michaeltoohig

"A little bit of slope makes up for a lot of y-intercept"

01/13/2012. From a lecture by Professor John Ousterhout at Stanford, class CS140

Here's today's thought for the weekend. A little bit of slope makes up for a lot of Y-intercept.

[Laughter]

my database had 72k annotations at the time I ran these benchmarks, here's the result:

$ python scripts/batch_bench.py conf/development-app.ini dumb
Memory summary: start
      types |   # objects |   total size
=========== | =========== | ============
       dict |       13852 |     12.46 MB
  frozenset |         349 |     11.85 MB
VM: 327.29Mb

Docker compose as a systemd unit

Create file /etc/systemd/system/[email protected]. SystemD calling binaries using an absolute path. In my case is prefixed by /usr/local/bin, you should use paths specific for your environment.

[Unit]
Description=%i service with docker compose
PartOf=docker.service
After=docker.service

This is a simple example of how to use loguru in your flask application

Just create a new InterceptHandler and add it to your app. Different settings should be configured in your config file, so that it is easy to change settings.

Logging is then as easy as:

from loguru import logger

logger.info("I am logging from loguru!")

Don’t Feed the Beast – the Great Recruitment Agency Infestation

“Don’t move to that London” warned my northern grandfather once. “It’s full of spivs”.

The Oxford Dictionary (somewhat chauvinistically) defines a spiv as:

A man, typically a flashy dresser, who makes a living by disreputable dealings

“But I work in IT” I told him. “engineers aren’t like that”.

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	from celery import Task
	from celery.task import task
	from my_app.models import FailedTask
	from django.db import models

	@task(base=LogErrorsTask)
	def some task():
	return result

	class LogErrorsTask(Task):

	from __future__ import with_statement
	from alembic import context
	from sqlalchemy import engine_from_config, pool, MetaData, Table, ForeignKeyConstraint
	from logging.config import fileConfig

	# this is the Alembic Config object, which provides
	# access to the values within the .ini file in use.
	config = context.config

	# Interpret the config file for Python logging.

	#!/usr/bin/env python
	# coding: UTF-8

	'''
	Setup apc mini colors without Ablton Live

	----------

	> brew install portmidi

	# Rate limiting with Celery + Django + Redis
	# Multiple Fixed Windows Algorithm inspired by Figma https://www.figma.com/blog/an-alternative-approach-to-rate-limiting/
	# and Celery's sometimes ambiguous, vague, and one-paragraph documentation
	#
	# Celery's Task is subclassed and the is_rate_okay function is added


	# celery.py or however your App is implemented in Django
	import os
	import math