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O11y Information – Bringing Monoliths into the Cloud Native World – Java Code Geeks

That is the fifth article within the sequence protecting my journey into the world of cloud native observability. In case you missed any of the earlier articles, head on again to 
the introduction for a fast replace.

After laying out the groundwork for this sequence within the preliminary article, I spent a while within the second article sharing who the observability gamers are. I additionally mentioned the groups that these gamers are on on this world of cloud native o11y. For the third article I seemed on the ongoing dialogue round monitoring pillars versus phases. Within the fourth article I talked about preserving your choices open with open supply requirements.

Being a developer from my early days in IT, it’s been very attention-grabbing to discover the complexities of cloud native o11y. Monitoring functions goes method past simply writing and deploying code, particularly within the cloud native world. One factor stays the identical, sustaining your group’s structure at all times requires each a vigilant outlook and an understanding of obtainable open requirements.

On this fifth article I’m going to have a look at architectural selections you may encounter when older monolithic functions and monitoring instruments are nonetheless a part of a company’s infrastructure panorama.

Having beforehand coated the virtues of open requirements and fewer official generally adopted requirements within the open supply o11y world, it’s now time to see how this identical impact has been utilized for monitoring older functions and companies.  

Not all cloud native

Not each cloud structure could make the declare to run solely cloud native or Kubernetes workloads. Many nonetheless have older elements, companies, or functions that run as monoliths. These monoliths will not be damaged up into microservices, don’t auto-scale in a container world, and may not even run in a container in any respect. 

This doesn’t preclude you from monitoring them in some style simply as you’ll your cloud native workloads. Previously this may need been achieved utilizing one of many Utility Efficiency Administration (APM) instruments, however typically requiring a number of instruments. 

There are such a lot of choices used over time, and opposite to common perception, not all architectures have moved on from monitoring their monoliths. We’ll at all times uncover darkish corners of getting old infrastructure that makes use of tooling we thought we’d by no means see once more within the mild of day. 

Let’s discover a couple of of the choices you usually tend to encounter as examples of how we will initially merge these getting old efforts into our cloud native observability platform.

Some (getting old) choices

Whilst you may discover nearly something beneath the solar nonetheless working within the older corners of some infrastructure architectures, it’s going to be robust to cowl all of them. Within the following part for instance, I’m not going to speak about Nagios. It may be built-in in the identical method because the choices I’m going to cowl beneath and I’m selecting to deal with examples that you just’re extra prone to encounter first.

Now that we’ve that settled, let’s discover a few of the options you’ll encounter for monitoring extra conventional monolithic options and infrastructure; Graphite, StatsD, and Collectd.


That is an open supply undertaking first launched in 2008 that supplied a time-series metrics monitoring system.  It was progressive in that it supplied an unique network-based method to ingesting and dealing with metrics information from exterior methods with out utilizing the SNMP protocol. 

The essential structure is pushing metrics from the monitoring targets utilizing a shopper. This shopper has to know that the central server amassing the metrics isn’t at all times capable of obtain and ingest information despatched. The server ingests and shops the time-series information permitting you to question it to create your monitoring dashboards. Metrics are pushed to the Graphite server in plain textual content, or as a binary set of metrics utilizing its personal protocol. For a extra in depth take a look at the Graphite platform, there may be an structure and ideas overview right here.

In case you encounter this platform, there are alternatives out there from the open supply undertaking Prometheus, the place you possibly can leverage the Graphite exporter. This provides you the prospect to combine your current monolithic functions and infrastructure metrics into your cloud native o11y efforts with out a right away migration effort.


From the exporter docs,
tips on how to arrange in place.

At one time, statsD was the most well-liked monitoring answer for instrumenting code and thereby exposing customized utility metrics. It’s open supply and lets you expose the within of your monolithic functions and could be very carefully tied with the ideas of Utility Efficiency Monitoring (APM). 

Builders instrument their code utilizing shopper libraries for no matter language they’re utilizing. There’s a central statsD daemon working that pulls metrics, generates aggregated metrics, and relays them onwards to a monitoring or graphing backend (a favourite being Graphite). It makes use of a hearth and neglect protocol for communication often called UDP, which means low communication overhead. It was very advantageous to have the metrics daemon indifferent from the applying and due to this fact not ever crashing or affecting utility efficiency.

From the exporter docs, 
tips on how to relay.

StatsD additionally has choices out there from the open supply undertaking Prometheus when trying to combine that current monolithic infrastructure into your cloud native o11y efforts. Integrating initially by utilizing the
statsD exporter from the Prometheus ecosystem avoids the necessity for a right away migration effort.


That is one other metrics information assortment answer with a plugin structure of greater than 100 choices. Some frequent collectd plugins are CPU, reminiscence, disk utilization, aggregation, community visitors, NGnix, MySQL, and many others. It’s written within the C language for efficiency and portability which suggests it might probably run on methods and not using a scripting language or cron daemons, corresponding to embedded units. It does have to be put in on each machine that it displays because it makes use of a push mechanism to ship it’s metrics to a backend system corresponding to Graphite. 

Integrating collectd into your modernization plans also can, you guessed it, be achieved utilizing the collectd exporter. This once more provides you the time to leverage current monolithic infrastructure in your cloud native o11y technique, shopping for a while to plan for the eventual migration path nonetheless wanted.

Open path to cloud native

What precisely do the three examples mentioned above have in frequent after we need to combine getting old monitoring infrastructure platforms and instruments into our cloud native o11y technique? All of them have the power to first be built-in by utilizing open supply monitoring options for cloud native environments.

Fortunate for us there are a lot of choices to make sure you can mitigate these outdated dependencies with out rip and changing them. The open supply world provides us the power to ingest older metrics platforms, API’s, and different elements utilizing an enormous ecosystem of exporters. This provides you time for bringing your monoliths into the cloud native o11y world. 

Subsequent up, I plan to start out exploring a brand new undertaking referred to as Perses. That is an up and coming open supply metrics dashboard undertaking and can be the place I begin getting some sensible expertise in my cloud native o11y journey.



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