The purpose of this article is to explore new Docker networking features introduced in version 1.9. We’ll apply them to a Docker Swarm cluster. For practice purposes we’ll be deploying containers to a Swarm cluster created locally with Vagrant and running Consul as a service registry and Registrator as a tool that will monitor Docker daemons and register/de-register containers we run/stop. When combined, Consul and Registrator will act as service discovery within our cluster. I won’t go into details how Docker Swarm or service discovery works. You can find more information about those subject in the following articles.
In the previous article we switched from manual to automatic deployment with Jenkins and Ansible. In the quest for zero-downtime we employed Consul to check health of our services and, if one of them fails, initiate deployment through Jenkins.
In this article we’ll explore how to scale individual services.
In the previous article we manually deployed the first version of our service together with a separate instance of the Mongo DB container. Both are (probably) running on different servers. Docker Swarm decided where to run our containers and Consul stored information about service IPs and ports as well as other useful information. That data was used to link one service with another as well as to provide information nginx needed to create proxy.
We’ll continue where we left and deploy a second version of our service. Since we’re practicing blue/green deployment, the first version was called blue and the next one will be green. This time there will be some additional complications. Deploying the second time is a bit more complicated since there are additional things to consider, especially since our goal is to have no downtime.
The previous article showed how scaling across the server farm looks like. We’ll continue where we left and explore details behind the presented implementation. Orchestration has been done through Ansible. Besides details behind tasks in Ansible playbooks, we’ll see how the same result could be accomplished using manual commands in case you might prefer a different orchestration/deployment framework.
Now it’s time to extend what we did in previous articles and scale services across any number of servers. We’ll treat all servers as one server farm and deploy containers not to predefined locations but to those that have the least number of containers running. Instead of thinking about each server as an individual place where we deploy, we’ll treat all of them as one unit.
With Docker there was not supposed to be a need to store logs in files. We should output information to stdout/stderr and the rest will be taken care by Docker itself. When we need to inspect logs all we are supposed to do is run docker logs [CONTAINER_NAME].
With Docker and ever more popular usage of micro services, number of deployed containers is increasing rapidly. Monitoring logs for each container separately quickly becomes a nightmare. Monitoring few or even ten containers individually is not hard. When that number starts moving towards tens or hundreds, individual logging is unpractical at best. If we add distributed services the situation gets even worst. Not only that we have many containers but they are distributed across many servers.
The solution is to use some kind of centralized logging. Our favourite combination is ELK stack (ElasticSearch, LogStash and Kibana). However, centralized logging with Docker on large-scale was not a trivial thing to do (until version 1.6 was released). We had a couple of solutions but none of them seemed good enough. Continue reading →
REST stands for Representational State Transfer. It relies on a stateless, client-server, cacheable communications. In most cases it is used with the HTTP protocol.
RESTful applications use HTTP requests to POST (create), PUT (create and/or update), GET (e.g., make queries), and DELETE data. REST uses HTTP for all four CRUD (Create/Read/Update/Delete) operations. Continue reading →