背景
监控是Spark非常重要的一部分。Spark的运行情况是由ListenerBus以及MetricsSystem 来完成的。通过Spark的Metrics系统，我们可以把Spark Metrics的收集到的信息发送到各种各样的Sink，比如HTTP、JMX以及CSV文件。
目前支持的Sink包括：
ConsoleSink
CSVSink
JmxSink
MetricsServlet
GraphiteSink
GangliaSink
有时我们需要实时获取metrics数据通过spark分析展示等需求，这个时候若有个KafkaSink将metrics指标数据实时往kafka发送那就太方便了，故有了这篇博文。
实践
所有的Sink都需要继承Sink这个特质：
private[spark] trait Sink { def start(): Unit def stop(): Unit def report(): Unit
}当该Sink注册到metrics系统中时，会调用start方法进行一些初始化操作，再通过report方式进行真正的输出操作，stop方法可以进行一些连接关闭等操作。直接上代码：
package org.apache.spark.metrics.sinkimport java.util.concurrent.TimeUnitimport java.util.{Locale, Properties}import com.codahale.metrics.MetricRegistryimport org.apache.kafka.clients.producer.KafkaProducerimport org.apache.spark.SecurityManagerimport org.apache.spark.internal.Loggingprivate[spark] class KafkaSink(val property: Properties, val registry: MetricRegistry,
securityMgr: SecurityManager) extends Sink with Logging{
val KAFKA_KEY_PERIOD = "period"
val KAFKA_DEFAULT_PERIOD = 10
val KAFKA_KEY_UNIT = "unit"
val KAFKA_DEFAULT_UNIT = "SECONDS"
val KAFKA_TOPIC = "topic"
val KAFKA_DEFAULT_TOPIC = "kafka-sink-topic"
val KAFAK_BROKERS = "kafka-brokers"
val KAFAK_DEFAULT_BROKERS = "XXX:9092"
val TOPIC = Option(property.getProperty(KAFKA_TOPIC)).getOrElse(KAFKA_DEFAULT_TOPIC)
val BROKERS = Option(property.getProperty(KAFAK_BROKERS)).getOrElse(throw new IllegalStateException("kafka-brokers is null!")) private val kafkaProducerConfig = new Properties()
kafkaProducerConfig.put("bootstrap.servers",BROKERS)
kafkaProducerConfig.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
kafkaProducerConfig.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer") private val producer = new KafkaProducer[String, String](kafkaProducerConfig) private val reporter: KafkaReporter = KafkaReporter.forRegistry(registry)
.topic(TOPIC)
.build(producer)
val pollPeriod = Option(property.getProperty(KAFKA_KEY_PERIOD)) match { case Some(s) => s.toInt case None => KAFKA_DEFAULT_PERIOD
}
val pollUnit: TimeUnit = Option(property.getProperty(KAFKA_KEY_UNIT)) match { case Some(s) => TimeUnit.valueOf(s.toUpperCase(Locale.ROOT)) case None => TimeUnit.valueOf(KAFKA_DEFAULT_UNIT)
}
override def start(): Unit = { log.info("I4 Metrics System KafkaSink Start ......")
reporter.start(pollPeriod, pollUnit)
}
override def stop(): Unit = { log.info("I4 Metrics System KafkaSink Stop ......")
reporter.stop()
producer.close()
}
override def report(): Unit = { log.info("I4 Metrics System KafkaSink Report ......")
