Getting Started

brharrington edited this page Mar 7, 2016 · 5 revisions

Generating Metrics with Servo

Metrics and Monitors

A Monitor in servo has a configuration (MonitorConfig) and a way to get its current value. The configuration is just a set of key=value pairs that uniquely identify the monitor. There is only one required key: name

A metric represents the value a given Monitor (identified by its MonitorConfig) returned at a particular point in time.

Splitting the attributes in key=value pairs allows for much greater query time flexibility. A backend system that collects metrics reported by many nodes can compute aggregations for any key/value combination. If all the interesting attributes for a metric were encoded in the name then it would be required to create and maintain fragile regular expressions (and in many cases the desired aggregation would be impossible to achieve.)

Monitor Types

Basic types

  • Gauges
  • Counters
  • Timers


A Gauge is simply a Monitor that returns a current value. For example the one minute load average, the size of a queue, the number of threads in a thread pool, etc.

Servo provides several implementations of gauges:


A BasicGauge invokes a given Callable to get the current value. For example:

 private final BlockingQueue<UpdateTasks> pushQueue;

 private final Gauge<Integer> pushQueueSize = new BasicGauge<Integer>(
            MonitorConfig.builder("pushQueue").build(), new Callable<Integer>() {
        public Integer call() throws Exception {
            return pushQueue.size();

MinGauge and MaxGauge

A MinGauge is a gauge that keeps track of the minimum value seen since the last reset. A MaxGauge is a gauge that keeps track of the maximum value seen since the last reset. Updates should be non-negative. In case of no updates the value 0 is returned.

  private final MinGauge minDiskFree = new MinGauge(MonitorConfig.builder("diskFreeMin").build());
  private final MaxGauge maxDiskFree = new MaxGauge(MonitorConfig.builder("diskFreeMax").build());

  private void updateDiskFreeStats(long curValue) {

NumberGauge: LongGauge and DoubleGauge

A NumberGauge just wraps a provided Number. The number needs to be be thread-safe for access by a background thread. For example:

  private final AtomicInteger n = new AtomicInteger(0);
  private final NumberGauge gauge = new NumberGauge(MonitorConfig.builder("someNumber").build(), n);

  private void update() {
    if (someCondition) {
    } else {

Two very common use cases are to wrap AtomicLong or AtomicDouble numbers, and the provided LongGauge and DoubleGauge do just that:

  private final LongGauge tp90 = new LongGauge(MonitorConfig.builder("tp90").build());
  private final DoubleGauge stdDev = new DoubleGauge(MonitorConfig.builder("stddev").build());

  private void computeStats() {
    // compute these
    long computedP90 = 10;
    double computedStdDev = 1.0;

    // then


Counters are monitors that provide increment() and increment(long delta) methods. There are several types of counters available in Servo. You can get a default Counter by calling Monitors.newCounter(String name), or you can instantiate a specific type of Counter. For example:

    Counter basic = new BasicCounter(MonitorConfig.builder("basic").build());
    Counter rate = new ResettableCounter(MonitorConfig.builder("resettable").build());
    Counter peakRate = new PeakRateCounter(MonitorConfig.builder("peak").build());

    void someMethod() {

The difference among counter types is what they report on the getValue() call.

  • BasicCounter returns a monotonically increasing value.
  • ResettableCounter returns a rate per second for the given polling interval. (See Resettable Monitors and Polling Intervals)
  • PeakRateCounter returns the maximum count for a given second during the polling interval.

At Netflix we send all counters to our backend as rates. That means we have to convert the value provided by BasicCounter. This is done using the Servo CounterToRateMetricTransform observer. In order to convert a BasicCounter to a rate two polling samples are required. A ResettableCounter can send the value to backend as a rate with just one polling sample.

public class Server {
    // just use the default implementation
    private final Counter totalConnections = Monitors.newCounter("totalConnections");
    private final Counter bytesIn = Monitors.newCounter("bytesIn");
    private final Counter bytesOut = Monitors.newCounter("bytesOut")

    private void setupNewConnection() {

    private void send(byte[] data) {
        // send data


Timers allow you to measure how long a particular event takes.


A BasicTimer is a composite monitor that generates four metrics per polling interval:

  • The minimum value that has been recorded. statistic=min
  • The maximum value that has been recorded. statistic=max
  • The total time recorded. statistic=totalTime
  • The number of times the event has been recorded. statistic=count
public class Server {
    private final Timer doSomethingTimer = new BasicTimer(
    private final Timer doSomethingElseTimer = new BasicTimer(

    public void doSomething() {
        Stopwatch s = doSomethingTimer.start();
        try {
            // do something
        } finally {

    public void doSomethingElse() {
        long timeInMs = thisReturnsTheTimeInMsItTakes();
        doSomethingElseTimer.record(timeInMs, TimeUnit.MILLISECONDS);

Assuming doSomething() was called twice and the recorded times were 5ms and 15ms the following metrics will be generated:

  • name=doSomething statistic=totalTime value=20
  • name=doSomething statistic=count value=2
  • name=doSomething statistic=min value=5
  • name=doSomething statistic=max value=15

Note that by publishing totalTime and count it becomes possible to get accurate averages across arbitrary aggregations later on.


