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5 Ways Log Analysis Augments APM

Gal Berg

While Application Performance Management (APM) is a vital tool to ensure application availability and performance, analysis of log data can augment APM to monitor, manage and optimize application performance.

From infrastructure, OS and web logs to home grown application logs and 3rd party software logs, you can extract valuable insights – using log analysis technology – that will help you better understand, measure and support your APM strategy.

The following are 5 ways log analysis can deliver results beyond APM tools:

1. Outlining the Steps in the Transaction Flow

The transaction flow of an application service is a set of interactions between the many components that enable that service. When a component fails in the transaction flow, it sets off a chain reaction of failures. This chain reaction can involve multiple steps across the environment, and other clouds, data centers and software components. You need to be able to efficiently analyze all of these many steps – and potential failure points – in the transaction flow, to identify the original component failure.

An ideal log analysis tool will enable you to identify – and even automatically collect – all the log events generated by a specific transaction flow. Once you have identified each log event type, it is very easy to collect all the information and investigate what happened. Each log event can provide insight into exactly what happened. You can see the chain of events, in chronological order, empowering you to track back through history to the root cause problem.

2. Determining the Root Cause of the Bottleneck

Bottlenecks are symptoms of an application performance problem, not the root cause. While APM tools can quickly identify a bottleneck, this is merely an indication that something went wrong. To fix the problem, you need to find the underlying cause of the bottleneck.

Bottlenecks in the transaction flow can be caused by a variety of factors. There are many breaking points where something can go wrong – infrastructure, software or within the application itself. In log files, you can find clues to what actually happened to cause the bottleneck.

If a single component within the transaction flow fails, this can start a bottleneck. When this occurs, a log analysis tool will enable you to see log events either from that specific component or from a component that interfaces with the failed component. These log events indicate where the problem lies.

For example, if a database fails, you will see a variety of bottlenecks across multiple transactions. This will trigger many logs with JDBC exceptions or data source exceptions across multiple sources. If you understand the messages within the logs – such as "database connection failed" or "out of memory exception" – you can determine the root cause of the bottleneck.

3. Detecting Configuration Changes

Configuration changes can have a significant negative impact on an application service. For example, configuration changes can introduce damaging infrastructure faults or application bugs. While a change may manifest as an application performance issue that you can identify, you need to discover the change that triggered this problem.

In these cases, it is challenging for APM tools to uncover the root cause of the problem because a configuration change does not directly create the problem. The change introduces a separate factor that causes the problem.

In order to fix the problem, you need to identify what was changed, and the effects of that change. Using log analysis, you can identify the original configuration change. Logs contain diagnostics information regarding failures and changes that happen during events. Leveraging these vital records, log analysis provides you with visibility into changes that happen on the application, infrastructure and configuration level.

4. Gaining Visibility into Stress

Stress on components in the transaction flow can cause application failures, so it is important to understand the impact of stress. With APM tools, you can see certain levels of load, but measuring load is not enough. You need broader visibility.

You need to look at what the application is doing semantically. Understanding stress is not only the number of threads that a server opens, or the number of transactions that the user is calling. Stress is something that eventually puts a tremendous load on the computation level, on the memory, on infrastructure resources.

A near real-time trend analysis dashboard – delivering valuable data gained from logs – will help you quickly identify potential events and growing trends that will become future problems. The dashboard can provide visibility into the stress on the application, not only from number of transactions, load, calls and logins. It also provides information on the number of complex queries the system is running right now; the average cost of each query; if the system is running a difficult cryptographic computation; the number of cryptographic computations, and so on. This provides visibility into which components are experiencing more stress over time, leading to potential performance issues.

5. Identifying Butterfly Events

Log analysis enables you to proactively search for "butterfly events" – mysterious events caused by an unexpected and complex chain reaction. The original cause of a butterfly event can be anything, and it is not clearly identifiable through traditional APM tools. Butterfly events are hard to find because there are millions of events out there and you don't know what to look for.

The right log analysis solution will show if an event occurred in the past; the first time the event happened; if an event disappeared from the system; and ultimately, if an event has meaning for the system, application or infrastructure. By scanning all log data silos, you can catch obscure events that may ultimately have an impact on the transaction flow. You can be proactive, and find and fix the problem before the chaotic nature of the environment translates into business chaos.

Using log analysis, you will be able to identify log patterns that have an impact on application performance, optimize your APM strategy, and drive better and faster APM results.

