When we try to optimize MTTR as if it’s a meaningful statistic, we run into trouble. This article does a great job of explaining why, drawing from concepts and techniques in manufacturing.

  Lorin Hochstein

This article introduces the concepts of “shared nothing” and “shared storage” in distributed systems and then explains why they chose shared storage for WarpStream.

  Richard Artoul — WarpStream

How much did that incident cost in lost revenue? This article says you should avoid including that number in your incident management process, because it’s a trap.

  Tom Webster — Rootly

Pushing a system to 100% CPU utilization can cause workloads to be slowed down. This article is about experimentally finding the sweet spot between utilizing CPUs as much as possible and avoiding performance issues.

  Andreas Strikos — GitHub

This article has a couple of strategies for handling concurrent updates to the same row in MySQL, with and without locking.

  Sönke Ruempler

They do it with a dead man’s switch, implemented using a backup alert provider.

  Lawrence Jones — incident.io

I came across part 6 first and I need to go back and read the rest, but I just had to share this now, because if the cool concept it contains: that efficiency and resiliency are at odds with each other.

  Uwe Friedrichsen

This is so cool! Their system automatically figures out which API calls are critical to each user journey and keeps the list updated.

  yakenji — Mercari

It’s a case of cascading failure, but with an interesting twist: their system was designed to handle floods but the safety mechanism was left unconfigured.

   Jamie Herre, Tom Walwyn, Christian ndres, Gabriele Viglianisi, Mik Kocikowski, and Rian van der Merwe — Cloudflare

Lorin takes apart the Cloudflare write-up with style, including a really insightful section on safety mechanisms in complex systems.

  Lorin Hochstein

Meta wanted to log details about the encrypted communications in their systems to help track key use, outdated algorithms, and the like. It’s a ton of telemetry, so they did smart sampling (which they call aggregation):

During the aggregation, a “count” is maintained for every unique event. When it comes time to flush, this count is exported along with the log to convey how often that particular event took place.

  Hussain Humadi, Sasha Frolov, Rafael Misoczki, Dong Wu — Meta

A primer on using Golang’s profiling tools including CPU profiling, memory profiling, goroutine leak analysis, and execution tracing.

  Gaurav Maheshwari — Oodle

A thought-provoking piece of automation, friction, and adaptive capacity. I especially enjoyed the section on decompensation.

  Fred Hebert

With various tools for different kinds of telemetry, these folks needed to up their game to be able to fully understand what happened in a customer request. They also needed a custom sampling strategy to make sure they didn’t miss anything important.

  Martin Fahy — Klaviyo

we’ll be looking at how Ably’s platform achieves scalability, and how, as a result, there’s no effective ceiling on the scale of applications that can be supported.

  Paddy Byers — Ably

Airbnb built a system for tracking and analyzing user actions to aid in personalization. Their system uses Flink and Kafka to handle over a million events per second.

  Kidai Kwon — Airbnb

The Lunch Exercise was my favorite part of the Blackrock3 training, and now Slack has adapted it for their ongoing training.

How Slack trains engineers in incident response by ordering lunch together.

  Scott Nelson Windels — Slack

Cloudflare runs programs written in their custom language Topaz in the hot path. They use formal verification in production(!) to ensure that the set of Topaz programs make sense.

  ames Larisch, Suleman Ahmad, and Marwan Fayed — Cloudflare

Distributed counting is a challenging problem in computer science. In this blog post, we’ll explore the diverse counting requirements at Netflix, the challenges of achieving accurate counts in near real-time, and the rationale behind our chosen approach, including the necessary trade-offs.

  Rajiv Shringi, Oleksii Tkachuk and Kartik Sathyanarayanan — Netflix

It’s hard, and this article explains why in excellent detail. It also includes a discussion of options to consider when designing a chat system.

  Ably

In anticipation of https://aws-news.com‘s busiest period of the year, I redesigned the API access patterns to support very effective caching. This resulted in significantly reduced backend load and a much faster frontend.

