Top Factors That Impact Application Performance 2016 - Part 3

In 2013, APMdigest published a list called 15 Top Factors That Impact Application Performance. Even today, this is one of the most popular pieces of content on the site. And for good reason – the whole concept of Application Performance Management (APM) starts with identifying the factors that impact application performance, and then doing something about it. However, in the fast moving world of IT, many aspects of application performance have changed in the 3 years since the list was published. And many new experts have come on the scene. So APMdigest is updating the list for 2016, and you will be surprised how much it has changed.

Start with Top Factors That Impact Application Performance 2016 - Part 1

Start with Top Factors That Impact Application Performance 2016 - Part 2

Part 3 of this list covers how the application interacts with the backend and the front end.

14. DATABASE

According to data from Gleanster Research, nearly 90 percent of application performance issues are related to the database layer. This is due to a number of factors — databases are at the heart of most applications; they are complex; they are difficult to scale horizontally; and last but not least, most operations teams have little to no visibility into what goes on inside them. Thankfully, solving that fourth challenge can help solve the others, too. Many database performance tools report that a query from a certain application may be slow, but they don't always show why or how to fix the problem. As a result, many IT departments often provision a larger database server or pay for faster storage. However, this doesn't always solve the problem. A better approach is to use wait time-based analytics to identify and solve database-related application performance bottlenecks.
Gerardo Dada
VP, Product Marketing and Strategy, SolarWinds

Database latency has a huge impact. Most applications have a database backend, and in some cases they can be very big, and the queries made against them very complicated, with many users hitting them constantly, and at the same time. The technology is available, with Hadoop, clusters, elastic scaling, etc., to improve database performance. Some enterprises like Google and Facebook are on top of that, but they have gazillions of dollars, which most enterprises do not. And many enterprises have too much legacy code to replace it all in one fell swoop, if ever, without starting from scratch.
Chris Bloom
Senior Manager, Technical Alliances, Savvius

Ensuring application availability is much more than up/down monitoring, as today's users demand near instant response no matter the device or network. While application front ends and back ends vary widely by application type, what almost all have in common is their dependency on transactional databases. To truly understand application performance, one must analyze database workloads in application context, from user response time to SQL response time. The strongest application teams use an APM tool that provides this context in one unified view.
Robert Anderson
VP of Product Management, Idera

The database has the distinct honor of being both overly blamed and often overlooked as the source of application performance problems. App developers will often blame the database for poor performance, when in fact close inspection shows that poorly written queries can be the true source of the problem. On the other hand, when an application experiences rapid growth in demand, the database may indeed become the genuine bottleneck. Most other parts of the infrastructure can scale more easily than the database. Scaling out – where the application makes use of multiple database servers simultaneously for scaling reads – is difficult at the application layer, requiring recoding to take advantage of readable secondaries. Technology that offloads the application, such as database load balancing software, can speed application performance, so organizations no longer have to modify the application for scale out or other performance-enhancing features like caching or connection management.
Michelle McLean
VP of Marketing, ScaleArc

15. DATA

Keep a watchful eye on the amount of data moving between tiers. Even after all these years, there are many applications that are still sending huge amounts of JSON or XML data to the front end to fill a huge multi-page table. Instead, IT should rewrite the UI to only request the pages of data it needs. The good news is most UI toolkits have a pattern to handle this.
Jim Cashman
Senior Product Manager, Ipswitch

16. BACKEND CALLS

In my experience, the number one factor that affects application performance is the backend calls the application makes. Whether it is a Database call or a HTTP call (such as SOAP, REST), or any other numerous types of backend calls, poorly performing backend can degrade application performance to a great extent. Adequate monitoring is critical for diagnosing this type of issues, as it may not be readily apparent at first.
Karun Subramanian
Application Support Expert, www.karunsubramanian.com

Plumbr have recently published the results of their quantitative study on top factors that impact application performance. This research was based on user experience data from more than a thousand real-world production applications running on the Java Virtual Machine. We found the top three culprits to be blocking calls to external web services, lock contention issues and slow database calls.
Priit Potter
Co-founder and CEO, Plumbr

17. BACKEND MODIFICATIONS

Among the various factors that influence application performance, one of the most critical is the performance (speed and reliability) of back-end transactional systems. According to a recent EMA survey, IT professionals cited "rapid growth in transaction usage taxes back-end capacity" as a top application support challenge. When it comes to optimizing application performance, many organizations are focused on front-end platforms, like customer-facing e-commerce systems. But these "systems of engagement" can only be as fast and reliable as the back-end "systems of record" supporting them. Essentially, multi-platform applications make these all one, unified system. Due to the sheer volume of users impacted, slight back-end modifications can have a significant positive or an equally negative impact on end users' experiences with transactional applications. Organizations need unified DevOps to ensure that a potential change does not produce unintended consequences.
Spencer Hallman
Product Manager, Compuware

