Debugging User-specific Issues in Cloud-native Applications

Disclaimer: This blog post was written as part of a collaboration with Lightrun.

Logging Overload and the Challenge of Troubleshooting User-Specific Issues

According to a survey by ChaosSearch, log data volumes are increasing significantly for many organizations, with an average daily ingest volume of 7.9 TB and 94% of participants ingesting at least 1 TB or more per day. This translates to roughly 34.05 billion daily log entries, assuming a typical log line of 35 words. The sheer volume of log data generated makes it increasingly challenging to identify and resolve user-specific issues in a timely and efficient manner. These challenges are further compounded by the associated costs.

In today's fast-paced digital landscape, troubleshooting user-specific problems has become a critical challenge that modern organizations must address. However, traditional logging often fails to meet the demands of this task effectively. As a result, alternatives have emerged as potential solutions to this pressing issue.

In this article, we will explore the challenges associated with troubleshooting user-specific problems in detail and examine the solution that can help organizations address them more effectively.

Is Traditional Logging Failing You?

In his 2017 Letter to Shareholders, Jeff Bezos emphasized the ever-evolving nature of customers' expectations. He noted that customers are "divinely discontent," constantly having a voracious appetite for a better way and leaving yesterday's "wow" in the dust as it quickly becomes today's "ordinary." Indeed, customer obsession is key to Amazon's success and one crucial facet of guaranteeing customer satisfaction is efficiently troubleshooting and fixing their problems, whether they are common or specific to a single user.

However, the complexity of identifying and fixing user-specific problems, coupled with the limitations of traditional logging approaches, poses a severe limitation. These issues refer to problems that affect only one user, such as errors that occur when a particular user attempts to access a specific feature or application. For instance, a user may encounter difficulties or errors when trying to complete a specific transaction on a website. When the transaction number is significant, such issues become "hidden" and can be challenging to identify and resolve, as they are specific to a user and may not be replicated by other users or developers themselves.

Traditional logging systems are often limited in their ability to address user-specific problems due to their design. These systems typically capture large amounts of data from the system's backend, such as server error logs, which provide little to no insights into the user's experience or actions. Furthermore, filtering through thousands of log lines to identify a prompt action that occurred during an unknown or approximate time frame can be like trying to find a specific grain of sand on a beach – a daunting task for developers that can lead to frustration.

Traditional logging systems and user-specific issues are two ingredients that do not mix well together. The resulting recipes can increase developers' frustration and limit their productivity. Additionally, users and customers may feel frustrated not just because an issue occurred, but also because of the time needed to troubleshoot and resolve their problems. This is what leads us to MMTR.

Inefficient troubleshooting can result in longer mean time to repair (MTTR), which means more downtimes and potentially lost revenue. Prolonged downtime can even lead to significant financial losses, affecting business continuity and reputation.

Furthermore, traditional logging systems are not optimized for modern use cases, resulting in increased logging costs over time.

Not being able to promptly and accurately resolve issues including the most specific ones, have multiple drawbacks starting from impacting your team’s productivity, your customer's loyalty, your business continuity as well as your operational costs.

The Solution - Conditional Logging

With traditional logging systems, developers must filter through thousands of log lines to identify the specific logs that may provide insights into the issue. And as seen, this can be a time-consuming and frustrating process.

Fortunately, better alternatives to traditional logging systems exist.

With Lightrun's conditional logging, developers can log only what's needed and quickly pinpoint the root cause of user-specific issues.

Lightrun is a developer-friendly tool designed to help your developers team debug specific, user-related issues in real time. The dynamic logging of Lightrun eliminates the need for redeployments, code changes, or restarts, which can lead to faster issue resolution times (MTTR).

In other words, a significant benefit of using Lightrun is the ability to debug applications without disrupting the user experience. Developers can analyze an application in real time and pinpoint the root cause of issues without having to stop and redeploy the application. This is particularly important for high-traffic websites or applications that must remain operational 24/7.

By using Lightrun, developers gain greater control over complex systems and make their applications more observable, thus making them easier to debug. This results in better overall performance and customer experience.

Additionally, the Lightrun Developer Observability Platform is designed to improve developer experience and productivity. It was specifically built for developers, so it is easy to use and integrates seamlessly with existing tools.

Debugging Made Easy: How Conditional Logging Simplifies Issue Resolution

To easily identify the root cause of problems that only arise in specific situations, developers using Lighrun can leverage the power of conditional logs.

The process is fast and straightforward:

Using their preferred IDE, IntelliJ IDEA, PyCharm, WebStorm, Visual Studio Code (VSCode), VSCode for the web (vscode.dev), or code-server, the developer inserts a log into the production environment at specific points in the code where they suspect an issue may be occurring.

This allows Lightrun to integrate into remote production without requiring any changes to the code.

These log insertions can be conditional, which means that they appear in the logs if and only if certain conditions are met. For example, a developer may want to log information about a specific user's activity on the site like purchase, registration, checkout, or authentication. In this case, they add a conditional log that only appears in the logs when that user performs these actions.

Once the log lines are added, the developer can monitor the logs in real-time using Lightrun's console, which is embedded in their IDE or logging platform (e.g. StatsD, Prometheus, Datadog, Slack, Logz.io, and Sentry). They can filter the logs by user, transaction, or any other variable, making it easier to pinpoint specific issues.

This approach can save tremendous amounts of time and effort, especially in highly-transactional industries like eCommerce and booking systems that handle thousands of requests and generate an army of log entries.

How Conditional Logging Increases Your Productivity and Reduces MTTR

It's essential to have the appropriate tool for any goal. Attempting to fix a car engine with a hammer and screwdriver would be futile, just like relying on traditional logging paradigms to solve modern cloud-native challenges.

Lightrun is a powerful tool that has been proven to be effective in reducing mean time to resolution (MTTR), with Start.io experiencing significant reductions of 50%-60% or Taboola saving 260+ debugging hours a month with Lightrun on AWS. Lightrun has also saved WhiteSource cycles of redeployments, making it a valuable tool for optimizing both R&D and operational processes.

With Lightrun, developers have the ability to set checkpoints at any point in the live running code and produce conditional log messages as a result. This allows for faster resolution times and eliminates the need for hours of debugging. By using Lightrun, organizations can achieve faster mean time to resolution, prevent downtimes due to redeployments, and ultimately improve user satisfaction.

Additionally, the platform's ability to reduce time spent on troubleshooting can free up developers to focus on coding, leading to faster development cycles and improved time-to-market.

In addition to these impacts related to user-specific issues, Lightrun platform as a whole can provide other significant business values.

Lightrun's real-time log analysis and conditional logging features make your team becoming proactive by providing better insights into system performance, helping your team identify issues before they become major problems. This contributes to increasing your competitiveness and stability as a business by reducing various risks including security and financial risks.

Moreover, by reducing log volumes and logging costs through dynamic logging, your business can save resources and allocate them more efficiently toward other business needs.

Conclusion

Traditional logging platforms can be used to resolve user-specific issues, but often at the expense of timely and efficient resolution. In addition to that, the costs and risks associated with the old paradigm are significant, making it crucial for organizations to consider more modern alternatives such as Lightrun conditional logging.

Additionally, the impacts of using Lightrun are wide-ranging from increasing your customers’ satisfaction and team productivity to reducing downtime risks and time to market.

To see it in action and understand better how Lightrun can help your business, you can request a demo here.

Alternatively, you can start off free by creating an account here. For a more technical overview, take a look at our Playground where you can play around with Lightrun in a real, live app without any configuration required.