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Native Docker Clustering with Swarm
Native Docker Clustering with Swarm

Native Docker Clustering with Swarm: Create and manage clusters of any size

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Profile Icon Fabrizio Soppelsa Profile Icon Chanwit Kaewkasi
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Arrow left icon
Profile Icon Fabrizio Soppelsa Profile Icon Chanwit Kaewkasi
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Paperback Dec 2016 280 pages 1st Edition
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Native Docker Clustering with Swarm

Chapter 2. Discover the Discovery Services

In Chapter 1, Welcome to Docker Swarm we created a simple yet well functioning local Docker Swarm cluster using the nodes:// mechanism. This system is not very practical, except for learning the Swarm fundamentals.

In fact, it is just a flat model that does not contemplate any true master-slave architecture, not to mention the high-level services, such as nodes discovery and auto-configuration, resilience, leader elections, and failover (high availability). In practice, it's not suitable for a production environment.

Apart from nodes://, Swarm v1 officially supports four discovery services; however, one of them, Token, is a trivial non-production one. Basically, with Swarm v1 you need to integrate a discovery service manually, while with Swarm Mode (from Docker 1.12), a discovery service, Etcd, is already integrated. In this chapter we're going to cover:

  • Discovery services
  • A test-grade discovery service: Token
  • Raft theory...

A discovery service

Imagine you're running a Swarm cluster on a static configuration, similar to the one in Chapter 1, Welcome to Docker Swarm, networking is flat and every container is assigned a specific task, for example a MySQL database. It's easy to locate the MySQL container because you assigned it a defined IP address or you run some DNS server. It's easy to notify whether this single container is working or not and it's a known fact that it won't change its port (tcp/3336). Moreover, it's not necessary that our MySQL container announces its availability as a database container with its IP and port: We, of course, already know that.

This is the pet model, mocked-up manually by a system administrator. However, since we're more advanced operators, we want to drive a cattle instead.

So, imagine you're running a Swarm made of hundreds of nodes, hosting several applications running a certain number of services (web servers, databases, key-value...

Token

Docker Swarm v1 includes an out-of-the-box discovery service, called Token. Token is integrated into the Docker Hub; hence, it requires all the Swarm nodes to be connected to the Internet and able to reach the Docker Hub. This is the main limitation of Token but, you will soon see, Token will allow us to make some practice in handling clusters.

In a nutshell, Token requires you to generate a UUID called, in fact, token. With this UUID, you can create a manager, act like a master, and join slaves to the cluster.

Re-architecting the example of Chapter 1 with token

If we want to keep it practical, it's time to take a look at an example. We'll use token to re-architect the example of Chapter 1, Welcome to Docker Swarm. As a novelty, the cluster will be not flat anymore, but it will consist of 1 master and 3 slaves and each node will have security enabled by default.

The master node will be the node exposing Swarm port 3376. We'll connect specifically to it in order to be able...

Raft

Consensus is an algorithm in distributed systems that forces agents in the system to agree on consistent values and elect a leader.

Some well-known consensus algorithms are Paxos and Raft. Paxos and Raft deliver similar performances but Raft is less complex, easier to understand, and therefore becoming very popular in distributed store implementations.

As consensus algorithms, Consul and Etcd implement Raft while ZooKeeper implements Paxos. The CoreOS Etcd Go library, implementing Raft, is included into SwarmKit and Swarm Mode as a dependency (in vendor/), so in this book we'll focus more on it.

Raft is described in detail in the Ongaro, Ousterhout paper, and it is available at https://ramcloud.stanford.edu/raft.pdf. In the upcoming section we'll summarize its basic concepts.

Raft theory

Raft was designed with simplicity in mind and compared to Paxos, it truly achieves this goal (there are even academic publications demonstrating this). For our purpose, the main difference between...

Etcd

Etcd is a highly available, distributed, and consistent key-value store that is used for shared configuration and service discovery. Some notable projects that use Etcd are SwarmKit, Kubernetes, and Fleet.

Etcd can gracefully manage master elections in case of network splits and can tolerate node failure, including the master. Applications, in our case Docker containers and Swarm nodes, can read and write data into Etcd's key-value storage, for example the location of services.

Re architecting the example of Chapter 1 with Etcd

We once again create an example with one manager and three nodes, this time by illustrating Etcd.

This time, we'll need a real discovery service. We can simulate a non-HA system by running the Etcd server inside Docker itself. We create a cluster made of four hosts, with the following names:

  • etcd-m will be the Swarm master and will host also the Etcd server
  • etcd-1: The first Swarm node
  • etcd-2: The second Swarm node
  • etcd-3: The third Swarm node

The operator...

A discovery service


Imagine you're running a Swarm cluster on a static configuration, similar to the one in Chapter 1, Welcome to Docker Swarm, networking is flat and every container is assigned a specific task, for example a MySQL database. It's easy to locate the MySQL container because you assigned it a defined IP address or you run some DNS server. It's easy to notify whether this single container is working or not and it's a known fact that it won't change its port (tcp/3336). Moreover, it's not necessary that our MySQL container announces its availability as a database container with its IP and port: We, of course, already know that.

This is the pet model, mocked-up manually by a system administrator. However, since we're more advanced operators, we want to drive a cattle instead.

So, imagine you're running a Swarm made of hundreds of nodes, hosting several applications running a certain number of services (web servers, databases, key-value stores, caches, and queues). These applications...

