SRE Weekly Issue #109


Asking five (or more) whys is outdated. So is trying to find a Root Cause Analysis. Take a look at the case against RCA.


Pusher had a problem: their service was being bombarded by connections from rogue clients, and they needed to enforce limits. This article is highly polished, with beautiful diagrams and well-constructed explanations.

This is the story of how we quelled the biggest threat to our service uptime for several years.

Structured logging can bring a lot of uniformity to your infrastructure, as lovingly explained in this article. Snyk explains how that uniformity allows for a standardized troubleshooting methodology that helps them get to the bottom of most problems in minutes.

Instead of focusing on the individual intricacies of each part of our system, we train on the common tools to be used for almost every kind of problem.

Feature flags are awesome! But there’s a downside: adding lots of conditional handling to your code can significantly increase code complexity, which can in turn decrease maintainability and increase risk.

Following up on her appearance in the New York Times last week, Charity Majors posted this excellent Twitter thread about the importance of vendor relationship management and generating business value, as any kind of engineer. I’d argue especially as an SRE.

Here’s the latest in Google’s CRE Life Lessons series. Previously, they explained how to build an Escalation Policy, and in this article, they analyze how it would be applied to several fictitious scenarios.

LinkedIn needed a way to test their HDFS cluster against real-world traffic patterns. The existing solutions didn’t meet their needs (for reasons they explain toward the end), so they created Dynamometer.

PagerDuty released a report this week entitled, “The State of IT Work-Life Balance”, which contains the results of their recent survey. This article is an overview, along with some related tidbits about alert fatigue.

Through an anecdote, Baron Schwartz cautions against the use of counter-factuals (“you should have…”) in analyzing the decisions leading up to an outage.

What it says on the tin. This article would make for a great checklist for deploys.


  • Singpass (Singapore ID system)
  • Uber
  • Fortnite
    • Fortnite hit a new peak of 3.4 million concurrent players last Sunday… and that didn’t come without issues!

      They suffered 6 different outages over two days, and they posted this highly-detailed incident analysis just 5 days later. Normally I tend not to include outages for MMO games because they have so many and rarely post in-depth analyses, but this one is worth a read.

  • Binance (cryptocurrency exchange)
  • Google App Engine
  • US stock brokerages
    • The US stock market had a rough week, and so did several brokerage websites as they dealt with the high trading volume.
  • Super Bowl Advertisers
    • Several companies that purchased expensive commercial slots during the SuperBowl (an american sportsball thing, for you folks outside the US) were unable to handle the web traffic they brought in.
  • Super Bowl
    • NBC had a 45-second blackout in their broadcast of the Superb Owl.

SRE Weekly Issue #108

Wow, I have a lot of great content to share with you this week!  Sometimes it seems like awesome articles come in waves… not sure what that’s about.


ChatOps continues to gain momentum in all industries. See what Jason Hand had to say about the progression.


This is the first in a series where New York Times CTO, Nick Rockwell, talks to leaders in the technology world about their work.

There’s so incredibly much awesome in this conversation, and I’ve already seen the internet alight with people quoting it. Charity says so many insightful things that I’m going to have to reread this a couple of times to absorb it all. It’s a must-read!

Xero SRE is back, this time with an article about their incident response process and an overview of their chatbot, Multivac. The bot assists with paging and information tracking and, crucially, guides incident responders through a checklist of actions such as determining severity.

Here’s a fun little distributed system debugging story from the founder of RavenDB.

This CNN article goes into a little more detail about what happened. To my eye, there’s not enough in those details to warrant firing, so there must be more than has been shared publicly.

LinkedIn’s growth from a single datacenter to multiple “hyperscale” locations was accompanied by a cultural shift. They transitioned from “‘Site-Up’ is priority #1” to “taking intelligent risks” as their overall reliability improved.

The program is nominally aimed toward “a variety of industries, including the aerospace, automotive, maritime, manufacturing, oil, chemical, power transmission, medical device, infrastructure planning and extreme event response sectors”, though I can’t help but wonder if it might be applicable to IT.

“Well I’d cut out the pizza and beer and instead pay for Splunk.”

This author pushes us to resist the urge to write something in-house and instead look for external services or software, when the tool is not key to delivering customer value.

Here’s a very well-articulated argument for using a third-party feature-flag service rather than writing your own. I’ve seen every pitfall they mention and more. This article is by, a feature-flag service, but they notably don’t mention their product even once, and they don’t need to. Nicely done, folks.

I think there’s another layer we get out of the postmortem process itself that hasn’t usually been part of the discussion: communicating about your service’s long-term stability.

We should look beyond merely preventing the same kind of incident in the future and improving our incident response process, says this article from PagerDuty.

How many times have you been paged for a server at 95% disk usage, only to find that it’s still months away from full? This article by SignalFX is about a feature on their platform, but its concepts are generally applicable to other tools.

A primer on testing failover in a MongoDB Atlas cluster.

Large numbers of SREs went scrambling last month when we realized that we may suddenly run out of resources on our NoSQL workloads. Here are some concrete numbers on how things actually turned out.


SRE Weekly Issue #107


Reactive, tactical, integrated, or holistic—where does your incident management fall? Read about incident management maturity to find out.


Here, “escalation policy” refers to ongoing work by SRE to get a service back into its SLO, rather than an escalation policy definition in PagerDuty (for example). This article describes the tactics a hypothetical Google SRE team has at their disposal to deal with an ailing service. It’s especially striking to me how this policy comes across as almost punitive in nature.

