Writing Metrics with the Wavefront SDK
18 April 2019

In version 3.0.0 my Wavefront SDK gained the ability to write metrics. But it wasn’t good enough. So, I’ve rewritten that part. It’s a breaking change from 3.0.0, hence version 4.0.0.

The big new feature is that when you write a metric it goes on a queue, which means you get a very quick return and can carry on with whatever important work you’re doing. Points are bundled up and flushed by worker threads, without you having to worry about it. The code handles retrying, chunking, and point validation, so you don’t have to.

Getting Started

Assuming you’ve installed the wavefront-sdk gem, and have a Wavefront account and whatnot, you only need to require the Wavefront::MetricHelper class. Its initializer looks like this.

def initialize(creds, writer_opts = {}, metric_opts = {})

creds is a mandatory argument, and it must be a hash of things which will enable a Wavefront::Writer to talk to Wavefront. The easiest way to get this object is via Wavefront::Credentials. Wavefront::Writer will need different information depending on how you ask it to send the points. If you’re using a proxy, use the credential object’s proxy method, or creds if you want to use the API. Or play it safe and use all to pass in both.

The writer_opts argument lets you pass objects to Wavefront::Writer. We’ll talk more about this later.

The final metric_opts option lets you control the way metrics are bundled up before being sent to Wavefront. The things you’re most likely to set are flush_interval, and delta_interval. Metrics go into an in-memory buffer, and are flushed to Wavefront every flush_interval seconds. This defaults to five seconds, but you can change it if you wish. We’ll come to delta_interval when we look at counters.

This, then, is all it takes to set up a metric helper.

require 'wavefront-sdk'

creds   = Wavefront::Credentials.new
metrics = Wavefront::MetricHelper.new(creds.all)

If you examine the metrics object, you’ll see it’s exposed some new methods. The ones we’re interested in are gauge, counter, and dist. If you look at those, say in irb, you’ll see that they’re all independent objects.

irb(main):021:0> metrics.class
=> Wavefront::MetricHelper
irb(main):022:0> metrics.gauge.class
=> Wavefront::MetricType::Gauge
irb(main):023:0> metrics.counter.class
=> Wavefront::MetricType::Counter
irb(main):024:0> metrics.dist.class
=> Wavefront::MetricType::Distribution

All those classes offer the same public interface. They expose a Ruby SimpleQueue, and your main interaction will be to put points on those queues via public methods called q, and qq.

I had a lot of trouble picking method names: nothing seemed right. write would get mixed up with Wavefront::Writer#write, send is already a Ruby method, and then I ran out of synonyms. (My cheap thesaurus is rubbish, and rubbish, and also rubbish.) I tried overloading #< and #<<, but that seemed wrong and dirty, so it’s #q to queue in short form, and #qq to do it in a longer fashion.

Throw points at the relevant objects, short-form or longhand, and they will periodically be flushed to Wavefront. It couldn’t (I hope) be simpler.


Gauges are the simplest metric. They’re a path, a value, and maybe some tags. That’s a point in Wavefront. There are, as I just mentioned, two ways to to that.

q takes two or three arguments, and lets you very quickly describe a point.

metrics.gauge.q('my.metric.path', 123)
metrics.gauge.q('my.metric.path', 123, { tag1: 'value 1', tag2: 'value 2' })

Wavefront needs to know the source and the timestamp, and #q fills those in for you. It sets the source as your local hostname, and the timestamp as “now”, however your environment describes it.

If you need more control over your metric descriptions, that is, you need to set the timestamp or the source, you can use #qq. This takes a hash, which fully describes a point.

metrics.gauge.qq(path: 'my.metric.path',
                value: 123,
                source: 'blog_example',
                ts:     Time.now.to_i,
                tags:   { tag1: 'value 1', tag2: 'value 2' })

You can also send qq an array of these points, and it will deal with them all. Some people might prefer to always use #qq, as it makes your code more explicit.

At any time, you may inspect the queue:

puts metrics.gauge.queue.size

puts metrics.gauge.queue.num_waiting



Wavefront has a one-second resolution, so if you send two gauge points with the same path and tags in the same wallclock second, only one will end up in Wavefront.

Often though, you want to count these fast moving events, and Wavefront gives you delta metrics to do that. But using deltas in a very busy application can really push your point rate up, and if they’re coming in very fast, may not play nicely with direct ingestion. To help you out, here is Wavefront::MetricHelper::Counter.

