5 Steps to Enhance APM with Log Data
May 20, 2014
Trevor Parsons
Share this

Logs have moved beyond a basic tool for debugging during development. A recent Logentries survey carried out across a sample of 25k users of log management software shows that the most common use case is using log data for production monitoring, which has traditionally been the stronghold of Application Performance Management (APM) and server monitoring tools.

Using logs for application monitoring comes with a major benefit. Logs not only allow you to look at trends in your data, but – unlike APM or server monitoring tools – they also maintain the evidence so that you can drill down to the log event level to understand exactly what led to a spike in response time or CPU for example.

Furthermore, you can also use logs to be proactive, such that you can create notifications or automated actions when particular events occur or thresholds are breached. That way you can get notified and react when symptoms of more serious issues begin to occur so you can react before a major incident happens.

So what are the most important steps to follow to investigate and resolve particular issues when they occur? When using your logs for performance monitoring, here are some useful steps you can follow to dig a little deeper into any issues that you identify:

1. Set up real-time alerts

The first step is to get notified in real time when something important happens. For example, if you get an OutOfMemoryException (one of the common Tomcat errors we identified from our analysis), this can be pretty critical. You want to know right away so you can react appropriately. If an OutOfMemoryException was caused by a slow memory leak, often a server restart will buy you some time so you might even want to have your notifications configured with your infrastructure API to automatically restart an instance upon a given issue. Make sure your logging supports alerts that can be configured with third-party APIs and are sent in real time - i.e. seconds not minutes.

2. Understand what user behavior caused the issue

Once you know there is a particular problem in the system, the next set of steps are usually related to figuring out what caused it. Understanding how your system was being used at the time of, or leading up to, an issue can be a big help. This can help you localize the problem to a set of system components or functions. If your hunch is that a single user action can lead to a problem (e.g. you released a new UI feature that crashed when users started to play with it), session- or transaction-tracing techniques can really help here. Session or transaction tracing allows you to follow a user’s steps through your system in the order in which they were carried out such as the order in which they navigated your app interface or the steps they took before they added something to a shopping cart, for example.

Tracing in this way can be achieved by following some logging best practices, which suggest you should add the following details to your log events:

- A timestamp

- A unique user identifier (e.g. user name, user ID, email address)

- A unique session or transaction ID

Combining these three parameters allows you to retrace the steps of a user before an incident occurred.

If, on the other hand, the system issue was caused by group user behavior rather than a single user action, which is often the case with an OutOfMemoryException that featured as a common issue that surfaced in our research analysis, tracing a given transaction or session may not be sufficient to identify the root cause. Instead you might want to understand what were the most common system functions that all users have been carrying out. A great way to do this is to group log events by user actions to get a break down of what the most common user behavior is and how this breaks down over the past hour, day or week for example.

This will give you an immediate view of how your system is being used by groups of users and can help you nail down actions that may be resulting in leaking memory. Correlating increases in a given user action over the past 24 hours with increases in your heap size over that same time period can be a good way to point you in the right direction of a leak.

3. Check resource usage

Resource usage data can also be streamed into your log data such that it can be correlated with application exceptions or system errors.

When a given issue occurs in your system it may or may not be related to exhausted system resources such as CPU or memory. Typically issues like slow response time, timeouts or memory leaks can be related to resource usage. A quick look at your system resource usage when there is an issue is almost always a good idea and can help save you time when troubleshooting.

4. Determine if performance was affected

One of the first things you will need to communicate across your team when there is a system issue is: which users were effected and how it affected them. Another logging best practice worth following is to log important performance parameters from your application code, web servers and database queries. Request response time, response size and slow queries can be particularly useful to track. Combining this information with unique user identifiers (see #2) allows you to track performance at the per-user level such that you can see if individual users have been affected by a given system issue.

Furthermore, real user monitoring (RUM) using client-side logging libraries will allow you to capture log data from a client device (smart phone/tablet) apps or web browser. With RUM, you will not only capture the time spent in the system backend, but can also capture the perceived performance from the client’s perspective capturing total time it took before the response was received by the client. This can also capture delays in the network or with page loading times for example.

5. Identify what part of the application code caused the issue

Once you have established the exception type, the user behavior that led to the issue, resource usage at the time as well as how users were affected, you will want to immediately dive into the low-level details to figure out the issue in your code or the system process that caused the problem. Examining exception stack traces in your logs can help identify the culprit. For example, in the case of a UI bug, tracing a user transaction (as outlined in #2 above) will often capture the exception caused by a particular action. Digging into the exception stack trace can show you the exact method/object/function and line number where a bug was introduced.

