Export Memory Dump Azure Kubernetes - azure

I need to export memory dump from Aks Cluster and save it in some location
How can I do it? Is easy to export to a storage account? Exist another solution? Can someone give me an step y step?

EDIT: the previous answer was wrong, I didn't paid attention you needed a dump. You'll actually will need to get it from Boot Diagnostic or some command line:
https://learn.microsoft.com/en-us/azure/virtual-machines/troubleshooting/boot-diagnostics#enable-boot-diagnostics-on-existing-virtual-machine

This question is quite old, but let me nevertheless share how I realized it:
Linux has an internal setting called RLIMIT_CORE which limits the size of the core dump you'll receive when your application crashes - this is what you find quite quickly.
Next, you have to define the location of where core files are saved, which is done in the file /proc/sys/kernel/core_pattern. The given path can either be a relative file name (saved next to the binary which crashed), an absolute path (absolute to the mounted namespace) or - here is where it gets interesting - a pipe followed by an absolute path to an executable (application or script). This script will (according to the docs - see headline Piping core dumps to a program) be started as user and group root - but furthermore, it will (according to this post in the Linux mailing list) also be executed in the global namespace - in other words, outside of the container.
If you are like me, and you do not have access to the image used for new nodes on your AKS cluster, you want to set these values using DaemonSets, a pod which runs once on every node.
Armed with all this knowledge, you can do the following:
Create a DaemonSet - a pod running on every machine performing the initial setup.
This DaemonSet will run as a privileged container to allow it to switch to the root namespace.
After having switched namespaces successfully, it can change the value of /proc/sys/kernel/core_pattern.
The value should be something like |/bin/dd of=/core/%h.%e.%p.%t (dd will take the stdin, the core file, and save it to the location defined by the parameter of). Core files will now be saved at /core/. The name of the file can be explained by the variables found in the docs for core files.
After knowing that the files will be saved to /core/ of the root namespace, we can mount our storage there - in my case Azure File Storage. Here's a tutorial of how to mount AzureFileStorage.
Pods have the RestartPolicy set to Always. Since the job of your pod is done, and you don't want it to restart automatically, let it remain running using sleep infinity.
This writeup is almost a copy of what I discovered while contacting the support from Microsoft. Here's the thread in their forum, which contains an almost finished configuration for a DaemonSet.
I'll leave some links here which I used during my research:
how to generate core file in docker container?
How to access docker host filesystem from privileged container
https://medium.com/#patnaikshekhar/initialize-your-aks-nodes-with-daemonsets-679fa81fd20e
Sidenote:
I could also just have mounted the AzureFileSystem into every container and set the value for /proc/sys/kernel/core_pattern to just /core/%h.%e.%p.%t but this would require me to mention the mount on every container. Going this way I could free the configuration of the pods of this administrative task and put it where it (in my opinion) belongs, to the initial machine setup.

Related

does docker manage filesystem like a standalone OS?

I have a program I'm running in a docker container. After 10-12 hours of run, the program terminated with filesystem-related errors (FileNotFoundError, or similar).
I'm wondering if the disk space got filled up or a similar filesystem-related issue or there was a problem in my code (e.g one process deleted the file pre-maturely).
I don't know much about docker management of files and wonder if inside docker it creates and manages its own FS or not. Here are three possibilities I'm considering and mainly wonder if #1 could be the case or not.
If docker manages it's own filesystem, could it be that although disk space is available on the host machine, docker container ran out of it's own storage space? (I've seen similar issues regarding running out of memory for a process that has limited memory artificially imposed using cgroups)
Could it be that host filesystem ran out of space and the files got corrupted or maybe didn't get written correctly?
There is some bug in my code.
This is likely a bug in your code. Most programs print the error they encounter, and when a program encounters out-of-space, the error returned by the filesystem is: "No space left on device" (errno 28 ENOSPC).
If you see FileNotFoundError, that means the file is missing. My best theory is that it's coming from your consumer process.
It's still possible though, that the file doesn't exist because the producer ran out of space and you didn't handle the error correctly - you'll need to check your logs.
It might also be a race condition, depending on your application. There's really not enough details to answer that.
As to the title question:
By default, docker just overlay-mounts an empty directory from the host's filesystem into the container, so the amount of free space on the container is the same as the amount on the host.
If you're using volumes, that depends on the storage driver you use. As #Dan Serbyn mentioned, the default limit for the devicemapper driver is 10 GB. The overlay2 driver - the default driver - doesn't have that limitation.
In the current Docker version, there is a default limitation on the Docker container storage of 10 GB.
You can check the disk space that containers are using by running the following command:
docker system df
It's also possible that the file your container is trying to access has access level restrictions. Try to make it available for docker or maybe everybody (chmod 777 file.txt).

