Can APM Really Handle Serverless? - Part 1
October 27, 2020

Chris Farrell
Instana

Share this

I remember the moment I heard about Serverless technology. On a bus back to the hotel at a conference, I overheard a CTO telling one of her developers about this "new" thing called Lambda. She said (and I'm paraphrasing): "so, the code is there, but it's not running anywhere — until you need it, then it appears, executes and disappears again."

I literally (YES, literally) got goosebumps. I had thought containers were cool, but this? O-M-G!!!

That night I had visions of millions of pieces of code just waiting in the wings for its time to be executed. Of course, the reality of today is that Serverless is a big part of modern application strategy, but not executing every workload like one might think.

There are three key reasons for that:

1. Architecting a serverless function into your operating applications isn't (or wasn't) the easiest thing in the world to do.

2. While the idea of serverless workload execution promises minimal cloud operating costs, the reality of serverless platform pricing is that sometimes it might be more.

3. The monitoring and performance management tools relied upon by IT shops around the globe couldn't handle serverless,

Now, you might be thinking "but wait. Many application monitoring tools struggled for years with containers, but that technology took off like a rocket."

And you would be right. That's one of the reasons I asked myself this important question: Can APM tools Manage Serverless Workloads?

And the answer is "No, not really."

No, don't go searching the web for serverless monitoring to look for a lack of functional claims. Every monitoring solution in the world claims support for monitoring serverless platforms (at least one of them).

What I mean by my answer is that the "APM" solutions we've come to love over the last 2 decades can't handle Serverless Functions or deliver the same performance and operational details that they deliver for other architectural constructs — including App Servers, Frameworks, Cloud, even Containers. And the reason is that they're methodologies for collecting performance data simply won't operate with the same characteristics as it would in persistent code.

To fully understand the nuanced differences between running an agent and capturing data from an API as it relates to monitoring, let's look at some of the operational costs of running serverless code.

Let's first look at what I call the Unicorn of Serverless application functionality — a seldomly called stateless functional piece of work — calculating a payment would be a good example. The inputs are the loan amount, the number of payments and the annual interest rate — the outputs are the interest payment and full payment. The function is called seldomly, requires very few resources to run (meaning little setup) and operates statelessly.

The Unicorn function can be loaded onto a serverless platform such as Lambda with zero permanent persistence (saves money). And a cold start doesn't hurt performance, so it can literally open up and shut down when you need it (also saving money). Now that we've established the perfect way to operate a serverless workload from a financial efficiency perspective, let's consider the three prerequisites:

■ Seldomly called — in the realm of efficient development, services that are never called are either deprecated or rolled into other functionality to make storage and operations as efficient as possible. Thus, a meaningful piece of code that is seldomly called is not really a thing anymore.

■ Requires few resources — again, in the realm of meaningful functions, the need for resources (memory, storage, I/O, etc.) is usually directly related to how important a piece of code is. Which maps back to the same decision point as seldomly called — a function that requires few resources is unlikely to operate on its own, instead being part of a shared service with active listeners, triggers, etc.

■ Is stateless — this is perhaps the least likely of scenarios to be present in today's microservice applications. Even plain old informational websites contain state of users — history, cache, setup, preferences, etc. The odds of having any kind of critical application service that doesn't have a personalized aspect to the workload is rare.

That's why the Unicorn Serverless operation is a rarity, and why cost isn't necessarily less anymore. Since (almost) every function requires some level of resources to use and/or a state — or access to state through a known memory location, two things become a concern.

First is performance — if you have to spin up resource libraries every time you want to run your piece of code, that can have a significant overhead, depending on how complex and resource intensive your piece of code is. I'm going to come back to this in a minute or two, so remember how just setting up your libraries can cause a relative performance impact of 50 — 500%.

Given the performance conundrum, the solution is to use functionality in the serverless platforms, like Lambda, to keep a warm pulse of libraries running so that there's no performance impact. This is referred to as a warm start serverless function.

Now, while this may address the performance issue, naturally it begins to detract from our cost savings. It's one thing to only pay for CPU cycles when you need to run the function — quite another when you're still ALWAYS paying for something, just a little less than you normally would.

Go to: Can APM Really Handle Serverless? - Part 2

Chris Farrell is Observability and APM Strategist at Instana
Share this

The Latest

April 15, 2024

Organizations recognize the value of observability, but only 10% of them are actually practicing full observability of their applications and infrastructure. This is among the key findings from the recently completed Logz.io 2024 Observability Pulse Survey and Report ...

April 11, 2024

Businesses must adopt a comprehensive Internet Performance Monitoring (IPM) strategy, says Enterprise Management Associates (EMA), a leading IT analyst research firm. This strategy is crucial to bridge the significant observability gap within today's complex IT infrastructures. The recommendation is particularly timely, given that 99% of enterprises are expanding their use of the Internet as a primary connectivity conduit while facing challenges due to the inefficiency of multiple, disjointed monitoring tools, according to Modern Enterprises Must Boost Observability with Internet Performance Monitoring, a new report from EMA and Catchpoint ...

April 10, 2024

Choosing the right approach is critical with cloud monitoring in hybrid environments. Otherwise, you may drive up costs with features you don’t need and risk diminishing the visibility of your on-premises IT ...

April 09, 2024

Consumers ranked the marketing strategies and missteps that most significantly impact brand trust, which 73% say is their biggest motivator to share first-party data, according to The Rules of the Marketing Game, a 2023 report from Pantheon ...

April 08, 2024

Digital experience monitoring is the practice of monitoring and analyzing the complete digital user journey of your applications, websites, APIs, and other digital services. It involves tracking the performance of your web application from the perspective of the end user, providing detailed insights on user experience, app performance, and customer satisfaction ...

April 04, 2024
Modern organizations race to launch their high-quality cloud applications as soon as possible. On the other hand, time to market also plays an essential role in determining the application's success. However, without effective testing, it's hard to be confident in the final product ...
April 03, 2024

Enterprises are experiencing a 13% year-over-year increase in customer-facing incidents, reflecting rising levels of complexity and risk as businesses drive operational transformation at scale, according to the 2024 State of Digital Operations study from PagerDuty ...

April 02, 2024

According to Grafana Labs' 2024 Observability Survey, it doesn't matter what industry a company is in or the number of employees they have, the truth is: the more mature their observability practices are, the more time and money they save. From AI to OpenTelemetry — here are four key takeaways from this year's report ...

April 01, 2024

In an age where technology evolves at a breakneck pace, it's crucial to explore how AI assistants can revolutionize our work processes and daily lives, ultimately enhancing overall performance ...

March 28, 2024

Nearly all (99%) globa IT decision makers, regardless of region or industry, recognize generative AI's (GenAI) transformative potential to influence change within their organizations, according to The Elastic Generative AI Report ...