My goal is to compare two docker solutions for my golang app:
use ubuntu as base image
use golang:alpine as base image
My Dockerfile is quite straight-forward, similar to:
FROM ubuntu:20.04
# FROM golang:alpine for alpine based images
# myApp binary is pre-built before running docker build
COPY bin/myApp app/myApp
COPY myApp-config.json app/myApp-config.json
CMD MYAPP_CONFIG=app/myApp-config.json ./app/myApp
With alpine-based images, I hit the same issue here, where /app/myApp cannot be started due to missing CGO dynamic links in my generated image. I addressed it by disabling CGO during go build:
CGO_ENABLED=0
Since I'm quite new to docker, my questions are:
Is there any risk disabling CGO? My understanding is that go build will fall back to native go implementation of CGO, but I don't know whether it has any hidden traps. My app does have critical dependency on net/http which seems to be requiring presence of CGO during runtime.
It seems to be that alpine images are the defacto base image to use for golang, what is the standard way to deal with this CGO problem?
Thanks!
If your app is net/http based - then really the only consideration you may need to worry about is with DNS resolution.
TL;DR
The only gotchas you may see is in a kubernetes environment with a hostname
ending in .local
With CGO_ENABLED=1 (the go build default) it will use the native OS's DNS resolver.
With CGO_ENABLED=0 used with scratch Docker builds - then Go's DNS resolver is used.
What's the difference? See the official Go docs:
The method for resolving domain names, whether indirectly with
functions like Dial or directly with functions like LookupHost and
LookupAddr, varies by operating system.
On Unix systems, the resolver has two options for resolving names. It
can use a pure Go resolver that sends DNS requests directly to the
servers listed in /etc/resolv.conf, or it can use a cgo-based resolver
that calls C library routines such as getaddrinfo and getnameinfo.
By default the pure Go resolver is used, because a blocked DNS request
consumes only a goroutine, while a blocked C call consumes an
operating system thread. When cgo is available, the cgo-based resolver
is used instead under a variety of conditions: on systems that do not
let programs make direct DNS requests (OS X), when the LOCALDOMAIN
environment variable is present (even if empty), when the RES_OPTIONS
or HOSTALIASES environment variable is non-empty, when the ASR_CONFIG
environment variable is non-empty (OpenBSD only), when
/etc/resolv.conf or /etc/nsswitch.conf specify the use of features
that the Go resolver does not implement, and when the name being
looked up ends in .local or is an mDNS name.
Personally, I've built dozens of net/http based microservices running in kubernetes in scratch Docker containers with CGO_ENABLED=0 without issue.
I have a couple of questions related to the usage of Docker in a development phase.
I am going to propose three different scenarios of how I think Docker could be used in a development environment. Let's imagine that we are creating a REST API in Java and Spring Boot. For this I will need a MySQL database.
The first scenario is to have a docker-compose for development with the MySQL container and a production docker-compose with MySQL and the Java application (jar) in another container. To develop I launch the docker-compose-dev.yml to start only the database. The application is launched and debugged using the IDE, for example, IntelliJ Idea. Any changes made to the code, the IDE will recognize and relaunch the application by applying the changes.
The second scenario is to have, for both the development and production environment, a docker-compose with the database and application containers. That way, every time I make a change in the code, I have to rebuild the image so that the changes are loaded in the image and the containers are lauched again. This scenario may be the most typical and used for development with Docker, but it seems very slow due to the need to rebuild the image every time there is a change.
The third scenario consists of the mixture of the previous two. Two docker-compose. The development docker-compose contains both containers, but with mechanisms that allow a live reload of the application, mapping volumes and using, for example, Spring Dev Tools. In this way, the containers are launched and, in case of any change in the files, the application container will detect that there is a change and will be relaunched. For production, a docker-compose would be created simply with both containers, but without the functionality of live reload. This would be the ideal scenario, in my opinion, but I think it is very dependent on the technologies used since not all allow live reload.
