[
https://issues.apache.org/jira/browse/YARN-5864?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wangda Tan updated YARN-5864:
-----------------------------
Description:
Currently, Capacity Scheduler at every parent-queue level uses relative
used-capacities of the chil-queues to decide which queue can get next available
resource first.
For example,
- Q1 & Q2 are child queues under queueA
- Q1 has 20% of configured capacity, 5% of used-capacity and
- Q2 has 80% of configured capacity, 8% of used-capacity.
In the situation, the relative used-capacities are calculated as below
- Relative used-capacity of Q1 is 5/20 = 0.25
- Relative used-capacity of Q2 is 8/80 = 0.10
In the above example, per today’s Capacity Scheduler’s algorithm, Q2 is
selected by the scheduler first to receive next available resource.
Simply ordering queues according to relative used-capacities sometimes causes a
few troubles because scarce resources could be assigned to less-important apps
first.
# Latency sensitivity: This can be a problem with latency sensitive
applications where waiting till the ‘other’ queue gets full is not going to cut
it. The delay in scheduling directly reflects in the response times of these
applications.
# Resource fragmentation for large-container apps: Today’s algorithm also
causes issues with applications that need very large containers. It is possible
that existing queues are all within their resource guarantees but their current
allocation distribution on each node may be such that an application which
needs large container simply cannot fit on those nodes.
Services:
# The above problem (2) gets worse with long running applications. With short
running apps, previous containers may eventually finish and make enough space
for the apps with large containers. But with long running services in the
cluster, the large containers’ application may never get resources on any nodes
even if its demands are not yet met.
# Long running services are sometimes more picky w.r.t placement than normal
batch apps. For example, for a long running service in a separate queue (say
queue=service), during peak hours it may want to launch instances on 50% of the
cluster nodes. On each node, it may want to launch a large container, say 200G
memory per container.
was:
YARN-4390 added preemption for reserved container. However, we found one case
that large container cannot be allocated even if all queues are under their
limit.
For example, we have:
{code}
Two queues, a and b, capacity 50:50
Two nodes: n1 and n2, each of them have 50 resource
Now queue-a uses 10 on n1 and 10 on n2
queue-b asks for one single container with resource=45.
{code}
The container could be reserved on any of the host, but no preemption will
happen because all queues are under their limits.
> YARN Capacity Scheduler - Queue Priorities
> ------------------------------------------
>
> Key: YARN-5864
> URL: https://issues.apache.org/jira/browse/YARN-5864
> Project: Hadoop YARN
> Issue Type: New Feature
> Reporter: Wangda Tan
> Assignee: Wangda Tan
> Attachments: YARN-5864.poc-0.patch
>
>
> Currently, Capacity Scheduler at every parent-queue level uses relative
> used-capacities of the chil-queues to decide which queue can get next
> available resource first.
> For example,
> - Q1 & Q2 are child queues under queueA
> - Q1 has 20% of configured capacity, 5% of used-capacity and
> - Q2 has 80% of configured capacity, 8% of used-capacity.
> In the situation, the relative used-capacities are calculated as below
> - Relative used-capacity of Q1 is 5/20 = 0.25
> - Relative used-capacity of Q2 is 8/80 = 0.10
> In the above example, per today’s Capacity Scheduler’s algorithm, Q2 is
> selected by the scheduler first to receive next available resource.
> Simply ordering queues according to relative used-capacities sometimes causes
> a few troubles because scarce resources could be assigned to less-important
> apps first.
> # Latency sensitivity: This can be a problem with latency sensitive
> applications where waiting till the ‘other’ queue gets full is not going to
> cut it. The delay in scheduling directly reflects in the response times of
> these applications.
> # Resource fragmentation for large-container apps: Today’s algorithm also
> causes issues with applications that need very large containers. It is
> possible that existing queues are all within their resource guarantees but
> their current allocation distribution on each node may be such that an
> application which needs large container simply cannot fit on those nodes.
> Services:
> # The above problem (2) gets worse with long running applications. With short
> running apps, previous containers may eventually finish and make enough space
> for the apps with large containers. But with long running services in the
> cluster, the large containers’ application may never get resources on any
> nodes even if its demands are not yet met.
> # Long running services are sometimes more picky w.r.t placement than normal
> batch apps. For example, for a long running service in a separate queue (say
> queue=service), during peak hours it may want to launch instances on 50% of
> the cluster nodes. On each node, it may want to launch a large container, say
> 200G memory per container.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
