Puppet Labs vs Zookeeper: What are the differences?
Puppet Labs: Server automation framework and application. Puppet is an automated administrative engine for your Linux, Unix, and Windows systems and performs administrative tasks (such as adding users, installing packages, and updating server configurations) based on a centralized specification; Zookeeper: Because coordinating distributed systems is a Zoo. A centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. All of these kinds of services are used in some form or another by distributed applications.
Puppet Labs can be classified as a tool in the "Server Configuration and Automation" category, while Zookeeper is grouped under "Open Source Service Discovery".
"Devops" is the primary reason why developers consider Puppet Labs over the competitors, whereas "High performance ,easy to generate node specific config" was stated as the key factor in picking Zookeeper.
Puppet Labs is an open source tool with 5.34K GitHub stars and 2.1K GitHub forks. Here's a link to Puppet Labs's open source repository on GitHub.
According to the StackShare community, Puppet Labs has a broader approval, being mentioned in 181 company stacks & 48 developers stacks; compared to Zookeeper, which is listed in 116 company stacks and 48 developer stacks.
What is Puppet Labs?
What is Zookeeper?
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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.
Since #ATComputing is a vendor independent Linux and open source specialist, we do not have a favorite Linux distribution. We mainly use Ubuntu , Centos Debian , Red Hat Enterprise Linux and Fedora during our daily work. These are also the distributions we see most often used in our customers environments.
For our #ci/cd training, we use an open source pipeline that is build around Visual Studio Code , Jenkins , VirtualBox , GitHub , Docker Kubernetes and Google Compute Engine.
For #ServerConfigurationAndAutomation, we have embraced and contributed to Ansible mainly because it is not only flexible and powerful, but also straightforward and easier to learn than some other (open source) solutions. On the other hand: we are not affraid of Puppet Labs and Chef either.
Currently, our most popular #programming #Language course is Python . The reason Python is so popular has to do with it's versatility, but also with its low complexity. This helps sysadmins to write scripts or simple programs to make their job less repetitive and automating things more fun. Python is also widely used to communicate with (REST) API's and for data analysis.
Initially, Stitch only supported real-time updates and addressed this problem with a MapReduce job named The Restorator that performed the following actions:
- Calculated the expected totals
- Queried Cassandra to get the values it had for each counter
- Calculated the increments needed to apply to fix the counters
- Applied the increments
Meanwhile, to stop the sand shifting under its feet, The Restorator needed to coordinate a locking system between itself and the real-time processors, so that the processors did not try to simultaneously apply increments to the same counter, resulting in a race-condition. It used ZooKeeper for this.
I'm using puppet to configure my servers. This makes it really simple to ensure that I have the same environment. There is a bit of a learning curve, but the repeatability definitely makes it worth the effort. I found puppet to be a little easier to pick up relative to chef, but I've used both. They're both great solutions.
I really like that there are a lot of modules available on the puppet forge that are being actively maintained.
Like many large scale web sites, Pinterest’s infrastructure consists of servers that communicate with backend services composed of a number of individual servers for managing load and fault tolerance. Ideally, we’d like the configuration to reflect only the active hosts, so clients don’t need to deal with bad hosts as often. ZooKeeper provides a well known pattern to solve this problem.
We provision all servers with puppet. We have one central Puppet server which uses puppet modules referenced by a Puppetfile. Those puppet modules are partly from forge and partly self written.
All modules which are self written, have to be tested using rspec-puppet and beaker.
Opstax uses puppet for role/profile based configuration management and the distribution of small/static code.
Configures or servers and allows us to be region independent we have 5 regions across the globe.
Zookeeper manages our state, and tells each node what version of code it should be running.
Used Zookeeper as the resource management system for Mesos/Marathon services.