This week we’re talking about Nix with Domen Kožar. The Nix ecosystem is a DevOps toolkit that takes a unique approach to package management and system configuration. Nix helps you make reproducible, declarative, and reliable systems. Domen is writing the Nix ecosystem guide at nix.dev and today he takes us on a deep dive on all things Nix.
At Channable we use Nix to build and deploy our services and to manage our development environments. This was not always the case: in the past we used a combination of ecosystem-specific tools and custom scripts to glue them together. Consolidating everything with Nix has helped us standardize development and deployment workflows, eliminate “works on my machine”-problems, and avoid unnecessary rebuilds. In this post we want to share what problems we encountered before adopting Nix, how Nix solves those, and how we gradually introduced Nix into our workflows.
Hamza Tahir on HackerNoon:
By now, chances are you’ve read the famous paper about hidden technical debt by Sculley et al. from 2015. As a field, we have accepted that the actual share of Machine Learning is only a fraction of the work going into successful ML projects. The resulting complexity, especially in the transition to “live” environments, lead to large amounts of failed ML projects never reaching production.
Continuous integration and continuous delivery are both terms we have heard, but what do they really mean? What does CI/CD look like when done well? What are some pitfalls we might want to avoid? In this episode Jérôme and Marko, authors of the book “CI/CD with Docker and Kubernetes” join us to share their thoughts.
While looking for these MLOps tools, I discovered some interesting points about the MLOps landscape:
- Increasing focus on deployment
- The Bay Area is still the epicenter of machine learning, but not the only hub
- MLOps infrastructures in the US and China are diverging
- More interests in machine learning production from academia
Raj Dutt is the founder and CEO of Grafana Labs. Grafana has become the world’s most popular open source technology used to compose observability dashboards (we use Grafana here at Changelog). Raj and team are 100% focused on building a sustainable business around open source. They have this “big tent” open source ecosystem philosophy that’s driving every aspect of building their business around their open source, as well as other projects in the open source community. But, to understand the wisdom Raj is leading with today, we have to go back to where things got started. To do that we had to go back like Prince to 1999…
What the what is DivOps?! That’s the question Jonathan Creamer is here to answer. In so doing, we cover the past, present, and future of frontend tooling.
In this post I share the latest 2020 and beyond details for changelog.com’s infrastructure.
Why Kubernetes? How is Kubernetes simpler than what we had before? What was our journey to running production on Kubernetes? What worked well? What could have been better? What comes next for changelog.com? Read this post and listen to episode #419 to learn all the details.
Tempo is cost-efficient, requiring only object storage to operate, and is deeply integrated with Grafana, Prometheus, and Loki. Tempo can be used with any of the open source tracing protocols, including Jaeger, Zipkin, and OpenTelemetry. It supports key/value lookup only and is designed to work in concert with logs and metrics (exemplars) for discovery.
Add this to the incredibly impressive open source portfolio at Grafana Labs.
This segment will be included in a podcast near you soon enough, but we thought it’d be fun to share the video as a standalone since we watched the whole thing play out via K9s.
kubectl is the new SSH. If you are using it to update production workloads, you are doing it wrong. See examples on how to automate application updates.
We’re using this in our new Kubernetes-based infrastructure (more details on that coming to a podcast near you). Keel runs as a single container, scanning Kubernetes and Helm releases for outdated images. Super cool stuff, and even has a web interface (which we’re not using yet, but should).
Chris Toomey shares a good idea (especially for read-heavy apps) around how you can do scheduled maintenance without taking your entire app offline (ie – Heroku’s maintenance page).
His solution is Rails-specific, but the general concept applies to any web app with similar use-case.
Everyone’s (or at least my) favorite system monitoring tool is still alive and kickin’ with a big 3.0 release. In addition to a new display option to show CPU frequency in CPU meters, optional vim key mapping mode, and many other goodies, the big news is this:
New maintainers - after a prolonged period of inactivity from Hisham, the creator and original maintainer, a team of community maintainers have volunteered to take over a fork at htop.dev and github.com/htop-dev to keep the project going.
