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From "Li Lu (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (YARN-3901) Populate flow run data in the flow_run & flow activity tables
Date Tue, 15 Sep 2015 22:06:46 GMT

    [ https://issues.apache.org/jira/browse/YARN-3901?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14746352#comment-14746352
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Li Lu commented on YARN-3901:
-----------------------------

Thanks [~sjlee0]! 

bq. It's essentially using AtomicLong.compareAndSet(). The few lines around it are mostly
to keep pace with the current time. I hope that makes sense.
Yes, the algorithm makes sense. I thought JVM may have some special optimization with the
getAndAdd method on x86 since x86 has a specific fetch-and-add (LOCK:XADD). However I'm not
sure if this is actually reflected in most current JVMs (https://bugs.openjdk.java.net/browse/JDK-6973482).
I'm OK with both, since the advantage of either one of them in our use case is nontrivial
(https://blogs.oracle.com/dave/entry/atomic_fetch_and_add_vs). If keeping up with current
time is important, we need to stick to the CAS based solution and _not_ to change it in future.


bq.  We create a new record in this table any time a new activity is done for a given day
for a flow.
Thanks. I missed the {{getTopOfTheDayTimestamp}} part. 

bq. The prefix is just cluster! and we can grab from the beginning. What you mention would
get activities for today only, which is slightly different.
Right. Thanks for the clarification! I meant activities for the past 24 hours. 

> Populate flow run data in the flow_run & flow activity tables
> -------------------------------------------------------------
>
>                 Key: YARN-3901
>                 URL: https://issues.apache.org/jira/browse/YARN-3901
>             Project: Hadoop YARN
>          Issue Type: Sub-task
>          Components: timelineserver
>            Reporter: Vrushali C
>            Assignee: Vrushali C
>         Attachments: YARN-3901-YARN-2928.1.patch, YARN-3901-YARN-2928.2.patch, YARN-3901-YARN-2928.3.patch,
YARN-3901-YARN-2928.4.patch, YARN-3901-YARN-2928.5.patch, YARN-3901-YARN-2928.6.patch, YARN-3901-YARN-2928.7.patch,
YARN-3901-YARN-2928.8.patch
>
>
> As per the schema proposed in YARN-3815 in https://issues.apache.org/jira/secure/attachment/12743391/hbase-schema-proposal-for-aggregation.pdf
> filing jira to track creation and population of data in the flow run table. 
> Some points that are being  considered:
> - Stores per flow run information aggregated across applications, flow version
> RM’s collector writes to on app creation and app completion
> - Per App collector writes to it for metric updates at a slower frequency than the metric
updates to application table
> primary key: cluster ! user ! flow ! flow run id
> - Only the latest version of flow-level aggregated metrics will be kept, even if the
entity and application level keep a timeseries.
> - The running_apps column will be incremented on app creation, and decremented on app
completion.
> - For min_start_time the RM writer will simply write a value with the tag for the applicationId.
A coprocessor will return the min value of all written values. - 
> - Upon flush and compactions, the min value between all the cells of this column will
be written to the cell without any tag (empty tag) and all the other cells will be discarded.
> - Ditto for the max_end_time, but then the max will be kept.
> - Tags are represented as #type:value. The type can be not set (0), or can indicate running
(1) or complete (2). In those cases (for metrics) only complete app metrics are collapsed
on compaction.
> - The m! values are aggregated (summed) upon read. Only when applications are completed
(indicated by tag type 2) can the values be collapsed.
> - The application ids that have completed and been aggregated into the flow numbers are
retained in a separate column for historical tracking: we don’t want to re-aggregate for
those upon replay
> 



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