Token


Docker Swarm v1 includes an out-of-the-box discovery service, called Token. Token is integrated into the Docker Hub; hence, it requires all the Swarm nodes to be connected to the Internet and able to reach the Docker Hub. This is the main limitation of Token but, you will soon see, Token will allow us to make some practice in handling clusters.

In a nutshell, Token requires you to generate a UUID called, in fact, token. With this UUID, you can create a manager, act like a master, and join slaves to the cluster.

Re-architecting the example of Chapter 1 with token

If we want to keep it practical, it's time to take a look at an example. We'll use token to re-architect the example of Chapter 1, Welcome to Docker Swarm. As a novelty, the cluster will be not flat anymore, but it will consist of 1 master and 3 slaves and each node will have security enabled by default.

The master node will be the node exposing Swarm port 3376. We'll connect specifically to it in order to be able to drive the...

Raft


Consensus is an algorithm in distributed systems that forces agents in the system to agree on consistent values and elect a leader.

Some well-known consensus algorithms are Paxos and Raft. Paxos and Raft deliver similar performances but Raft is less complex, easier to understand, and therefore becoming very popular in distributed store implementations.

As consensus algorithms, Consul and Etcd implement Raft while ZooKeeper implements Paxos. The CoreOS Etcd Go library, implementing Raft, is included into SwarmKit and Swarm Mode as a dependency (in vendor/), so in this book we'll focus more on it.

Raft is described in detail in the Ongaro, Ousterhout paper, and it is available at https://ramcloud.stanford.edu/raft.pdf. In the upcoming section we'll summarize its basic concepts.

Raft theory

Raft was designed with simplicity in mind and compared to Paxos, it truly achieves this goal (there are even academic publications demonstrating this). For our purpose, the main difference between Raft and...

Etcd


Etcd is a highly available, distributed, and consistent key-value store that is used for shared configuration and service discovery. Some notable projects that use Etcd are SwarmKit, Kubernetes, and Fleet.

Etcd can gracefully manage master elections in case of network splits and can tolerate node failure, including the master. Applications, in our case Docker containers and Swarm nodes, can read and write data into Etcd's key-value storage, for example the location of services.

Re architecting the example of Chapter 1 with Etcd

We once again create an example with one manager and three nodes, this time by illustrating Etcd.

This time, we'll need a real discovery service. We can simulate a non-HA system by running the Etcd server inside Docker itself. We create a cluster made of four hosts, with the following names:

  • etcd-m will be the Swarm master and will host also the Etcd server

  • etcd-1: The first Swarm node

  • etcd-2: The second Swarm node

  • etcd-3: The third Swarm node

The operator, by connecting...

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Key benefits

  • *Get to grips with Docker Swarm, one of the key components of the Docker ecosystem.
  • Optimize Swarm and SwarmKit features for scaling massive applications through containers.
  • Learn about Docker’s scheduling tricks, high availability, security, and platform scalability.

Description

Docker Swarm serves as one of the crucial components of the Docker ecosystem and offers a native solution for you to orchestrate containers. It’s turning out to be one of the preferred choices for Docker clustering thanks to its recent improvements. This book covers Swarm, Swarm Mode, and SwarmKit. It gives you a guided tour on how Swarm works and how to work with Swarm. It describes how to set up local test installations and then moves to huge distributed infrastructures. You will be shown how Swarm works internally, what’s new in Swarmkit, how to automate big Swarm deployments, and how to configure and operate a Swarm cluster on the public and private cloud. This book will teach you how to meet the challenge of deploying massive production-ready applications and a huge number of containers on Swarm. You'll also cover advanced topics that include volumes, scheduling, a Libnetwork deep dive, security, and platform scalability.

Who is this book for?

If you are a Linux admin or a Docker user who wants to natively manage Docker clusters, then this is the book for you.

What you will learn

  • *Create and manage Swarm Mode clusters of any size
  • *Get a backstage view of the biggest Swarms ever built : Swarm2k and Swarm3k, with their 2,300 and 4,700 nodes
  • *Discovery mechanisms and Raft
  • *Deploy your containerized app on Swarm?
  • *Administer Swarm clusters on AWS, Azure, and DigitalOcean
  • *Integrate Flocker volumes with Swarm
  • *Create and manage Swarms on OpenStack Magnum?
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Publication date : Dec 20, 2016
Length: 280 pages
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Language : English
ISBN-13 : 9781786469755
Vendor :
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Table of Contents

11 Chapters
1. Welcome to Docker Swarm Chevron down icon Chevron up icon
2. Discover the Discovery Services Chevron down icon Chevron up icon
3. Meeting Docker Swarm Mode Chevron down icon Chevron up icon
4. Creating a Production-Grade Swarm Chevron down icon Chevron up icon
5. Administer a Swarm Cluster Chevron down icon Chevron up icon
6. Deploy Real Applications on Swarm Chevron down icon Chevron up icon
7. Scaling Up Your Platform Chevron down icon Chevron up icon
8. Exploring Additional Features of Swarm Chevron down icon Chevron up icon
9. Securing a Swarm Cluster and the Docker Software Supply Chain Chevron down icon Chevron up icon
10. Swarm and the Cloud Chevron down icon Chevron up icon
11. What is next? Chevron down icon Chevron up icon
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