Over the years, the process of application performance monitoring and observability has evolved significantly. What was once a one-size-fits-all endeavor has now transformed into a nuanced and tailored approach, reflecting the diverse needs and complexities of today's digital landscape. As organizations strive to optimize their applications for peak performance and user satisfaction, the implementation of best practices becomes not just a choice, but a strategic imperative. Let's take a look at five best practices in application performance monitoring and observability that organizations can implement to set themselves up for success.

1. Align observability goals with overall business objectives

Monitoring the performance of your applications can provide access to a treasure trove of data, but without clear objectives, it's like navigating a maze blindfolded. Establishing performance objectives goes beyond mere focus; it builds a foundation of accountability within your organization. Yet, setting these goals is only the starting point. To achieve them, you need a strategic approach that considers several crucial elements:

• Baseline behavior: Before aiming for improvement, you need a clear understanding of your current state. This involves collecting and analyzing metrics, events, logs, and traces (MELT) data to establish baseline metrics for key performance indicators (KPIs). Analyzing industry standards provides a benchmark for comparison and helps identify areas where your application might be underperforming compared to similar offerings.

• End-user experience: Who are your core user groups? What are their expectations for application performance? Identify their top frustrations, including slow loading times, frequent errors, and more, through surveys, user testing, and support tickets. Translate these frustrations into quantifiable goals. For instance, surveys might reveal common complaints about mobile devices' slow loading times, leading to a quantifiable goal: Reduce application load time by 30% on mobile platforms. Additionally, breaking down the user journey into key stages—like login, navigation, and checkout—allows for setting specific performance objectives for each stage. This ensures a seamless and smooth experience across the entire user journey, addressing frustrations and enhancing overall satisfaction.

• Cross-functional collaboration: Encourage close collaboration among application development, operations, and business strategy teams. This ensures that observability goals are not just aligned with business objectives but also seamlessly woven into development and operational workflows. Through this partnership, teams can identify potential bottlenecks and areas for improvement early in the development life cycle. For instance, during feature planning, discussions can focus on defining relevant observability metrics, estimating the impact on the user experience, and setting up monitoring strategies for post-release performance. By fostering this integrated approach and eliminating silos, you pave the way for a unified effort in achieving business goals through observability.

2. Integrate observability practices across your SDLC

With the increasing complexity of distributed applications and the rapid pace of development cycles, the traditional approach to application performance monitoring falls short. DevOps ecosystems now demand observability to glean insights across every stage of the DevOps life cycle, comprehend planned and unplanned changes, and stay ahead of the curve. However, implementing this requires a strategic approach. Here's a breakdown of observability best practices for each phase of the software development life cycle (SDLC):

• Phase 1: Plan
  Collaborate with product stakeholders to define service level objectives (SLOs) and service level agreements (SLAs) for the applications and features being developed. Create an inventory of existing monitoring tools and data sources within your organization. Evaluate if these tools are sufficient to meet your observability goals. If necessary, research and plan for the integration of additional tools to ensure comprehensive data collection across your application ecosystem.

• Phase 2: Develop
  Integrate instrumentation libraries and frameworks into your code from the beginning. This allows you to collect performance data during development, and identify and address potential issues early on. Additionally, focus on instrumenting critical code paths and areas, especially the ones that are tricky.

• Phase 3: Continuous integration
  Integrate observability practices within your CI pipeline. Run automated tests that collect and analyze performance data to ensure code changes don't cause performance regressions. Furthermore, set clear thresholds for performance metrics and failed builds that exceed these thresholds. This will help developers prioritize performance optimization throughout the development process.

• Phase 4: Deployment
  Deploy new code versions to a small subset of users first to identify and address performance issues before a wider release. Simulate real-world user journeys and transactions to proactively monitor application performance before deployments.

• Phase 5: Operate
  Implement distributed tracing to track requests across the entire application stack, pinpointing the root cause of issues in complex microservice architectures. Analyze application logs to identify errors, anomalies, and performance bottlenecks. Regularly review data collected to identify trends and areas for improvement. Use these insights to optimize your overall application performance monitoring and observability strategy.

3. Minimize tool proliferation

To keep up with their growing IT landscape, organizations often adopt
multiple monitoring tools (both proprietary and open-source) at a rapid pace. This uncoordinated adoption of various tools leads to fragmented visibility, as each tool collects its own data.

