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https://issues.apache.org/jira/browse/HELIX-655?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16010960#comment-16010960
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ASF GitHub Bot commented on HELIX-655:
--------------------------------------
Github user jiajunwang commented on a diff in the pull request:
https://github.com/apache/helix/pull/89#discussion_r116554013
--- Diff:
helix-core/src/main/java/org/apache/helix/controller/stages/BestPossibleStateCalcStage.java
---
@@ -90,60 +96,86 @@ private BestPossibleStateOutput compute(ClusterEvent
event, Map<String, Resource
BestPossibleStateOutput output = new BestPossibleStateOutput();
- for (String resourceName : resourceMap.keySet()) {
- logger.debug("Processing resource:" + resourceName);
+ // Reset current INIT/RUNNING tasks on participants for throttling
+ JobRebalancer.resetActiveTaskCount(cache.getLiveInstances().keySet(),
currentStateOutput);
- Resource resource = resourceMap.get(resourceName);
- // Ideal state may be gone. In that case we need to get the state
model name
- // from the current state
- IdealState idealState = cache.getIdealState(resourceName);
+ PriorityQueue<JobResourcePriority> jobResourceQueue = new
PriorityQueue<JobResourcePriority>();
--- End diff --
SortedSet is an interface, so you mean using TreeSet?
Why do you think that will be better? I think there is no big difference
between a TreeSet and PriorityQueue here.
Besides, we do want to implement some kind of job priority here, as you can
see. So PriorityQueue is easier for others to understand.
> Helix per-participant concurrent task throttling
> ------------------------------------------------
>
> Key: HELIX-655
> URL: https://issues.apache.org/jira/browse/HELIX-655
> Project: Apache Helix
> Issue Type: New Feature
> Components: helix-core
> Affects Versions: 0.6.x
> Reporter: Jiajun Wang
> Assignee: Junkai Xue
>
> h1. Overview
> Currently, all runnable jobs/tasks in Helix are equally treated. They are all
> scheduled according to the rebalancer algorithm. Means, their assignment
> might be different, but they will all be in RUNNING state.
> This may cause an issue if there are too many concurrently runnable jobs.
> When Helix controller starts all these jobs, the instances may be overload as
> they are assigning resources and executing all the tasks. As a result, the
> jobs won't be able to finish in a reasonable time window.
> The issue is even more critical to long run jobs. According to our meeting
> with Gobblin team, when a job is scheduled, they allocate resource for the
> job. So in the situation described above, more and more resources will be
> reserved for the pending jobs. The cluster will soon be exhausted.
> For solving the problem, an application needs to schedule jobs in a
> relatively low frequency (what Gobblin is doing now). This may cause low
> utilization.
> A better way to fix this issue, at framework level, is throttling jobs/tasks
> that are running concurrently, and allowing setting priority for different
> jobs to control total execute time.
> So given same amount of jobs, the cluster is in a better condition. As a
> result, jobs running in that cluster have a more controllable execute time.
> Existing related control mechanisms are:
> * ConcurrentTasksPerInstance for each job
> * ParallelJobs for each workflow
> * Threadpool limitation on the participant if user customizes
> TaskStateModelFactory.
> But none of them can directly help when concurrent workflows or jobs number
> is large. If an application keeps scheduling jobs/jobQueues, Helix will start
> any runnable jobs without considering the workload on the participants.
> The application may be able to carefully configures these items to achieve
> the goal. But they won't be able to easily find the sweet spot. Especially
> the cluster might be changing (scale out etc.).
> h2. Problem summary
> # All runnable tasks will start executing, which may overload the participant.
> # Application needs a mechanism to prioritize important jobs (or workflows).
> Otherwise, important tasks may be blocked by other less important ones. And
> allocated resource is wasted.
> h2. Feature proposed
> Based on our discussing, we proposed 2 features that can help to resolve the
> issue.
> # Running task throttling on each participant. This is for avoiding overload.
> # Job priority control that ensures high priority jobs are scheduled earlier.
> In addition, application can leverage workflow/job monitor items as feedback
> from Helix to adjust their stretgy.
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