A timer that - in addition to min, max, count, totalTime - maintains a number of "buckets". For example this can be used to keep track of how many times a call was under (or equal to) 50ms, between 51-500ms, and above 500ms.

public class Server {
    private final Timer bucketTimer = new BucketTimer(
        new BucketConfig.Builder()
            .withBuckets(new long[] { 50, 500 })

    public void getRecommendations() {
        Stopwatch s = bucketTimer.start();

        // call some external services


Assuming in the above code that the getRecommendations() method was called 7 times, and the values reported were: 49ms, 40ms, 400ms, 50ms, 500ms, 2000ms, 501ms. The following metrics would be available:

  • name=getRecommendations statistic=totalTime (49 + 40 + 400 + 50 + 500 + 2000) value=3039
  • name=getRecommendations statistic=min value=40
  • name=getRecommendations statistic=max value=2000
  • name=getRecommendations statistic=count servo.bucket=bucket=050ms value=3
  • name=getRecommendations statistic=count servo.bucket=bucket=500ms value=2
  • name=getRecommendations statistic=count servo.bucket=overflow value=1

The main use of BucketTimer is as an alternative to percentiles that allows a backend service to aggregate the values correctly. For example in an AWS deployment we might want to graph the distribution of values across buckets per availability zone, or per autoScalingGroup, etc. To get the total count we'll have to use statistic=count and ignore the servo.bucket tag, using an aggregation function of sum.


A StatTimer can collect different statistics for time measurements. For example:

  • min, max, count, totalTime (as in a BasicTimer)
  • standard deviation and variance
  • average
  • arbitrary percentiles. For example: 10th, 50th, 99th, 99.5th

This is a much more expensive monitor, and it doesn't allow proper aggregation by backend metrics systems. (For example if each machine will be reporting the 99th percentile for a metric, there's no way to get an arbitrary 99th percentile across multiple machines).

A critical configuration setting is the sample size, which should be set to roughly the number of expected calls per polling interval (usually one minute) for the timer.

public class Server {
    private static StatsTimer newTimer(String name) {
        final double [] percentiles = {50.0, 95.0, 99.0, 99.5};
        final StatsConfig statsConfig = new StatsConfig.Builder()
        final MonitorConfig config = MonitorConfig.builder(name).build();
        return new StatsTimer(config, statsConfig);

    private final StatsTimer requestTimer = newTimer("requestTimer");
    void doRequest() {
        Stopwatch s = requestTimer.start();
        // do something

This will publish the following metrics:

  • name=requestTimer statistic=count
  • name=requestTimer statistic=totalTime
  • name=requestTimer statistic=stdDev
  • name=requestTimer statistic=percentile_50
  • name=requestTimer statistic=percentile_95
  • name=requestTimer statistic=percentile_99
  • name=requestTimer statistic=percentile_99.50

Internally this timer uses a double buffering technique. It creates and reuses two buffers (StatsBuffer) for the current and previous update intervals. Updates record a sample in the 'current' buffer, and a background thread computes the statistics from data on the 'previous' buffer handling the swapping of current and previous in a thread safe manner. By default a single threaded ScheduledExecutorService is used by all stat monitors, but you can pass your own.


This class creates a Proxy monitor that tracks all calls to methods of an interface.

AmazonS3 client = TimedInterface.newProxy(AmazonS3.class,
        new AmazonS3Client(new InstanceProfileCredentialsProvider()));

Every time you call a method implemented by AmazonS3 the time the method takes will be tracked by a BasicTimer.

For example calling:

    client.putObject("bucket", "some.key", new File("/some/file"));
    for (String key : ImmutableList.of("key-a", "key-b", "key-c")) {
        client.getObject("bucket", key);

would create three BasicTimers: one with name="listObjects", one with name="putObject", and one with name="getObject". Each will have a tag interface=AmazonS3 and a tag class=AmazonS3Client. Additionally we could have created the TimedInterface proxy using an extra id parameter to differentiate among multiple instances of the timers.

For registration purposes (see the next section) you can cast the 'client' to a CompositeMonitor.


A CompositeMonitor is a special type of Monitor that is composed of a number of sub-monitors. For example a BasicTimer is a CompositeMonitor that is composed of:

  • totalTime: A counter that keeps track of the total time spent
  • count: A counter that keeps track of the number of times the 'record' method has been invoked (directly or indirectly through Stopwatch#stop() )
  • min: A MinGauge to keep the minimum time spent
  • max: A MaxGauge to keep that maximum time spent

This can be safely ignored if you're just using the existing monitors but it becomes very useful when you want to create your own. In particular if you want to return a variable set of Monitors at runtime this is how to do it. DynamicCounter / DynamicTimer are implemented using this feature.

