Tag Archives: Scaling

To Replicas Or Not To Replicas In Kubernetes Deployments And StatefulSets?

Knowing that HorizontalPodAutoscaler (HPA) manages auto-scaling of our applications, the question might arise regarding replicas. Should we define them in our Deployments and StatefulSets, or should we rely solely on HPA to manage them? Instead of answering that question directly, we’ll explore different combinations and, based on results, define the strategy.

First, let’s see how many Pods we have in our cluster right now.

You might not be able to use the same commands since they assume that go-demo-5 application is already running, that the cluster has HPA enabled, that you cloned the code, and a few other things. I presented the outputs so that you can follow the logic without running the same commands.

kubectl -n go-demo-5 get pods

The output is as follows.

NAME    READY STATUS  RESTARTS AGE
api-... 1/1   Running 0        27m
api-... 1/1   Running 2        31m
db-0    2/2   Running 0        20m
db-1    2/2   Running 0        20m
db-2    2/2   Running 0        21m

We can see that there are two replicas of the api Deployment, and three replicas of the db StatefulSets.
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Auto-Scaling Docker Swarm Services Using Instrumented Metrics

If you prefer reading, please visit Auto-Scaling Docker Swarm Services Using Instrumented Metrics tutorial from Docker Flow Monitor documentation. Otherwise, feel free to watch the video that follows.

The DevOps 2.2 Toolkit: Self-Sufficient Docker Clusters

The article you just read is the summary of the progress we made in The DevOps 2.2 Toolkit: Self-Healing Docker Clusters. You can find the hands-on exercises that build the system in the book.

If you liked this article, you might be interested in The DevOps 2.2 Toolkit: Self-Sufficient Docker Clusters book. The book goes beyond Docker and schedulers and tries to explore ways for building self-adaptive and self-healing Docker clusters. If you are a Docker user and want to explore advanced techniques for creating clusters and managing services, this book might be just what you’re looking for.

Please get a copy from Amazon, LeanPub, or look for it through your favorite book seller.

Give the book a try and let me know what you think.

Docker Swarm Introduction (Tour Around Docker 1.12 Series)

Docker SwarmDocker just published a new Docker Engine v1.12. It is the most significant release since v1.9. Back then, we got Docker networking that, finally, made containers ready for use in clusters. With v1.12, Docker is reinventing itself with a whole new approach to cluster orchestration. Say goodbye to Swarm as a separate container that depends on an external data registry and welcome the new Docker Swarm. Everything you’ll need to manage your cluster is now incorporated into Docker Engine. Swarm is there. Service discovery is there. Improved networking is there.
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Docker Flow – Walkthrough

Docker Flow is a project aimed towards creating an easy to use continuous deployment flow. It depends on Docker Engine, Docker Compose, Consul, and Registrator. Each of those tools is proven to bring value and are recommended for any Docker deployment.

The goal of the project is to add features and processes that are currently missing inside the Docker ecosystem. The project, at the moment, solves the problems of blue-green deployments, relative scaling, and proxy service discovery and reconfiguration. Many additional features will be added soon.

The current list of features is as follows.

Docker Flow: Proxy – On-Demand HAProxy Service Discovery and Reconfiguration

A lot changed since I published that article. The Swarm as a standalone container is deprecated in favor of Swarm Mode bundled inside Docker Engine 1.12+. On the other hand, the Docker Flow: Proxy advanced and became more feature rich and advanced. I suggest you check out the project README instead this article.

The goal of the Docker Flow: Proxy project is to provide a simple way to reconfigure proxy every time a new service is deployed or when a service is scaled. It does not try to “reinvent the wheel”, but to leverage the existing leaders and combine them through an easy to use integration. It uses HAProxy as a proxy and Consul as service registry. On top of those two, it adds custom logic that allows on-demand reconfiguration of the proxy.
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Clustering And Scaling Services

Organizations which design systems … are constrained to produce designs which are copies of the communication structures of these organizations” - M. Conway

Many will tell you that they have a scalable system. After all, scaling is easy. Buy a server, install WebLogic (or whichever other monster application server you’re using) and deploy your applications. Then wait for a few weeks until you discover that everything is so “fast” that you can click a button, have some coffee, and, by the time you get back to your desk, the result will be waiting for you. What do you do? You scale. You buy few more servers, install your monster applications servers and deploy your monster applications on top of them. Which part of the system was the bottleneck? Nobody knows. Why did you duplicate everything? Because you must. And then some more time passes, and you continue scaling until you run out of money and, simultaneously, people working for you go crazy. Today we do not approach scaling like that. Today we understand that scaling is about many other things. It’s about elasticity. It’s about being able to quickly and easily scale and de-scale depending on variations in your traffic and growth of your business, and that, during that process, you should not go bankrupt. It’s about the need of almost every company to scale their business without thinking that IT department is a liability. It’s about getting rid of those monsters.
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Microservices: When the Stars Aligned

In The History of Failed Initiatives I mentioned that microservices as a concept existed for a long time. And yet, most of those who tried organizing their architecture around microservices failed or, to use different words, realized that benefits are shadowed by the increase in complexity (especially in operations). The spark that was born long ago became a star only recently. In order to understand why microservices became a hot topic not long ago, we need to understand better why they failed in the past. The explanation is simple. We neither had the tools nor understood the logic we had to apply. Do we have the tools today? Are we capable of putting in place the right procedures and have the mindset microservices require. I’d say yes, we have and we do. When I say we I mean people with the will and resources to make this happen. As in most other cases, once something new is proven and adopted by some, years need to pass until that something is applied by many. The good news is that I think that we are getting there and, slowly, the more conservative part of the industry (in other words, most of it) is starting to at least consider microservices as an option. Even Gartner had positive words for it. And when Gartner says jump, enterprise jumps.
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