Legacy Application Performance Management (APM) vs Modern Observability - Part 2
May 10, 2022

Colin Fallwell
Sumo Logic

Share this

In Part 1 of this series, we introduced APM and Modern Observability. If you haven't read it, you can find it here.

For the past decade, Application Performance Management has been a capability provided by a very small and exclusive set of vendors. These vendors provided a bolt-on solution that provided monitoring capabilities without requiring developers to take ownership of instrumentation and monitoring. You may think of this as a benefit, but in reality, it was not.

Operations usually bought APM and would almost always struggle with finding and improving signal quality, having too much data, having the wrong data, and interpreting the data. Developers didn't have to care about how things were observed and had no real ownership in the journey of keeping things reliable. This has almost always led to a higher degree of low-quality software and higher MTTR.

The High Cost of Exclusivity

APM vendors have struggled with Cloud-Native architectures. Their agents were never designed for the Cloud and are almost always overkill for small microservices and ephemeral containers. Their agent code remains exclusive, lacks interoperability with one another, and provides features (such as heap analysis and thread dumps) that are no longer relevant in the cloud.

Despite this, legacy APM vendors today are touting support for Modern Observability and Open Telemetry. There is a caveat in that they provide this support by requiring customers to continue leveraging their proprietary agents (for the broadest support).

Keeping customers dependent on the vendor-owned code to equal out-of-the-box CNCF capabilities to me is counter-intuitive. The primary reason for this mindset and approach stems from their legacy beginnings. Generally speaking, their backends are not compatible with modern open-schemas of metadata and tags. To work around the limitations of being born in the legacy world, they must leverage proprietary agents as an abstraction layer to transform and map open standards to their closed ecosystem. This benefits these vendors but leaves customers locked into a single vendor's agent codebase (or more likely, multiple vendors' agent codebases to cover different domains such as logging, metrics, and traces), which come loaded with technical debt and are serviceable by only a small team of developers.

In relation to modern observability, the only argument we could try to make for proprietary agents might center around the following:

■ The agents are good at abstracting the control plane, simplifying telemetry acquisition via remote management and UI.

■ They provide features for dynamic instrumentation of the services, and environments they operate in.

Fortunately for the industry at large, this benefit is rapidly eroding with projects such as OpAmp (Open telemetry's Open Agent Management Protocol) and recent significant advances in auto-instrumentation frameworks and capabilities like span-events. The future does not look good for vendors pushing organizations to remain locked in exclusive, black box software to acquire their telemetry.

We are seeing more and more organizations realizing the enormous benefits that come with owning their telemetry from the outset. These companies are ditching proprietary agents and embracing open standards for telemetry.

Indeed, there is a new mantra emerging in the industry, "Supply vendors your telemetry, don't rely on you vendors to supply your telemetry."

Over the years, I have worked at many APM companies and have witnessed the downsides of exclusivity. For the customers, they've had to endure an extremely high cost of ownership related to:

■ Agent deployment and version maintenance

■ Massive tech debt in agent codebases

■ Specialized and expensive training

■ Ever-changing pricing models to support cloud-architectures

Exclusivity was born out of complexity. Simply put, it used to be very hard to collect telemetry in this way. APM vendors were truly successful at abstracting the complexity of acquiring telemetry.

In the early days, there were only a handful of developers in the world that really understood Java well enough under the hood and could build an agent capable of dynamically rewriting byte-code at runtime to capture the timings of code execution without breaking the application.

Some vendors fared worse than others supporting "dynamic" languages such as Python, PHP, etc. Nearly all of them struggle to maintain support for new frameworks and stacks and lag the market. This is in stark contrast to how Open Source contributions and innovation happen today. The net result is a yearly backlog of unhappy customers and support cases to resolve broken correlations in trace collection while waiting for vendors to support, for example, the next version of NodeJS or React that's been out for months.

Legacy APM is a great choice for the legacy, monolithic, on-prem environment. It is not my preferred choice for Cloud-Native architectures where things evolve quickly, are small down to the size of a function, and are highly ephemeral.

None of the legacy APM vendors invested in logging and even downplayed logging as unnecessary if you could trace it. This brought up questions from them such as:

Why log if you can capture errors and stack traces in the APM world?

Who wants to clean up all the exception logging to understand and rely on log content for knowing if something is healthy?