The questions are as follows.
Which of these scenarios is the most typical when using Docker for phase?
Is scenario 1 well raised? That is, dockerize only external services, such as databases, queues, etc. and perform the development and debugging of the application with the IDE without using Docker for it.
The doubts and the scenarios that I raise came up after I raised the problem that scenario 2 has. With each change in the code, having to rebuild the image and start the containers again is a significant waste of time. In short, a question would be: How to avoid this?
Thanks in advance for your time.
NOTE: It may be a question subject to opinion, but it would be nice to know how developers usually deal with these problems.
Disclaimer: this is my own opinion on the subject as asked by Mr. Mars. Even though I did my best to back my answer with actual sources, it's mostly based on my own experience and a bit of common sense
Which of these scenarios is the most typical when using Docker for development?
I have seen all 3 scenarios iin several projects, each of them with their advantages and drawbacks. However I think scenario 3 with a Docker Compose allowing for dynamic code reload is the most advantageous in term of flexibility and consistency:
Dev and Prod Docker Compose are close matches, meaning Dev environment is as close as possible to Prod environment
You do not have to rebuild the image constantly when developping, but it's easy to do when you need to
Lots of technologies support such scenario, such as Spring Dev Tools as you mentionned, but also Python Flask, etc.
You can easily leverage Docker Compose extends a.k.a configuration sharing mechanism (also possible with scenario 2)
Is scenario 1 well raised? That is, dockerize only external services, such as databases, queues, etc. and perform the development and debugging of the application with the IDE without using Docker for it.
Scenario 1 is quite common, but the IDE environment would probably be different than the one from the Docker container (and it would be difficult to maintain a match of version for each libs, dependencies, etc. from IDE environment to Docker environment). It would also probably require to go through an intermediate step between Dev and Production to actually test the Docker image built after Dev is working before going to Production.
In my own experience doing this is great when you do not want to deal too much with Docker when actually doing dev and/or the language or technology you use is not adapted for dynamic reload as described in scenario 3. But in the end it only adds a drift between your environments and more complexity between Dev and Prod deployment method.
having to rebuild the image and start the containers again is a significant waste of time. In short, a question would be: How to avoid this?
Beside the scenarios you describe, you have ways to decently (even drastically) reduce image build time by leveraging Docker build cache and designing your Dockerfile. For example, a Python application would typically copy code as the last (or almost last) step of the build to avoid invalidating the cache, and for Java app it would be possible to split code so as to avoid compiling the entire application everytime a bit of code changes - that would depend on your actual setup.
I personally use a workflow roughly matching scenario 3 such as:
a docker-compose.yml file corresponding to my Production environment
a docker-compose.dev.yml which will override some aspect of my main Docker Compose file such as mouting code from my machine, adding dev specific flags to commands, etc. - it would be run such as
docker-compose -f docker-compose.yml -f docker-compose.dev.yml
but it would also be possible to have a docker-compose.override.yml as Docker Compose uses by default for override
in some situation I would have to use other overrides for specific situations such as docker-compose.ci.yml on my CI, but usually the main Docker Compose file is enough to describe my Prod environment (and if that's not the case, docker-compose.prod.yml does the trick)
I've seen them all used in different scenarios. There are some gotchas to avoid:
Applications inside of a container shouldn't depend on something running outside of a container on the host. So all of your dependencies should be containerized first.
File permissions with host volumes can be complicated depending on your version of docker. Some of the newer Docker Desktop installs automatically handle uid mappings, but if you develop directly on Linux you'll need to ensure the containers run as the same uid as your host user.
Avoid making changing inside the container if that isn't mapped into a host volume, since those changes will be lost when the container is recreated.
Looking at each of the options, here's my assessment of each:
Containerizing just the DB: This works well when developers already have a development environment for the language of choice, and there's no risk of external dependencies creeping in, e.g. a developer upgrading their JDK install to a newer version than the image is built with. It follows the idea of containerizing the dependencies first, while also giving developers the familiar IDE integration with their application.
