I remember some recent version of YARN has a configuration parameter which controls the amount of memory (or cores) a job can use. I tried to find it from the Web but I couldn't yet. If you know the parameter, please let me know.
I know one way to go about this is to use some kind of scheduler but for now I need a job level control so that the job doesn't abuse the entire system.
Thanks!
You can control maximum and minimum resource which are allocated to each containers.
yarn.scheduler.minimum-allocation-mb: Minimum memory allocation for each container
yarn.scheduler.maximum-allocation-mb: Maximum memory allocation for each container
yarn.scheduler.minimum-allocation-vcores: Minimum core allocation for each container
yarn.scheduler.maximum-allocation-vcores: Maximum core allocation for each container
If you want to avoid abuse of user jobs, yarn.scheduler.maximum-allocation-* can be solution because RM refuses the request which requires above these restriction by throwing InvalidResourceRequestException.
ref: yarn-default.xml
Related
when I use flink execute one job that read from hive to deal ,hive include about 1000 files,the flink show the parallelism is 1000,flink request resources used all resources of my cluster that caused others job request slot faild,others job executed faild.each file of 1000 files is small. the job maybe not need occupy the all resources.how can I tune the flink param that use less resource to execute the job
Yarn perspective
I don't recommend usage of Yarn's memory management. Yarn kills containers instantly when they exceed the limits. Usually you need to disable memory checks to overcome this kind of problems.
"yarn.nodemanager.vmem-check-enabled":"false",
"yarn.nodemanager.pmem-check-enabled":"false"
Flink perspective
You can't limit slot resource usage. You have to tune your task managers on your needs. By reducing slots or running multiple task managers on each node . You can set task manager resource usage limit by taskmanager.memory.process.size.
Alternatively you can use flink on kubernetes. You can create Flink clusters for each job which will give you more flexibility. It will create task managers for each job and destroy them when jobs are completed.
There are also stateful functions which you can deploy job pipeline operators into separate containers. This will allow you to manage each function resources separately beside task managers. This allows you to reduce pressure on task managers.
Flink also supports Reactive Mode. This also can reduce pressure on workers by scaling up/down operators automatically based on cpu kind of metrics.
You need to discover this kind of features and find best solution for your needs.
What are the Redis capacity requirements to support 50k consumers within one consumer group to consume and process the messages in parallel? Looking for testing an infrastructure for the same scenario and need to understand considerations.
Disclaimer: I worked in a company which used Redis in a somewhat large scale (probably less consumers than your case, but our consumers were very active), however I wasn't from the infrastructure team, but I was involved in some DevOps tasks.
I don't think you will find an exact number, so I'll try to share some tips and tricks to help you:
Be sure to read the entire Redis Admin page. There's a lot of useful information there. I'll highlight some of the tips from there:
Assuming you'll set up a Linux host, edit /etc/sysctl.conf and set a high net.core.somaxconn (RabbitMQ suggests 4096). Check the documentation of tcp-backlog config in redis.conf for an explanation about this.
Assuming you'll set up a Linux host, edit /etc/sysctl.conf and set vm.overcommit_memory = 1. Read below for a detailed explanation.
Assuming you'll set up a Linux host, edit /etc/sysctl.conf and set fs.file-max. This is very important for your use case. The Open File Handles / File Descriptors Limit is essentially the maximum number of file descriptors (each client represents a file descriptor) the SO can handle. Please check the Redis documentation on this. RabbitMQ documentation also present some useful information about it.
If you edit the /etc/sysctl.conf file, run sysctl -p to reload it.
"Make sure to disable Linux kernel feature transparent huge pages, it will affect greatly both memory usage and latency in a negative way. This is accomplished with the following command: echo never > /sys/kernel/mm/transparent_hugepage/enabled." Add this command also to /etc/rc.local to make it permanent over reboot.
In my experience Redis is not very resource-hungry, so I believe you won't have issues with CPU. Memory are directly related to how much data you intend to store in it.
If you set up a server with many cores, consider using more than one Redis Server. Redis is (mostly) single-threaded and will not use all your CPU resources if you use a single instance in a multicore environment.
Redis server also warns about wrong/risky configurations on startup (sorry for the old image):
Explanation on Overcommit Memory (vm.overcommit_memory)
Setting overcommit_memory to 1 says Linux to relax and perform the fork in a more optimistic allocation fashion, and this is indeed what you want for Redis [from Redis FAQ]
There are three possible settings for vm.overcommit_memory.
