Why Observability Matters: My Early Lessons in Performance Optimization

My experience with observability goes back to 2011Ā  when I worked as a Product Marketing Manager for a U.S.-based e-commerce startup built on a dropshipping model. The engineering team launched the MVP (minimal viable product) website with a curated selection of products, rapidly expanding the catalog and introducing new features on a regular basis. My role was to handle product marketing through various channelsā€”search engines, social media, paid per click ads, and more.

Identifying the root cause of slow load times

One day, I noticed the websiteā€™s loading speed was unusually slow. Given that internet speeds were slower back then, I initially thought my web browser, loaded with work-related plugins, might be the issue. However, when I saw a drop in sales that lasted over a week, I knew we needed to dig deeper.

Using Google Analytics, I saw a clear decline in our search traffic over the last 10 days. Google Webmaster Tool (now Google Search Console) confirmed my suspicionā€”our website was losing rankings for several important keywords. This was alarming. I initially thought competitors might be outranking us with better SEO or that weā€™d made some SEO missteps ourselves. However, after checking various factors like seasonal trends, content changes, and even Googleā€™s famous algorithm updates (Google Dance – as we knew them), I turned to the last possibility: a technical issue.

In hindsight, I wish Iā€™d checked this sooner and saved our time. Technical problems like server errors or slow load times can greatly affect search rankings. A SearchEngineJournal.com article confirmed my hunch, noting that page speed was a major Google ranking factor. Google PageSpeed Insights, launched in 2010, allowed anyone to check a websiteā€™s load speed. Using it, I found that our site was loading much slower than our competitors, with a low score of 30/100 for both desktop and mobile. This was a big red flag.

Long-Term Solutions: Shifting Focus to Code Optimization

When it comes to technical issues, there are both hardware and software aspects to consider. Since cloud computing wasnā€™t yet widespread, we relied on dedicated private or virtual private servers. I reached out to our engineering team, who quickly implemented a temporary fix. That improved our PageSpeed score to 40/100, but I was aiming for 80/100 or higher for a real solution. When I asked the engineering manager about their approach, his response was surprising. He explained that our virtual private server had accumulated a massive amount of log files that were taking up disk space, which he simply deleted. Yes, Iā€™m not kidding.

Deleting server logs is generally considered a poor practice because server logs provide a critical record of system events, user activity, errors, and security incidents. These logs are invaluable for troubleshooting issues, optimizing system performance, and investigating security breaches.

Unsatisfied, I asked the team to look into the siteā€™s code for potential optimizations. Thankfully, Google Chromeā€™s Inspect Element tool, released around that time, allowed us to diagnose page performance issues. After some analysis, we identified five main reasons for the slow load times:

  1. Large Image and Media Files: Our load times were being dragged down by high-resolution images and media. We later turned to Akamai Technologies as a robust CDN solution to manage our media files efficiently.
  2. Unoptimized Code: The issue was worsened by our HTML, CSS, and JavaScript being cluttered and inefficient. This added unnecessary bulk, slowing down page load times and affecting overall performance.
  3. Excessive HTTP Requests: Too many requests for resources like scripts and images were further slowing the page.
  4. Slow Server Response Time: This was delaying content delivery, but implementing a CDN could significantly improve performance.
  5. Render-Blocking Resources: Certain CSS and JavaScript elements were blocking the page from loading smoothly.

 

The engineering team initially suggested upgrading the virtual servers to get more RAM and storage, which would have been a costly band-aid, not a solution. Ultimately, we needed a shift in engineering practices. It took two challenging months, but the team eventually prioritized optimizing code over pushing new product features. This change in focus paid off, improving both site speed and our Google rankings. Solving this problem was a time-consuming process, but Iā€™m glad we tackled it the right way.