You can send as many counter metrics as you like, using exactly the same #q and #qq syntax as we saw for gauges. When the buffer flushes, the MetricHelper class will bundle up all counters with the same path, source, and tags, and turn them into a single delta metric. By default they’re rolled-up over a five-second window, which is the same as the flush interval, but you can change this using delta_interval in the metric_opts hash when you create the MetricHelper class. The only rule is that delta_interval must be an exact divisor of flush_interval. If it is not, you’ll get a Wavefront::Exception::InvalidInterval.

Let’s make a little example. I’m going to deliberately set a short flush interval, and an even shorter delta interval so you can see the mechanics of the thing.

require 'wavefront-sdk/credentials'
require 'wavefront-sdk/metric_helper'
require 'logger'

Here’s the output, showing the interleaving

$ ./example_01
I, [2020-09-30T15:41:09.222877 #3735]  INFO -- : gauge 1
I, [2020-09-30T15:41:09.223297 #3735]  INFO -- : counter
I, [2020-09-30T15:41:09.323607 #3735]  INFO -- : counter
I, [2020-09-30T15:41:09.424194 #3735]  INFO -- : counter
I, [2020-09-30T15:41:09.524630 #3735]  INFO -- : gauge 2
I, [2020-09-30T15:41:19.532467 #3735]  INFO -- : gauge 1
I, [2020-09-30T15:41:19.532814 #3735]  INFO -- : counter
I, [2020-09-30T15:41:19.633257 #3735]  INFO -- : counter
I, [2020-09-30T15:41:19.733827 #3735]  INFO -- : counter
I, [2020-09-30T15:41:19.834384 #3735]  INFO -- : gauge 2
I, [2020-09-30T15:41:24.227324 #3735]  INFO -- : ∆metric_helper.example.01.counter 3 1601476879 source=box type="counter" method="q"
I, [2020-09-30T15:41:24.227584 #3735]  INFO -- : ∆metric_helper.example.01.counter 3 1601476869 source=box type="counter" method="q"
I, [2020-09-30T15:41:24.239931 #3735]  INFO -- : metric_helper.example.01.gauge 1 1601476869 source=box type="gauge" method="q"
I, [2020-09-30T15:41:24.240055 #3735]  INFO -- : metric_helper.example.01.gauge 2 1601476869.5247834 source=box type="gauge" method="qq"
I, [2020-09-30T15:41:24.240144 #3735]  INFO -- : metric_helper.example.01.gauge 2 1601476879 source=box type="gauge" method="q"
I, [2020-09-30T15:41:24.240234 #3735]  INFO -- : metric_helper.example.01.gauge 4 1601476879.83451 source=box type="gauge" method="qq"
I, [2020-09-30T15:41:29.844477 #3735]  INFO -- : gauge 1
I, [2020-09-30T15:41:29.844991 #3735]  INFO -- : counter
I, [2020-09-30T15:41:29.945511 #3735]  INFO -- : counter
I, [2020-09-30T15:41:30.046152 #3735]  INFO -- : counter
I, [2020-09-30T15:41:30.146794 #3735]  INFO -- : gauge 2
I, [2020-09-30T15:41:39.238670 #3735]  INFO -- : ∆metric_helper.example.01.counter 1 1601476894 source=box type="counter" method="q"
I, [2020-09-30T15:41:39.238908 #3735]  INFO -- : ∆metric_helper.example.01.counter 2 1601476889 source=box type="counter" method="q"
I, [2020-09-30T15:41:39.255049 #3735]  INFO -- : metric_helper.example.01.gauge 3 1601476889 source=box type="gauge" method="q"
I, [2020-09-30T15:41:39.255289 #3735]  INFO -- : metric_helper.example.01.gauge 6 1601476890.1469483 source=box type="gauge" method="qq"
I, [2020-09-30T15:41:40.156389 #3735]  INFO -- : gauge 1
I, [2020-09-30T15:41:40.156636 #3735]  INFO -- : counter
I, [2020-09-30T15:41:40.256968 #3735]  INFO -- : counter
I, [2020-09-30T15:41:40.357829 #3735]  INFO -- : counter
I, [2020-09-30T15:41:40.458373 #3735]  INFO -- : gauge 2
I, [2020-09-30T15:41:50.468476 #3735]  INFO -- : gauge 1
I, [2020-09-30T15:41:50.468840 #3735]  INFO -- : counter
I, [2020-09-30T15:41:50.569265 #3735]  INFO -- : counter
I, [2020-09-30T15:41:50.669872 #3735]  INFO -- : counter
I, [2020-09-30T15:41:50.770446 #3735]  INFO -- : gauge 2
I, [2020-09-30T15:41:54.243509 #3735]  INFO -- : ∆metric_helper.example.01.counter 3 1601476914 source=box type="counter" method="q"
I, [2020-09-30T15:41:54.243767 #3735]  INFO -- : ∆metric_helper.example.01.counter 3 1601476904 source=box type="counter" method="q"
I, [2020-09-30T15:41:54.276223 #3735]  INFO -- : metric_helper.example.01.gauge 4 1601476900 source=box type="gauge" method="q"
I, [2020-09-30T15:41:54.276983 #3735]  INFO -- : metric_helper.example.01.gauge 8 1601476900.458564 source=box type="gauge" method="qq"
I, [2020-09-30T15:41:54.277212 #3735]  INFO -- : metric_helper.example.01.gauge 5 1601476910 source=box type="gauge" method="q"
I, [2020-09-30T15:41:54.277380 #3735]  INFO -- : metric_helper.example.01.gauge 10 1601476910.7705927 source=box type="gauge" method="qq"
I, [2020-09-30T15:42:00.780483 #3735]  INFO -- : loops have finished. Shut down the helper

