Best practice to automatically backup remotely hosted server - docker

I am trying to setup a server for team note taking, and I am wondering what is the best way to backup its data, A.K.A my notes, automatically.
Currently I plan to run the server in a docker image.
The docker image will be hosted by a hosting service (such as Google).
I found a free hosting service that fits my need, but it does not allow mounting volumes to a docker image.
Therefore, I think the only way for me to backup my data is to transfer them to some other cloud services.
However, this requires that I have to store some sort of sensitive data for authentication in my docker image, apparently this is not cool.
So:
Is it possible to transfer data from a docker image to a cloud service without taking the risk of leaking password/private key?
Is there any other way to backup my data?
I don't have to use docker as all I need is actually Node.js.
But the server must be hosted on some remote machines because I don't have the ability/time/money to host a machine on my own...

I use borg backup to backup our servers (including docker volumes) ... and it's saved the day many times due to failure and stupidity.
It transfers over SSH so comms are encrypted. The repositories it uses are also encrypted on disk so that makes all your data safe. It de-duplicates, snapshots, prunes, compresses ... the feature list is quite large.
After the first backup, subsequent backups are much faster because it only submits the changes since the previous backup.
You can also mount the snapshots as filesystems so you can hunt down the single file you deleted or just restore the whole lot. The mounts can also be done remotely.
I've configured ours to backup /home, /etc and the /var/lib/docker/volumes directories (among others).
We rent a few cheap storage VPSs and send the data up to them nightly. They're in different geographic locations with different hosting providers, you know, because we're paranoid.

Beside docker swarm secrets, don't forget bind mounts strategies: you could have your data in a volume.
In that case, you can have a backup strategy done on the host (instead of the container at runtime), which would take that volume, compress it and save it elsewhere. See for instance this answer or this one.

Related

How to host Artifactory in ECS

We are planning to host our Artifactory on ECS (Fargate) and mount the data to EFS. We will use an ALB in front of the containers (8081 and 8082) We still have some open issues:
Can we use multiple containers at the same time or will there be upload/write issues to EFS?
Is EFS a good solution or is S3 better?
What about the metadata. I read Artifactory is hosting this in some Derby database. What if we redeploy a new container? Will the data be gone? Can this data be persisted on EFS or do we need RDS?
Can we use multiple containers at the same time or will there be upload/write issues to EFS?
Ans: Yes you can use multiple containers to host Artifactory instances in a single host. However it is generally recommended to use multiple host to avoid the 'single point of failure' scenario. I don't anticipate any RW issues with EFS/S3.
Is EFS a good solution or is S3 better?
Ans: In my opinion both S3 and EFS are better known as scalable solutions rather than high performance oriented and it completely depends on the use-case. You can overcome this issue by enabling cache-fs in Artifactory which will store the frequently used binaries in a defined place (like a local disk with higher RW speeds). You can read more about cache-fs here: https://jfrog.com/knowledge-base/what-is-cache-fs-video/
What about the metadata. I read Artifactory is hosting this in some Derby database. What if we redeploy a new container? Will the data be gone? Can this data be persisted on EFS or do we need RDS?
Ans: when you are configuring more than one Artifactory node it is mandatory to have an external database (RDS) to store the configurations/references. On a side note: Artifactory generates the metadata for the packages/artifacts and store them in the FS only. However the references will be stored in the DB

Sharing docker volumes within the workplace

I have taken some time to create a useful Docker volume for use at work. It has a restored backup of one of our software databases (SQL Server) on it, and I use it for testing/debug by just attaching it to whatever Linux SQL Container I feel like running at the time.
When I make useful Docker images at work, I share them with our team using either the Azure Container Registry or the AWS Elastic Container Registry. If there's a DockerFile I've made as part of a solution, I can store that in our GIT repo for others to access.
But what about volumes? Is there a way to share these with colleagues so they don't need to go through the process I went through to build the volume in the first place? So if I've got this 'databasevolume' is there a way to source control it? Or share it as a file to other users of Docker within my team? I'm just looking to save them the time of creating a volume, downloading the .bak file from its storage location, restoring it etc.
The short answer is that there is no default docker functionality to export the contents of a docker volume and docker export explicitly does not export the contents of the volumes associated with the container. You can backup, restore or migrate data volumes.
Note: if your're backing up a database I'd suggest using the appropriate tools for that database.

