2018-08-25 11:35:56 -05:00

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layout	page_title	sidebar_current	description
guides	Apache Spark Integration - Monitoring Output	guides-spark-monitoring	Learn how to monitor Spark application output.

Monitoring Spark Application Output

By default, spark-submit in cluster mode will submit your application to the Nomad cluster and return immediately. You can use the spark.nomad.cluster.monitorUntil configuration property to have spark-submit monitor the job continuously. Note that, with this flag set, killing spark-submit will not stop the spark application, since it will be running independently in the Nomad cluster.

Spark UI

In cluster mode, if spark.ui.enabled is set to true (as by default), the Spark web UI will be dynamically allocated a port. The Web UI will be exposed by Nomad as a service, and the UI’s URL will appear in the Spark driver’s log. By default, the Spark web UI will terminate when the application finishes. This can be problematic when debugging an application. You can delay termination by setting spark.ui.stopDelay (e.g. 5m for 5 minutes). Note that this will cause the driver process to continue to run. You can force termination immediately on the “Jobs” page of the web UI.

Spark History Server

It is possible to reconstruct the web UI of a completed application using Spark’s history server. The history server requires the event log to have been written to an accessible location like HDFS or Amazon S3.

Sample history server job file:

job "spark-history-server" {
  datacenters = ["dc1"]
  type = "service"

  group "server" {
    count = 1

    task "history-server" {
      driver = "docker"
      
      config {
        image = "barnardb/spark"
        command = "/spark/spark-2.1.0-bin-nomad/bin/spark-class"
        args = [ "org.apache.spark.deploy.history.HistoryServer" ]
        port_map {
          ui = 18080
        }
        network_mode = "host"
      }

      env {
        "SPARK_HISTORY_OPTS" = "-Dspark.history.fs.logDirectory=hdfs://hdfs.service.consul/spark-events/"
        "SPARK_PUBLIC_DNS"   = "spark-history.service.consul"
      }

      resources {
        cpu    = 1000
        memory = 1024
        network {
          mbits = 250
          port "ui" {
            static = 18080
          }
        }
      }

      service {
        name = "spark-history"
        tags = ["spark", "ui"]
        port = "ui"
      }
    }

  }
}

The job file above can also be found here.

To run the history server, first deploy HDFS and then create a directory in HDFS to store events:

$ hdfs dfs -fs hdfs://hdfs.service.consul:8020 -mkdir /spark-events

You can then deploy the history server with:

$ nomad job run spark-history-server-hdfs.nomad

You can get the private IP for the history server with a Consul DNS lookup:

$ dig spark-history.service.consul

Find the public IP that corresponds to the private IP returned by the dig command above. You can access the history server at http://PUBLIC_IP:18080.

Use the spark.eventLog.enabled and spark.eventLog.dir configuration properties in spark-submit to log events for a given application:

$ spark-submit \
    --class org.apache.spark.examples.JavaSparkPi \
    --master nomad \
    --deploy-mode cluster \
    --conf spark.executor.instances=4 \
    --conf spark.nomad.cluster.monitorUntil=complete \
    --conf spark.eventLog.enabled=true \
    --conf spark.eventLog.dir=hdfs://hdfs.service.consul/spark-events \
    --conf spark.nomad.sparkDistribution=https://s3.amazonaws.com/nomad-spark/spark-2.1.0-bin-nomad.tgz \
    https://s3.amazonaws.com/nomad-spark/spark-examples_2.11-2.1.0-SNAPSHOT.jar 100

Logs

Nomad clients collect the stderr and stdout of running tasks. The CLI or the HTTP API can be used to inspect logs, as documented in Accessing Logs. In cluster mode, the stderr and stdout of the driver application can be accessed in the same way. The Log Shipper Pattern uses sidecar tasks to forward logs to a central location. This can be done using a job template as follows:

job "template" {
  group "driver" {

    task "driver" {
      meta {
        "spark.nomad.role" = "driver"
      }
    }

    task "log-forwarding-sidecar" {
      # sidecar task definition here
    }
  }

  group "executor" {

    task "executor" {
      meta {
        "spark.nomad.role" = "executor"
      }
    }

    task "log-forwarding-sidecar" {
      # sidecar task definition here
    }
  }
}

Next Steps

Review the Nomad/Spark configuration properties.

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