And here’s a chart.

You can see all our counter increments went through as a single point. If I ran the script again, I might see two counter points. The final delta would be the same, but all our counters wouldn’t have landed in the same bucket.

Note that delta metrics are now a first-class datatype. View them with cs() rather than ts().


You can also write distributions. These have a slightly different q and qq syntax, because distributions are not the same as points. They accept multiple values, and they need to be told a bucket size. So:

def q(path, interval, value, tags = nil)

A distribution can be written in two ways. Firstly, as an array of what Wavefront calls “centroids”. They are pairs of numbers where the second number is the value and the first is how many times that value ocurred. For instance:

[[3, 1], [1, 2], [4, 3], [2, 4]]

But say you have some code which spits out numbers you want to plot as a distribution, it’s a bit of an inconvenience to have to write code to turn that random data into centroids. So dist.q will accept the a array of values. So the data above could also be represented as

[1, 1, 1, 2, 3, 3, 3, 3, 4, 5]

and you’d get exactly the same thing.

Let’s have a look. This time we’ll use the default flush interval.

#!/usr/bin/env ruby

require 'wavefront-sdk/credentials'
require 'wavefront-sdk/metric_helper'

creds   = Wavefront::Credentials.new(profile: :beta)
metrics = Wavefront::MetricHelper.new(creds.all, { verbose: true })

10.times do
  random_dist = Array.new(10).map { |a| rand(10) }

  puts "[#{Time.now}] distribution is #{random_dist}"

  metrics.dist.q('metric_helper.example.002', :m, random_dist)
  sleep 50

puts "[#{Time.now}] loops have finished. Shut down the helper"

The script, you can probably tell, makes up a random ten-element distribution every fifty seconds. It does this ten times. The default flush time is three minutes, so we’ll get one somewhere near in the middle, then have to force one at the end.

$ ./example_002
[2019-04-26 09:57:20 +0100] distribution is [9, 6, 6, 0, 8, 6, 4, 2, 4, 0]
[2019-04-26 09:58:10 +0100] distribution is [5, 5, 5, 7, 8, 5, 1, 2, 5, 7]
[2019-04-26 09:59:00 +0100] distribution is [8, 2, 3, 1, 5, 6, 2, 5, 3, 3]
[2019-04-26 09:59:50 +0100] distribution is [4, 5, 9, 8, 4, 9, 4, 5, 6, 7]
[2019-04-26 10:00:40 +0100] distribution is [7, 2, 4, 0, 3, 4, 2, 2, 7, 9]
[2019-04-26 10:01:30 +0100] distribution is [6, 9, 1, 8, 2, 4, 4, 9, 7, 2]
SDK INFO: !M 1556269040 #1 9.0 #3 6.0 #2 0.0 #1 8.0 #2 4.0 #1 2.0 metric_helper.example.002 source=box
SDK INFO: !M 1556269090 #5 5.0 #2 7.0 #1 8.0 #1 1.0 #1 2.0 metric_helper.example.002 source=box
SDK INFO: !M 1556269140 #1 8.0 #2 2.0 #3 3.0 #1 1.0 #2 5.0 #1 6.0 metric_helper.example.002 source=box
SDK INFO: !M 1556269190 #3 4.0 #2 5.0 #2 9.0 #1 8.0 #1 6.0 #1 7.0 metric_helper.example.002 source=box
SDK INFO: !M 1556269240 #2 7.0 #3 2.0 #2 4.0 #1 0.0 #1 3.0 #1 9.0 metric_helper.example.002 source=box
SDK INFO: !M 1556269290 #1 6.0 #2 9.0 #1 1.0 #1 8.0 #2 2.0 #2 4.0 #1 7.0 metric_helper.example.002 source=box
[2019-04-26 10:02:20 +0100] distribution is [3, 7, 3, 5, 3, 7, 5, 1, 1, 5]
[2019-04-26 10:03:10 +0100] distribution is [7, 6, 6, 2, 2, 4, 3, 3, 8, 7]
[2019-04-26 10:04:00 +0100] distribution is [0, 2, 4, 7, 3, 5, 2, 1, 0, 4]
[2019-04-26 10:04:50 +0100] distribution is [0, 3, 3, 2, 5, 0, 2, 4, 5, 6]
[2019-04-26 10:05:40 +0100] loops have finished. Shut down the helper
SDK INFO: !M 1556269340 #3 3.0 #2 7.0 #3 5.0 #2 1.0 metric_helper.example.002 source=box
SDK INFO: !M 1556269390 #2 7.0 #2 6.0 #2 2.0 #1 4.0 #2 3.0 #1 8.0 metric_helper.example.002 source=box
SDK INFO: !M 1556269440 #2 0.0 #2 2.0 #2 4.0 #1 7.0 #1 3.0 #1 5.0 #1 1.0 metric_helper.example.002 source=box
SDK INFO: !M 1556269490 #2 0.0 #2 3.0 #2 2.0 #2 5.0 #1 4.0 #1 6.0 metric_helper.example.002 source=box