When choosing your logging solution, make sure it can handle multi-line events, as exception traces are essentially single events that can span 10s or 100s of lines. With some solutions, it can be very frustrating when you search for an exception and do not get the full trace. Solutions that support multi-line events and show surrounding events around a given search can make life a lot easier when dealing with exception traces.

ABOUT Trevor Parsons

Trevor Parsons, PhD, is Co-founder and Chief Scientist of Logentries. Parsons is responsible for product strategy and direction. He works closely with customers and partners to continuously understand what they need, and to validate product market fit. Parsons also leads the product management and UX teams and assures the best possible user experience. Parsons enjoys speaking at local devops meet-ups and events, and is always looking for how log data and analytics can be applied in more and more powerful use cases. Parsons was a post doctoral researcher and member of the Performance Engineering Lab at the School of Computer Science and Informatics in University College Dublin, Ireland. He received a PhD from University College Dublin for his thesis titled Automatic Detection of Performance Design and Deployment Antipatterns in Component Based Enterprise Systems.

Share this

The Latest

February 29, 2024

Despite the growth in popularity of artificial intelligence (AI) and ML across a number of industries, there is still a huge amount of unrealized potential, with many businesses playing catch-up and still planning how ML solutions can best facilitate processes. Further progression could be limited without investment in specialized technical teams to drive development and integration ...

February 28, 2024

With over 200 streaming services to choose from, including multiple platforms featuring similar types of entertainment, users have little incentive to remain loyal to any given platform if it exhibits performance issues. Big names in streaming like Hulu, Amazon Prime and HBO Max invest thousands of hours into engineering observability and closed-loop monitoring to combat infrastructure and application issues, but smaller platforms struggle to remain competitive without access to the same resources ...

February 27, 2024

Generative AI has recently experienced unprecedented dramatic growth, making it one of the most exciting transformations the tech industry has seen in some time. However, this growth also poses a challenge for tech leaders who will be expected to deliver on the promise of new technology. In 2024, delivering tangible outcomes that meet the potential of AI, and setting up incubator projects for the future will be key tasks ...

February 26, 2024

SAP is a tool for automating business processes. Managing SAP solutions, especially with the shift to the cloud-based S/4HANA platform, can be intricate. To explore the concerns of SAP users during operational transformations and automation, a survey was conducted in mid-2023 by Digitate and Americas' SAP Users' Group ...

February 22, 2024

Some companies are just starting to dip their toes into developing AI capabilities, while (few) others can claim they have built a truly AI-first product. Regardless of where a company is on the AI journey, leaders must understand what it means to build every aspect of their product with AI in mind ...

February 21, 2024

Generative AI will usher in advantages within various industries. However, the technology is still nascent, and according to the recent Dynatrace survey there are many challenges and risks that organizations need to overcome to use this technology effectively ...

February 20, 2024

In today's digital era, monitoring and observability are indispensable in software and application development. Their efficacy lies in empowering developers to swiftly identify and address issues, enhance performance, and deliver flawless user experiences. Achieving these objectives requires meticulous planning, strategic implementation, and consistent ongoing maintenance. In this blog, we're sharing our five best practices to fortify your approach to application performance monitoring (APM) and observability ...

February 16, 2024

In MEAN TIME TO INSIGHT Episode 3, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at Enterprise Management Associates (EMA) discusses network security with Chris Steffen, VP of Research Covering Information Security, Risk, and Compliance Management at EMA ...

February 15, 2024

In a time where we're constantly bombarded with new buzzwords and technological advancements, it can be challenging for businesses to determine what is real, what is useful, and what they truly need. Over the years, we've witnessed the rise and fall of various tech trends, such as the promises (and fears) of AI becoming sentient and replacing humans to the declaration that data is the new oil. At the end of the day, one fundamental question remains: How can companies navigate through the tech buzz and make informed decisions for their future? ...

February 14, 2024

We increasingly see companies using their observability data to support security use cases. It's not entirely surprising given the challenges that organizations have with legacy SIEMs. We wanted to dig into this evolving intersection of security and observability, so we surveyed 500 security professionals — 40% of whom were either CISOs or CSOs — for our inaugural State of Security Observability report ...