Observability as a Solution for Sustainable Performance

Observability platforms like New Relic and Datadog have changed the game for handling such issues. These tools offer comprehensive log management, allowing users to collect, process, and store logs without storage limitations, which provides crucial advantages:

  1. Single Pane of Glass: Observability tools unify diverse log and performance data from every microservice into one cohesive view, enabling teams to monitor, diagnose, and optimize the health of the entire application in real-time. This holistic visibility is crucial for maintaining reliability and agility across complex, distributed systems.
  2. Enhanced Troubleshooting: With full log histories across services, teams can trace issues back to their origin faster, reducing downtime and helping ensure that even the smallest anomalies are addressed before they escalate.
  3. In-Depth Analysis: Access to vast log data helps engineers detect patterns and anomalies at a granular level, providing insights into the complex interactions among services and empowering proactive optimization.
  4. Historical Context: Observability platforms retain data over extended periods, making it easy to compare current performance with past trends. This historical context helps identify areas where improvements or degradation have occurred over time.
  5. Compliance and Auditing: Robust log storage supports compliance audits and regulatory needs, as complete historical data can be retained and accessed as required.
  6. Performance Optimization: With detailed, real-time insights across all services, observability allows teams to better optimize resource allocation, identify bottlenecks, and ensure smooth operations.

Industry Trends: The Global Rise of Observability Adoption

In recent years, the adoption of observability has been on a steady rise, as more companies recognize its value for maintaining high-performing, reliable applications in an increasingly complex, distributed world. Yet, despite this growth, the industry is still in the early stages of fully understanding and integrating observability practices across the board. Observability has yet to become a core part of every engineering teamā€™s toolkit, and the data surrounding its adoption reflects this.

Globally, we see some countries leading the way in observability practices, though the adoption varies widely. According to recent industry reports, the United States and Canada are top adopters, with around 60% of organizations reporting some level of observability implementation, especially in tech-forward sectors like finance, e-commerce, and cloud services. Europe follows close behind, with countries like the United Kingdom, Germany, and France showing about 50% of companies investing in observability solutions. In Asia, countries such as Japan, South Korea, and Singapore are rapidly catching up, with around 40% of businesses beginning to implement observability. India, meanwhile, is emerging as a key player, with approximately 35% of companies adopting observability practices, primarily driven by the countryā€™s vast IT sector.

However, even in these leading countries, full-scale observabilityā€”where teams actively monitor, log, and analyze every aspect of their applicationsā€”remains limited. The gap often stems from a lack of understanding about observabilityā€™s impact. Many engineering teams, like the one I worked with, fail to realize the value of logs and structured data. Often, logs are viewed as clutter rather than vital information, leading to situations where teams delete logs or overlook them because they donā€™t see the immediate benefit. This mindset is common and highlights the need for continued education around the importance of observability.

If most developers truly understood how essential observability is, especially when paired with advanced analytics and machine learning, weā€™d see a more consistent uptake. Observability isnā€™t just about monitoring performance; itā€™s about being proactive and learning from every error, every spike, and every system interaction to create stronger applications. Emerging AI-driven observability tools will help bridge this gap by automating many of these insights, making it easier for teams to detect and resolve issues without manual intervention. But until observability practices become as standard as coding itself, thereā€™s still a long journey ahead for the industry to fully embrace and integrate this powerful approach.

Observabilityā€™s Future: From Monitoring to Self-Healing Applications

In my opinion, observability has become essential for tackling the challenges of performance, reliability, and compliance, especially in the era of complex, distributed systems. But the journey doesnā€™t end here. With new advancements on the horizon, observability is evolving into something even more powerful. Emerging technologies, particularly AI and machine learning, are set to transform how we monitor and maintain applications, introducing the potential for systems to autonomously detect, diagnose, and resolve issues in real time.

Imagine an ecosystem where the applications can self-heal, preemptively adjust to changing conditions, and learn from past behaviors to optimize performanceā€”all without human intervention. Itā€™s a future that could redefine whatā€™s possible in system reliability and operational efficiency. So, how close are we to this reality, and what challenges still lie ahead?

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