Disparate data sources create blind spots, making it difficult to see the bigger picture and correlate events across the application stack. Furthermore, tool sprawl creates a deluge of data that makes identifying critical insights a burden. Additionally, the sheer volume of alerts can lead to alert fatigue, which can cause critical issues to go unnoticed, impacting application performance and the user experience.

Observability demands a shift away from the tool-for-every-issue mindset towards a centralized resource that consolidates insights, streamlines processes, and simplifies monitoring strategies. The rationale behind this consolidation lies in the need for a unified platform that aggregates all relevant data in one place.

With this approach, teams can more easily correlate data points across different parts of the application, gaining a holistic understanding of its behavior and performance. While replacing every single tool might not be feasible, an effective APM solution can replace at least a subset of these tools while seamlessly integrating with the rest. This tool consolidation approach can involve three phases:

• Plan smart: This phase involves creating a clear picture of your existing monitoring landscape (all tools) and how they contribute to your desired outcomes. Define your ideal future state with a streamlined set of tools.

• Prepare for success: Build use cases that demonstrate the value of consolidation in specific scenarios. Pilot these use cases to identify the best approach and potential challenges. Develop a timeline for migrating to the new tool set.

• Execute smoothly: Implement the chosen platform, migrate data and configurations, and train your teams on the new tools. Develop and socialize documentation to ensure everyone understands the new approach.

4. Automate remediation and incident response

Application monitoring is crucial, but let's be honest—pinpointing the why behind issues can be extremely time-consuming. Manual remediation further eats into valuable resources. This is where AI-powered automation steps in, transforming your monitoring from reactive to proactive.

Imagine a scenario where a specific container within your web application deployment consistently breaches its memory limit, causing the entire application to crash and restart repeatedly. Traditional monitoring might alert you to these application crashes, but AI automation can identify the culprit and trigger a horizontally scaling event, preventing further clashes. Over time, such tasks can be automated entirely, freeing your team to focus on innovation.
Here is a three-step approach to maximize the benefits of AI-driven automation for remediation and incident response:

• Step 1: Choose tasks that truly require automation: Not every task needs automation. Focus on repetitive, high-volume tasks like anomaly detection, log analysis, and basic incident response. These tasks often have clear patterns and minimal decision-making variability, making them ideal candidates for efficient automation with significant operational improvements.

• Step 2: Enhance issue diagnosis: Dive deep into affected components to gain comprehensive insights and prevent issues from escalating. Automation aids in obtaining richer context, facilitating faster and more accurate diagnosis.

• Step 3: Streamline incident resolution: Navigate incident resolution efficiently by automating remediation actions with minimal human intervention. Implement intelligent workflows that can trigger actions like auto-scaling, service restarts, or configuration adjustments based on predefined criteria. Concurrently, establish a streamlined response system, directing issues to individuals or teams equipped with the precise expertise needed for resolution.

5. Prioritize the end-user experience

While server-side metrics provide a crucial foundation for APM and observability, they only tell part of the story. Focusing solely on these metrics can mask issues that significantly impact users. Consider a scenario where your Apdex score drops from say a solid 0.9 to 0.65 after a new application launch. This could be sluggish load times, issues specific to certain user journeys, or geographical performance inconsistencies.

A robust end-user experience monitoring strategy allows you to pinpoint whether these problems stem from slow load times due to a recent feature update or an influx of concurrent user sessions. Consider implementing these three best practices:

• Set up synthetic transaction monitoring: Simulate real-user actions with varying scenarios (paths, session lengths, interactions) to identify potential problems before they impact real users. This approach will enable you to test the performance of your application across various global locations, providing insights into how users experience your application under different circumstances.

• Track and optimize real-user metrics: Employ a real user monitoring approach that will help you capture metrics like page load times, successful transactions, error rates, and more. Focus your optimization efforts on critical user pathways that significantly impact user satisfaction and align with business objectives. This will allow you to detect performance bottlenecks and swiftly address issues that might hinder a smooth user experience.

• Adopt an integrated approach: Correlate backend infrastructure metrics with frontend performance to obtain a holistic perspective. Establish a continuous feedback loop between backend and frontend teams to promote collaboration and insight-sharing. This iterative approach enables cohesive efforts in addressing performance challenges, optimizing the app, and ensuring a seamless user experience.

Transform your approach to application performance monitoring and observability with ManageEngine Applications Manager. This tool helps you define and track performance goals, ensuring your applications consistently meet predefined benchmarks.

Interested in learning more about Applications Manager? Schedule a free, personalized demo with one of our solution experts today, or explore on your own with a free, 30-day trial.