In this post, we’ll provide a technical walk-through of how we used the Play Framework and the Akka Actor Model to build the massive infrastructure that keeps track of the online status of millions of members at any given moment. We’ll describe how it distributes thousands of changes per second in the online status of these members to millions of other connected members in real time. You will also learn how to apply these techniques to your own applications.

This article from LaunchDarkly is about assuming failure and mitigating harm, through the lens of feature-flag-based deployment.

New Relic shares this list of the categories of tools that SREs use to standardize the systems they support.

As Liz [Fong-Jones] told Matthew Flaming, New Relic vice president of software engineering, “One SRE team is going to have a really difficult time supporting 50 different software engineering teams if they’re each doing their own separate thing, and they’re each using separate tooling.”

In the final article of this series, Tyler Treat lays out a design for a new distributed log based on NSQ.

While perhaps not strictly SRE-related, hiring is still critically important for SRE teams. I really love Honeycomb’s approach to hiring as laid out in this blog post.

Why indeed? This issue of The Morning Paper discusses a paper on the effectiveness of random testing in distributed systems. More specifically, it goes over the mathematics behind why randomized testing in Jepsen is actually useful, despite classical theories that it ought not be.


  • Pinterest
  • Google Cloud Storage
    • This one’s worth a read. Google’s original status posting stated 100% impact to cloud storage in its US region, but their followup post retroactively reduced that to 2.0% average and 3.6% peak.
  • Netflix
    • This one happened seemingly at the same time as the Google Cloud Storage outage, but that may be a spurious correlation. This is the first time that I learned that Netflix does have a status page of sorts: it’s an article in their help center entitled “Is Netflix Down?” and they update it live. Who knew?
  • Facebook/Instagram
  • National Health Service (UK)

SRE Weekly Issue #106


See how AlienVault focuses their incident management on collaboration and shared responsibility while relying on the rules engine of the VictorOps Transmogrifier.


Chaos engineering is extremely useful, and Mathias Lafeldt has written plenty about its virtues. But as with everything, it’s important to be aware of its pitfalls and shortcomings too.

There’s been a lot of talk of firing (or worse) the person whose actions led to the false alarm in Hawaii. That’s why I’m especially glad to see this excellent analysis by Don Norman (The Design of Everyday Things and others). Bonus content: another article along the same vein with some more interesting tidbits.

Think twice before you disable swap, says Chris Down, an author of the upcoming cgroup v2 in the Linux kernel.

Catchpoint is running a survey of SREs and SRE-like folks, and I’d really appreciate it if you’d take a moment to fill it out. Not only will the resulting data be very interesting, but Catchpoint is donating $5 to charity for every survey completed. Let’s stuff that ballot box and get them to hit their cap of $3000!

The awesome continues this week with a discussion of the importance of simplicity in the design of a reliable system.

This article from Heidi Waterhouse at Launch Darkly starts off with a really interesting take on the Y2K bug and continues on to discuss risk management in operations.

This short article has an extremely cogent point: design your system to be flexible enough to allow the user to do something seemingly incorrect, because they might need to while responding to an incident!

LinkedIn had a problem: their on-call system was so dysfunctional that they had to scramble to find coverage for an engineer that had been scheduled to be on call when they were on vacation. They explain how they identified the problem, came up with a solution, and implemented it, including automation and cultural fixes.

If the phrase “a DevOps World” makes you feel ill, don’t dismiss this article from ACM Queue out of hand. It’s got some great points about designing effective monitoring, and I like the introduction of the “Real Systems Monitoring” concept (akin to “Real User Monitoring” or RUM).


  • Heroku
    • Heroku had a 29-hour impairment to their application log routing platform.

SRE Weekly Issue #105

A quick note: Friday was my last day at Heroku/Salesforce, so don’t be surprised if you see my “full disclosure” notices change.


See how CloudBees Jenkins Solutions & VictorOps work together to bridge the on-call gap for CI/CD in this webinar. Register today.


PagerDuty put a call out on Twitter, asking what folks are doing to improve the on-call experience at their companies.

Here’s part three in the series. This one’s about sharding, horizontal scaling, and client versus server complexity.

Here’s how Azure’s new availability zones change the way highly available apps can be designed on Azure.

The meltdown patch seems to be having a disproportionate impact on Redis performance. Here’s Grab’s story of how they figured out what was up and what they did to deal with it.

I don’t often do the Twitter thing, but this chain by Charity Majors is worth reading. Is that what they call it? a chain?

Google on the advantages of Cloud Spanner’s strong consistency and why to use it. I’m still looking out for an explanation of what the downside to Spanner is…

Just to be clear, this is about how critical it is that Facebook keep their machine learning applications running, rather than using machine learning to design disaster recovery solutions.

This article is about useful error messages, which are important both for the customer experience and for operations. I’m not sure what really qualifies as a “mainframe” these days, though….

LinkedIn is open-sourcing two tools that they use for troubleshooting during incidents. Fossor automates running data-gathering can and Ascii Etch displays graphs using ASCII art.


  • LastPass
  • Slack
  • Spotify
  • Bitbucket
    • Bitbucket has had severe performance problems due to a failure in their storage layer.
  • Kraken (cryptocurrency exchange)
    • This appears to have been a scheduled upgrade that blew up in complexity, preventing Kraken from coming back up for two days. From the article:

      Most astonishing of all, about 36 hours after the upgrade began, Kraken apparently sent their engineers home to take a nap!

      Not that astonishing! Tired engineers make mistakes, after all.

  • Missile threat alert for Hawaii a false alarm
    • There’s so much more to this story than we’ve been told, and I really wish I could be a fly on the wall during the retrospective.
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