Registration of Metrics

In servo all monitors need to be registered to be reported. One critically important thing to remember is that they need to be registered exactly once. A few utility classes, such as DynamicCounters, are provided that will handle creation and registration of monitors automatically.

The main ways to achieve this:

  • Monitors are static members of the class and registered in a static initializer block.
public final class AlertEvaluator {
    private static final Counter alertNotificationFailures = new ResettableCounter(
                .withTag("class", "AlertEvaluator")
    static {

Please note that we're manually adding a class tag. This is a convention that lets us namespace metrics generated by different libraries.

  • Monitors are members of a singleton and registered in the constructor.
public enum SomeSingleton {

    private final Counter someCount = Monitors.newCounter("someCount");

    SomeSingleton() {

Note that the call to Monitors.registerObject(this) will use reflection to add all instances of Monitors that have been declared, and also add a tag class with the value set to the class' simple name (class.getSimpleName()).

  • Monitors are members of a class that will have very few instances that can be meaningfully distinguished.
public class Server  {
    private final Timer t = Monitors.newTimer("someTimer");

    public Server(String vip) {
        Monitors.registerObject(vip, this);


Note that in this case in addition to the class tag servo will also add an 'id' tag to distinguish metrics among different instances.

  • Use a helper class that will create or register a Monitor once.
public class Servo {
    private static final ConcurrentMap<MonitorConfig, Counter> counters = 
        new ConcurrentHashMap<MonitorConfig, Counter>();
    private static final ConcurrentMap<MonitorConfig, Timer> timers = 
        new ConcurrentHashMap<MonitorConfig, Timer>();

    public static Counter getCounter(MonitorConfig config) {
        Counter v = counters.get(config);
        if (v != null) return v;
        else {
            Counter counter = new BasicCounter(config);
            Counter prevCounter = counters.putIfAbsent(config, counter)
                if (prevCounter != null) return prevCounter;
                else {
                    return counter;

    public static Counter getCounter(Class c, String n) {
        MonitorConfig config = MonitorConfig.builder(n)
            .withTag("class", c.getSimpleName()).build();
        return getCounter(config); 

    // similar methods for Timers

public class Server {
    final Counter errors = Servo.getCounter(Server.class, "fooNumErrors");
    final Timer t = Servo.getTimer(Server.class, "fooTimer");

    void doFoo() {
        Stopwatch s = t.start();
        try {
            // do something
        } catch (Throwable t) {
            throw t;
  • Use DynamicTimer / DynamicCounter. These helper classes will dynamically register a Monitor the first time it is used, and will also take care of expiring Monitors that been idle for a certain period of time (by default 15 minutes).
public class Server {
    void doStuff() {
        DynamicCounter.increment("numRequests", "class", "Server");
        try {
        } catch (Throwable t) {
            DynamicCounter.increment("numErrors", "class", "Server");
            throw t;

    void callSomeService() {
        Stopwatch s = DynamicTimer.start("someService", "class", "Server");
        try {
            // do the actual call here
        } catch (Throwable t) {
            DynamicCounter.increment("someServiceErrors", "class", "Server",
                "error", t.getMessage());
            // or t.getClass().getSimpleName() 
            throw t;

        // note that we don't call s.stop() since we don't want to record
        // the times for failed attempts

    void callBar() {
        Stopwatch s = DynamicTimer.start("bar", "class", "Server");
        try {
            // do bar()
        } catch (Throwable t) {
            // manually record the time it took for a particular exception
            // type
                .withTag("class", "Server")
                .withTag("error", t.getClass().getSimpleName())
                .build(), s.getDuration());
            throw t;

While these utility classes are very simple to use they do come with a performance cost. It is much more expensive to invoke DynamicCounter.increment() for example than simply counter.increment() (counter being a field in an instance) since the former invokes an expensive map lookup operation every time the counter is incremented. Using the helper class method is a more performant way to achieve this, but extra care has to be taken to only create needed monitors since they won't be expired after periods of inactivity.

Polling Intervals

The previous section explained that all monitors need to be registered. Their values are polled at regular intervals. These are called Polling Intervals. At Netflix a common configuration is to have two polling intervals:

  • A slow polling interval that runs every 60 seconds

  • A fast polling interval that runs every 10 seconds

Some policies determine the set of metrics (potentially aggregated using some function like sum or count) that will be sent to our backend. The fast polling interval will only send a very small number of metrics per machine (what we consider critical metrics) and usually all the metrics collected during the slow polling interval will be sent to a different set of backends.

For correctness some Monitors must be aware of the polling intervals. For example a MaxGauge should report the maximum value that was recorded during a particular interval. In the case of multiple pollers (a slow and a fast one) normally each will get a different value. The system property servo.pollers is used for this purpose. It tells servo how many pollers will be used and their frequency. For example: -Dservo.pollers=60000,10000 tells servo that there will be two pollers: poller at index 0 will run once a minute (60,000 ms) and poller at index 1 will run every 10 seconds.

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