mirror of https://github.com/docker/docs.git
104 lines
3.8 KiB
Markdown
104 lines
3.8 KiB
Markdown
---
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advisory: swarm-standalone
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hide_from_sitemap: true
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description: Try Swarm at scale
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keywords: docker, swarm, scale, voting, application, architecture
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title: Learn the application architecture
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---
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On this page, you learn about the Swarm at scale example. Make sure you have
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read through [the introduction](index.md) to get an idea of the skills and time
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required first.
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## Learn the example back story
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Your company is a pet food company that has bought a commercial during the
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Superbowl. The commercial drives viewers to a web survey that asks users to vote
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– cats or dogs. You are developing the web survey.
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Your survey must ensure that millions of people can vote concurrently without
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your website becoming unavailable. You don't need real-time results, a company
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press release announces the results. However, you do need confidence that every
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vote is counted.
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## Understand the application architecture
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The voting application is composed of several microservices. It uses a parallel
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web frontend that sends jobs to asynchronous background workers. The
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application's design can accommodate arbitrarily large scale. The diagram below
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shows the application's high level architecture:
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All the servers are running Docker Engine. The entire application is fully
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"Dockerized" in that all services are running inside of containers.
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The frontend consists of a load balancer with *N* frontend instances. Each
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frontend consists of a web server and a Redis queue. The load balancer can
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handle an arbitrary number of web containers behind it (`frontend01`-
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`frontendN`). The web containers run a simple Python application that takes a
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vote between two options. It queues the votes to a Redis container running on
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the datastore.
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Behind the frontend is a worker tier which runs on separate nodes. This tier:
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* scans the Redis containers
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* dequeues votes
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* deduplicates votes to prevent double voting
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* commits the results to a Postgres database
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Just like the frontend, the worker tier can also scale arbitrarily. The worker
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count and frontend count are independent from each other.
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The application's Dockerized microservices are deployed to a container network.
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Container networks are a feature of Docker Engine that allows communication
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between multiple containers across multiple Docker hosts.
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## Swarm Cluster Architecture
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To support the application, the design calls for a Swarm cluster with a single
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Swarm manager and four nodes as shown below.
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All four nodes in the cluster are running the Docker daemon, as is the Swarm
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manager and the load balancer. The Swarm manager is part of the cluster and is
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considered out of band for the application. A single host running the Consul
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server acts as a keystore for both Swarm discovery and for the container
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network. The load balancer could be placed inside of the cluster, but for this
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demonstration it is not.
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After completing the example and deploying your application, this
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is what your environment should look like.
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As the previous diagram shows, each node in the cluster runs the following containers:
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- `frontend01`:
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- Container: voting-app
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- Container: Swarm agent
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- `frontend02`:
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- Container: voting-app
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- Container: Swarm agent
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- `worker01`:
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- Container: voting-app-worker
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- Container: Swarm agent
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- `dbstore`:
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- Container: voting-app-result-app
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- Container: db (Postgres 9.4)
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- Container: redis
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- Container: Swarm agent
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After deploying the application, you'll configure your local system so that you
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can test the application from your local browser. In production, of course, this
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step wouldn't be needed.
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## Next step
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Now that you understand the application architecture, you need to deploy a
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network configuration that can support it. In the next step, you
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[deploy network infrastructure](deploy-infra.md) for use in this sample.
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