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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.metrics
import com.codahale.metrics.{Metric, MetricFilter, MetricRegistry}
import java.util.Properties
import java.util.concurrent.TimeUnit
import scala.collection.mutable
import org.apache.spark.{SparkConf, Logging}
import org.apache.spark.metrics.sink.{MetricsServlet, Sink}
import org.apache.spark.metrics.source.Source
/**
* Spark Metrics System, created by specific "instance", combined by source,
* sink, periodically poll source metrics data to sink destinations.
*
* "instance" specify "who" (the role) use metrics system. In spark there are several roles
* like master, worker, executor, client driver, these roles will create metrics system
* for monitoring. So instance represents these roles. Currently in Spark, several instances
* have already implemented: master, worker, executor, driver, applications.
*
* "source" specify "where" (source) to collect metrics data. In metrics system, there exists
* two kinds of source:
* 1. Spark internal source, like MasterSource, WorkerSource, etc, which will collect
* Spark component's internal state, these sources are related to instance and will be
* added after specific metrics system is created.
* 2. Common source, like JvmSource, which will collect low level state, is configured by
* configuration and loaded through reflection.
*
* "sink" specify "where" (destination) to output metrics data to. Several sinks can be
* coexisted and flush metrics to all these sinks.
*
* Metrics configuration format is like below:
* [instance].[sink|source].[name].[options] = xxxx
*
* [instance] can be "master", "worker", "executor", "driver", "applications" which means only
* the specified instance has this property.
* wild card "*" can be used to replace instance name, which means all the instances will have
* this property.
*
* [sink|source] means this property belongs to source or sink. This field can only be source or sink.
*
* [name] specify the name of sink or source, it is custom defined.
*
* [options] is the specific property of this source or sink.
*/
private[spark] class MetricsSystem private (val instance: String,
conf: SparkConf) extends Logging {
val confFile = conf.getOrElse("spark.metrics.conf", null)
val metricsConfig = new MetricsConfig(Option(confFile))
val sinks = new mutable.ArrayBuffer[Sink]
val sources = new mutable.ArrayBuffer[Source]
val registry = new MetricRegistry()
// Treat MetricsServlet as a special sink as it should be exposed to add handlers to web ui
private var metricsServlet: Option[MetricsServlet] = None
/** Get any UI handlers used by this metrics system. */
def getServletHandlers = metricsServlet.map(_.getHandlers).getOrElse(Array())
metricsConfig.initialize()
registerSources()
registerSinks()
def start() {
sinks.foreach(_.start)
}
def stop() {
sinks.foreach(_.stop)
}
def registerSource(source: Source) {
sources += source
try {
registry.register(source.sourceName, source.metricRegistry)
} catch {
case e: IllegalArgumentException => logInfo("Metrics already registered", e)
}
}
def removeSource(source: Source) {
sources -= source
registry.removeMatching(new MetricFilter {
def matches(name: String, metric: Metric): Boolean = name.startsWith(source.sourceName)
})
}
def registerSources() {
val instConfig = metricsConfig.getInstance(instance)
val sourceConfigs = metricsConfig.subProperties(instConfig, MetricsSystem.SOURCE_REGEX)
// Register all the sources related to instance
sourceConfigs.foreach { kv =>
val classPath = kv._2.getProperty("class")
try {
val source = Class.forName(classPath).newInstance()
registerSource(source.asInstanceOf[Source])
} catch {
case e: Exception => logError("Source class " + classPath + " cannot be instantialized", e)
}
}
}
def registerSinks() {
val instConfig = metricsConfig.getInstance(instance)
val sinkConfigs = metricsConfig.subProperties(instConfig, MetricsSystem.SINK_REGEX)
sinkConfigs.foreach { kv =>
val classPath = kv._2.getProperty("class")
try {
val sink = Class.forName(classPath)
.getConstructor(classOf[Properties], classOf[MetricRegistry])
.newInstance(kv._2, registry)
if (kv._1 == "servlet") {
metricsServlet = Some(sink.asInstanceOf[MetricsServlet])
} else {
sinks += sink.asInstanceOf[Sink]
}
} catch {
case e: Exception => logError("Sink class " + classPath + " cannot be instantialized", e)
}
}
}
}
private[spark] object MetricsSystem {
val SINK_REGEX = "^sink\\.(.+)\\.(.+)".r
val SOURCE_REGEX = "^source\\.(.+)\\.(.+)".r
val MINIMAL_POLL_UNIT = TimeUnit.SECONDS
val MINIMAL_POLL_PERIOD = 1
def checkMinimalPollingPeriod(pollUnit: TimeUnit, pollPeriod: Int) {
val period = MINIMAL_POLL_UNIT.convert(pollPeriod, pollUnit)
if (period < MINIMAL_POLL_PERIOD) {
throw new IllegalArgumentException("Polling period " + pollPeriod + " " + pollUnit +
" below than minimal polling period ")
}
}
def createMetricsSystem(instance: String, conf: SparkConf): MetricsSystem =
new MetricsSystem(instance, conf)
}
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