grouped together. You can create a new hierarchy at anytime to group related logs.

Installation

pip install tremors

Usage

A function can be wrapped in a logger context with the logged decorator. If you call the function without a logger argument, one will automatically be injected into it.

import logging

import tremors
from tremors import collector


@tremors.logged
def fn(*, logger: tremors.Logger = tremors.from_logged) -> None:
    logger.info("hello")


logging.basicConfig(
    format="Tremors > %(levelname)s:%(name)s:%(message)s",
    level=logging.INFO,
)
fn()

The context automatically logs entered, and exited messages before, and after each function call. The logger uses the configured standard root logger by default to log the messages.

Tremors > INFO:root:entered: fn
Tremors > INFO:root:hello
Tremors > INFO:root:exited: fn

You may specify a standard logger by name for the Tremors logger to use as its underlying logger.

@tremors.logged(logger_name=__name__)
def fn(*, logger: tremors.Logger = tremors.from_logged) -> None:
    logger.info("hello")


fn()

The messages are logged by the specified underlying logger. Based on our standard logging configuration, the messages propagate from the underlying logger to the standard root logger, which emits them.

Tremors > INFO:__main__:entered: fn
Tremors > INFO:__main__:hello
Tremors > INFO:__main__:exited: fn

Next let’s use a collector to measure the elapsed time since the function started each time a message is logged. When a message is logged, the logger runs the collector, and adds its updated state to the message’s LogRecord. We use a standard logging filter to inspect and modify the record before it is emitted. We format the collector state, then add the formatted state to the elapsed custom attribute of the record. Finally, we configure the root logger’s formatter to incorporate the elapsed attribute.

import copy
import time


def flt(record: logging.LogRecord) -> logging.LogRecord:
    record = copy.copy(record)
    elapsed = collector.elapsed.formatter(record)
    record.elapsed = f"{elapsed} " if elapsed else ""
    return record


@tremors.logged(collector.elapsed.factory())
def fn(*, logger: tremors.Logger = tremors.from_logged) -> None:
    logger.info("sleeping for 1s...")
    time.sleep(1)


logging.basicConfig(
    format="%(elapsed)s%(levelname)s:%(name)s:%(message)s",
    level=logging.INFO,
    force=True,
)
logging.root.handlers[0].addFilter(flt)
fn()

The messages contain elapsed information, according to the formatter configuration, that is sourced from the record’s elapsed custom attribute.

0.000 INFO:root:entered: fn
0.000 INFO:root:sleeping for 1s...
1.000 INFO:root:exited: fn

A Logger can have any number of collectors. Here, in addition to the elapsed collector from the previous example, we add a counter collector. A collector has a level, and will only run if the message is being logged at that level or higher. Our counter level is ERROR. We can also control which custom record attribute has the formatted collector state via the collector’s name. This is useful if you have multiple of the same collector on a single logger. Here, we name the counter errors, so record.errors will contain a formatted string with the running total number of errors that have been logged by a single function call. Finally, we an control the format of the counter state via the fmt argument of the counter’s formatter.

def flt(record: logging.LogRecord) -> logging.LogRecord:
    record = copy.copy(record)
    errors = collector.counter.formatter(
        record, name="errors", fmt="errors={counter}"
    )
    record.errors = f"{errors} " if errors else ""
    elapsed = collector.elapsed.formatter(record)
    record.elapsed = f"{elapsed} " if elapsed else ""
    return record


@tremors.logged(
    collector.counter.factory(name="errors", level=logging.ERROR),
    collector.elapsed.factory(),
)
def fn(*, logger: tremors.Logger = tremors.from_logged) -> None:
    logger.info("hello")
    time.sleep(1)
    logger.error("uh-ho!")


logging.basicConfig(
    format="%(elapsed)s%(errors)s%(levelname)s:%(name)s:%(message)s",
    level=logging.INFO,
    force=True,
)
logging.root.handlers[0].addFilter(flt)
fn()

The messages contain information from both collectors.

0.000 errors=0 INFO:root:entered: fn
0.000 errors=0 INFO:root:hello
1.001 errors=1 ERROR:root:uh-ho!
1.001 errors=1 INFO:root:exited: fn

In the previous example, a new counter collector was used each time the function is called. Let’s reuse the same collector to keep a tally of errors across all calls to the function.

fn_errors = collector.counter.factory(name="errors", level=logging.ERROR)


@tremors.logged(fn_errors)
def fn(*, logger: tremors.Logger = tremors.from_logged) -> None:
    logger.error("uh-ho!")


fn()
fn()

The error count doesn’t reset in the second function call.

errors=0 INFO:root:entered: fn
errors=1 ERROR:root:uh-ho!
errors=1 INFO:root:exited: fn
errors=1 INFO:root:entered: fn
errors=2 ERROR:root:uh-ho!
errors=2 INFO:root:exited: fn

Another way we can tally the count across all function calls is to pass the same logger with each call.

def fn(*, logger: tremors.Logger) -> None:
    logger.error("uh-ho!")


with tremors.Logger(
    collector.counter.factory(name="errors", level=logging.ERROR),
    name="context",
) as logger:
    fn(logger=logger)
    fn(logger=logger)

We only get entering and exiting messages for the context block. But the single logger used in both function calls maintains its state between calls.

errors=0 INFO:root:entered: context
errors=1 ERROR:root:uh-ho!
errors=2 ERROR:root:uh-ho!
errors=2 INFO:root:exited: context

See the tremors.collector module for how you can define your own collectors, and bundles.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tremors-0.3.3.tar.gz (16.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

tremors-0.3.3-py3-none-any.whl (14.6 kB view details)

Uploaded Python 3

File details

Details for the file tremors-0.3.3.tar.gz.

File metadata

  • Download URL: tremors-0.3.3.tar.gz
  • Upload date:
  • Size: 16.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.0

File hashes

Hashes for tremors-0.3.3.tar.gz
Algorithm Hash digest
SHA256 487a97e6de9a2cb2883bedb6649db2923b78e3c044ff8e6a247dc491f69e51a7
MD5 cd6522cac2a6acf63b3394c0061923d9
BLAKE2b-256 e539334a04a0e0936926d1cb4729eadf216388d9e6467fa8f473771b3400f0b1

See more details on using hashes here.

File details

Details for the file tremors-0.3.3-py3-none-any.whl.

File metadata

  • Download URL: tremors-0.3.3-py3-none-any.whl
  • Upload date:
  • Size: 14.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.0

File hashes

Hashes for tremors-0.3.3-py3-none-any.whl
Algorithm Hash digest
SHA256 587c1615bbc851def09233fdae3dd4f6dbab38e3d4c3de4b60f3e6370f89f247
MD5 5bc2507bc6793d8ba5c7aa9a2cd29569
BLAKE2b-256 8af0ff00b33db3f55dafb6f2cbe75441bf43e68cca58736bc2f43522414f0843

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page