0 (zero): Check if enough memory is available and, if so, allow the allocation. If there isn’t enough memory, deny the request and return an error to the application.
1 (one): Permit memory allocation in excess of physical RAM plus swap, as defined by vm.overcommit_ratio. The vm.overcommit_ratio parameter is a
percentage added to the amount of RAM when deciding how much the kernel can overcommit. For instance, a vm.overcommit_ratio of 50 and 1 GB of
RAM would mean the kernel would permit up to 1.5 GB, plus swap, of memory to be allocated before a request failed.
2 (two): The kernel’s equivalent of "all bets are off", a setting of 2 tells the kernel to always return success to an application’s request for memory. This is absolutely as weird and scary as it sounds.
I am running managed Instance groups whose overall c.p.u is always below 30% but if i check instances individually then i found some are running at 70 above and others are running as low as 15 percent.
Keep in mind that Managed Instance Groups don't take into account individual instances as whether a machine should be removed from the pool or not. GCP's MIGs keep a running average of the last 10 minutes of activity of all instances in the group and use that metric to determine scaling decisions. You can find more details here.
Identifying instances with lower CPU usage than the group doesn't seem like the right goal here, instead I would suggest focusing on why some machines have 15% usage and others have 70%. How is work distributed to your instances, are you using the correct strategies for load balancing for your workload?
Maybe your applications have specific endpoints that cause large amounts of CPU usage while the majority of them are basic CRUD operations, having one machine generating a report and displaying higher usage is fine. If all instances render HTML pages from templates and return the results one machine performing much less work than the others is a distribution issue. Maybe you're using a RPS algorithm when you want a CPU utilization one.
In your use case, the best option is to create an Alert notification that will alert you when an instance goes over the desired CPU usage. Once you receive the notification, you will then be able to manually delete the VM instance. As it is part of the Managed Instance group, the VM instance will automatically recreate.
I have attached an article on how to create an Alert notification here.
There is no metric within Stackdriver that will call the GCE API to delete a VM instance .
There is currently no such automation in place. It should't be too difficult to implement it yourself though. You can write a small script that would run on all your machines (started from Cron or something) that monitors CPU usage. If it decides it is too low, the instance can delete itself from the MIG (you can use e.g. gcloud compute instance-groups managed delete-instances --instances ).
Is it possible to notify an application running on a Google Compute VM when the VM migrates to different hardware?
I'm a developer for an application (HMMER) that makes heavy use of vector instructions (SSE/AVX/AVX-512). The version I'm working on probes its hardware at startup to determine which vector instructions are available and picks the best set.
We've been looking at running our program on Google Compute and other cloud engines, and one concern is that, if a VM migrates from one physical machine to another while running our program, the new machine might support different instructions, causing our program to either crash or execute more slowly than it could.
Is there a way to notify applications running on a Google Compute VM when the VM migrates? The only relevant information I've found is that you can set a VM to perform a shutdown/reboot sequence when it migrates, which would kill any currently-executing programs but would at least let the user know that they needed to restart the program.
We ensure that your VM instances never live migrate between physical machines in a way that would cause your programs to crash the way you describe.
However, for your use case you probably want to specify a minimum CPU platform version. You can use this to ensure that e.g. your instance has the new Skylake AVX instructions available. See the documentation on Specifying the Minimum CPU Platform for further details.
As per the Live Migration docs:
Live migration does not change any attributes or properties of the VM
itself. The live migration process just transfers a running VM from
one host machine to another. All VM properties and attributes remain
unchanged, including things like internal and external IP addresses,
instance metadata, block storage data and volumes, OS and application
state, network settings, network connections, and so on.
Google does provide few controls to set the instance availability policies which also lets you control aspects of live migration. Here they also mention what you can look for to determine when live migration has taken place.
Live migrate
By default, standard instances are set to live migrate, where Google
Compute Engine automatically migrates your instance away from an
infrastructure maintenance event, and your instance remains running
during the migration. Your instance might experience a short period of
decreased performance, although generally most instances should not
notice any difference. This is ideal for instances that require
constant uptime, and can tolerate a short period of decreased
performance.
When Google Compute Engine migrates your instance, it reports a system
event that is published to the list of zone operations. You can review
this event by performing a gcloud compute operations list --zones ZONE
request or by viewing the list of operations in the Google Cloud
Platform Console, or through an API request. The event will appear
with the following text:
compute.instances.migrateOnHostMaintenance
In addition, you can detect directly on the VM when a maintenance event is about to happen.