reporter.report()
}
}KafkaReporter类：
package org.apache.spark.metrics.sink;import com.alibaba.fastjson.JSONObject;import com.codahale.metrics.*;import com.twitter.bijection.Injection;import com.twitter.bijection.avro.GenericAvroCodecs;import org.apache.avro.Schema;import org.apache.avro.generic.GenericData;import org.apache.avro.generic.GenericRecord;import org.apache.kafka.clients.producer.KafkaProducer;import org.apache.kafka.clients.producer.ProducerRecord;import org.slf4j.Logger;import org.slf4j.LoggerFactory;import java.util.Map;import java.util.SortedMap;import java.util.concurrent.TimeUnit;public class KafkaReporter extends ScheduledReporter { private static final Logger LOGGER = LoggerFactory.getLogger(KafkaReporter.class); public static KafkaReporter.Builder forRegistry(MetricRegistry registry) { return new KafkaReporter.Builder(registry);
} private KafkaProducer producer; private Clock clock; private String topic; private KafkaReporter(MetricRegistry registry,
TimeUnit rateUnit,
TimeUnit durationUnit,
MetricFilter filter,
Clock clock,
String topic,
KafkaProducer producer) { super(registry, "kafka-reporter", filter, rateUnit, durationUnit); this.producer = producer; this.topic = topic; this.clock = clock;
} @Override
public void report(SortedMap<String, Gauge> gauges, SortedMap<String, Counter> counters, SortedMap<String, Histogram> histograms, SortedMap<String, Meter> meters, SortedMap<String, Timer> timers) { final long timestamp = TimeUnit.MILLISECONDS.toSeconds(clock.getTime()); // Gauge
for (Map.Entry<String, Gauge> entry : gauges.entrySet()) {
reportGauge(timestamp,entry.getKey(), entry.getValue());
} // Histogram// for (Map.Entry<String, Histogram> entry : histograms.entrySet()) {// reportHistogram(timestamp, entry.getKey(), entry.getValue());// }
} private void reportGauge(long timestamp, String name, Gauge gauge) {
report(timestamp, name, gauge.getValue());
} private void reportHistogram(long timestamp, String name, Histogram histogram) { final Snapshot snapshot = histogram.getSnapshot();
report(timestamp, name, snapshot.getMax());
} private void report(long timestamp, String name, Object values) {
JSONObject jsonObject = new JSONObject();
jsonObject.put("name",name);
jsonObject.put("timestamp",timestamp);
jsonObject.put("value",values);
producer.send(new ProducerRecord(topic,name, jsonObject.toJSONString()));
} public static class Builder { private final MetricRegistry registry; private TimeUnit rateUnit; private TimeUnit durationUnit; private MetricFilter filter; private Clock clock; private String topic; private Builder(MetricRegistry registry) { this.registry = registry; this.rateUnit = TimeUnit.SECONDS; this.durationUnit = TimeUnit.MILLISECONDS; this.filter = MetricFilter.ALL; this.clock = Clock.defaultClock();
} /**
* Convert rates to the given time unit.
*
* @param rateUnit a unit of time
* @return {@code this}
*/
public KafkaReporter.Builder convertRatesTo(TimeUnit rateUnit) { this.rateUnit = rateUnit; return this;
} /**
* Convert durations to the given time unit.
*
* @param durationUnit a unit of time
* @return {@code this}
*/
public KafkaReporter.Builder convertDurationsTo(TimeUnit durationUnit) { this.durationUnit = durationUnit; return this;
} /**
* Use the given {@link Clock} instance for the time.
*
* @param clock a {@link Clock} instance
* @return {@code this}
*/
public Builder withClock(Clock clock) { this.clock = clock; return this;
} /**
* Only report metrics which match the given filter.
*
* @param filter a {@link MetricFilter}
* @return {@code this}
*/
public KafkaReporter.Builder filter(MetricFilter filter) { this.filter = filter; return this;
} /**
* Only report metrics which match the given filter.
*
* @param topic a
* @return {@code this}
*/
public KafkaReporter.Builder topic(String topic) { this.topic = topic; return this;
} /**
* Builds a {@link KafkaReporter} with the given properties, writing {@code .csv} files to the
* given directory.
*
* @return a {@link KafkaReporter}
*/
public KafkaReporter build(KafkaProducer producer) { return new KafkaReporter(registry,
rateUnit,
durationUnit,
filter,
clock,
topic,
producer);
}
}
}其中的report方法就是获取各种类型指标，并进行对应的输出操作的时机。
如何使用
可在配置文件或者程序中设定需要注册的sink，并带上对应的参数即可：
spark.metrics.conf.*.sink.kafka.class=org.apache.spark.metrics.sink.KafkaSink spark.metrics.conf.*.sink.kafka.kafka-brokers=XXX:9092
作者：BIGUFO
链接：https://www.jianshu.com/p/cee005368b61