Most developers I worked with over my career did not want to take on that effort as technical debt.

In these APM solutions, the metrics being collected and presented were only those that were included when you installed the agent. Rarely did they provide an easy way of capturing custom metrics, nor was there really much in way of metric correlation across the layers of the stacks. These platforms lacked scalability and suffered from an architecture that didn't include time-series datastores. In fact, the scaling factor has always been the achilles heel of legacy APM vendors because none were born cloud-native and all must support proprietary data schemas, and progress on re-writing APM platforms to be compliant with the modern cloud has been painfully slow.

In the final installment (Part 3) of this series, I dive into the birth and history of modern observability.

Colin Fallwell is Field CTO of Sumo Logic
Share this

The Latest

May 26, 2022

Site reliability engineers are development-focused IT professionals who work on developing and implementing solutions that solve reliability, availability, and scale problems. On the other hand, DevOps engineers are ops-focused workers who solve development pipeline problems. While there is a divide between the two professions, both sets of engineers cross the gap regularly, delivering their expertise and opinions to the other side and vice versa ...

May 25, 2022

Site reliability engineering (SRE) is fast becoming an essential aspect of modern IT operations, particularly in highly scaled, big data environments. As businesses and industries shift to the digital and embrace new IT infrastructures and technologies to remain operational and competitive, the need for a new approach for IT teams to find and manage the balance between launching new systems and features and ensuring these are intuitive, reliable, and friendly for end users has intensified as well ...

May 24, 2022

The most sophisticated observability practitioners (leaders) are able to cut downtime costs by 90%, from an estimated $23.8 million annually to just $2.5 million, compared to observability beginners, according to the State of Observability 2022 from Splunk in collaboration with the Enterprise Strategy Group. What's more, leaders in observability are more innovative and more successful at achieving digital transformation outcomes and other initiatives ...

May 23, 2022

Programmatically tracked service level indicators (SLIs) are foundational to every site reliability engineering practice. When engineering teams have programmatic SLIs in place, they lessen the need to manually track performance and incident data. They're also able to reduce manual toil because our DevOps teams define the capabilities and metrics that define their SLI data, which they collect automatically — hence "programmatic" ...

May 19, 2022

Recently, a regional healthcare organization wanted to retire its legacy monitoring tools and adopt AIOps. The organization asked Windward Consulting to implement an AIOps strategy that would help streamline its outdated and unwieldy IT system management. Our team's AIOps implementation process helped this client and can help others in the industry too. Here's what my team did ...

May 18, 2022

You've likely heard it before: every business is a digital business. However, some businesses and sectors digitize more quickly than others. Healthcare has traditionally been on the slower side of digital transformation and technology adoption, but that's changing. As healthcare organizations roll out innovations at increasing velocity, they must build a long-term strategy for how they will maintain the uptime of their critical apps and services. And there's only one tool that can ensure this continuous availability in our modern IT ecosystems. AIOps can help IT Operations teams ensure the uptime of critical apps and services ...

May 17, 2022

Between 2012 to 2015 all of the hyperscalers attempted to use the legacy APM solutions to improve their own visibility. To no avail. The problem was that none of the previous generations of APM solutions could match the scaling demand, nor could they provide interoperability due to their proprietary and exclusive agentry ...

May 16, 2022

The DevOps journey begins by understanding a team's DevOps flow and identifying precisely what tasks deliver the best return on engineers' time when automated. The rest of this blog will help DevOps team managers by outlining what jobs can — and should be automated ...

May 12, 2022

A survey from Snow Software polled more than 500 IT leaders to determine the current state of cloud infrastructure. Nearly half of the IT leaders who responded agreed that cloud was critical to operations during the pandemic with the majority deploying a hybrid cloud strategy consisting of both public and private clouds. Unsurprisingly, over the last 12 months, the majority of respondents had increased overall cloud spend — a substantial increase over the 2020 findings ...

May 11, 2022

As we all know, the drastic changes in the world have caused the workforce to take a hybrid approach over the last two years. A lot of that time, being fully remote. With the back and forth between home and office, employees need ways to stay productive and access useful information necessary to complete their daily work. The ability to obtain a holistic view of data relevant to the user and get answers to topics, no matter the worker's location, is crucial for a successful and efficient hybrid working environment ...