Docker vs Salt: What are the differences?
Docker: Enterprise Container Platform for High-Velocity Innovation. The Docker Platform is the industry-leading container platform for continuous, high-velocity innovation, enabling organizations to seamlessly build and share any application — from legacy to what comes next — and securely run them anywhere; Salt: Fast, scalable and flexible software for data center automation. Salt is a new approach to infrastructure management. Easy enough to get running in minutes, scalable enough to manage tens of thousands of servers, and fast enough to communicate with them in seconds Salt delivers a dynamic communication bus for infrastructures that can be used for orchestration, remote execution, configuration management and much more..
Docker and Salt are primarily classified as "Virtual Machine Platforms & Containers" and "Server Configuration and Automation" tools respectively.
Some of the features offered by Docker are:
- Integrated developer tools
- open, portable images
- shareable, reusable apps
On the other hand, Salt provides the following key features:
- Remote execution is the core function of Salt. Running pre-defined or arbitrary commands on remote hosts.
- Salt modules are the core of remote execution. They provide functionality such as installing packages, restarting a service, running a remote command, transferring files, and infinitely more
- Building on the remote execution core is a robust and flexible configuration management framework. Execution happens on the minions allowing effortless, simultaneous configuration of tens of thousands of hosts.
"Rapid integration and build up" is the primary reason why developers consider Docker over the competitors, whereas "Flexible" was stated as the key factor in picking Salt.
Docker and Salt are both open source tools. Docker with 54K GitHub stars and 15.6K forks on GitHub appears to be more popular than Salt with 10.1K GitHub stars and 4.59K GitHub forks.
According to the StackShare community, Docker has a broader approval, being mentioned in 3525 company stacks & 3445 developers stacks; compared to Salt, which is listed in 110 company stacks and 20 developer stacks.
What is Docker?
What is Salt?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to add, upvote and see more prosMake informed product decisions
What are the cons of using Salt?
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
By 2014, the DevOps team at Lyft decided to port their infrastructure code from Puppet to Salt. At that point, the Puppet code based included around "10,000 lines of spaghetti-code,” which was unfamiliar and challenging to the relatively new members of the DevOps team.
“The DevOps team felt that the Puppet infrastructure was too difficult to pick up quickly and would be impossible to introduce to [their] developers as the tool they’d use to manage their own services.”
To determine a path forward, the team assessed both Ansible and Salt, exploring four key areas: simplicity/ease of use, maturity, performance, and community.
They found that “Salt’s execution and state module support is more mature than Ansible’s, overall,” and that “Salt was faster than Ansible for state/playbook runs.” And while both have high levels of community support, Salt exceeded expectations in terms of friendless and responsiveness to opened issues.
Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).
It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear
vagrant up or
vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.
I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as
vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.
We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.
If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.
The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).
Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.
I have got a small radio service running on Node.js. Front end is written with React and packed with Webpack . I use Docker for my #DeploymentWorkflow along with Docker Swarm and GitLab CI on a single Google Compute Engine instance, which is also a runner itself. Pretty unscalable decision but it works great for tiny projects. The project is available on https://ch1ller.com
For automating deployment or system admin tasks, Shell/Perl are more than enough. Specially Perl one liners, that I use heavily, even to make changes in xml files. But quite often the need is to just check the state of system and run scripts without fear. Thats where I actually needed some scripting language with "state mechanism" associated with it. Salt provided me above similar kind of experience. I tested salt first on a small scenario. Installation of 60 RPMS on a machine. I was pleased that I could achieve that in around 25 lines of code using salt. And eventually I was also able to keep data and code separate. This was another plus point. henceforth I was able to use salt to deploy a large potion Datacenter (apps deployment). I am still working towards orchestration and finding it quite promising. The use of pure python whenever needed to deal with more complex scenario is awesome.
Docker is the new kid on the block disrupting virtualization nowadays. You're able to save up to 70% of your development cost on AWS (or any other cloud) switching to Docker. For example instead of paying for many small VMs you can spin up a large one with many Docker containers to drastically lower your cost. That alone is only one of the reasons why Docker is the future and it's not even the best feature: isolation, testability, reproducibility, standardization, security, and upgrading / downgrading / application versions to name a few. You can spin up 1000's of Docker containers on an ordinary Laptop, but you would have trouble spinning up 100's of VMs. If you haven't already checked out Docker you're missing out on a huge opportunity to join the movement that will change development/production environments forever
The support for macOS is a fake.
I can't work with docker in macOS because de network and comunications with the container don't works correctly.
Currently experimenting. The idea is to isolate any services where I'm not confident yet in their security/quality. The hope is that if there is an exploit in a given service that an attacker won't be able break out of the docker container and cause damage to my systems.
An example of a service I would isolate in a docker container would be a minecraft browser map application I use. I don't know who wrote it, I don't know who's vetting it, I don't know the source code. I would feel a lot better putting this in a container before I expose it to the internet.
I believe I will follow this process for anything that's not properly maintained (not in an trusted apt-repo or some other sort of confidence)
We are testing out docker at the moment, building images from successful staging builds for all our APIs. Since we operate in a SOA (not quite microservices), developers have a dockerfile that they can run to build the entirety of our api infrastructure on their machines. We use the successful builds from staging to power these instances allowing them to do some more manual integration testing across systems.
Each component of the app was launched in a separate container, so that they wouldn't have to share resources: the front end in one, the back end in another, a third for celery, a fourth for celery-beat, and a fifth for RabbitMQ. Actually, we ended up running four front-end containers and eight back-end, due to load constraints.
Linux containers are so much more lightweight than VMs which is quite important for my limited budget. However, Docker has much more support and tooling for it unlike LXC, hence why I use it. rkt is interesting, although I will probably stick with Docker due to being more widespread.
We are running primarily as a micro-services platform and Docker lets us iterate on these smaller units consistently from dev to staging to production. It is also integral to our continuous deployment system for rolling out or rolling back new features.
When it comes to provisioning tens to hundreds of servers, you need a tool that can handle the load, as well as being extremely customisable. Fortunately, Salt has held that gauntlet for us consistently through any kind of issue you can throw at it.
We've built something using SaltStack and Debian Linux to help us deploy and administer at scale the servers we provide for our part- and fully-managed hosting customers.