Getting Live Migration Notices
The metadata server provides information about an instance's
scheduling options and settings, through the scheduling/
directory and the maintenance-event attribute. You can use these
attributes to learn about a virtual machine instance's scheduling
options, and use this metadata to notify you when a maintenance event
is about to happen through the maintenance-event attribute. By
default, all virtual machine instances are set to live migrate so the
metadata server will receive maintenance event notices before a VM
instance is live migrated. If you opted to have your VM instance
terminated during maintenance, then Compute Engine will automatically
terminate and optionally restart your VM instance if the
automaticRestart attribute is set. To learn more about maintenance
events and instance behavior during the events, read about scheduling
options and settings.
You can learn when a maintenance event will happen by querying the
maintenance-event attribute periodically. The value of this
attribute will change 60 seconds before a maintenance event starts,
giving your application code a way to trigger any tasks you want to
perform prior to a maintenance event, such as backing up data or
updating logs. Compute Engine also offers a sample Python script
to demonstrate how to check for maintenance event notices.
You can use the maintenance-event attribute with the waiting for
updates feature to notify your scripts and applications when a
maintenance event is about to start and end. This lets you automate
any actions that you might want to run before or after the event. The
following Python sample provides an example of how you might implement
these two features together.
You can also choose to terminate and optionally restart your instance.
Terminate and (optionally) restart
If you do not want your instance to live migrate, you can choose to
terminate and optionally restart your instance. With this option,
Google Compute Engine will signal your instance to shut down, wait for
a short period of time for your instance to shut down cleanly,
terminate the instance, and restart it away from the maintenance
event. This option is ideal for instances that demand constant,
maximum performance, and your overall application is built to handle
instance failures or reboots.
Look at the Setting availability policies section for more details on how to configure this.
If you use an instance with a GPU or a preemptible instance be aware that live migration is not supported:
Live migration and GPUs
Instances with GPUs attached cannot be live migrated. They must be set
to terminate and optionally restart. Compute Engine offers a 60 minute
notice before a VM instance with a GPU attached is terminated. To
learn more about these maintenance event notices, read Getting live
migration notices.
To learn more about handling host maintenance with GPUs, read
Handling host maintenance on the GPUs documentation.
Live migration for preemptible instances
You cannot configure a preemptible instances to live migrate. The
maintenance behavior for preemptible instances is always set to
TERMINATE by default, and you cannot change this option. It is also
not possible to set the automatic restart option for preemptible
instances.
As Ramesh mentioned, you can specify the minimum CPU platform to ensure you are only migrated to an instance which has at least the minimum CPU platform you specified. At a high level it looks like:
In summary, when you specify a minimum CPU platform:
Compute Engine always uses the minimum CPU platform where available.
If the minimum CPU platform is not available or the minimum CPU platform is older than the zone default, and a newer CPU platform is
available for the same price, Compute Engine uses the newer platform.
If the minimum CPU platform is not available in the specified zone and there are no newer platforms available without extra cost, the
server returns a 400 error indicating that the CPU is unavailable.
Since I don't have root rights on the machines in a compute pool, and thus cannot adapt the load parameters of atd for batch, I'm looking for an alternative way to do job scheduling. Since the machines are used by multiple users, it should be able to take the load into account. Optionally, I'm looking for a way to do this for all the machines it the pool, I.e. there is one central queue with jobs that need to be ran, and a script that distributes them (over ssh) over the machines that are under a certain load. Any ideas?
First: go talk to the system administrators of the compute pool. Enterprise wide job schedulers have become a rather common component in infrastructures these days. Typically, these schedulers do not take into account system load though.
If the above doesn't lead to a good solution, you should carefully consider what load your jobs will impose on the machine: your jobs could be stressing the cpu more, consume large amounts of memory, generate lots of network or disk IO activity. Consequently, determining whether your job should start may depend on a lot of measurement, some of which you would not be able to do as an ordinary user (depends a bit on the kind of OS you are running, and how tight security is). In any case: you would only be able to take into account the load at the job's start up. Obviously, if every user would do this, you're back at square one in no time...
It might be a better idea to see with your system administrator if they have some sort of resource controls in place (e.g. projects in Solaris) through which they can make sure your batches are not tearing down the nodes in the compute pool. Next, write your batch jobs in such a way that they can cope with the OS declining requests for resources.
EDIT: As for the distributed nature: queueing up the jobs and having clients on all nodes point to the same queue, consuming as much as they can in the context of the resource controls...