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Can APM Really Handle Serverless? - Part 2

Chris Farrell

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.

Start with: Can APM Really Handle Serverless? - Part 1

And Then There's "Observability"

There are three ways conventional tools deliver service performance data to your monitoring tools:

1. API built into the platform — the consummate example of this is Lambda and Xray. This at least provides some level of performance detail, but it's nowhere near the richness and depth DevOps teams are used to (or need). PLUS: X-Ray provides data about the specific instance, AND ONLY the specific instance; but applications are distributed connected things — getting information about a single service without any knowledge of connected systems doesn't help understand what is getting in the way of distributed performance issues.

2. Pre-instrument the code — Like the way that some application monitoring tools tackled the container incompatibility issue, you could always run the code through an instrumentation step. While this allows the APM solution to get its hooks into the code, it loses the benefit of years of technology advancement in real-time instrumentation which allows decisions to be made on how much (or how little) to measure.

3. Open Source Observability — one or more of the observability APIs could always be put into place — of course, this requires some, if not a ton of, developer time to put the API instrumentation into their code:

■ Deciding what to instrument

■ Selecting which metrics to provide

■ Coding it in

■ Identifying those metrics for the tool

■ Selecting a visualization (If possible)

■ Analyzing logs for serverless events

All three of these approaches actually run counter to the value and efficiency promise of using Serverless Functions in a distributed application.

Option (1) simply doesn't have the juice to provide the detailed information needed for complex applications — and ZERO information about distributed functions, their dependencies (upstream and downstream) with other services, and no context or understanding of traces or end users to examine performance against.

(2) and (3) have similar visibility problems, depending on how much instrumentation is turned on and how much time you're willing to invest in your developers writing performance monitoring instead of their functional code. However, even though those decision points aren't trivial, the real problem comes in the way of cost and performance overhead.

After all, regardless of whether you load code pre-instrumented with a tool or code that your developers added monitoring lines of code, you are essentially operating at 10, 20, even 50% more code, cycles, overhead and cost than just your functional code. Replicate that overhead enough times and not only are you impacting your user service levels, you're blowing through all your serverless "savings" by paying for additional non-functional code.

There Are Options

Look, all is not doom and gloom. There are methods and ways to get the performance data you need across your distributed application, without blowing your budget or your error budget. Look for non-traditional APM tools that don't rely on either legacy instrumentation methods OR open source observability (BONUS, though, if the tool can actually run its own monitoring AND support observability instrumentation).

The key to these tools is that they're more intricately connected with the serverless infrastructure than a legacy APM tool might have. Good news — this means that there are solutions out there that can instrument serverless on the fly, using their connections with the infrastructure. Bad news — if the tool and infrastructure don't match up, you're back to square one. Sometimes that means you may change your infrastructure choice — and sometimes, that means you have to go with the basic instance-based metrics — and use your EUM to the best of your ability.

Anyway, don't be discouraged by this. You can still effectively use Serverless functions to create a more cost effective and efficient multi-cloud application ... and you don't necessarily have to give up that application visibility you've become accustomed to seeing. You will have to check (up front, hopefully) that you have the right tools and right infrastructure to do both. Happy Serverlessing!!!!

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Can APM Really Handle Serverless? - Part 2

Chris Farrell

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.

Start with: Can APM Really Handle Serverless? - Part 1

And Then There's "Observability"

There are three ways conventional tools deliver service performance data to your monitoring tools:

1. API built into the platform — the consummate example of this is Lambda and Xray. This at least provides some level of performance detail, but it's nowhere near the richness and depth DevOps teams are used to (or need). PLUS: X-Ray provides data about the specific instance, AND ONLY the specific instance; but applications are distributed connected things — getting information about a single service without any knowledge of connected systems doesn't help understand what is getting in the way of distributed performance issues.

2. Pre-instrument the code — Like the way that some application monitoring tools tackled the container incompatibility issue, you could always run the code through an instrumentation step. While this allows the APM solution to get its hooks into the code, it loses the benefit of years of technology advancement in real-time instrumentation which allows decisions to be made on how much (or how little) to measure.

3. Open Source Observability — one or more of the observability APIs could always be put into place — of course, this requires some, if not a ton of, developer time to put the API instrumentation into their code:

■ Deciding what to instrument

■ Selecting which metrics to provide

■ Coding it in

■ Identifying those metrics for the tool

■ Selecting a visualization (If possible)

■ Analyzing logs for serverless events

All three of these approaches actually run counter to the value and efficiency promise of using Serverless Functions in a distributed application.

Option (1) simply doesn't have the juice to provide the detailed information needed for complex applications — and ZERO information about distributed functions, their dependencies (upstream and downstream) with other services, and no context or understanding of traces or end users to examine performance against.

(2) and (3) have similar visibility problems, depending on how much instrumentation is turned on and how much time you're willing to invest in your developers writing performance monitoring instead of their functional code. However, even though those decision points aren't trivial, the real problem comes in the way of cost and performance overhead.

After all, regardless of whether you load code pre-instrumented with a tool or code that your developers added monitoring lines of code, you are essentially operating at 10, 20, even 50% more code, cycles, overhead and cost than just your functional code. Replicate that overhead enough times and not only are you impacting your user service levels, you're blowing through all your serverless "savings" by paying for additional non-functional code.

There Are Options

Look, all is not doom and gloom. There are methods and ways to get the performance data you need across your distributed application, without blowing your budget or your error budget. Look for non-traditional APM tools that don't rely on either legacy instrumentation methods OR open source observability (BONUS, though, if the tool can actually run its own monitoring AND support observability instrumentation).

The key to these tools is that they're more intricately connected with the serverless infrastructure than a legacy APM tool might have. Good news — this means that there are solutions out there that can instrument serverless on the fly, using their connections with the infrastructure. Bad news — if the tool and infrastructure don't match up, you're back to square one. Sometimes that means you may change your infrastructure choice — and sometimes, that means you have to go with the basic instance-based metrics — and use your EUM to the best of your ability.

Anyway, don't be discouraged by this. You can still effectively use Serverless functions to create a more cost effective and efficient multi-cloud application ... and you don't necessarily have to give up that application visibility you've become accustomed to seeing. You will have to check (up front, hopefully) that you have the right tools and right infrastructure to do both. Happy Serverlessing!!!!

Hot Topics

The Latest

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...

In March, New Relic published the State of Observability for Media and Entertainment Report to share insights, data, and analysis into the adoption and business value of observability across the media and entertainment industry. Here are six key takeaways from the report ...

Regardless of their scale, business decisions often take time, effort, and a lot of back-and-forth discussion to reach any sort of actionable conclusion ... Any means of streamlining this process and getting from complex problems to optimal solutions more efficiently and reliably is key. How can organizations optimize their decision-making to save time and reduce excess effort from those involved? ...

As enterprises accelerate their cloud adoption strategies, CIOs are routinely exceeding their cloud budgets — a concern that's about to face additional pressure from an unexpected direction: uncertainty over semiconductor tariffs. The CIO Cloud Trends Survey & Report from Azul reveals the extent continued cloud investment despite cost overruns, and how organizations are attempting to bring spending under control ...

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Azul