18. SOCIAL MEDIA AND MARKETING OVERHEAD

Do you ever wonder why when you want to check the local weather on your favorite website it takes well over the 6-8 second threshold to render completely, which is more like 30 -50 seconds when you time it? This goes for all of the popular quick hit social media and informational sites with high traffic volumes. The banner adds, click tracking, and java client-side scripts running to create web trending metrics have created such an overhead that we continue to rely on Moore’s law and upgrade to the newest device or PC when it comes out. It’s crazy when you consider the extra overhead being imposed on us to track everything we do. That’s why it’s important to establish baselines on performance no matter how simple the monitoring may seem. If you know what “normal” looks like, it will actually pay dividends when trying to figure out if you have a problem with your application or not.
Larry Dragich
Director of Customer Experience Management at the Auto Club Group and Founder of the APM Strategies Group on LinkedIn.

19. DEVICE HEALTH

Performance and health of the broad range of end user devices … The average workforce end user relies on three or more devices to access business critical applications throughout their day – smartphone on their commute, laptop or PC in the office, tablet when interacting with customers. Application performance must be of consistently high quality so the user can navigate seamlessly between these devices as they conduct their business. And designing mobile apps for the broad range of consumer devices is a well-known challenge. Therefore, one of the top factors that impacts application performance is the performance and health of the broad range of end user devices used to access them
Mike Marks
Chief Product Evangelist, Aternity

Just from my own experience over the past week and having to work a lot more while on-the-go, it's clear that with the increase of the number of users interfacing via mobile devices such as tablets, smartphones and a growing number of IoT connected devices, that mobile apps, desktop apps and most any service accessible via desktop now also has to be designed with "mobile" platforms in mind. This changing factor warrants added focus on maintaining a consistent end-user experience across devices with a medley of screen resolution, memory, processing power and operating system combinations. Even further, this creates added demand on back-end resources with this upsurge in popularity of smart devices.
Hayden James
Linux Systems Analyst, haydenjames.io

Read Top Factors That Impact Application Performance 2016 - Part 4 covering the application itself.

Related Links

The Latest

Enterprise Edge AI Reaches Inflection Point

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

AI Is Hitting Operational Limits

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Almost Half of AI-Generated Code Fails in Production

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Top Factors That Impact Application Performance 2016 - Part 3

In 2013, APMdigest published a list called 15 Top Factors That Impact Application Performance. Even today, this is one of the most popular pieces of content on the site. And for good reason – the whole concept of Application Performance Management (APM) starts with identifying the factors that impact application performance, and then doing something about it. However, in the fast moving world of IT, many aspects of application performance have changed in the 3 years since the list was published. And many new experts have come on the scene. So APMdigest is updating the list for 2016, and you will be surprised how much it has changed.

Start with Top Factors That Impact Application Performance 2016 - Part 1

Start with Top Factors That Impact Application Performance 2016 - Part 2

Part 3 of this list covers how the application interacts with the backend and the front end.

14. DATABASE

According to data from Gleanster Research, nearly 90 percent of application performance issues are related to the database layer. This is due to a number of factors — databases are at the heart of most applications; they are complex; they are difficult to scale horizontally; and last but not least, most operations teams have little to no visibility into what goes on inside them. Thankfully, solving that fourth challenge can help solve the others, too. Many database performance tools report that a query from a certain application may be slow, but they don't always show why or how to fix the problem. As a result, many IT departments often provision a larger database server or pay for faster storage. However, this doesn't always solve the problem. A better approach is to use wait time-based analytics to identify and solve database-related application performance bottlenecks.
Gerardo Dada
VP, Product Marketing and Strategy, SolarWinds

Database latency has a huge impact. Most applications have a database backend, and in some cases they can be very big, and the queries made against them very complicated, with many users hitting them constantly, and at the same time. The technology is available, with Hadoop, clusters, elastic scaling, etc., to improve database performance. Some enterprises like Google and Facebook are on top of that, but they have gazillions of dollars, which most enterprises do not. And many enterprises have too much legacy code to replace it all in one fell swoop, if ever, without starting from scratch.
Chris Bloom
Senior Manager, Technical Alliances, Savvius

Ensuring application availability is much more than up/down monitoring, as today's users demand near instant response no matter the device or network. While application front ends and back ends vary widely by application type, what almost all have in common is their dependency on transactional databases. To truly understand application performance, one must analyze database workloads in application context, from user response time to SQL response time. The strongest application teams use an APM tool that provides this context in one unified view.
Robert Anderson
VP of Product Management, Idera

