Okay, another question.
In AWS I have EBS, which allows me to create volumes, define iops/size for them, mount to desired EC2 machines and take snapshots.
How can I achieve same features in Jelastic? I have option to create "Storage Container" but it belongs only to one environment. How can I backup this volume?
Also, what's the best practice of managing storage devices for things like databases? Use separate storage container?
I have option to create "Storage Container" but it belongs only to one environment.
Yes the Storage Container belongs to 1 environment (either part of one of your other environments, or its own), but you can mount it in 1+ other containers (i.e. inside containers of other environments).
You can basically consider a storage container to be similar to AWS EBS: it can be mounted anywhere you like (multiple times even) in containers within environments in the same region.
How can I backup this volume?
Check your hosting provider's backup policy. In our case we perform backups of all containers for our customers for free. Customers do not need to take additional backups themselves. No need for those extra costs and steps... It might be different at some other Jelastic providers so please check this with your chosen provider(s).
If you wish to make your own backups, you can define a script to do it and set it in cron for example. That script can transfer archives to S3 or anything you wish.
what's the best practice of managing storage devices for things like databases?
Just like with AWS, you may experience performance issues if you use remote storage for database access. Jelastic should generally give you lower latency than EBS, but even so I recommend to keep your database storage local (not via storage containers).
Unlike AWS EC2, you do not have the general risk of local storage disappearing (i.e. your Jelastic containers local storage is not ephemeral; you can safely write data there and expect it to be persistent).
If you need multiple database nodes, it is recommended to use database software level clustering features (master-master or master-slave replication for example) instead of sharing the filesystem.
Remember that any shared filesystem is a shared (single) point of failure. What you gain in application / software convenience you may also lose in reliability / high availability. It's often worth making the extra steps in your application to handle this issue another way, or perhaps consider using lsyncd (there are Jelastic marketplace addons for this) to replicate parts of your filesystem instead of mounting a shared storage container.
Related
I'm starting to learn Kubernetes recently and I've noticed that among the various tutorials online there's almost no mention of Volumes. Tutorials cover Pods, ReplicaSets, Deployments, and Services - but they usually end there with some example microservice app built using a combination of those four. When it comes to databases they simply deploy a pod with the "mongo" image, give it a name and a service so that other pods can see it, and leave it at that. There's no discussion of how the data is written to disk.
Because of this I'm left to assume that with no additional configuration, containers are allowed to write files to disk. I don't believe this implies files are persistent across container restarts, but if I wrote a simple NodeJS application like so:
const fs = require("fs");
fs.writeFileSync("test.txt", "blah");
const value = fs.readFileSync("test.txt", "utf8");
console.log(value);
I suspect this would properly output "blah" and not crash due to an inability to write to disk (note that I haven't tested this because, as I'm still learning Kubernetes, I haven't gotten to the point where I know how to put my own custom images in my cluster yet -- I've only loaded images already on Docker Hub so far)
When reading up on Kubernetes Volumes, however, I came upon the Ephemeral Volume -- a volume that:
get[s] created and deleted along with the Pod
The existence of ephemeral volumes leads me to one of two conclusions:
Containers can't write to disk without being granted permission (via a Volume), and so every tutorial I've seen so far is bunk because mongo will crash when you try to store data
Ephemeral volumes make no sense because you can already write to disk without them, so what purpose do they serve?
So what's up with these things? Why would someone create an ephemeral volume?
Container processes can always write to the container-local filesystem (Unix permissions permitting); but any content that goes there will be lost as soon as the pod is deleted. Pods can be deleted fairly routinely (if you need to upgrade the image, for example) or outside your control (if the node it was on is terminated).
In the documentation, the types of ephemeral volumes highlight two major things:
emptyDir volumes, which are generally used to share content between containers in a single pod (and more specifically to publish data from an init container to the main container); and
injecting data from a configMap, the downward API, or another data source that might be totally artificial
In both of these cases the data "acts like a volume": you specify where it comes from, and where it gets mounted, and it hides any content that was in the underlying image. The underlying storage happens to not be persistent if a pod is deleted and recreated, unlike persistent volumes.
Generally prepackaged versions of databases (like Helm charts) will include a persistent volume claim (or create one per replica in a stateful set), so that data does get persisted even if the pod gets destroyed.
So what's up with these things? Why would someone create an ephemeral volume?
Ephemeral volumes are more of a conceptual thing. The main need for this concept is driven from microservices and orchestration processes, and also guided by 12 factor app. But why?