Rebuilding the Image for Every Change: This tends to be the least ideal for developer workflow, but the quickest to implement when you're not familiar with the tooling. I'll give a 4th option that I consider an improvement to this.
Everything in a container, volume mounts, and live reloading: This is the most complicated to implement, and requires the language itself to support things like live reloading. However, when they do, it is nearly seamless for the developers and gets them up to speed on a new project quickly without needing to install any other tooling to get started.
Rebuild the app in the container with volume mounts: This is a halfway point between 2 and 3. When you don't have live reloading, you likely need to recompile or restart the interpreter to see any change. Rather than rebuilding the image, I put the recompile step in the entrypoint of a development image. I'll mount the code into the container, and run a full JDK instead of just a JRE (or whatever compiler is needed). I use named volumes for any dependency caches so they don't need to download on every restart. Then the method to see the changes is to restart that one container. The steps are identical to a compiled binary outside of a container, stop the old service, recompile, and restart the service, but now it happens inside of a container that should have the same tools used when building the production image.
For option 4, I tend to use a multi-stage build that has stages for build, develop, and release. The build stage pulls in the code and compiles it, the develop stage is the same base image as build but with an entrypoint that does the compile/run, and the release stage copies the result of the build stage into a minimal runtime. Developers then have a compose file for development that creates the development image and runs that with volume mounts and any debugging ports opened.
First of all, docker-compose is just for development and also testing phase, not for production. Example:
With a minimal and basic docker-compose, all your containers will run in the same machine? For development purposes it is ok, but in production, put all the apps in just one machine is a risk
Official link https://docs.docker.com/compose/production/
We will assume
01 java api
01 mysql database
01 web application that needs the api
all of these applications are already in production
Quick Answer
If you need to fix or add new feature to the java api, I advice you to use an ide like eclipse or IntelliJ Idea. Why?
Because java needs compilation.
Compile inside a docker container will take more time due to maven dependencies
IDE has code auto completion
etc
In this development phase, Docker helps you with one of its most powerful features: "Bring the production containers to your localhost". Yeah, in this case, docker-compose.yml is the best option because with one file, you could start everything you need : mysql database and web app but not your java api. Open your java api with your favorite ide.
Anyway if you want to use docker to "develop", you just need the Dockerfile and perform a docker build ... when you need to run your source code in your localhost
Basic Devops Life cycle with docker
Developer push source code changes using git
Your continuous integration (C.I) platform detect this change and perform
docker build ... (In this step, unit test are triggered)
docker push to your private hub. Container is uploaded in this step and will be used to deployments on other servers.
docker run or container deploy to the next environment : testing
Human testers ,selenium or another automation start their work
If no errors are detected, your C.I perform perform a final deploy of the uploaded container to your production environment. No docker build are required, just deploy or docker run.
Some Tips
Docker features are awesome but sometimes add too much complexity. So stop using volumes , hard disk dependency, logs, or complex configurations. If you use volumes, what will happen when your containers are in different hosts?
Java and Nodejs are a stable languages and your rest api or web apps does not need crazy configurations. Just maven compilation and java -jar ... or npm install and npm run start.
For logs you could use https://www.graylog.org/, google stasckdriver or another log management.
And like Heroku, stop using hard disk dependency as much as possible. In heroku platform disk are disposable, it means disappear when app is restarted. So instead of local file storage, you could use another file storage service with a lot of functionalities.
With this approaches, your containers can be deployed anywhere in a simple way
I'm using something similar to your 3rd scenario for my web dev, but it is Node-based. So I have 3 docker-compose files (actually 4, one is base and having all common stuff for others) for dev, staging and production environments.
Staging docker-compose config is similar to production config excluding SSL, ports and other things that may not allow to use it locally.