Open source FTW!
More good news: Hisham has agreed to join us on Maintainer Spotlight!
How do you respond when someone asks:
Is Kubernetes right for us?
Where do you start? Let’s talk about IT modernisation, beginning with the problem that needs to be solved, and exploring any constraints that are obvious.
Infra, Devops, Systems Engineer, SRE, and the list goes on and on. What do these terms mean? Why does every job listing for the same role seem to entail different responsibiliities? Why is it important for developers to be familiar with the infrastructure their code is running on? Tune in to gain some insights into all of this and more!
The impact of COVID-19 is multifaceted. Our infrastructure team observed an exhaustion of our server resource pool for auto scaling due to a drastic traffic increase! Learn how we achieved 2× faster application run with only 1/3 of the servers by tuning auto scaling rules and switching to Puma threads.
DevOps for deep learning is well… different. You need to track both data and code, and you need to run multiple different versions of your code for long periods of time on accelerated hardware. Allegro AI is helping data scientists manage these workflows with their open source MLOps solution called Trains. Nir Bar-Lev, Allegro’s CEO, joins us to discuss their approach to MLOps and how to make deep learning development more robust.
Monitoror is a single file app written in Go. It can run on Linux, macOS, or Windows. You can view a live demo here.
This tool is surrounded by mountains of marketing speak, but it does seem like it offers a quick way to spin up different dev environments, which is cool. It has built-in recipes for WordPress, Drupal, LAMP, MEAN, and more. Here’s how you get started on Drupal 7, for example:
lando init \ --source remote \ --remote-url https://ftp.drupal.org/files/projects/drupal-7.59.tar.gz \ --remote-options="--strip-components 1" \ --recipe drupal7 --webroot . \ --name hello-drupal7
You can use these out of the box or start with a base language and mix in the things you need from there. Kinda like Docker Compose? Yeah, kinda like Docker Compose:
You can think of Lando as both an abstraction layer and superset of Docker Compose as well as a Docker Compose utility.
Arijit Mukherji on The New Stack:
We all have our favorite urban legends. From cow tipping to chupacabras, these myths persist despite a lack of definitive proof (and often evidence to the contrary). Technology isn’t immune to this phenomenon. It has its own set of urban legends and myths that emerge alongside new technologies and continue well into mass adoption. As organizations consider the shift from monitoring to Observability, I hear three common misperceptions. It’s time to debunk the myths.
Includes interview questions, notes, and useful links to other resources to continue your learning.
Christine Yen (co-founder and CEO of Honeycomb) joined the show to talk about her upcoming talk at Strange Loop titled “Observability: Superpowers for Developers.” We talk practically about observability and how it delivers on these superpowers. We also cover the biggest hurdles to observability, the cultural shifts needed in teams to implement observability, and even the gains the entire organization can enjoy when you deliver high-quality code and you’re able to respond to system failure with resilience.
If you are a system administrator, or just a regular Linux user, there is a very high chance that you worked with Syslog, at least one time. On your Linux system, pretty much everything related to system logging is linked to the Syslog protocol. Designed in the early 80’s by Eric Allman (from Berkeley University), the syslog protocol is a specification that defines a standard for message logging on any system.
This is pitched as “everything that you need to know about Syslog.” From what I can tell, it might just live up to that pitch. It’s high quality and thorough.
Almost any slog can be turned into a do-nothing script. A do-nothing script is a script that encodes the instructions of a slog, encapsulating each step in a function. For the example procedure above, we could write the following do-nothing script:
Containerization technologies are one of the trendiest topics in the cloud economy and the IT ecosystem. The container ecosystem can be confusing at times, this post may help you understand some confusing concepts about Docker and containers. We are also going to see how the containerization ecosystem evolved and the state of containerization in 2019.
Put on your swimming suit, because this is a deep dive. 🏊♀️🏊