As per the documentation I am building and deploying my code to Cloud Run. I have configured the machine it's running on the have 2 CPU cores.
Since Cloud Run manages scaling automatically, will I get additional performance benefits from using the Node cluster module to utilize both CPU cores on the host machine?
If your code can leverage 2 (or more) CPU in the same time to process the same request, using more than 1 CPU makes sense.
If, as the majority of the developers, you use NodeJS as-is, a single-thread runtime, don't set 2 CPU on your cloud run service. Set one, and let Cloud Run scaling automatically the number of parallel instances.
At high level, it's like having a cluster of VM; or a big multi CPU VM with 1 thread per CPU. It's the power of horizontal scaling by keeping the code simple.
Related
I am using a Docker Setup that consists of 14 different containers. Every container gets a cpu_limit of 2 and a mem_limit of 2g.
To create and run these containers, I've written a Python script that uses the docker-py library. As of now, the containers are created sequentially, which takes approximately 2 minutes.
Now I'm thinking about parallelizing the process. So now instead of doing (its pseudocode):
for container in containers_to_start:
create_container(container)
I do
from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(4)
pool.map(create_container, containers_to_start)
And as a result the 14 containers are created 2x faster. BUT: The applications within the containers take a significant longer time to boot. At the end of the day, i dont gain really much, the time until every application is reachable is more or less the same, no matter if with or without multithreading.
But I don't really know why, because every container gets the same amount of CPU and memory resources, so I would expect the same boot time no matter how many containers are starting at the same time. Clearly this is not the case. Maybe I'm missing some knowledge here, any explanation would be greatly appreciated.
System Specs
CPU: intel i7 # 2.90 GHz
32GB RAM
I am using Windows 10 with Docker installed in WSL2 backend.
The project in which I am working develops a Java service that uses MarkLogic 9 in the backend.
We are running a Jenkins build server that executes (amongst others) several tests in MarkLogic written in XQuery.
For those tests MarkLogic is running in a docker container on the Jenkins host (which is running Ubuntu Linux).
The Jenkins host has 12 GB of RAM and 8 GB of swap configured.
Recently I have noticed that the MarkLogic instance running in the container uses a huge amount of RAM (up to 10 GB).
As there are often other build jobs running in parallel, the Jenkins starts to swap, sometimes even eating up all swap
so that MarkLogic reports it cannot get more memory.
Obviously, this situation leads to failed builds quite often.
To analyse this further I made some tests on my PC running Docker for Windows and found out that the MarkLogic tests
can be run successfully with 5-6 GB RAM. The MarkLogic logs show that it sees all the host memory and wants to use everything.
But as we have other build processes running on that host this behaviour is not desirable.
My question: is there any possibility to tell the MarkLogic to not use so much memory?
We are preparing the docker image during the build, so we could modify some configuration, but it has to be scripted somehow.
The issue of the container not detecting memory limit correctly has been identified, and should be addressed in a forthcoming release.
In the meantime, you might be able to mitigate the issue by:
changing the group cache sizing from automatic to manual and setting cache sizes appropriate for the allocated resources. There area variety of ways to set these configs, whether deploying and settings configs from ml-gradle project, making your own Manage API REST calls, or programmatically:
admin:group-set-cache-sizing
admin:group-set-compressed-tree-cache-partitions
admin:group-set-compressed-tree-cache-size
admin:group-set-expanded-tree-cache-partitions
admin:group-set-expanded-tree-cache-size
admin:group-set-list-cache-partitions
admin:group-set-list-cache-size
reducing the in-memory-limit
in memory limit specifies the maximum number of fragments in an in-memory stand. An in-memory stand contains the latest version of any new or changed fragments. Periodically, in-memory stands are written to disk as a new stand in the forest. Also, if a stand accumulates a number of fragments beyond this limit, it is automatically saved to disk by a background thread.
I am running a micro-service application in docker container and have to test that using JMeter tool. So I am running JMeter on my host machine and my host machine has 4 cores. I allocate 2 cores to the container using --cpu=2 flag while running the container. so it means it can use up to 2 cores as per it needs while running. I leave the remaining 2 cores for the JMeter and other applications and system usage.
Here I need a clarification that what will happen if JMeter and other application needs more than 2 cores and container also needs allocated 2 cores fully ?
Is there any way to allocate 2 cores fully to the container? (It means any other applications or system can't use that 2 cores)
Thank you in advance.
The answer is most probably "no", the explanations will differ depending on your operating system.
You can try to implement this by playing with CPU affinity, however CPU is not only one metric you should be looking at, I would rather be concerned about RAM and Disk usage.
In general having load generator and application under test on the same physical machine is a very bad idea because they are both very resource intensive so consider using 2 separate machines for this otherwise both will suffer from context switches and you will not be able to monitor resources usage of JMeter and the application under test using JMeter PerfMon Plugin
We are running hundreds of Mule Java 8 apps in a machine currently. A single JVM appears to be running that is sharing hundreds of megs of Jars with the apps at run-time. If we were to run each app in a docker container would each container run a separate JVM at run-time? If so this would incur a major RAM penalty!
Yes, each container would run its own java process and thus its own JVM.
You may consider partitioning your apps though so that, rather than going from many apps on one server|VM to many containers each running one app on one server, that you go to some number of containers each running several apps on one server.
Yes, you'd have to duplicate shared jars for each container. Yes, you'd increase CPU, RAM and network traffic.
But, you'd gain more flexibility in duplicating app servers for scale-out and moving them onto different machines to better reflect their CPU, memory, bandwidth needs.
I'm trying to build a system which runs pieces of code in consistent conditions, and one way I imagine this being possible is to run the various programs in docker containers with the same layout, reserving the same amount of memory, etc. However, I can't seem to figure out how to keep CPU usage consistent.
The closest thing I can seem to find are "cpu shares," which, if I understand the documentation, limit cpu usage with respect to what other containers/other processes are running on the system, and what's available on the system. They do not seem to be capable of limiting the container to an absolute amount of cpu usage.
Ideally, I'd like to set up docker containers that would be limited to using a single cpu core. Is this at all possible?
If you use a newer version of Docker, you can use --cpuset-cpus="" in docker run to specify the CPU cores you want to allocate:
docker run --cpuset-cpus="0" [...]
If you use an older version of Docker (< 0.9), which uses LXC as the default execution environment, you can use --lxc-conf to configure the allocated CPU cores:
docker run --lxc-conf="lxc.cgroup.cpuset.cpus = 0" [...]
In both of those cases, only the first CPU core will be available to the docker container. Both of these options are documented in the docker help.
I've tried to provide a tutorial on container resource alloc.
https://gist.github.com/afolarin/15d12a476e40c173bf5f