How to store and update a file in a docker container which is read by a SpringBoot application?

I'm setting up an environment which could contain multiple docker container. Each container contains the same SpringBoot Application. During the runtime of the SpringBoot application an .ini-file is needed to work through different things. Furthermore the .ini-file might be updated from outside the containers. This update or new .ini-file should be distributed among all other containers so that it is available at the other SpringBoot apps at the end. Distributing the file is not the problem at this point but how to store the file because the classpath can't be used.
I'm using hazelcast to use its cluster feature. With the help of it I'm able to distribute the new file over all other members in the cluster. At the beginning I stored the .ini-file within the classpath. But if the .ini-file changes it makes no sense to have it in the classpath because you cannot write within a jar. Also, if the container goes down, the memory of the hazelcast is lost because it only has a in-memory database.
What I expect is a process where I can easily substitute the .ini-file. For example a container already knows the file (all newer versions of the .ini-file will have the same name) during build or something like that. If the container was down, it is able to find the file by itself again. And, as I already mentioned, I need to change the .ini-file during runtime. Then the container, or to be more specific, the SpringBoot app has to recognize this change automatically. In my opinion a changing of the file could be done via a REST call which stores the file anywhere within the container or a place where it is allowed to write because classpath doesn't work.
As your question is holding a tag "kubernetes", I will try to answer you in context of this specific container orchestrator.
The feature you are looking for is called ConfigMap in Kubernetes.
Think of it as a key-value pairs created from data source (in your case ini config file).
kubectl create configmap game-config --from-file=.ini-file
You can then use ConfigMap data in two ways inside your containers:
As a container environment variables
As a populated Volume, mounted inside container under specific path
Important thing to note here is, that mounted ConfigMaps are updated automatically. If you are interested in this concept please read more about it here.

Docker - Access host /proc

This is a duplicate of a post I have created in the docker forum. Thus I am going to close this / the other one once this problem is solved. But since no one answers in the docker forum and my problem persists, I'll post it again, looking forward to get an answer.
I would like to expose a server monitoring app as a docker container. The app I have written relies on /proc to read system information like CPU utilization or disk stats. Thus I have to forward the information provided in hosts /proc virtual file system to my docker container.
So I made a simple image (using the first or second intro on docker website: Link) and started it:
docker run -v=/proc:/host/proc:ro -d hostfiletest
Assuming the running container could read from /host/proc to obtain information about the host system.
I fired up a console inside the container to check:
docker exec -it {one of the funny names the container get} bash
And checked the content of /host/proc.
Easiest way to check it was getting the content of /host/proc/sys/kernel/hostname - that should yield the hostname of the vm I am working on.
But I get the hostname of the container, while /host/proc/uptime gets me the correct uptime of the vm.
Do I miss something here? Maybe something conceptual?
Docker version 17.05.0-ce, build 89658be running on Linux 4.4.0-97-generic (VM)
Update:
I found several articles describing how to run a specific monitoring app inside a containing using the same approach I mentioned above.
Update:
Just tried using an existing Ubuntu image - same behavior. Running the image privileged and with pid=host doesn't help.
Greetings
Peepe
The reason of this problem is that /proc is not a normal filesystem. According to procfs, it is like an interface to access some kernel data and system information. This interface provides a file-like structure, so it can make people misunderstand that it is a normal directory.
Files in /proc are also not normal files. They are empty (size = 0). You can check by yourself.
$ stat /proc/sys/kernel/hostname
File: /proc/sys/kernel/hostname
Size: 0 Blocks: 0 IO Block: 1024 regular empty file
So the file doesn't hold any data, but when you read the file, the kernel will dynamically return to you a corresponding system information.
To answer your question, /proc/sys/kernel/hostname is just an interface to access the hostname. And depending on where you access that interface, on the host or on the container, you will get the corresponding hostname. This is also applied when you use bind mount -v /proc:/hosts/proc:ro, since bind mount will provide an alternative view of /proc. If you call the interface /hosts/proc/sys/kernel/hostname, the kernel will return the hostname of the box where you are in (the container).
In short, think about/proc/sys/kernel/hostname as a mirror, if your host stands in front of it, it will reflect the host. If it is the container, it will reflect the container.
I know its a few months later no but I came across the same problem today.
In my case I was using psutil in Python to read disk stats of the hosts from inside a docker container.
The solution was to mount the whole host files system as read only into the docker container with -v /:/rootfs:ro and specify the path to proc as psutil.PROCFS_PATH = '/rootfs/proc'.
Now the psutil.disk_partitions() lists all partitions from the host files system. As the hostname is also contained within the proc hierarchy, I guess this also works for other host system information as long the the retrieving command points to /rootsfs/proc.