I have a separate container for each service (like DB, queue), and for dev, I also have additional dev DB and queue containers mostly for running auto-tests. In dev environment, all source are mounted into containers, so it allows to use IDE/editor of choice outside the container, and see changes inside.
I use supervisor to manage my workers inside a container with workers and have some commands to restart my workers manually when I need this. Maybe you can have something similar to recompile/restart your Java app. Or if you have an idea of how to organize app source code changes detection and your app auto-reloading, than could be the best variant. By the way, you gave me an idea to research something similar suitable for my case.
For staging and production environment, my source code is included inside the corresponding container using production Dockerfile. And I have some commands to restart all stuff using an environment I need, and this typically includes rebuilding containers, but because of Docker cache, it doesn't take much time (about 20 seconds). And taking into account that switching between environments is not a too frequent operation I feel quite comfortable with this.
Production docker-compose config is used only during deployment because it enables SSL, proper ports and has some additional production stuff.
Update for details on backend app restarting using Supervisor:
This is how I use it in my projects:
A part of my Dockerfile with installing Supervisor:
FROM node:10.15.2-stretch-slim
RUN apt-get update && apt-get install -y \
# Supervisor
supervisor \
...
...
# Configs for services/workers managed by supervisor
COPY some/path/worker-configs/*.conf /etc/supervisor/conf.d/
This is an example of one of Supervisor configs for a worker:
[program:myWorkerName]
command=/usr/local/bin/node /app/workers/my-worker.js
user=root
numprocs=1
stopsignal=INT
autostart=true
autorestart=true
startretries=10
In this example in your case command should run your Java app.
And this is an example of command aliases for convenient managing Supervisor from outside of containers. I'm using Makefile as a universal runner of all commands, but this can be something else.
# Used to run all workers
su-start:
#docker exec -t MY-WORKERS-CONTAINER-NAME supervisorctl start all
# Used to stop all workers
su-stop:
#docker exec -t MY-WORKERS-CONTAINER-NAME supervisorctl stop all
# Used to restart all workers
su-restart:
#docker exec -t MY-WORKERS-CONTAINER-NAME supervisorctl restart all
# Used to check status of all workers
su-status:
#docker exec -t MY-WORKERS-CONTAINER-NAME supervisorctl status
As I described above these Supervisor commands need to be run manually, but I think it is possible to implement maybe another Node-based worker or some watcher outside of a container with workers that will detect file system changes for sources directory and run these commands automatically. I think it is possible to implement something like this using Java as well like this or this.
On the other hand, it needs to be done carefully to avoid constant restarting workers on every little change.
I'm building my first Dockerfile for a go application and I don't understand while go build or go install are considered a necessary part of the docker container.
I know this can be avoided using muilt-stage but I don't know why it was ever put in the container image in the first place.
What I would expect:
I have a go application 'go-awesome'
I can build it locally from cmd/go-awesome
My Dockerfile contains not much more than
COPY go-awesome .
CMD ["go-awesome"]
What is the downside of this configuration? What do I gain by instead doing
COPY . .
RUN go get ./...
RUN go install ./..
Links to posts showing building go applications as part of the Dockerfile
https://www.callicoder.com/docker-golang-image-container-example/
https://blog.codeship.com/building-minimal-docker-containers-for-go-applications/
https://www.cloudreach.com/blog/containerize-this-golang-dockerfiles/
https://medium.com/travis-on-docker/how-to-dockerize-your-go-golang-app-542af15c27a2
You are correct that you can compile your application locally and simply copy the executable into a suitable docker image.
However, there are benefits to compiling the application inside the docker build, particularly for larger projects with multiple collaborators. Specifically the following reasons come to mind:
There are no local dependencies (aside from docker) required to build the application source. Someone wouldn't even need to have go installed. This is especially valuable for projects in which multiple languages are in use. Consider someone who might want to edit an HTML template inside of a go project and see what that looked like in the container runtime..
The build environment (version of go, dependency managment, file paths...) is constant. Any external dependencies can be safely managed and maintained via the Dockerfile.