GCP: how to access cloud storage bucket from a VM instance

I'm trying to deploy and run a docker image in a GCP VM instance.
I need it to access a certain Cloud Storage Bucket (read and write).
How do I mount a bucket inside the VM? How do I mount a bucket inside the Docker container running in my VM?
I've been reading google cloud documentation for a while, but I'm still confused. All guides show how to access a bucket from a local machine, and not how to mount it to VM.
https://cloud.google.com/storage/docs/quickstart-gsutil
Found something about Fuse, but it looks overly complicated for just mounting a single bucket to VM filesystem.
Google Cloud Storage is a object storage API, it is not a filesystem. As a result, it isn't really designed to be "mounted" within a VM. It is designed to be highly durable and scalable to extraordinarily large objects (and large numbers of objects).
Though you can use gcsfuse to mount it as a filesystem, that method has pretty significant drawbacks. For example, it can be expensive in operation count to do even simple operations for a normal filesystem.
Likewise, there are many surprising behaviors that are a result of the fact that it is an object store. For example, you can't edit objects -- they are immutable. To give the illusion of writing to the middle of an object, the object is, in effect, deleted and recreated whenever a call to close() or fsync() happens.
The best way to use GCS is to design your application to use the API (or the S3 compatible API) directly. That way the semantics are well understood by the application, and you can optimize for them to get better performance and control your costs. Thus, to access it from your docker container, ensure your container has a way to authenticate through GCS (either through the credentials on the instance, or by deploying a key for a service account with the necessary permissions to access the bucket), then have the application call the API directly.
Finally, if what you need is actually a filesystem, and not specifically GCS, Google Cloud does offer at least 2 other options if you need a large mountable filesystem that is designed for that specific use case:
Persistent Disk, which is the baseline filesystem that you get with a VM, but you can mount many of these devices on a single VM. However, you can't mount them read/write to multiple VMs at once -- if you need to mount to multiple VMs, the persistent disk must be read only for all instances they are mounted to.
Cloud Filestore is a managed service that provides an NFS server in front of a persistent disk. Thus, the filesystem can be mounted read/write and shared across many VMs. However it is significantly more expensive (as of this writing, about $0.20/GB/month vs $0.04/GB/month in us-central1) than PD, and there are minimum size requirements (1TB).
Google Cloud Storage buckets cannot be mounted in Google Compute instances or containers without third-party software such as FUSE. Neither Linux nor Windows have built-in drivers for Cloud Storage.
GCS VM comes with google cloud SDK installed. So without mounting you can copy in and out files using those commands.
gsutil ls gs://

Where should production critical and non-production non-critical data stored?

I was asked this question in an interview and i m not sure of the correct answer hence I would like your suggestions.
I was asked whether we should persist production critical data inside of the docker instance or outside of it? What would be my choice and the reasons for it.
Would your answer differ incase we have a non-prod non critical data ?
Back your answers with reasons.
Most data should be managed externally to containers and container images. I tend to view data constrained to a container as temporary (intermediate|discardable) data. Otherwise, if it's being captured but it's not important to my business, why create it?
The name "container" is misleading. Containers aren't like VMs where there's a strong barrier (isolation) between VMs. When you run multiple containers on a single host, you can enumerate all their processes using ps aux on the host.
There are good arguments for maintaining separation between processes and data and running both within a single container makes it more challenging to retain this separation.
Unlike processes, files in container layers are more isolated though. Although the layers are manifest as files on the host OS, you can't simply ls a container layer's files from the host OS. This makes accessing the data in a container more complex. There's also a performance penalty for effectively running a file system atop another file system.
While it's common and trivial to move container images between machines (viz docker push and docker pull), it's less easy to move containers between machines. This isn't generally a problem for moving processes as these (config aside) are stateless and easy to move and recreate, but your data is state and you want to be able to move this data easily (for backups, recovery) and increasingly to move amongst a dynamic pool of nodes that perform processing upon it.
Less importantly but not unimportantly, it's relatively easy to perform the equivalent of a rm -rf * with Docker by removing containers (docker container rm ...) and thereby deleting the application and your data.
The two very most basic considerations you should have here:
Whenever a container gets deleted, everything in the container filesystem is lost.
It's extremely common to delete containers; it's required to change many startup options or to update a container to a newer image.
So you don't really want to keep anything "in the container" as its primary data storage: it's inaccessible from outside the container, and will get lost the next time there's a critical security update and you must delete the container.
In plain Docker, I'd suggest keeping
...in the image: your actual application (the compiled binary or its interpreted source as appropriate; this does not go in a volume)
...in the container: /tmp
...in a bind-mounted host directory: configuration files you need to push into the container at startup time; directories of log files produced by the container (things where you as an operator need to directly interact with the files)
...in either a named volume or bind-mounted host directory: persistent data the container records in the filesystem
On this last point, consider trying to avoid this layer altogether; keeping data in a database running "somewhere else" (could be another container, a cloud service like RDS, ...) simplifies things like backups and simplifies running multiple replicas of the same service. A host directory is easier to back up, but on some environments (MacOS) it's unacceptably slow.
My answers don't change here for "production" vs. "non-production" or "critical" vs. "non-critical", with limited exceptions you can justify by saying "it's okay if I lose this data" ("because it's not the master copy of it").

