I have a problem with docker compose and build order. Below is my dockerfile for starting my .net application
As you can see as part of my build process I run some tests using "RUN dotnet test backend_test/backend_test.csproj"
These tests require a mongodb database to be present.
I try to solve this dependency with docker-compose and its "depends_on" feature, see below.
However this doesn't seem to work as when I run "docker-compose up" I get the following:
The tests eventually timeout since there is no mongodb present.
Does depends_on actually affect build order at all or does it only affect start-order (i.e builds everything the proceeds to start in correct order) ?
Is there another way of doing this ? (I want tests to run as part of building my final app)
Ty in advance, let me know If you need extra information
As you guessed, depends_on is for runtime order only, not build time - it just affects docker-compose up and docker-compose stop.
I highly recommend you make all the builds independent of each other. Perhaps you need to consider separate builder and runtime images here, and / or use a Docker-based CI (Gitlab, Travis, Circle etc) to have these dependencies available for testing.
Note also, depends_on often disappoints people - as it just waits for Docker's startup to finish, not the application startup. So your DB / service / whatever may still be starting up when the container that depends on it start will start using it, causing timeouts etc. This is why HEALTH_CHECK now exists (with a similar healthcheck feature in Docker Compose)
Related
How to run docker-compose across different lifecycle environments (say dev, qa, staging, production).
Sometimes a larger VM is being shared by multiple developers, so would like to start the containers with appropriate developer specific suffixes (say dev1, dev2, dev3 ..). Should port customization be handled manually via the environment file (i.e. .env file)
This is an unusual use case for docker-compose, but I'll leave some tips anyway! :)
There's two different ways to name stuff you start with docker-compose. One is to name the service that you specify under the main services: key of your docker-compose.yml file. By default, individual running containers will be assigned names indicating what project they are from (by default, the name of the directory from which your docker-compose file is in), what service they run (this is what's specified under your services: key), and which instance of that service they are (this number changes if eg. you're using replicas). Eg. default container names for a service named myservice specified in a compose file ~/my_project/docker/docker-compose.yml will have a name like docker_myservice_1 (or _2, _3, etc if more than one container is supposed to run).
You can use environment variables to specify a lot of key-value pairs in docker-compose files, but you can't conditionally specify the service name - service keys are only allowed to have alphanumeric characters in them and compose files can't look like eg:
version: "3"
services:
${ENVVAR}:
image: ubuntu:20.04
However, you can override the container naming scheme by using the container_name field in your docker-compose file (documentation for usage here). Maybe a solution you could use looks like this:
version: "3"
services:
myservice:
image: ubuntu:20.04
container_name: ${DEVELOPER_ENVVAR?err}
this will require a developer to specify DEVELOPER_ENVVAR at runtime, either by exporting it in their shell or by running docker-compose like DEVELOPER_ENVVAR=myservice_dev1 docker-compose up. Note that using container_name is incompatible with using replicas to run multiple containers for the same service - the names have to be unique for those running containers, so you'll either have to define separate services for each name, or give up on using container_name.
However, you're in a pickle if you expect multiple developers to be able to run containers with different names using the same compose file in the same directory. That's because when starting a service, docker-compose has a Recreating step where, if there's already containers implementing that service running, they'll wait for that container to finish. Ultimately, I think this is for the best - if multiple developers were trying to run the exact same compose project at once, should a developer have control over other developers' running containers? Probably not, right?
If you want multiple developers to be able to run services at once in the same VM, I think you probably want to do two things:
first, (and you may well have already done this! but it's still a good reminder) make sure that this is a good idea. Are there going to be resource contention issues (eg. for port-forwarding) that make different running instances of your project conflict? For many Docker services, there are going to be, but there probably won't be for eg. images that are meant to be run in a swarm.
second, have different compose files checked out in different directories, so that there are separate compose projects for each developer. To use .env files one way one obvious option is to just maintain separate copies, one per developer directory. If, for your use case, it's unsatisfactory to maintain one copy of .env per developer this way, you could use symlinks named .env (or whatever your env file is named) to the same file somewhere else on the VM.
After you've done this, you'll be able to tell from the container names who is running what.
If none of these are satisfactory, you might want to consider, eg. using one VM per developer, or maybe even considering using a different container management system than docker-compose.
I have done very similar automation and I've used Ansible to create "docker compose" config on the fly.
So based on input-Environment , the ansible playbook will create the relevant docker-compose file. So basically I have a docker-compose template in my git repository with values that are dynamic and ansible playbook populates them etc.
and also you can use ansible to trigger such creation or automation one after another
A similar sample has been posted at ansible_docker_splunk repository.
Basically the whole project is to automate end-to-end docker cluster from CSV file
I have a python app that builds a dataset for a machine learning task on GCP.
