This is the fourth blog post in a 4-part series on building a microservice architecture with Spring Boot and Docker. If you would like to read the previous posts in the series, please see Part 1, Part 2, and Part 3.
So now that we have a solid understanding of microservices and Docker, stood up a MongoDB container and Spring Boot microservice container and had them talk to each other via container linking (reference part 4/start from our Git repo to catch up), let’s put together a few more quick microservices. To complete our initial use case, we’ll need two more microservices — missions and rewards. I’ll jump ahead and build out those two in the exact same manner as we did for the employee microservice. You can reference branch ‘part4/step1’ to get these two extra service containers. Now, if we do a Docker ps, we’ll have (some columns removed for brevity):
CONTAINER ID IMAGE PORTS NAMES 86bd9bc19917 microservicedemo/employee 0.0.0.0:32779->8080/tcp employee 1c694e248c0a microservicedemo/reward 0.0.0.0:32775->8080/tcp reward c3b5c56ff3f9 microservicedemo/mission 0.0.0.0:32774->8080/tcp mission 48647d735188 mongo 0.0.0.0:32771->27017/tcp mongodb
This is all very simple, but not exactly functional because none of the microservices provide any direct value aside from simple CRUD at this point. Let’s start layering on some code changes to provide a bit more value and functionality. We’ll make some changes to one of our microservices, then deal with updating our running container to understand what goes into versioning containers. Since employees earn points by completing missions, we need to track their mission completions, point totals (earned and active), and rewards redeemed. We’ll add some additional classes to our employee model — these won’t be top-level business objects, so they won’t get their own microservices, but will instead provide context within the employee object. Once these changes are made (see ‘part4/step2’ branch) we’ll have some structural changes that need to be synchronized throughout the stack. The steps to update our container are:
docker build -t microservicedemo/employee .You’ll notice some messages at the end:
Removing intermediate container 5ca297c19885
Successfully build 088558247
docker stop employee
docker rm employee
docker run -P -d --name employee --link mongodb microservicedemo/employee
The important thing to note is that the code within the running container is not updated. Another core principle of containers and microservices is that the code and configuration within a container is immutable. To put it another way: you don’t update a container, you replace it. This can pose some problems for some container use cases, such as using them for databases or other persistent resources.
If, like me, you had other work to do between following this seres of articles, ensuring all of the various command line parameters to link up these containers can be a little frustrating. Organizing a fleet of containers is the purpose of Docker Compose (previously known as Fig). You define your set of containers in a YAML configuration file, and it manages the runtime configuration of the containers. In many ways, think of it as an orchestrator of “running” the containers with the correct options/configuration. We will create one for our application to do all of the things we’ve managed via command line parameters.
employee: build: employee ports: - "8080" links: - mongodb reward: build: reward ports: - "8080" links: - mongodb mission: build: mission ports: - "8080" links: - mongodb mongodb: image: mongo
Then, from a command prompt, you type:
docker-compose up -dAnd the entire fleet comes up. Pretty handy! Many docker commands have analogs in docker-compose. If we run “docker-compose ps,” we see:
Name Command State Ports ------------------------------------------------------------- git_employee_1 java -Dspring.data.mongodb ... Up 0.0.0.0:32789->8080/tcp git_mission_1 java -Dspring.data.mongodb ... Up 0.0.0.0:32785->8080/tcp git_mongodb_1 /entrypoint.sh mongod Up 27017/tcp git_reward_1 java -Dspring.data.mongodb ... Up 0.0.0.0:32784->8080/tcp
But that’s not all that docker-compose can do. If you run “docker-compose scale [compose container name]=3,” it will create mutiple instances of your container — running “docker-compose scale employee=3” then “docker-compose ps” we see:
Name Command State Ports --------------------------------------------------------------------------------- git_employee_1 java -Dspring.data.mongodb ... Up 0.0.0.0:32789->8080/tcp git_employee_2 java -Dspring.data.mongodb ... Up 0.0.0.0:32791->8080/tcp git_employee_3 java -Dspring.data.mongodb ... Up 0.0.0.0:32790->8080/tcp git_mission_1 java -Dspring.data.mongodb ... Up 0.0.0.0:32785->8080/tcp git_mongodb_1 /entrypoint.sh mongod Up 27017/tcp git_reward_1 java -Dspring.data.mongodb ... Up 0.0.0.0:32784->8080/tcp
and our employee container now has three instances! Docker-compose remembers the number you set, so the next time you run, it will start up 3 employee instances. Personally, I think this should be in the docker-compose.yml file, but it’s not.
Hopefully, you are starting to see a problem developing here. How are we supposed to make an end-user application that can actually use these microservices, since their ports change, and in a clustered Docker server environment (e.g. Docker Swarm), the “host” IP address could change too? There are some advanced solutions (Kubernetes and AWS’ ECS), but for now we’ll look at a (relatively) simple option. We’ll start with a very easy way to load balance multiple container instances. Tutum, a company that is structuring a multi-cloud container organization capability, has put out to the Docker community an extension of the HAProxy, which can auto-configure itself based on linked containers. Let’s add a load balancer to balance our multiple employee containers we now have. WE’ll just add that into our docker-compose.yml file:
… ha_employee: image: tutum/haproxy links: - employee ports: - "8080:80"
Then we run
"docker-compose up -d” and it will download and start the missing container. Now we can run our tests against a specific port (8080), which will load balance against all the running employee instances. After this, we can hit our employee service “cluster” at 192.168.99.100:8080, which will round robin (by default) across the three running instances. Talk about easy street! There are a lot of additional features and functionality within this HAProxy Docker container; I suggest looking at https://github.com/tutumcloud/haproxy for more information.
This HAProxy approach works fantastic for load balancing against multiple instances of a specific container and would be the ideal choice for a single container environment. However, we don’t have that here, do we? We could set up multiple HAProxy instances to handle container clusters exposing each proxy on a different host port, so our employee service is at port 8080, our mission is on port 8081, and the reward is on port 8082 (reference part4/step3 in the Git repository). If we were to go full production, we could pursue a leveraging nginx to create a reverse proxy that would mask all service requests to a single IP/port (route to the container based upon URL path /employee/ vs /reward/). Or we could use a more robust service discovery route, such as this, which leverages etcd and some impressive Docker meta-data scripting and template engines from Jason Wilder’s Docker-gen system (https://hub.docker.com/r/jwilder/docker-gen/), as well as a myriad of additional self-managed service discovery solutions. We’ll keep the simple HAProxy solution for now, as it gives us a solid understanding in managing container clusters.
This is a good place to wrap up this series for now. There are many additional areas that I could pontificate on, including:
What other areas around Docker and Microservices would you like to know? Let me know at email@example.com. I plan on making additional posts on this topic, continuing this use case scenario, and would be happy to pick the direction based on your feedback!
This blog is the fourth of four parts. The entire series: