Spark Metrics Save

A library to expose more of Apache Spark's metrics system

Project README


A library to expose more of Apache Spark's metrics system. This library allows you to use APIs like the Dropwizard/Codahale Metrics library on Spark applications to publish metrics that are aggregated across all executors.


Spark 2.x

spark-metrics by default will be targeting the Spark 2.x line of releases. To use this library for a Spark 2.x application, add the following dependency:


Note that spark-metrics targets Scala 2.11 by default, as that is the default Scala version supported by Spark 2.x. To support Scala 2.10 on the Spark 2.x releases, this library will need to be recompiled with the Spark dependencies that target Scala 2.10.

Spark 1.x

To use this library with the Spark 1.x line of releases, add a dependency to spark-metrics_spark-1.x instead:


The default Spark version targeted for spark-metrics_spark-1.x is Spark 1.6.3, but it is compatible with 1.5 and 1.4 as well. Note that spark-metrics_spark-1.x targets Scala 2.10 by default, as that is the default Scala version supported by Spark 1.x. To support Scala 2.11 on the Spark 1.x releases, this library will need to be recompiled with the Spark dependencies that target Scala 2.11.

Updates to spark-metrics will be backported to spark-metrics_spark-1.x whenever possible, but support for spark-metrics_spark-1.x will be discontinued at some point in the future.


Include this import in your main Spark application file:

import org.apache.spark.groupon.metrics.UserMetricsSystem

In the Spark driver, add the following call to UserMetricsSystem.initialize() right after the application's SparkContext is instantiated:

val sparkContext = new SparkContext()
UserMetricsSystem.initialize(sparkContext, "MyMetricNamespace")

(Technically, it need not necessarily be right after the SparkContext is created, so long as initialize is called before SparkMetric instances are created. But invoking it here will help prevent issues related to initialization from occuring, so it is highly recommended.)

After this, you can create SparkMetric instances that report to Spark's metrics servlet anywhere in your application. These instances must be declared lazy for this library to work properly.

lazy val counter: SparkCounter = UserMetricsSystem.counter("MyCounter")

lazy val gauge: SparkGauge = UserMetricsSystem.gauge("MyGauge")

lazy val histogram: SparkHistogram = UserMetricsSystem.histogram("MyHistogram")

lazy val meter: SparkMeter = UserMetricsSystem.meter("MyMeter")

lazy val timer: SparkTimer = UserMetricsSystem.timer("MyTimer")

The metricName parameter is the only identifier for metrics, so different metric types cannot have the same name (e.g. a Counter and Histogram both with the same metricName).

The APIs for these are kept as close as possible to Dropwizard's APIs, but they don't actually extend a common interface at the language level. The only significant difference is in the SparkGauge class. Whereas Dropwizard's Gauge class is instantiated basically by passing in a function that knows how to obtain the value for the Gauge, this version simply has a set() method to set its value.

Viewing and Publishing Metrics

This library is integrated with Spark's built-in metrics servlet. This means that metrics collected using this library are visible at the /metrics/json/ endpoint. Here, any of the metrics from this library will be published with the key <appName>.<metricNamespace>.<metricName>.

These metric JSONs look something like this:

application_1454970304040_0030.driver.MyAppName.MyMetricNamespace.MyTimer: {
    count: 4748,
    max: 21683.55228,
    mean: 662.7780978119895,
    min: 434.211779,
    p50: 622.795788,
    p75: 672.358402,
    p95: 1146.214833,
    p98: 1146.214833,
    p99: 1146.214833,
    p999: 1572.286154,
    stddev: 163.37016417936547,
    m15_rate: 0.06116903443100036,
    m1_rate: 0.019056182723172856,
    m5_rate: 0.051904011476711955,
    mean_rate: 0.06656686539563786,
    duration_units: "milliseconds",
    rate_units: "calls/second"

Other methods of publishing these metrics are also possible by configuring Spark. Any of the sinks listed here will also report the metrics collected by this library as long as the driver instance is enabled. See this page for a sample metrics configuration.

How It Works

This library is implemented using a combination of Spark's internal RPC APIs and the Dropwizard APIs. The Dropwizard APIs are used on the driver to aggregate metrics that get collected across different executors and the driver. Spark itself uses the Dropwizard library for some of its own metrics, so this library integrates with Spark's existing metrics system to report user metrics alongside Spark's built-in metrics.

When a SparkMetric instance is created in an executor or the driver, it sets up a connection to the MetricsReceiver on the driver, which gets set up by the call to UserMetricsSystem.initialize. Whenever a metric is collected (e.g. calling meter.mark(), gauge.set(x), etc.), that value is sent to the MetricsReceiver, which uses those values to update its corresponding stateful Dropwizard Metric instance. A SparkMetric instance is stateless, in that there are no actual values stored there - its only functionality is to send values to the MetricsReceiver. A metric is uniquely identified by its name, so, for example, all values sent by instances of a SparkHistogram named MyHistogram will get aggregated on the MetricsReceiver in a single instance of a Dropwizard Histogram that corresponds to MyHistogram.

Metrics are sent to the MetricsReceiver using a MetricMessage, which contains the actual metric value and metadata about that metric. This metadata contains information that determines how its corresponding Dropwizard metric will be instantiated in the MetricsReceiver. For example, a MetricMessage for a SparkHistogram contains not only the metric value and name, but also the Dropwizard Reservoir class used to determine what kind of windowing behavior the histogram will have. Having this metadata allows for metrics to be created dynamically during runtime, rather than having to define them all beforehand. This can, for example, enable the creation of a Meter which is named after an Exception, where what the Exception instance could be doesn't need to be known ahead of time:



  • A NotInitializedException is thrown:

    The most likely reason is that UserMetricsSystem.initialize was not called on the driver before a SparkMetric instance was created. A SparkMetric instance needs to connect to the MetricsReceiver when instantiated, so if initialize was not invoked, there is no MetricsReceiver to connect to. Another likely reason is that the SparkMetric instance was not declared lazy. This is important because, even if initialize was called on the driver, there's no guarantee that the SparkMetric instance will be instantiated on a remote JVM after the MetricsReceiver is set up. The only way to have this guarantee is to delay instantiating the SparkMetric until it is actually used by the application, which means that these need to be lazy. This isn't the most user-friendly API, so future work will aim to not require these lazy declarations.

  • A SparkContextNotFoundException is thrown:

    This can happen if UserMetricsSystem.initialize is called before a SparkContext exists. This error can also happen if a SparkMetric instance isn't declared lazy and is instantiated as a field on the driver singleton object. A broken example:

    object OffsetMigrationTool {
      val myHistogram = UserMetricsSystem.histogram("MyHistogram")
      def main(args: Array[String]) {
        val sc = new SparkContext()
        // Rest of driver code...

    myHistogram above needs to instead be declared lazy:

    lazy val myHistogram = UserMetricsSystem.histogram("MyHistogram")
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