Move docker data volume containers between CoreOS hosts

For some scenarios a clustered file system is just too much. This is, if I got it right, the use case for the data volume container pattern. But even CoreOS needs updates from time to time. If I'd still like to minimise the down time of applications, I'd have to move the data volume container with the app container to an other host, while the old host is being updated.
Are there best practices existing? A solution mentioned more often is the "backup" of a container with docker export on the old host and docker import on the new host. But this would include scp-ing of tar-files to an other host. Can this be managed with fleet?
#brejoc, I wouldn't call this a solution, but it may help:
Alternative
1: Use another OS, which does have clustering, or at least - doesn't prevent it. I am now experimenting with CentOS.
2: I've created a couple of tools that help in some use cases. First tool, retrieves data from S3 (usually artifacts), and is uni-directional. Second tool, which I call 'backup volume container', has a lot of potential in it, but requires some feedback. It provides a 2-way backup/restore for data, from/to many persistent data stores including S3 (but also Dropbox, which is cool). As it is implemented now, when you run it for the first time, it would restore to the container. From that point on, it would monitor the relevant folder in the container for changes, and upon changes (and after a quiet period), it would back up to the persistent store.
Backup volume container: https://registry.hub.docker.com/u/yaronr/backup-volume-container/
File sync from S3: https://registry.hub.docker.com/u/yaronr/awscli/
(docker run yaronr/awscli aws s3 etc etc - read aws docs)

Neo4j Azure hosting and Database location

I know that we can use the VM Depot to get started with the Neo4J in Azur but one thing that is not clear is where should we physically store the DB files. I tried to look around in the net if there are any recommendations on where the physical files would be stored so that then a VM crashes or restarts, the data is not lost.
can someone share their thoughts or point me to a address where some more details can be found on do and don'ts of Neo4j on Azure for a production environment.
Regards
Kiran
When you set up a Neo4j VM via VM Depot, that image, by default, configures the database files to reside within the same VM as the server itself. The location is specified in neo4j-server.properties. This lets you simply spin up the VM and start using Neo4j immediately.
However: You'll soon discover that your storage space is limited (I believe the VM instances are set up with a 127GB disk). To work with larger databases, you'll need to attach an additional disk (or disks), each disk up to 1TB in size. These disks, as well as the main VM disk, are backed by blob storage, meaning they're durable - persistent disks.
How you ultimately configure this is up to you, depending on the size of the database and its purpose. The only storage to avoid, if you need persistence, is the scratch disk provided (which is a locally-attached drive with no durability).
The documentation announcing that VM doesn't say. But when you install neo4j as a package on to other similar linux systems (the VM in question is a linux VM) then the data usually goes into /var/lib/neo4j/data. Here's an example:
user#host:/var/lib/neo4j/data$ pwd
/var/lib/neo4j/data
user#host:/var/lib/neo4j/data$ ls
graph.db keystore log neo4j-service.pid README.txt rrd
user#host:/var/lib/neo4j/data$ cat README.txt
Neo4j Data
=======================================
This directory contains all live data managed by this server, including
database files, logs, and other "live" files.
The main directory you really have to have is the "graph.db" directory. That's going to contain the bulk of the data. May as well back up the entirety of this directory. Some of the files (like the .pid file and the README.txt) of course aren't needed.
Now, there's no guarantee that in the VM that it's going to be /var/lib/neo4j/data but it's going to be something very similar. And what you're going to want is going to be a directory whose name ends in .db since that's the default for new neo4j databases.
To narrow down further, once you get that VM running, just run updatedb then locate *.db | grep neo4j and that's almost certain to find it quickly.

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