Skip to main content

Paper on Gray Failures

A great paper annotated by The Morning Paper, this time on the subject of gray failures. https://blog.acolyer.org/2017/06/15/gray-failure-the-achilles-heel-of-cloud-scale-systems/ There are a handful of interesting takeaways from this one:

  • It is important that monitoring on a system aligns with the clients of the system's definition of failure. The cycle of failure is inevitable unless proper root causes are identified.

Some personal observations:

  • A potential observability gap is the difference between proximate and root cause. For example a service may fail because a proxy server returns an error. The error may be due to timing out a request to an upstream server. The cause of that could be the fact that latency has increased on the upstream server. The root cause of that could be that the working set of the server no longer fully fits in memory and the server is swapping leading to latency. The server itself is not failing any requests, but the proxy is throwing the results away because it is too late arriving.
  • The "masked failure" area is a great area to search for indicators of failure. If metrics can be found that correlate strongly with later gray failures, then remediation can take place before customers even notice. The trick is finding the correlating metrics.

Comments

Post a Comment

Popular posts from this blog

First Post

Hello and welcome to the inane ramblings of an Irish software developer. The title of the blog comes from Lewis Carroll's, Through the Looking Glass . In the book, Alice goes running with the Red Queen, but they don't seem to make any progress. Alice remarks on this, saying, "Well in our country, you'd generally get to somewhere else - if you ran very fast for a long time as we've been doing." The Red Queen replies, "A slow sort of country. Now, here, you see, it takes all the running you can do, to stay in the same place." The Red Queen Effect is quite applicable to the software industry, and as I probably will be talking quite a bit about the software industry, I thought it would be a good name for a blog. I have a few objectives for my new blog. By writing here, I hope to learn how to write well. That is, I hope to learn how to write clearly and concisely, and be interesting at the same time. I also hope that this blog will become a good prof
Read more

Learning Forth

One of my side projects for this year is to learn the programming language, Forth. Some people might consider this an odd language to learn. It is not a popular language. There are no hot startups using it (that I know of). It doesn't even show up in the top 100 languages in the TIOBE Index . However, I am convinced learning it is worthwhile. Some of my reasons for this are: Forth is probably the most successful and widely deployed language that nobody has heard of. It is the language used to develop OpenFirmware . This boot loader is installed on the laptops of the One Laptop Per Child Project , on PowerPC based Apple Mac computers, and on SPARC based computers from SUN Microsystems. It has also been used to develop to develop control software for the National Radio Astronomy Observatory , which is where it was developed. While not as widely used as C/C++, Forth is used a lot in embedded applications and has been ported to most micro-controllers. For example, the Forth, Inc. w
Post a Comment
Read more

Operational Metrics and Alerts for Distributed Software Systems

This post will be about operational metrics and alerts for distributed software systems. What do I mean by that? I mean the metrics and alerts that allow operations personel to detect failure of of a distributed software system and helps them to quickly diagnose what is wrong. Metrics The metrics are measurements of characteristics of the system collected at regular(ish) intervals and stored somewhere for processing - rendering into graphs, triggering alert notifications, etc. Metrics can be divided into 3 categories: input metrics, output metrics, and process metrics. Input metrics are measures of the inputs to the system, for example, the number of user requests, counts of particular characteristics of the requests - where they are from, how large the request data is, counts of particular features in the request (for example, which resources/items/products are being asked for). Output metrics are measures of the output of the system. Examples of these would include orders s
Post a Comment
Read more