Currently I have to start an instance of a VM that we have, and then SSH in, and run the app, which will complete in 2-24 hours depending on the size of the dataset requested.
Once the dataset is complete the VM needs to be shutdown so we don't incur additional charges.
I am looking to streamline this process as much as possible, so that we have a "1 click" or "1 command" solution, but I'm not sure the best way to go about it.
From what I've read about so far it seems like containers might be a good way to go, but I'm inexperienced with docker.
Can I setup a container that will pip install the latest app from our private GitHub and execute the dataset build before shutting down? How would I pass information to the container such as where to get the config file etc? It's conceivable that we will have multiple datasets being generated at the same time based on different config files.
Is there a better gcloud feature that suits our purpose more effectively than containers?
I'm struggling to get information regarding these basic questions, it seems like container tutorials are dominated by web apps.
It would be useful to have a batch-like container service that runs a container until its process completes. I'm unsure whether such a service exists. I'm most familiar with Google Cloud Platform and this provides a wealth of compute and container services. However -- to your point -- these predominantly scale by (HTTP) requests.
One possibility may be Cloud Run and to trigger jobs using Cloud Pub/Sub. I see there's async capabilities too and this may be interesting (I've not explored).
Another runtime for you to consider is Kubernetes itself. While Kubernetes requires some overhead in having Google, AWS or Azure manage a cluster for you (I strongly recommend you don't run Kubernetes yourself) and some inertia in the capacity of the cluster's nodes vs. the needs of your jobs, as you scale the number of jobs, you will smooth these needs. A big advantage with Kubernetes is that it will scale (nodes|pods) as you need them. You tell Kubernetes to run X container jobs, it does it (and cleans-up) without much additional management on your part.
I'm biased and approach the container vs image question mostly from a perspective of defaulting to container-first. In this case, you'd receive several benefits from containerizing your solution:
reproducible: the same image is more probable to produce the same results
deployability: container run vs. manage OS, app stack, test for consistency etc.
maintainable: smaller image representing your app, less work to maintain it
One (beneficial!?) workflow change if you choose to use containers is that you will need to build your images before using them. Something like Knative combines these steps but, I'd stick with doing-this-yourself initially. A common solution is to trigger builds (Docker, GitHub Actions, Cloud Build) from your source code repo. Commonly you would run tests against the images that are built but you may also run your machine-learning tasks this way too.
Your containers would container only your code. When you build your container images, you would pip install, perhaps pip install --requirement requirements.txt to pull the appropriate packages. Your data (models?) are better kept separate from your code when this makes sense. When your runtime platform runs containers for you, you provide configuration information (environment variables and|or flags) to the container.
The use of a startup script seems to better fit the bill compared to containers. The instance always executes startup scripts as root, thus you can do anything you like, as the command will be executed as root.
A startup script will perform automated tasks every time your instance boots up. Startup scripts can perform many actions, such as installing software, performing updates, turning on services, and any other tasks defined in the script.
Keep in mind that a startup script cannot stop an instance but you can stop an instance through the guest operating system.
This would be the ideal solution for the question you posed. This would require you to make a small change in your Python app where the Operating system shuts off when the dataset is complete.
Q1) Can I setup a container that will pip install the latest app from our private GitHub and execute the dataset build before shutting down?
A1) Medium has a great article on installing a package from a private git repo inside a container. You can execute the dataset build before shutting down.
Q2) How would I pass information to the container such as where to get the config file etc?
A2) You can use ENV to set an environment variable. These will be available within the container.
You may consider looking into Docker for more information about container.
I'm not sure I completely understand the role of Docker in the process of development and deployment.
Say, I create a Dockerfile with nginx, some database and something else which creates a container and runs fine.
I drop it somewhere in the cloud and execute it to install and configure all the dependencies and environment settings.
Next, I have a repository with a web application which I want to run inside the container I created and deployed in the first 2 steps. I regularly work on it and push the changes.
Now, how do I integrate the web application into the container?
Do I put it as a dependency inside the Dockerfile I create in the 1st step and recreate the container each time from scratch?
Or, do I deploy the container once but have procedures inside Dockerfile that install utils that pull the code from repo by command or via hooks?
What if a container is running but I want to change some settings of, say, nginx? Do I add these changes into Dockerfile and recreate the image?
In general, what's the role of Docker in the daily app development routine? Is it used often if the infrastructure is running fine and only code is changing?
I think there is no singl "use only this" answer - as you already outlined, there are different viable concepts available.
Deployment to staging/production/pre-production
a)
Do I put it as a dependency inside the Dockerfile I create in the 1st step and recreate the container each time from scratch?
This is for sure the most docker`ish way and aligns fully with he docker-philosophy. It is highly portable, reproducible and suites anything, from one container to "swarm" thousands of. E.g. this concept has no issue suddenly scaling horizontally when you need more containers, lets say due to heavy traffic / load.
