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A Docker Swarm is a container orchestration tool running the Docker application. It has been configured to join together in a cluster. The activities of the cluster are controlled by a swarm manager, and machines that have joined the cluster are referred to as nodes.
In a Docker Swarm, there are typically several worker nodes and at least one manager node that is responsible for handling the worker nodes' resources efficiently and ensuring that the cluster operates efficiently. Docker Swarm is still included in docker-ce, but there is no longer a software-as-a-service for Docker Swarm.
To contextualize our understanding of a Docker Swam, let's take a step back and define some of the more basic terms surrounding containers and the docker application.
Docker is a software platform that enables software developers to easily integrate the use of containers into the software development process. The Docker platform is open source and available for Windows and Mac, making it accessible for developers working on a variety of platforms. The application provides a control interface between the host operating system and containerized applications.
Containers and their utilization and management in the software development process are the main focus of the docker application. Containers allow developers to package applications with all of the necessary code and dependencies that are necessary for them to function in any computing environment. As a result, containerized applications run reliably when moved from one computing environment to another. In the docker application, a container is launched by running an image.
An Image is a package of executable files that contains all of the code, libraries, runtime, binaries and configuration files necessary to run an application. A container can be described as the runtime instance of an image.
A Dockerfile is a name given to the type of file that defines the contents of a portable image. Imagine you were going to write a program in the Java programming language. Your computer does not understand Java on its own, so you'll need a way to convert your code into machine code. The libraries, configuration files, and programs needed to do this are collectively called the "Java Runtime Environment (JRE)." In Docker, all of these assets would be included in the Dockerfile.
So, instead of installing the JRE onto your computer, you could simply download a portable JRE as an image and include it in the container with your application code. When launching the application from the container, all of the resources necessary for the application to run smoothly will be present in the isolated containerized environment.
The difference between Docker Swarm and Docker Compose is that Compose is used for configuring multiple containers in the same host. Docker Swarm is different in that it is a container orchestration tool. This means that Docker Swarm lets you connect containers to multiple hosts similar to Kubernetes.
Docker Swarm has two types of services: replicated and global.
Replicated services: Swarm mode replicated services functions based on a specified number of replica tasks for the swarm manager to assign to available nodes.
Global services: Global services function by using the swam manager to schedule one task to each available node that meets the services constraints and resource requirements.
A Docker Swarm is comprised of a group of physical or virtual machines operating in a cluster. When a machine joins the cluster, it becomes a node in that swarm. In a Docker Swarm, there are typically several worker nodes and at least one manager node that is responsible for handling the worker nodes' resources efficiently and ensuring that the cluster operates efficiently. Docker Swarm's load balancer runs on every node and is capable of balancing load requests across multiple containers and hosts.
The docker swarm function recognizes three different types of nodes, each with a different role within the docker swarm ecosystem:
The primary function of manager nodes is to assign tasks to worker nodes in the swarm. Manager nodes also help to carry out some of the managerial tasks needed to operate the swarm. Docker recommends a maximum of seven manager nodes for a swarm.
When a cluster is established, the Raft consensus algorithm is used to assign one of them as the "leader node." The leader node makes all of the swarm management and task orchestration decisions for the swarm. If the leader node becomes unavailable due to an outage or failure, a new leader node can be elected using the Raft consensus algorithm.
In a docker swarm with numerous hosts, each worker node functions by receiving and executing the tasks that are allocated to it by manager nodes. By default, all manager modes are also worker nodes and are capable of executing tasks when they have the resources available to do so.
Here are three simple reasons why companies use Docker Swarm:
Developers love using docker swarm because it fully leverages the design advantages offered by containers. Containers allow developers to deploy applications or services in self-contained virtual environments, a task that was previously the domain of virtual machines. Containers are proving a more lightweight version of virtual machines, as their architecture allows them to make more efficient use of computing power.
One of the main benefits of Docker Swarms is increasing application availability through redundancy. In order to function, a docker swarm must have a swarm manager that can assign tasks to worker nodes. By implementing multiple managers, developers ensure that the system can continue to function even if one of the manager nodes fails. Docker recommends a maximum of seven manager nodes for each cluster.
Docker Swarm schedules tasks using a variety of methodologies to ensure that there are enough resources available for all of the containers. Through a process that can be described as automated load balancing, the swarm manager ensures that container workloads are assigned to run on the most appropriate host for optimal efficiency.
Traditional Linux-based tools that are designed to run on a single host and rely on analyzing log files on disk don’t scale well to multi-container clustered applications. Unless they are written to a data volume, they don’t monitor single container apps well because disk content is not persisted when containers are shut down.
Companies need a centralized approach to log management using container-aware monitoring tools.
Sumo Logic delivers a comprehensive strategy for continuous monitoring of Docker infrastructures. Correlate container events, configuration information, and host and daemon logs to get a complete overview of your Docker environment.
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