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From "Ming Ma (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (YARN-2082) Support for alternative log aggregation mechanism
Date Fri, 23 May 2014 19:18:03 GMT

    [ https://issues.apache.org/jira/browse/YARN-2082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14007562#comment-14007562

Ming Ma commented on YARN-2082:

Folks, thanks for the feedbacks and other jiras; quite useful. We have been discussing internally
how to mitigate log aggregation's impact on NN for some time. Here are more context and comments.

1. We discussed Vinod's suggestion of post processing before. The issue is that the write
RPC hit on NN has already happened. Even more, post processing introduces more hits on NN.

2. Agree that HDFS needs to be more scalable. Some improvements have been done; some are still
being worked on. My opinion is we should do both if possible, improve HDFS and improve how
applications use HDFS.

3. Reducing the impact on NN in large cluster is our primary motivation, similar to what Jason
mentioned in YARN-1440. We use the approach mentioned by [~jira.shegalov], [~ctrezzo] in YARN-221
to mitigate the issue.

4. YARN-1440 also suggested making it pluggable, but it seems the primary motivation is to
make it easy for other tools to integrate with yarn logs. If that is the case, we have two
requirements to make it make log aggregation pluggable, easy to integrate with other tools
and reduce pressure on NN.

5. We discussed writing logs to key-value store before. At that point, we didn't go with that
approach as it introduces yarn depend on external component like HBase. Based on recent discussion
with [~jira.shegalov] and [~ctrezzo], it sounds like a reasonable approach.  a) timeline store
has dependency on HBase and b) the size of logs is small and suitable for HBase scenario.

6. Regarding Zhijie's suggestion of using timeline store, that sounds like an interesting
idea, if timeline store is highly available.

7. Regarding Steve' comment for the long running job support. It wasn't our primary goal;
just want to make sure if we do end up changing log aggregation, the framework needs to support
that scenario as well. If there is long running container and we rotate the logs, is there
a plan to aggregate them before the container finishes?

> Support for alternative log aggregation mechanism
> -------------------------------------------------
>                 Key: YARN-2082
>                 URL: https://issues.apache.org/jira/browse/YARN-2082
>             Project: Hadoop YARN
>          Issue Type: New Feature
>            Reporter: Ming Ma
> I will post a more detailed design later. Here is the brief summary and would like to
get early feedback.
> Problem Statement:
> Current implementation of log aggregation create one HDFS file for each {application,
nodemanager }. These files are relative small, in the range of 1-2 MB. In a large cluster
with lots of application and many nodemanagers, it ends up creating lots of small files in
HDFS. This creates pressure on HDFS NN on the following ways.
> 1. It increases NN Memory size. It is mitigated by having history server deletes old
log files in HDFS.
> 2. Runtime RPC hit on HDFS. Each log aggregation file introduced several NN RPCs such
as create, getAdditionalBlock, complete, rename. When the cluster is busy, such RPC hit has
impact on NN performance.
> In addition, to support non-MR applications on YARN, we might need to support aggregation
for long running applications.
> Design choices:
> 1. Don't aggregate all the logs, as in YARN-221.
> 2. Create a dedicated HDFS namespace used only for log aggregation.
> 3. Write logs to some key-value store like HBase. HBase's RPC hit on NN will be much
> 4. Decentralize the application level log aggregation to NMs. All logs for a given application
are aggregated first by a dedicated NM before it is pushed to HDFS.
> 5. Have NM aggregate logs on a regular basis; each of these log files will have data
from different applications and there needs to be some index for quick lookup.
> Proposal:
> 1. Make yarn log aggregation pluggable for both read and write path. Note that Hadoop
FileSystem provides an abstraction and we could ask alternative log aggregator implement compatable
FileSystem, but that seems to an overkill.
> 2. Provide a log aggregation plugin that write to HBase. The scheme design needs to support
efficient read on a per application as well as per application+container basis; in addition,
it shouldn't create hotspot in a cluster where certain users might create more jobs than others.
For example, we can use hash($user+$applicationId} + containerid as the row key.

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