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.fior...@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.sukmanow...@gmail.com> > Date: Tuesday, March 22, 2016 at 3:13 PM > To: Silvio Fiorito <silvio.fior...@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.fior...@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.sukmanow...@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 >> >
