yairst’s gists

yairst / private_fork.md

Created December 22, 2022 16:15 — forked from 0xjac/private_fork.md

Create a private fork of a public repository

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare [email protected]:usi-systems/easytrace.git

yairst / approx_dynamic_burn_rate.py

Created January 15, 2022 21:52

Approximated dynamic burn rate based on resampled total number of events in the SLO period

	df['aprox dynamic burn rate'] = df['total_hits_last_28_days_resampled'] / (df['hits']) * (EBP / 100)

	fig, ax = plt.subplots(figsize=(16, 5))
	df_sample.plot(x='date',y='hits', ax=ax, logy=True)
	df_sample.plot(x='date',y='dynamic burn rate', ax=ax, logy=True)
	df_sample.plot(x='date',y='aprox dynamic burn rate', ax=ax, logy=True, linestyle='--')
	ax.grid()

yairst / resampling_total_events_in_SLO_period.py

Created January 15, 2022 21:46

Resampling total events in the SLO period

	# resample
	tmp = df.set_index('date')['total_hits_last_28_days'].asfreq('1D')
	tmp.loc[tmp.index[-1] + pd.Timedelta('1D')] = 0 # dummy row in purpose to get all hours in the last day
	df['total_hits_last_28_days_resampled'] = tmp.resample('1H').ffill().iloc[:-1].reset_index()['total_hits_last_28_days']

	# plot
	fig, ax = plt.subplots(figsize=(16, 5))
	df_sample.plot(x='date',y='total_hits_last_28_days', ax=ax)
	df_sample.plot(x='date',y='total_hits_last_28_days_resampled', ax=ax)

yairst / actual_ebp.py

Created January 15, 2022 21:19

Actual percentage of error budget consumption when using Google's static burn rate

	df['actual_ebp'] = ((24 * SLO_PERIOD) * df['hits']) / (ALERTING_WIN * df['total_hits_last_28_days']) * EBP

	fig, ax = plt.subplots(figsize=(16, 5))

	df_sample.plot(x='date', y='ebp', ax=ax)
	df_sample.plot(x='date', y='actual_ebp',ax=ax)
	plt.ylabel('Percentage of Error Budget\nConsumption Threshold (%)')
	ax.grid(axis='y')

yairst / static_vs_dynamic_burn_rate.py

Last active January 15, 2022 21:13

static vs dynamic burn rate

	import pandas as pd
	from matplotlib import pyplot as plt

	SLO_PERIOD = 28
	EBP = 2 # percent
	ALERTING_WIN = 1

	df = pd.read_csv('sav_2013_2017.csv', parse_dates=['date'])

	# aviod divide by zero

yairst / latency90_eb_alert.yml

Last active January 29, 2022 07:26

Prometheus alert definition on error budget of 90% percentile latency SLO

	- alert: Latency90ErrorBudgetBurn
	expr: \|2
	job:latency90_eb_http_request_duration_seconds:increase1h > (0.02 * 0.1 * job:http_requests_total:increase30d)
	and
	job:latency90_eb_http_request_duration_seconds:increase5m > ((0.02 / 12) * 0.1 * job:http_requests_total:increase30d)
	for: 0m
	labels:
	period: 1h
	severity: critical
	annotations:

yairst / latency90_eb.yml

Last active January 27, 2022 17:46

	- record: job:latency90_eb_http_request_duration_seconds:increase1h
	expr: \|2
	sum by (job) (increase(http_request_duration_seconds_count[1h]))
	-
	sum by (job) (increase(http_request_duration_seconds_bucket{le="0.45"}[1h]))

Yair Stark yairst