mahmoud / Lithoxyl
Application instrumentation and logging, with a geological bent.
Stars: ✭ 141
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Lithoxyl
Nanolog
Nanosecond scale logger inspired by https://github.com/PlatformLab/NanoLog
Stars: ✭ 220 (+56.03%)
Mutual labels: logging, performance
Home
Project Glimpse: Node Edition - Spend less time debugging and more time developing.
Stars: ✭ 260 (+84.4%)
Mutual labels: logging, performance
Timber Elixir
🌲 Great Elixir logging made easy
Stars: ✭ 226 (+60.28%)
Mutual labels: logging, instrumentation
Appmetrics
App Metrics is an open-source and cross-platform .NET library used to record and report metrics within an application.
Stars: ✭ 1,986 (+1308.51%)
Mutual labels: instrumentation, performance
Capture Thread
Lock-free framework for loggers, tracers, and mockers in multithreaded C++ programs.
Stars: ✭ 93 (-34.04%)
Mutual labels: logging, instrumentation
Marathon
Cross-platform test runner written for Android and iOS projects
Stars: ✭ 250 (+77.3%)
Mutual labels: instrumentation, performance
Rz Go
Ripzap - Fast and 0 allocs leveled JSON logger for Go ⚡️. Dependency free.
Stars: ✭ 256 (+81.56%)
Mutual labels: logging, performance
Caliper
Caliper is an instrumentation and performance profiling library
Stars: ✭ 162 (+14.89%)
Mutual labels: instrumentation, performance
Werelogs
A logging library providing efficient raw logging in the form of JSON data.
Stars: ✭ 16 (-88.65%)
Mutual labels: logging, performance
Inspectit
inspectIT is the leading Open Source APM (Application Performance Management) tool for analyzing your Java (EE) applications.
Stars: ✭ 513 (+263.83%)
Mutual labels: instrumentation, performance
thundra-agent-nodejs
Thundra Lambda Node.js Agent
Stars: ✭ 31 (-78.01%)
Mutual labels: logging, instrumentation
Easy.logger
A modern, high performance cross platform wrapper for Log4Net.
Stars: ✭ 118 (-16.31%)
Mutual labels: logging, performance
Reckless
Reckless logging. Low-latency, high-throughput, asynchronous logging library for C++.
Stars: ✭ 358 (+153.9%)
Mutual labels: logging, performance
Hawktracer
HawkTracer is a highly portable, low-overhead, configurable profiling tool built in Amazon Video for getting performance metrics from low-end devices.
Stars: ✭ 108 (-23.4%)
Mutual labels: instrumentation, performance
Orbit
C/C++ Performance Profiler
Stars: ✭ 2,291 (+1524.82%)
Mutual labels: instrumentation, performance
Wperf
A simple HTTP load testing utility with detailed performance metrics.
Stars: ✭ 138 (-2.13%)
Mutual labels: performance
Countwords
Playing with counting word frequencies (and performance) in various languages.
Stars: ✭ 136 (-3.55%)
Mutual labels: performance
Web Vitals Module
Web Vitals: Essential module for a healthy Nuxt.js
Stars: ✭ 138 (-2.13%)
Mutual labels: performance
Fragment Cache
WordPress plugin for partial and async caching.
Stars: ✭ 135 (-4.26%)
Mutual labels: performance
Js Search
JS Search is an efficient, client-side search library for JavaScript and JSON objects
Stars: ✭ 1,920 (+1261.7%)
Mutual labels: performance
lithoxyl
Application instrumentation and logging, with a geological bent. Documentation is available on Read the Docs.
An infomercial of sorts
"Has this ever happened to you?"
Here's an example of some ostensibly well-instrumented code.
import logging
def create_user(name):
logging.info('creating user with name %r', name)
try:
success = _create_user(name)
if success:
logging.info('successfully created user %r', name)
else:
logging.error('failed to create user %r', name)
except Exception:
logging.critical('exception encountered while creating user %r',
name, exc_info=True)
return success
Notice how the logging statements tend to dominate the code, almost drowning out the meaning of the code.
Here's lithoxyl's take:
from lithoxyl import stderr_log
def create_user(name):
with stderr_log.critical('user creation', username=name, reraise=False) as r:
success = _create_user(name)
if not success:
r.failure()
return success
Feature brief
- Transactional logging
- Semantic instrumentation
- Pure Python
- Pythonic context manager API minimizes developer errors
- Decorator syntax is convenient and unobtrusive
- Human-readable structured logs
- Reparseability thanks to autoescaping
- Statistical accumulators for prerolled metrics
- Programmatic configuration with sensible defaults just an import away
- Synchronous mode for simplicity
- Asynchronous operation for performance critical applications
- Log file headers for metadata handling
- Heartbeat for periodic output and checkpointing
- Automatic, fast log parser generation (TBI)
- Sinks
- EWMASink
- DebuggerSink
- MomentSink
- QuantileSink
- StreamSink
- SyslogSink
- and more
Reasons to use Lithoxyl
- More specific: distinguishes between level and status
- Safer: Transactional logging ensures that exceptions are always recorded appropriately
- Lower overhead: Lithoxyl can be used more places in code (e.g., tight loops), as well as more environments, without concern of excess overhead.
- More Pythonic: Python's logging module is a port of log4j, and it shows.
- No global state: Lithoxyl has virtually no internal global state, meaning fewer gotchas overall
- Higher concurrency: less global state and less overhead mean fewer places where contention can occur
- More succinct: Rather than try/except/finally, use a simple with block
- More useful: Lithoxyl represents a balance between logging and profiling
- More composable: Get exactly what you want by recombining new and provided components
- More lightweight: Simplicity, composability, and practicality, make Lithoxyl something one might reach for earlier in the development process. Logging shouldn't be an afterthought, nor should it be a big investment that weighs down development, maintenance, and refactoring.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].