  Luc van Donkersgoed — AWS News

Recover means that not only is everything back online, but the system is performing well and satisfying any QoS or SLAs AND a preventative approach has been implemented.

  Will Searle — Causely

Here’s a list of recommended talks for SREs attending re:Invent, with short descriptions explaining why they’re interesting.

  Jamie Baker

In this post, I’ll share exactly how we link our code to the team that owns it, so errors and alerting are routed to the right place with minimal maintenance burden.

  Martha Lambert — incident.io

Most fascinating air incident report I’ve seen in awhile! The pilots deviated from the non-normal checklist, and it immediately made me think of runbooks. On the one hand, you want the runbook to be simple and easy to handle in an incident. On the other hand, it can be very useful to tell the operator why they should do something.

  Mentour Pilot

With their claimed 14.5% of all websites depending on Cloudflare’s DNS, they had to be super careful with this migration. Lots of good stuff in here including:

• replacing direct DB access by multiple services with an API
• keeping the old and new DB in sync
• ensuring both forward and reverse migration were possible in case of rollback

  Alex Fattouche and Corey Horton — Cloudflare

I didn’t get to experience the value of a good tracing tool until recently in my career, and I didn’t understand the hype. If you’re in the same boat, this article may help you understand the value of tracing.

  Sam Starling — incident.io

About a year ago, Honeycomb git rid of incident severity levels in favor of incident types, which are purposefully not sortable. Here’s how their experiment has gone so far.

  Fred Hebert — Honeycomb

  Full disclosure: Honeycomb is my employer.

Is Service Level Indicator (SLI) the same as Key Performance Indicator (KPI)?

There’s a really cool framing in there: KPIs are moonshots, so we aim high and rarely hit all of them, while with SLOs, we under-promise and over-deliver.

  Alex Ewerlöf

A fun dive into some unix/linux internals with nine different methods to run a program with timeouts and retries. If you have a soft spot in your heart for signals and system calls, this one’s for you.

  Philippe Gaultier

Cosmos DB is Azure’s answer to Amazon’s DynamoDB. This article gives a nice overview and compares it to various other data stores to help you decide whether it’s right for your use case.

  Adam Gordon Bell — Pulumi

An engineer at Mercari shares their plan for migrating to their new payment system in this five-part article series, all of which are published now. They created their design after reading 80(!) similar articles from folks at other companies.

  resotto — Mercari

If you’re heading to KubeCon this week, here are some talks to consider.

  JJ Tang — Rootly

This article shows you how to manage Postgres indexes: when you need one, what type of index to choose, and how to set it up.

  Milly Leadley — incident.io

It’s neat that Cloudflare can see evidence of a BGP route leak in a third party that affected OVHcloud.

  Bryton Herdes, David Belson, and Tanner Ryan — Cloudflare

In this post, we’ll explore how we leveraged Temporal’s own capabilities to expand our infrastructure from AWS to Google Cloud, the challenges we faced along the way, and how we solved them using cloud-agnostic workflows.

  Raphaël Beamonte — Temporal

This deeply opinionated piece advocates against the “Architect” role, at least in certain forms. Among other problems, the Architect role breaks ownership models and impedes others.

  Alex Ewerlöf

These folks were using Kubernetes for their product that provides hosted developer environments (build systems, toolchains, and the like). While they directly acknowledge that their use case is not the same as common production environments, I still found it pretty interesting to learn about the problems they ran into that ultimately caused them to find another platform.

  Christian Weichel and Alejandro de Brito Fontes — Gitpod

This extensive guide shows you how to build an LLM-based agent to assist with incident response. It includes python code snippets and shows you how to provide the LLM agent with documentation and access to external data sources like PagerDuty.

  Eric Abruzzese — Aptible

A primer on the Swiss Cheese Model for modeling how accidents happen. I especially like the section at the end that suggests more nuanced thinking.

  Jonathan Cheyne — Johnson Winter Slattery