It also aligns with the idea that only the configuration/data should be dynamic in a docker container, not code / binaries /artifacts
This strategy should be chosen for production use, so when not as frequent deployments happen. If you care about downtimes during container-rebuilds (on upgrade), there are good concepts to deal with that too.
We use this for production and pre-production intances.
b)
Or, do I deploy the container once but have procedures inside
Dockerfile that install utils that pull the code from repo by command
or via hooks?
This is a more common practice for very frequent deployment. You can go the pull ( what you said ) or the push (docker cp / ssh scp) concept, while i guess the latter is preferred in this kind of environment.
We use this for any kind strategy for staging instances, which basically should reflect the current "codebase" and its status. We also use this for smoke-tests and CI, but depending on the application. If the app actually changes its dependencies a lot and a clean build requires a rebuild with those to really ensure stuff is tested as it is supposed to, we actually rebuild the image during CI.
Configuration management
1.
What if a container is running but I want to change some settings of,
say, nginx? Do I add these changes into Dockerfile and recreate the
image?
I am not using this as c) since this is configuration management, not applications deployment and the answer to this can be very complicated, depending on your case. In general, if redeployment needs configuration changes, it depends on your configuration management, if you can go with b) or always have to go a).
E.g. if you use https://github.com/markround/tiller with consul as the backend, you can push the configuration changes into consul, regenerating the configuration with tiller, while using consul watch -prefix /configuration tiller as a watch-task to react on those value changes.
This enables you to go b) and fix the configuration
You can also use https://github.com/markround/tiller and on deployment, e.g. change ENV vars or some kind of yml file ( tiller supports different backends ), and call tiller during deployment yourself. This most probably needs you to have ssh or you ssh on the host and use docker cp and docker exec
Development
In development, you generally reuse your docker-compose.yml file you use for production, but overload it with docker-compose-dev.yml to e.g. mount your code-folder, set RAILS_ENV=development, reconfigurat / mount some other configurations like xdebug or more verbose nginx loggin, whatever you need. You can also add some fake MTA-services like fermata and so on
docker-compose -f docker-compose.yml -f docker-compose-dev.yml up
docker-compose-dev.yml only overloads some values, it does not redefine it or duplicate it.
Depending on how powerful your configuration management is, you can also do a pre-installation during development stack up.
We actually use scaffolding for that, we use https://github.com/xeger/docker-compose and after running it, we use docker exec and docker cp to preinstall a instance or stage something. Some examples are here https://github.com/EugenMayer/docker-sync/wiki/7.-Scripting-with-docker-sync
If you are developing under OSX and you face performance issues due to OSXFS / code shares, you probably want to have a look at http://docker-sync.io ( i am biased though )
We need to use Jenkins to test some web apps that each need:
a database (postgres in our case)
a search service (ElasticSearch in our case, but only sometimes)
a cache server, such as redis
So far, we've just had these services running on the Jenkins master, but this causes problems when we want to upgrade Postgres, ES or Redis versions. Not all apps can move in lock step, and we want to run the tests on new versions before committing to move an app in production.
What we'd like to do is have these services provided on a per-job-run basis, each one running in its own container.
What's the best way to orchestrate these containers?
How do you start up these ancillary containers and tear them down, regardless of whether to job succeeds or not?
how do you prevent port collisions between, say, the database in a run of a job for one web app and the database in the job for another web app?
Check docker-compose and write a docker-compose file for your tests.
The latest network features of Docker (private network) will help you to isolate builds running in parallel.
However, start learning docker-compose as if you only had one build at the same time. When confident with this, look further for advanced docker documentation around networking.
I have a repository which builds three different images:
powerpy-base
powerpy-web
powerpy-worker
Both powerpy-web and powerpy-worker inherit from powerpy-base using the FROM keyword in their Dockerfile.
I'm using Docker Compose in the project to run a Redis and RabbitMQ container. Is there a way for me to tell Docker Compose that I'd like to build the base image first and then the web and worker images?
You can use depends_on to enforce an order, however that order will also be applied during "runtime" (docker-compose up), which may not be correct.
If you're only using compose to build images it should be fine.
You could also split it into two compose files. a docker-compose.build.yml which has depends_on for build, and a separate one for running the images as services.
These is a related issue: https://github.com/docker/compose/issues/295
About run containers:
It was bug before, but they fixed it since docker-compose v1.10.
https://blog.docker.com/2016/02/docker-1-10/
Start linked containers in correct order when restarting daemon: This is a little thing, but if you’ve run into it you’ll know what a headache it is. If you restarted a daemon with linked containers, they sometimes failed to start up if the linked containers weren’t running yet. Engine will now attempt to start up containers in the correct order.
About build:
You need to build base image first.