Why Traditional APM Tools Are Insufficient for Modern Enterprise Applications
September 26, 2019

Navin Israni

Share this

APM tools are your window into your application's performance — its capacity and levels of service. These tools help admins conduct regular health checks on the app so they can tell the state of the app without any ambiguity.

Any application is made up of its layers and its subsystems — the servers, the virtualization layers, the dependencies, and its components. The purpose of such tools has traditionally been to monitor the performance of all the subsystems.

A traditional approach to APM involved the use of arbitrary sampling strategies, algorithm-based data completion, and a fair bit of prediction to analyze the root cause. So, the agents had to come up with a hypothesis of why things were wrong and devise a sampling strategy to test that theory. Any data gaps were predictively filled by algorithms.

Automation is one of the many ways that founders can scale their business. As organizations grow, their automated processes will only generate more data, not less. As automation seeps into every facet of the digital enterprise, the applications interfacing organizations with their audience generate large swathes of raw, unsampled data.

Traditional APM tools are now struggling due to the mismatch between their specifications and expectations.

Modern application architectures are multi-faceted; they contain hybrid components across a variety of on-premise and cloud applications. Modern enterprises often generate data in silos with each outflow having its own data structure. This data comes from several tools over different periods of time.

Such diversity in sources, structure, and formats present unique challenges for traditional enterprise tools.

1. Inability to handle massive, multi-dimensional data

As discussed before, modern applications are not atomic; they are constituent of several components and subsystems all of which contribute to its overall performance. 

Each subsystem can produce several terabytes of data. Such scale of data brings forth at least a few problems with the earlier-generation APM tools:

■ The efficient storage of and access to this data is a peculiar challenge.

■ Real-time analysis of this data on the mammoth-scale is an even bigger challenge for traditional APM tools.

■ Often the data may be multiple types of data sources — in flat files, structured query-based databases, or even complete systems of their own with API-based access.

2. Propagation of fragmentation into APM tools

Often, we see new tools for each functional area even within the same data center. This fuels silo creation as segregated teams support individual tools for managing the server, network, storage, and virtual layers. 

A count of anywhere between 6 to 10 tools would not be uncommon. Each of these proprietary tools may come with vendor lock-in, forcing companies to continue using them with restrictions or pay more when the usage increases.

This is not ideal for enterprises as most modern applications are dynamic and interdependent in nature. For example, as user-base increases, a single business request to increase capacity will mean synchronous updating and coordination among silos for databases, servers, networks, and virtual layers.

At the intersection of these functional areas, agents do the job of coordinating the data and passing on the configurations. Without a cohesive plan to manage these agents (automated or manual), it becomes difficult to collectively address issues to optimize their efficiency. 

Due to the fragmentation in tools, other issues like long-term licensing come to surface and companies have to keep paying for these tools over the long term. One possible solution is to outsource product development. This way companies can target multiple functionalities with a single custom-developed app and finite vendor contracts.

3. Security risks during seasonal spikes

To proactively identify problems, these tools rely on detecting anomalies in data sources that are infrastructure-centric. This would typically include log files, memory metrics, CPU usage, and so on. 

If there are seasonal spikes, such as massive holiday sales like Black Friday, the admins would be flooded with spikes across the board. Hiding an attack in between these spikes becomes easier as most traditional APM tools can't differentiate between these spikes from distributed denial of service (DDoS) attacks.

4. Difficulty in root-cause analysis

Agents can stitch together data from various systems to identify root cause of major problems. To detect anomalies, agents identify patterns and then use queries to confirm their assumptions of a diagnosis.

Because of human involvement in the diagnosis process, there is a strong possibility of selection/sampling bias being introduced in the process.

Also, these analyses are estimates at best as they rely on testing a hypothesis.

An accurate, tools-agnostic analysis of the root cause requires not only identifying anomalies but patterns of these aberrations over time. This is where traditional APM tools fall short and predictive analysis tools truly shine.

Final Words

Traditional APM tools lack the capacity to handle the scale of data being generated by modern applications. Also, these applications generally occupy status of legacy apps in enterprises, which makes replacing them even more difficult.

So, while management is likely to see them as roadblocks, removing these legacy apps completely from the enterprise would mean ripping the band-aid off. It is a hard decision to make and one that requires a fair bit of convincing and strategy.

This might look like hard work, but it is better than letting these roadblocks continue to slow your processes down. It is important to take action before the damage becomes critical.

Navin Israni is a Senior Content Writer at Arkenea
Share this

The Latest

December 11, 2019

As the New Year approaches, it is time for APMdigest's 10th annual list of Application Performance Management (APM) predictions. Industry experts offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2020 ...

December 10, 2019

Enterprises with services operating in the cloud are overspending by millions due to inefficiencies with their apps and runtime environments, according to a poll conducted by Lead to Market, and commissioned by Opsani. 69 Percent of respondents report regularly overspending on their cloud budget by 25 percent or more, leading to a loss of millions on unnecessary cloud spend ...

December 09, 2019

For IT professionals responsible for upgrading users to Windows 10, it's crunch time. End of regular support for Windows 7 is nearly here (January 14, 2020) but as many as 59% say that only a portion of their users have been migrated to Windows 10 ...

December 05, 2019

Application performance monitoring (APM) has become one of the key strategies adopted by IT teams and application owners in today’s era of digital business services. Application downtime has always been considered adverse to business productivity. But in today’s digital economy, what is becoming equally dreadful is application slowdown. When an application is slow, the end user’s experience accessing the application is negatively affected leaving a dent on the business in terms of commercial loss and brand damage ...

December 04, 2019

Useful digital transformation means altering or designing new business processes, and implementing them via the people and technology changes needed to support these new business processes ...

December 03, 2019
The word "digital" is today thrown around in word and phrase like rice at a wedding and never do two utterances thereof have the same meaning. Common phrases like "digital skills" and "digital transformation" are explained in 101 different ways. The outcome of this is a predictable cycle of confusion, especially at business management level where often the answer to business issues is "more technology" ...
December 02, 2019

xMatters recently released the results of its Incident Management in the Age of Customer-Centricity research study to better understand the range of various incident management practices and how the increased focus on customer experience has caused roles across an organization to evolve. Findings highlight the ongoing challenges organizations face as they continue to introduce and rapidly evolve digital services ...

November 26, 2019

The new App Attention Index Report from AppDynamics finds that consumers are using an average 32 digital services every day — more than four times as many as they realize. What's more, their use of digital services has evolved from a conscious decision to carry around a device and use it for a specific task, to an unconscious and automated behavior — a digital reflex. So what does all this mean for the IT teams driving application performance on the backend? Bottom line: delivering seamless and world-class digital experiences is critical if businesses want to stay relevant and ensure long-term customer loyalty. Here are some key considerations for IT leaders and developers to consider ...

November 25, 2019

Through the adoption of agile technologies, financial firms can begin to use software to both operate more effectively and be faster to market with improvements for customer experiences. Making sure there is the necessary software in place to give customers frictionless everyday activities, like remote deposits, business overdraft services and wealth management, is key for a positive customer experience ...

November 21, 2019

For the past two years, Couchbase has been digging into enterprises' digital strategies. Can they deliver the experiences and services their end-users need? What pressure are they under to innovate and succeed? And what is driving investments in new technologies? ...