In order of first appearance in The Morning Paper.
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SRE Practices | |
Weekly Ops Review | |
Lunch and Learn | |
Pre-mortem | |
Maturity Model | |
SRE Games | |
State of the Union | |
RACI | |
OOPs Meeting |
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In [1]: class One: | |
...: def __init__(self): | |
...: self.bw_rate = 1 | |
...: | |
In [2]: class Two: | |
...: def __init__(self): | |
...: self.bw_rate = 0 | |
...: |
This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.
- Stores data elements based on an sequential, most commonly 0 based, index.
- Based on tuples from set theory.
- They are one of the oldest, most commonly used data structures.
Topics | |
---|---|
Service Overview | What is it, who uses it, where does it fit in overall |
Technical Architecture | Overview, upstream dependencies, sub-components |
Development Process | Source control, external dependencies, build, test, tools |
Change Management / Deployment | Process, technology, cadence, gates, rollback |
Configuration Management | Process, technology, source control |
Demand Forecasting, Capacity Management | How do you shift load, or scale? How do you load test? Can you shed load? |
Availability level | Downtime per year | Downtime per quarter | Downtime per month | Downtime per week | Downtime per day | Downtime per hour |
---|---|---|---|---|---|---|
90% ("one nine") | 36.5 days | 9 days | 3 days | 16.8 hours | 2.4 hours | 6 minutes |
95% | 18.25 days | 4.5 days | 1.5 days | 8.4 hours | 1.2 hours | 3 minutes |
99% ("two-nines") | 3.65 days | 21.6 hours | 7.2 hours | 1.68 hours | 14.4 minutes | 36 seconds |
99.50% | 1.83 days | 10.8 hours | 3.6 hours | 50.4 minutes | 7.20 minutes | 18 seconds |
99.9%("three nines") | 8.76 hours | 2.16 hours | 43.2 minutes | 10.1 |
A checklist for designing and developing internet scale services, inspired by James Hamilton's 2007 paper "On Desgining and Deploying Internet-Scale Services."
- Does the design expect failures to happen regularly and handle them gracefully?
- Have we kept things as simple as possible?
I hereby claim:
- I am dastergon on github.
- I am dastergon (https://keybase.io/dastergon) on keybase.
- I have a public key ASCWcL_1JH05ltGCQ6HWe-ZJvFnUN0PwfIz1W-Hm857bEQo
To claim this, I am signing this object:
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
"""dbsnap is a DB snapshot management tool for Amazon RDS. | |
Demo tool used for educational purposes in http://blog.codebender.cc/2015/12/08/automating-db-snapshots-at-amazon-rds/ | |
""" | |
from __future__ import print_function | |
import boto3 |
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import java.io.BufferedReader; | |
import java.io.InputStreamReader; | |
import java.net.HttpURLConnection; | |
import java.net.URL; | |
public class HttpURLCon { | |
private final String USER_AGENT = "Mozilla/5.0"; | |
public static void main(String[] args) throws Exception { |
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