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5 Ways Log Analysis Augments APM

Gal Berg

While Application Performance Management (APM) is a vital tool to ensure application availability and performance, analysis of log data can augment APM to monitor, manage and optimize application performance.

From infrastructure, OS and web logs to home grown application logs and 3rd party software logs, you can extract valuable insights – using log analysis technology – that will help you better understand, measure and support your APM strategy.

The following are 5 ways log analysis can deliver results beyond APM tools:

1. Outlining the Steps in the Transaction Flow

The transaction flow of an application service is a set of interactions between the many components that enable that service. When a component fails in the transaction flow, it sets off a chain reaction of failures. This chain reaction can involve multiple steps across the environment, and other clouds, data centers and software components. You need to be able to efficiently analyze all of these many steps – and potential failure points – in the transaction flow, to identify the original component failure.

An ideal log analysis tool will enable you to identify – and even automatically collect – all the log events generated by a specific transaction flow. Once you have identified each log event type, it is very easy to collect all the information and investigate what happened. Each log event can provide insight into exactly what happened. You can see the chain of events, in chronological order, empowering you to track back through history to the root cause problem.

2. Determining the Root Cause of the Bottleneck

Bottlenecks are symptoms of an application performance problem, not the root cause. While APM tools can quickly identify a bottleneck, this is merely an indication that something went wrong. To fix the problem, you need to find the underlying cause of the bottleneck.

Bottlenecks in the transaction flow can be caused by a variety of factors. There are many breaking points where something can go wrong – infrastructure, software or within the application itself. In log files, you can find clues to what actually happened to cause the bottleneck.

If a single component within the transaction flow fails, this can start a bottleneck. When this occurs, a log analysis tool will enable you to see log events either from that specific component or from a component that interfaces with the failed component. These log events indicate where the problem lies.

For example, if a database fails, you will see a variety of bottlenecks across multiple transactions. This will trigger many logs with JDBC exceptions or data source exceptions across multiple sources. If you understand the messages within the logs – such as "database connection failed" or "out of memory exception" – you can determine the root cause of the bottleneck.

3. Detecting Configuration Changes

Configuration changes can have a significant negative impact on an application service. For example, configuration changes can introduce damaging infrastructure faults or application bugs. While a change may manifest as an application performance issue that you can identify, you need to discover the change that triggered this problem.

In these cases, it is challenging for APM tools to uncover the root cause of the problem because a configuration change does not directly create the problem. The change introduces a separate factor that causes the problem.

In order to fix the problem, you need to identify what was changed, and the effects of that change. Using log analysis, you can identify the original configuration change. Logs contain diagnostics information regarding failures and changes that happen during events. Leveraging these vital records, log analysis provides you with visibility into changes that happen on the application, infrastructure and configuration level.

4. Gaining Visibility into Stress

Stress on components in the transaction flow can cause application failures, so it is important to understand the impact of stress. With APM tools, you can see certain levels of load, but measuring load is not enough. You need broader visibility.

You need to look at what the application is doing semantically. Understanding stress is not only the number of threads that a server opens, or the number of transactions that the user is calling. Stress is something that eventually puts a tremendous load on the computation level, on the memory, on infrastructure resources.

A near real-time trend analysis dashboard – delivering valuable data gained from logs – will help you quickly identify potential events and growing trends that will become future problems. The dashboard can provide visibility into the stress on the application, not only from number of transactions, load, calls and logins. It also provides information on the number of complex queries the system is running right now; the average cost of each query; if the system is running a difficult cryptographic computation; the number of cryptographic computations, and so on. This provides visibility into which components are experiencing more stress over time, leading to potential performance issues.

5. Identifying Butterfly Events

Log analysis enables you to proactively search for "butterfly events" – mysterious events caused by an unexpected and complex chain reaction. The original cause of a butterfly event can be anything, and it is not clearly identifiable through traditional APM tools. Butterfly events are hard to find because there are millions of events out there and you don't know what to look for.

The right log analysis solution will show if an event occurred in the past; the first time the event happened; if an event disappeared from the system; and ultimately, if an event has meaning for the system, application or infrastructure. By scanning all log data silos, you can catch obscure events that may ultimately have an impact on the transaction flow. You can be proactive, and find and fix the problem before the chaotic nature of the environment translates into business chaos.

Using log analysis, you will be able to identify log patterns that have an impact on application performance, optimize your APM strategy, and drive better and faster APM results.

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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