Because, one major use case is when you are deploying a number of microservices (and their replicas) using containers across multiple machines in a cluster you don't want a container to be reliant on its own storage. If containers rely on their on storage, shutting them down and starting new ones affects the way your app behaves, and this is something everyone wants to avoid. Everyone wants to be able to start and stop containers whenever they want, because this allows easy scaling, updates, etc.
When you actually need a service with persistent data (like DB) you need a special type of configuration, especially if you are running on a cluster of machines. If you are running on one machine, you could use a mounted volume, just to be sure that your data will persist even after container is stopped. But if you want to just load balance across hundreds of stateless API services, ephemeral containers is what you actually want.
I have this idea for what I think should happen with my project, but I want to check in and see if this works on a theoretical level first. Basically I am working on a Django site that is run on Kubernetes, but am struggling a little bit about which way I should set up my replicationSet/statefulSet to manage uploaded content (images).
My biggest concern is trying to find out how to scale and maintain uploaded content. My first idea is that I need to have a single volume that has these files written to it, but can I have multiple pods write to the same volume that way while scaling?
From what I have gathered, it doesn't seem to work that way. It sounds more like each pod, or at least each node, would have it's own volume. But would a request for an image reach the volume it is stored on? Or should I create a custom backend program to move things around so that it is served off of a NGINX server like my other static content?
FYI - this its my first scalable project lol. But I am really just trying to find the best way to manage uploads... or a way in general. I would appreciate any explanations, thoughts, or fancy diagrams on how something like this might work!
Hello I think you should forget kubernetes a bit and think of the architecture and capabilities of your Django application. I guess you have built a web app, that offers some 'upload image' functionality, and then you have code that 'stores' this image somewhere. On the very simple scenario if you run your app on your laptop, the you web app, is configured to save this content to a local folder, a more advanced example is that you deploy your application to a VM or a cloud vm e.g an AWS EC2 instance, and your app is saving the files to the local storage of this EC2 instance. The question is twofold - what happens if we have 2 instances of your web app deployed - can the be configured and run - so that they 'share' the same folder to save the images? I guess this is what you want, other wise your app would not scale horizontally , each user would have to hit each individual instance - in order to upload or retrieve specific images. So having that in mind that is a design decision of your application, which I am pretty sure you have already worked it out, the you need to think - how can I share a folder? a bucket so that all my instances of my web app can save files? If you spinned 3 different vms, on any cloud, you would have to use some kind of clour storage, so that all three instances point to the same physical storage location, or an NFS drive or you could save your data to a cloud storage service S3!
Having all the above in mind, and clearly understanding that you need to decouple your application from the notion of locale storage especially if you want to make it as as stateless as it gets (whatever that means to you), having your web app, which is packaged as a docker container and deployed in a kubernetes cluster as a pod - and saving files to the local storage is not going to get any far, since each pod, each docker container will use the underlying kubernetes worker (vm) storage to save files, so another instance will be saving files on some other vm etc etc.
Kubernetes provides this kind of abstraction for applications (pods) that want to 'share' within the kubernetes cluster, some local storage and of course persist it. Something that I did not add above is that pod and worker storage (meaning if you save files in the kubernetes worker or pod) once this vm / instance is restarted you will loose your data. So you want something durable.
To cut a long story short,
1) you can either to deploy your application / pod along with a Persistent Volume Claim assuming that your kubernetes cluster supports it. What is happening is that you can mount to your pod some kind of folder / storage which will be backed up by whatever is available to your cluster - some kind of NFS store. https://kubernetes.io/docs/concepts/storage/persistent-volumes/
2) You can 'outsource' this need to share a common local storage to some external provider, e.g a common case use an S3 bucket, and not tackle the problem on kubernetes - just keep and provision the app within kubernetes.
I hope I gave you some basic ideas.
Note: Kubernetes 1.14 now (March 2019) comes with Durable Local Storage Management is Now GA, which:
Makes locally attached (non-network attached) storage available as a persistent volume source.
Allows users to take advantage of the typically cheaper and improved performance of persistent local storage kubernetes/kubernetes: #73525, #74391, #74769 kubernetes/enhancements: #121 (kep)
That might help securing a truly persistent storage for your case.
As noted by x-yuri in the comments:
See more with "Kubernetes 1.14: Local Persistent Volumes GA", from Michelle Au (Google), Matt Schallert (Uber), Celina Ward (Uber).
you could use ipfs https://pypi.org/project/django-ipfs-storage/
creating a container with this image https://hub.docker.com/r/ipfs/go-ipfs/ in the same pod you can ref as 'localhost'
I have a node app that allows people to upload their profile picture. Profile pictures are stored on the file system.
I now what to turn my node app into a docker container.
