RE: Better way of measuring custom application metrics

[Shao, Saisai]

I started to know your requirement, maybe there’s some limitations in current 
MetricsSystem, I think we can improve it either.

Thanks
Jerry

From: Enno Shioji [mailto:eshi...@gmail.com]
Sent: Sunday, January 4, 2015 5:46 PM
To: Shao, Saisai
Cc: user@spark.apache.org
Subject: Re: Better way of measuring custom application metrics

Hi Jerry, thanks for your answer.

I had looked at MetricsSystem, but I couldn't see how I could use it in my use 
case, which is:

    stream
        .map { i =>
          Metriker.mr.meter(Metriker.metricName("testmetric123")).mark(i)
          i * 2
        }

From what I can see, a Source accepts an object and describes how to poll it 
for metrics. Presumably that's why Sources have only Gauges and never Meters, 
for example. In my case, I don't have a state that I want Spark's MetricSystem 
to poll.

If I could get a reference to an internal metricRegistry instance AND an task 
identifier in my functions, I could achieve the same thing while using Spark's 
metric configuration, but I couldn't find a way to do this either...








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On Sun, Jan 4, 2015 at 2:46 AM, Shao, Saisai 
<saisai.s...@intel.com<mailto:saisai.s...@intel.com>> wrote:
Hi,

I think there’s a StreamingSource in Spark Streaming which exposes the Spark 
Streaming running status to the metrics sink, you can connect it with Graphite 
sink to expose metrics to Graphite. I’m not sure is this what you want.

Besides you can customize the Source and Sink of the MetricsSystem to build 
your own and configure it in metrics.properties with class name to let it 
loaded by metrics system, for the details you can refer to 
http://spark.apache.org/docs/latest/monitoring.html or source code.

Thanks
Jerry

From: Enno Shioji [mailto:eshi...@gmail.com<mailto:eshi...@gmail.com>]
Sent: Sunday, January 4, 2015 7:47 AM
To: user@spark.apache.org<mailto:user@spark.apache.org>
Subject: Better way of measuring custom application metrics

I have a hack to gather custom application metrics in a Streaming job, but I 
wanted to know if there is any better way of doing this.

My hack consists of this singleton:

object Metriker extends Serializable {
  @transient lazy val mr: MetricRegistry = {
    val metricRegistry = new MetricRegistry()
    val graphiteEndpoint = new 
InetSocketAddress("ec2-54-220-56-229.eu-west-1.compute.amazonaws.com<http://ec2-54-220-56-229.eu-west-1.compute.amazonaws.com>",
 2003)
    GraphiteReporter
      .forRegistry(metricRegistry)
      .build(new Graphite(graphiteEndpoint))
      .start(5, TimeUnit.SECONDS)
    metricRegistry
  }

  @transient lazy val processId = ManagementFactory.getRuntimeMXBean.getName

  @transient lazy val hostId = {
    try {
      InetAddress.getLocalHost.getHostName
    } catch {
      case e: UnknownHostException => "localhost"
    }
  }

   def metricName(name: String): String = {
    "%s.%s.%s".format(name, hostId, processId)
  }
}


which I then use in my jobs like so:

    stream
        .map { i =>
          Metriker.mr.meter(Metriker.metricName("testmetric123")).mark(i)
          i * 2
        }

Then I aggregate the metrics on Graphite. This works, but I was curious to know 
if anyone has a less hacky way.


ᐧ