You can see in the SDK INFO messages that the raw arrays of numbers have been converted into Wavefront format centroids.

Here’s the chart, applying the max(), avg() and min() functions to those distributions.

Things Always Go Wrong

What happens if the queue is full? That’s up to you. By default, writes to Ruby SizedQueues, which do the real work, block. That is, if the queue is full and your thread tries to add something to it, your thread will block until the queue becomes available. Chances are, whatever your main thread is doing is more important than your metrics, so I decided to make all writes to the queue default to be non blocking. Ruby raises a ThreadError exception it makes a non-blocking call to an unavailable queue, and by default the SDK will also handle that for you, simply logging a warning.

Naturally, you can control all this, through fields in the MetricHelper#new’s metric_opts hash. If you want to handle the ThreadError yourself, set { suppress_errors: false }, and if you want the normal blocking behaviour, set { nonblock: true }.

If your Wavefront endpoint suddenly becomes unavailable, the writer class will throw a Wavefront::Exception::InvalidEndpoint. This would normally kill the metric sending thread, so you’d lose all your metrics even if your endpoint came back. Thus, we catch that exception, log an error, and carry on.

What happens to your points when they can’t be written? They’re put back in the queue for next time. Counter points are put back on the queue in their aggregated form, which helps keep the size of the queue down during an endpoint outage.

Another thing to know about counter points is that, like our attitudes, they should never be negative. This follows convention: Wavefront deltas are monotonic. If you send a negative value, you’ll get a Wavefront::Exception::InvalidCounterValue. All sorts of validation is done on the points you send. If you wish to turn it off, include no_validation: true in your metric options hash. I don’t know why you would, though, and I haven’t really tested the way the code handles totally insane data, so caveat emptor.

Writer is Your Friend

Wavefront::MetricHelper doesn’t actually send any metrics anywhere. For that it uses Wavefront::Write. This is good, because Wavefront::Write has some nice features.

Firstly, you can write to different endpoints. In the examples above we sent our points to a proxy, using the standard Unix socket protocol. If we’d added writer to the first options hash, we could have sent the points directly to Wavefront (writer: :api); to a proxy over HTTP (writer: :http); or to a local Unix socket (writer: :unix).

We can also pass in a hash of point tags. Then, any points, of any kind, written through your MetricHelper will get those tags, as well as any you send when you write an individual metric.

The following will set up a MetricHelper which will write directly to Wavefront, and tag every point with an entirely pointless global_tag.

metrics = Wavefront::MetricHelper.new(creds.all,
                                      { verbose: true
                                        writer:  :api,
                                        tags:    { global_tag: 'yes!' }

Wavefront::Write also takes care of breaking large amounts of metrics up into manageable chunks, so you don’t have to worry about sending unmanageable payloads if your application suddenly gets very busy.

That’s pretty much all for now, but the MetricHelper code is very modular, so it should be straghtforward to add other metric types, should you be able to think of any. Why not have a go, and send me a PR?