The database has the distinct honor of being both overly blamed and often overlooked as the source of application performance problems. App developers will often blame the database for poor performance, when in fact close inspection shows that poorly written queries can be the true source of the problem. On the other hand, when an application experiences rapid growth in demand, the database may indeed become the genuine bottleneck. Most other parts of the infrastructure can scale more easily than the database. Scaling out – where the application makes use of multiple database servers simultaneously for scaling reads – is difficult at the application layer, requiring recoding to take advantage of readable secondaries. Technology that offloads the application, such as database load balancing software, can speed application performance, so organizations no longer have to modify the application for scale out or other performance-enhancing features like caching or connection management.
Michelle McLean
VP of Marketing, ScaleArc

15. DATA

Keep a watchful eye on the amount of data moving between tiers. Even after all these years, there are many applications that are still sending huge amounts of JSON or XML data to the front end to fill a huge multi-page table. Instead, IT should rewrite the UI to only request the pages of data it needs. The good news is most UI toolkits have a pattern to handle this.
Jim Cashman
Senior Product Manager, Ipswitch

16. BACKEND CALLS

In my experience, the number one factor that affects application performance is the backend calls the application makes. Whether it is a Database call or a HTTP call (such as SOAP, REST), or any other numerous types of backend calls, poorly performing backend can degrade application performance to a great extent. Adequate monitoring is critical for diagnosing this type of issues, as it may not be readily apparent at first.
Karun Subramanian
Application Support Expert, www.karunsubramanian.com

Plumbr have recently published the results of their quantitative study on top factors that impact application performance. This research was based on user experience data from more than a thousand real-world production applications running on the Java Virtual Machine. We found the top three culprits to be blocking calls to external web services, lock contention issues and slow database calls.
Priit Potter
Co-founder and CEO, Plumbr

17. BACKEND MODIFICATIONS

Among the various factors that influence application performance, one of the most critical is the performance (speed and reliability) of back-end transactional systems. According to a recent EMA survey, IT professionals cited "rapid growth in transaction usage taxes back-end capacity" as a top application support challenge. When it comes to optimizing application performance, many organizations are focused on front-end platforms, like customer-facing e-commerce systems. But these "systems of engagement" can only be as fast and reliable as the back-end "systems of record" supporting them. Essentially, multi-platform applications make these all one, unified system. Due to the sheer volume of users impacted, slight back-end modifications can have a significant positive or an equally negative impact on end users' experiences with transactional applications. Organizations need unified DevOps to ensure that a potential change does not produce unintended consequences.
Spencer Hallman
Product Manager, Compuware

18. SOCIAL MEDIA AND MARKETING OVERHEAD

Do you ever wonder why when you want to check the local weather on your favorite website it takes well over the 6-8 second threshold to render completely, which is more like 30 -50 seconds when you time it? This goes for all of the popular quick hit social media and informational sites with high traffic volumes. The banner adds, click tracking, and java client-side scripts running to create web trending metrics have created such an overhead that we continue to rely on Moore’s law and upgrade to the newest device or PC when it comes out. It’s crazy when you consider the extra overhead being imposed on us to track everything we do. That’s why it’s important to establish baselines on performance no matter how simple the monitoring may seem. If you know what “normal” looks like, it will actually pay dividends when trying to figure out if you have a problem with your application or not.
Larry Dragich
Director of Customer Experience Management at the Auto Club Group and Founder of the APM Strategies Group on LinkedIn.

19. DEVICE HEALTH

Performance and health of the broad range of end user devices … The average workforce end user relies on three or more devices to access business critical applications throughout their day – smartphone on their commute, laptop or PC in the office, tablet when interacting with customers. Application performance must be of consistently high quality so the user can navigate seamlessly between these devices as they conduct their business. And designing mobile apps for the broad range of consumer devices is a well-known challenge. Therefore, one of the top factors that impacts application performance is the performance and health of the broad range of end user devices used to access them
Mike Marks
Chief Product Evangelist, Aternity

Just from my own experience over the past week and having to work a lot more while on-the-go, it's clear that with the increase of the number of users interfacing via mobile devices such as tablets, smartphones and a growing number of IoT connected devices, that mobile apps, desktop apps and most any service accessible via desktop now also has to be designed with "mobile" platforms in mind. This changing factor warrants added focus on maintaining a consistent end-user experience across devices with a medley of screen resolution, memory, processing power and operating system combinations. Even further, this creates added demand on back-end resources with this upsurge in popularity of smart devices.
Hayden James
Linux Systems Analyst, haydenjames.io

Read Top Factors That Impact Application Performance 2016 - Part 4 covering the application itself.

Related Links

The Latest

Enterprise Edge AI Reaches Inflection Point

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

AI Is Hitting Operational Limits

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Almost Half of AI-Generated Code Fails in Production

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...