I need help with my Alibaba Cloud Elastic Compute Service!
screenshot of df -h
screenshot of iotop -a
My storage was only 40GB and I used 33GB, on the next day it became fully consumed. The usage was at 40GB even though I had not uploaded any files.
I decided to increase my storage to 60GB, but 3 days later it has become fully consumed again.
I badly need help.
Thank you.
Free up some space and install iotop. Using iotop -a you can leave it running for a while and check back to find which processes are consuming your disk.
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I am investigating a topic, which I will call “Docker swarm and memory management”.
It states in this article here that docker does not recommend using swap memory, but I can’t find (googling) a place where disadvantages of using swap memory in docker context is explained.
Can a kind soul enlighten me? :-)
It is normal to disable SWAP memory in ALL applications or services that are used in production.
SWAP memory is based on using the hard disk as a substitute when the RAM is full. This may seem beneficial but the RAM has a speed from 2.1 GB/s the oldest to 25.6 GB/s the newest. Contrary to the speed of a hard drive with HDDs on average at 135MB/s, newer M.2 SSDs at 1.2GB/s.
As we can see we would be greatly slowing down the service if we were using SWAP.
I create my docker (python flask).
How can I calculate what is the limit to put for memory and CPU?
Do we have some tools that run performance tests on docker with different limitation and then advise what is the best limitation numbers to put?
With an application already running inside of a container, you can use docker stats to see the current utilization of CPU and memory. While there it little harm in setting CPU limits too low (it will just slow down the app, but it will still run), be careful to keep memory limits above the worst case scenario. When apps attempt to exceed their memory limit, they will be killed and usually restarted by a restart policy/orchestration tool. If the limit is set too low, you may find your app in a restart loop.
This is more about the consumption of your specific Flask application, you can probably take use the resource module in Python to calculate them.
More information here and here.
There are a number of really tiny Linux Docker images that weigh in around 4-5M and the "full" distros that start around 100M and climb to twice that.
Setting aside storage space and download time from a repo, are there runtime considerations to small vs large images? For example if I have a compiled Go program, one running on Busybox and the other on Ubuntu, and I run say 10 of them on a machine, in what ways (if any) does it matter than one image is tiny and the other pretty heavy? Does one consume more runtime resources than the other?
I never saw any real difference in consuming other resources than storage and RAM if the image is bigger, however, as Docker containers should be single process why having the big overhead of unused clutter in your containers?
When trimming things down to small containers, there some advantages you may consider:
Faster transfer when deploying (esp. important if you wan't to do rolling upgrades)
Costs: The most time I used big containers, I ran exactly into storage issues on small VMs
Distributed File Systems: When using some File Storage like GlusterFS or other attached storage, big containers slow down, when bootet and updated heavily
massive overhead of data: if you have 500 MB clutter, you'll have it on your dev-machine, your CI/CD-Server, your registry AND every node of your production servers. This CAN matter depending on your use case.
I would say: If you just use a handful containers internally then the size is less important, if ever, than using hundrets of containers in production.
I am deploying a Flask-based website on the server of Digital Ocean. And the website deployed is mainly static pages, config files and jsons.
This morning I found the memory usage has exceeded 51%. Here is the snapshot.
My memory is 512MB. Would someone please instruct me how to lower the memory usage? Thanks so much!
Update: I've use the "top" command in shell as suggested. Here is the snapshot, does it mean that it is the server itself eaten up those memories?
The memory issue is not related to my application.
I just received the answer from Digital Ocean. Here it is:
Hi there!
Thank you for contacting us! We can help with any memory issues you're having!
Since the Droplet is set up with only 512MB of RAM, once the system and any installed services start, it doesn't take much to push it past 50%. As a result, I don't think what you're seeing is necessarily abnormal under the circumstances. This leaves a few options: the Droplet can be resized and made larger to provide more memory (see https://www.digitalocean.com/community/tutorials/how-to-resize-your-droplets-on-digitalocean), you can add swap space to use part of the Droplet's file system as RAM (see https://www.digitalocean.com/community/tutorials/how-to-add-swap-on-ubuntu-14-04), or you can review the applications and services running on the Droplet and attempt to optimize them to reduce memory use.
We hope this is helpful! Please let us know if there is anything else we can do!
Regards,
I am assuming your are running a Linux server. If so, you can use the top command. It shows you all of the running processes and the system resources they are using. You would then be able to optimize from there.
I found out the cause! Linux borrows unused memory for disk caching. This makes it look like you are low on memory, but you are not! Everything is fine! If your application, or any other process needs more memory, Linux will automatically clear the cache and give memory for your application. Linux does this to speed up the system for you.
If, however, you find yourself needing to clear some RAM quickly to workaround another issue, like a VM misbehaving, you can force Linux to nondestructively drop caches using:
echo 3 | sudo tee /proc/sys/vm/drop_caches
I'm pretty sure at this stage that Redis needs a certain amount of free memory on the OS in order to run. In the past few weeks, I've seen Redis (Linux) run out of memory with a couple of gigabytes of RAM still free, and on Windows, it refuses to start when you are using a lot of memory on the system but still have a bunch left free, as in the screenshot below.
The error on Windows gives a hint as to why this is happening (although I'm not assuming it's the same on Linux). However, my question is more generic. How much free memory does Redis need in order to operate?
Redis requires RAM between x2 to x3 the size of your data. The maxheap flag is Windows-specific.
According to Redis FAQ, without a specific Linux configuration, it might need 2x the memory of your dataset. From the document:
Short answer: echo 1 > /proc/sys/vm/overcommit_memory :)
With this configuration, the forked process (responsible for saving the dataset to disk) will be able to share memory pages more easily with the original process, so it won't need that much memory.
You can read more about this here: https://redis.io/topics/faq#background-saving-fails-with-a-fork-error-under-linux-even-if-i-have-a-lot-of-free-ram