I would like to be able to deploy it pretty much anywhere (Amazon, etc.) and realise that storing files within the file system is a no-go.
So:
Option 1: store files on Amazon's S3 (or something equivalent)
Option 2: creating a "data volume. This makes me wonder: if I deploy this remotely, will this work? Would this be a long-term way to go about it?
Are volumes what I want to do here? Is this how you use docker volumes in Amazon?
(Damn this stuff is hard to crack...)
The answer is: that depends hehehe
Option 1 is good, resilient, and works just "out-of-the-box", but creates a vendor lock-in. Meaning that if you ever decide to stop using AWS, you'll have some code to refactor. Plus bills for S3 will be high if you perform lots and LOTs of requests.
Option 2 works partially: your docker containers will likely run on VMs on AWS, Azure, etc. which are also ephemeral. Meaning your data can disappear just as quick as if they were on containers, unless you backup them.
Other options I know:
Option 3: AWS have a NFS service (sorry, can't remember its name right now), which seems VERY interesting. In theory, it would be like plugging an USB drive storage to VM instances, which can be mounted as volumes inside the containers. I have never done this myself, but seems viable to reduce S3 costs. However this also generates vendor lock-in.
Option 4: AWS S3 with a caching mechanism for files in the VMs.
If you are just testing, option 1 sounds good! But later on you might have to work on that.
I've been making some tests with docker and so far I'm wondering why it's considered a good practice to separate the DB and the app in two containers.
Having two containers seems to be cumbersome to manage and I don't really see the value in it.
Whereas I like the idea of having a self sustainable container per app.
One reason is the separation of data storage and application. If you you put both in their own container, you can update them independently. In my experience this is a common process, because usually the application will evolve faster than the underlying database.
It also frees you to run the containers in different places, which might be a constraint in your operations. Or to run multiple containers from the same database image with different applications.
Often it is also a good thing to be able to scale the UI from one instance to multiple instance, all connected to the same database (or cache instance or HTTP backend). This is mentioned briefly in the docker best practices.
I also understand the urge to run multiple processes in one container. That's why so many minimalist init systems/supervisors like s6 came up lately. I prefer this for demos of applications which require a couple things, like an nginx for frontend, a database and maybe a redis instance. But you could also write a basic docker-compose file and run the demo with multiple containers.
It depends on what you consider your "DB", is it the database application or the content.
The latter is easy, the content needs to be persisted outside the lifetime of the application. The convention used to be to have a "data" container, which simplified linking it with the application (e.g. using the Docker Engine create command --volumes-from parameter). With Docker 1.9 there is a new volume API which has superceded the concept of "data" containers. But you should never store your data in the overlay filesystem (if not only for persistence, but for performance).
If you are referring to a database application, you really enter a semi-religious debate with the microservices crowd. Docker is built to run single process. It is built for 12-factor apps. It is built for microservices. It is definitely possible to run more than one process in a container, but with it you have to consider the additional complexity of managing/monitoring these processes (e.g. using an init process like supervisord), dealing with logging, etc.
I've delivered both. If you are managing the container deployment (e.g. you are hosting the app), it is actually less work to use multiple containers. This allows you to use Docker's abstraction layers for networking and persistent storage. It also provides maximum portability as you scale the application (perhaps you may consider using convoy or flocker volume drivers or an overlay network for hosting containers across multiple servers). If you are developing a product for distribution, it is more convenient to deliver a single Docker Repository (with one Image). This minimizes the support costs as you guide customers through deployment.
I'm currently using RethinkDB across cloud servers by manually joining each server at setup. I'm interested in moving over to a Swarm approach to make scaling and failover easier. The current approach is cumbersome to scale.
In the current manual approach, I simply create a local folder on each server for RDB and mount as a volume to store its data. However, using a Swarm means that I'd need to handle volumes more dynamically. Each container will need a distinct volume to keep data separate in case of errors.
Any recommendations on how to handle this scenario? A lot of the tutorials I've seen so far mention Flocker to manage persistent storage, but I can't see that being handled dynamically.
Currently I am struggling with a situation like this. I've created a temporary fix with GlusterFS.
What you do is install GlusterFS on all the Docker nodes and mount the folders. This way the data exists on all the nodes. But this is less than ideal if you have a lot of writes. This could be slow because of the way Gluster treats your data replication to prevent data loss. It is solid, but I have some issues with the speed.
In your case I would suggest looking into Flocker. Flocker is a volume plugin that migrates your data when a container moves to another host. I haven't had any experience with it, but in my case the concept of Flocker renders useless, I need my data in multiple containers on multiple hosts (Read only) This is where Gluster came into play