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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

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

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 ...