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From Mike Sukmanowsky <mike.sukmanow...@gmail.com>
Subject Re: Spark Metrics Framework?
Date Wed, 23 Mar 2016 13:17:47 GMT
Thanks Ted and Silvio. I think I'll need a bit more hand holding here,
sorry. The way we use ES Hadoop is in pyspark via
org.elasticsearch.hadoop.mr.EsOutputFormat
<org.elasticsearch.hadoop.mr.EsOutputFormathttps://github.com/elastic/elasticsearch-hadoop/blob/master/mr/src/main/java/org/elasticsearch/hadoop/mr/EsOutputFormat.java>
in a saveAsNewAPIHadoopFile call. Given the Hadoop interop, I wouldn't
assume that the EsOutputFormat class
<https://github.com/elastic/elasticsearch-hadoop/blob/master/mr/src/main/java/org/elasticsearch/hadoop/mr/EsOutputFormat.java>
could be modified to define a new Source and register it via
MetricsSystem.createMetricsSystem. This feels like a good feature request
for Spark actually: "Support Hadoop Counters in Input/OutputFormats as
Spark metrics" but I wanted some feedback first to see if that makes sense.

That said, some of the custom RDD classes
<https://github.com/elastic/elasticsearch-hadoop/tree/master/spark/core/main/scala/org/elasticsearch/spark/rdd>
could
probably be modified to register a new Source when they perform
reading/writing from/to Elasticsearch.

On Tue, 22 Mar 2016 at 15:17 Silvio Fiorito <silvio.fiorito@granturing.com>
wrote:

> Hi Mike,
>
> It’s been a while since I worked on a custom Source but I think all you
> need to do is make your Source in the org.apache.spark package.
>
> Thanks,
> Silvio
>
> From: Mike Sukmanowsky <mike.sukmanowsky@gmail.com>
> Date: Tuesday, March 22, 2016 at 3:13 PM
> To: Silvio Fiorito <silvio.fiorito@granturing.com>, "user@spark.apache.org"
> <user@spark.apache.org>
> Subject: Re: Spark Metrics Framework?
>
> The Source class is private
> <https://github.com/apache/spark/blob/v1.4.1/core/src/main/scala/org/apache/spark/metrics/source/Source.scala#L22-L25>
> to the spark package and any new Sources added to the metrics registry must
> be of type Source
> <https://github.com/apache/spark/blob/v1.4.1/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala#L144-L152>.
> So unless I'm mistaken, we can't define a custom source. I linked to 1.4.1
> code, but the same is true in 1.6.1.
>
> On Mon, 21 Mar 2016 at 12:05 Silvio Fiorito <silvio.fiorito@granturing.com>
> wrote:
>
>> You could use the metric sources and sinks described here:
>> http://spark.apache.org/docs/latest/monitoring.html#metrics
>>
>> If you want to push the metrics to another system you can define a custom
>> sink. You can also extend the metrics by defining a custom source.
>>
>> From: Mike Sukmanowsky <mike.sukmanowsky@gmail.com>
>> Date: Monday, March 21, 2016 at 11:54 AM
>> To: "user@spark.apache.org" <user@spark.apache.org>
>> Subject: Spark Metrics Framework?
>>
>> We make extensive use of the elasticsearch-hadoop library for
>> Hadoop/Spark. In trying to troubleshoot our Spark applications, it'd be
>> very handy to have access to some of the many metrics
>> <https://www.elastic.co/guide/en/elasticsearch/hadoop/current/metrics.html>
>> that the library makes available when running in map reduce mode. The library's
>> author noted
>> <https://discuss.elastic.co/t/access-es-hadoop-stats-from-spark/44913>
>> that Spark doesn't offer any kind of a similar metrics API where by these
>> metrics could be reported or aggregated on.
>>
>> Are there any plans to bring a metrics framework similar to Hadoop's
>> Counter system to Spark or is there an alternative means for us to grab
>> metrics exposed when using Hadoop APIs to load/save RDDs?
>>
>> Thanks,
>> Mike
>>
>

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