How do I use Docker on cloud or datacenter

I couldn't have enough courage to start using docker now I'm feel like came from last century. I want to clear my doubts about docker before get started. My question is mainly for deploying/running docker images on cloud or hosting environment.
Can I build a docker image with any type of server (eg. wildfly, payara) and/or database server (eg. mysql, oracle) and will it work on docker enabled cloud/datacenter?
If it's yes how about persistent datas like database files and static storages (eg. images, uploaded documents, logs) those are stored in docker images or somewhere else? What will happen to those files when I update my application and redeploy new image?
I read posts about what is docker but I couln't find specific answer. Forgive me for not doing enough googling.
I have run docker on AWS and other cloud providers. It is really not that hard if you have some experience with system administration and or devops. Regarding cloud hosters and getting started, most providers have some sort of tutorial on how to get started using docker with their infrastructure:
http://docs.aws.amazon.com/AmazonECS/latest/developerguide/docker-basics.html
https://azure.microsoft.com/en-us/documentation/articles/virtual-machines-linux-dockerextension/
Can I build a docker image with any type of server (eg. wildfly,
payara) and/or database server (eg. mysql, oracle) and will it work on
docker enabled cloud/datacenter?
To get a server up and running, you just need the docker engine installed on the host, there are packages for many distros:
https://docs.docker.com/engine/installation/
After docker engine is installed, you can create dockerfiles for basically any server or service. Hopefully you do not need to, in most cases, since there are countless docker files and pre-configured, vendor maintained images already available on dockerhub (I use wildfly, elk-stack, and mysql for example). Be careful about selecting images are maintained, otherwise you end up with security issues in your images that might never get fixed! Or you have to do it yourself!
Example images:
https://hub.docker.com/r/jboss/wildfly/
https://hub.docker.com/_/mysql/
https://hub.docker.com/_/oraclelinux/
https://hub.docker.com/u/payara/
If it's yes how about persistent datas like database files and static
storages (eg. images, uploaded documents, logs) those are stored in
docker images or somewhere else? What will happen to those files when
I update my application and redeploy new image?
In general, you will want to store persistent data external to the docker image and mount it into the image as a volume:
https://docs.docker.com/engine/tutorials/dockervolumes/
Some cloud based storage providers might be easier to mount or connect to in other ways, but this volume approach is standard, IMO.
For logfiles, I actually push them to an ELK server, so having a volume for the logs is not necessarily required. However, since the ELK server is also a docker image, it does have a volume where the data is persisted.
So you have:
documentation from your cloud hoster (or docker themselves)
a host in your cloud running docker engine
0..n images that you can either grab from dockerhub or build yourself.
storage for persistent data on this host or mounted from elsewhere that you mount into your docker images on startup. this is where e.g. mysql data folders live, or where you can persist logs, etc.
Of course, it can get much more complex from there, e.g. how to transparently scale and update your environment etc., but that is something for e.g. kubernetes or docker swarm or some other solution (I've scripted a bit on my own but do not need the robustness or elastic scalability of large systems).
Regarding cluster management, it should be noted that Swarm is now included in the Docker Core. This has created some controversy in the community and even talks of a fork of the core:
https://technologyconversations.com/2015/11/04/docker-clustering-tools-compared-kubernetes-vs-docker-swarm/
https://jaxenter.com/docker-1-12-is-probably-the-most-important-release-since-1-0-129080.html
http://searchitoperations.techtarget.com/news/450303918/Docker-fork-talk-prompts-container-standardization-brawl
http://www.infoworld.com/article/3118345/cloud-computing/why-kubernetes-is-winning-the-container-war.html
I have experience running docker on Alibaba cloud and AWS as well. I did not see any difference in working with docker on both cloud providers. Docker images can be build same way on all linux platform regardless of the cloud provider. However, persistence of data need to be taken care using docker volumes. However, it is recommended to use managed service such as RDS in Alibaba cloud for databases instead of using docker.
Can I build a docker image with any type of server (eg. wildfly,
payara) and/or database server (eg. mysql, oracle) and will it work on
docker enabled cloud/datacenter?
You can build your own Docker images or use solutions that are already pre-packaged and proven by cloud providers. For example, here is an auto-clustering Docker-based implementation of GlassFish that can be run and managed on Jelastic PaaS.
If it's yes how about persistent datas like database files and static
storages (eg. images, uploaded documents, logs) those are stored in
docker images or somewhere else? What will happen to those files when
I update my application and redeploy new image?
With the above mentioned cluster, all data is kept inside containers and stays without changes after restart. As an option, you can also connect a separate data storage container if you wish to share it across other containers.

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