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https://issues.apache.org/jira/browse/YARN-3816?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15067321#comment-15067321
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Sangjin Lee commented on YARN-3816:
-----------------------------------
It seems the latest patch (v.4.1) is mostly a rebase change, so I'll wait for
an updated patch that addresses the comments. To comment on some of the
questions and comments,
{quote}
That sounds a reasonable concern here. I agree that we should get rid of
metrics get messed up between system metrics and application's metrics.
However, I think our goal here is not just aggregate/accumulate container
metrics, but also provide aggregation service to applications' metrics (other
than MR). Isn't it? If so, may be a better way is to aggregate metrcis along
not only metric name but also its original entity type (so memory metrics for
ContainerEntity won't be aggregated against memory metrics from Application
Entity). Sangjin Lee, What do you think?
{quote}
If I understood your suggestion correctly, you're talking about qualifying (or
scoping) the metric with the entity type so that they don't get mixed up, right?
I still see that this can be problematic. Let me illustrate an example. Suppose
there is an app framework called "Foo". Let's suppose Foo has a notion of
"jobs" (entity type = "FooJob"), "tasks" (entity type = "FooTask") and
"subtasks" (entity type = "FooSubTask"), so that a job is made up of a bunch of
tasks, and each task can be made up of subtasks. Furthermore, suppose all of
them emit metrics called "MEMORY" where the sum of all subtasks' memory is the
same as the parent task's memory, and the sum of all tasks' memory is the same
as the parent job's memory.
With the idea of qualifying metrics with the entity type, still all these types
will contribute MEMORY to aggregation (FooJob-to-application,
FooTask-to-application, and FooSubTask-to-application), in addition to the
YARN-generic container-to-application aggregation. But given their nature,
things like FooSubTask-to-application and FooTask-to-application aggregation
are very much redundant and thus wasteful. It's basically doing the same
summation multiple times.
As you suggested later, we could utilize the "toAggregate" flag for
applications to exclude certain metrics from aggregation (in this case FOO
would need to set toAggregate = false for all its types). But I think we need
to determine how valuable it is to open this up to app-specific metrics.
Also, if we were to qualify the metric names with the entity type, another
complicating factor is the HBase column names for metrics. Now the aggregated
metric names in the application table would need to be prefixed (or encoded in
some form) with the entity type. We need to think about the implication of
queries, filters, etc.
To me, the most important thing we need to get right is the *YARN-generic
container-to-application aggregation*. That needs to be correct and perform
well in all cases. Supporting \*-to-application aggregation for app-specific
metrics is somewhat secondary IMO. How about keeping it simple, and focusing on
the container-to-application aggregation?
> [Aggregation] App-level aggregation and accumulation for YARN system metrics
> ----------------------------------------------------------------------------
>
> Key: YARN-3816
> URL: https://issues.apache.org/jira/browse/YARN-3816
> Project: Hadoop YARN
> Issue Type: Sub-task
> Components: timelineserver
> Reporter: Junping Du
> Assignee: Junping Du
> Labels: yarn-2928-1st-milestone
> Attachments: Application Level Aggregation of Timeline Data.pdf,
> YARN-3816-YARN-2928-v1.patch, YARN-3816-YARN-2928-v2.1.patch,
> YARN-3816-YARN-2928-v2.2.patch, YARN-3816-YARN-2928-v2.3.patch,
> YARN-3816-YARN-2928-v2.patch, YARN-3816-YARN-2928-v3.1.patch,
> YARN-3816-YARN-2928-v3.patch, YARN-3816-YARN-2928-v4.patch,
> YARN-3816-feature-YARN-2928.v4.1.patch, YARN-3816-poc-v1.patch,
> YARN-3816-poc-v2.patch
>
>
> We need application level aggregation of Timeline data:
> - To present end user aggregated states for each application, include:
> resource (CPU, Memory) consumption across all containers, number of
> containers launched/completed/failed, etc. We need this for apps while they
> are running as well as when they are done.
> - Also, framework specific metrics, e.g. HDFS_BYTES_READ, should be
> aggregated to show details of states in framework level.
> - Other level (Flow/User/Queue) aggregation can be more efficient to be based
> on Application-level aggregations rather than raw entity-level data as much
> less raws need to scan (with filter out non-aggregated entities, like:
> events, configurations, etc.).
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