I have an application which can be installed with ansible. No I want to create docker image that includes installed application.
My idea is to up docker container from some base image, after that start installation from external machine, to this docker container. After that create image from this container.
I am just starting with dockers, could you please advise if it is good idea and how can I do it?
This isn’t the standard way to create a Docker image and it isn’t what I’d do, but it will work. Consider looking at a tool like Hashicorp’s Packer that can automate this sequence.
Ignoring the specific details of the tools, the important thing about the docker build sequence is that you have some file checked into source control that an automated process can use to build a Docker image. An Ansible playbook coupled with a Packer JSON template would meet this same basic requirement.
The important thing here though is that there are some key differences between the Docker runtime environment and a bare-metal system or VM that you’d typically configure with Ansible: it’s unlikely you’ll be able to use your existing playbook unmodified. For example, if your playbook tries to configure system daemons, install a systemd unit file, add ssh users, or other standard system administrative tasks, these generally aren’t relevant or useful in Docker.
I’d suggest making at least one attempt to package your application using a standard Dockerfile to actually understand the ecosystem. Don’t expect to be able to use an Ansible playbook unmodified in a Docker environment; but if your organization has a lot of Ansible experience and you can easily separate “install the application” from “configure the server”, the path you’re suggesting is technically fine.
You can use multi-stage builds in Docker, which might be a nice solution:
FROM ansible/centos7-ansible:stable as builder
COPY playbook.yaml .
RUN ansible-playbook playbook.yaml
FROM alpine:latest # Include whatever image you need for your application
# Add required setup for your app
COPY --from=builder . . # Copy files build in the ansible image, aka your app
CMD ["<command to run your app>"]
Hopefully the example is clear enough for you to create your Dockerfile
I have a Node.JS based application consisting of three services. One is a web application, and two are internal APIs. The web application needs to talk to the APIs to do its work, but I do not want to hard-code the IP address and ports of the other services into the codebase.
In my local environment I am using the nifty envify Node.JS module to fix this. Basically, I can pretend that I have access to environment variables while I'm writing the code, and then use the envify CLI tool to convert those variables to hard-coded strings in the final browserified file.
I would like to containerize this solution and deploy it to Kubernetes. This is where I run into issues...
I've defined a couple of ARG variables in my Docker image template. These get turned into environment variables via RUN export FOO=${FOO}, and after running npm run-script build I have the container I need. OK, so I can run:
docker build . -t residentmario/my_foo_app:latest --build-arg FOO=localhost:9000 BAR=localhost:3000
And then push that up to the registry with docker push.
My qualm with this approach is that I've only succeeded in punting having hard-coded variables to the container image. What I really want is to define the paths at pod initialization time. Is this possible?
Edit: Here are two solutions.
PostStart
Kubernetes comes with a lifecycle hook called PostStart. This is described briefly in "Container Lifecycle Hooks".
This hook fires as soon as the container reaches ContainerCreated status, e.g. the container is done being pulled and is fully initialized. You can then use the hook to jump into the container and run arbitrary commands.
In our case, I can create a PostStart event that, when triggered, rebuilds the application with the correct paths.
Unless you created a Docker image that doesn't actually run anything (which seems wrong to me, but let me know if this is considered an OK practice), this does require some duplicate work: stopping the application, rerunning the build process, and starting the application up again.
Command
Per the comment below, this event doesn't necessarily fire at the right time. Here's another way to do it that's guaranteed to work (and hence, superior).
A useful Docker container ends with some variant on a CMD serving the application. You can overwrite this run command in Kubernetes, as explained in the "Define a Command and Arguments for a Container" section of the documentation.
So I added a command to the pod definition that ran a shell script that (1) rebuilt the application using the correct paths, provided as an environment variable to the pod and (2) started serving the application:
command: ["/bin/sh"]
args: ["./scripts/build.sh"]
Worked like a charm.