Many monitoring tools prioritize quantity over quality, overwhelming users with an abundance of data points and metrics rather than providing clear insights into what’s happening and how to resolve issues. In this blog post, we’ll explore what’s truly needed and how to create more effective solutions.
Quality Is Important
Monitoring solutions are great at collecting metrics of all kinds—they can pull data from operating systems, networks, CPUs, memory, disks, runtimes, virtual machines, databases, and more. They even integrate with these components seamlessly, requiring minimal effort from users to start gathering data points.
But here’s the problem: that’s not enough.
While raw data is valuable for system administrators, it falls short when it comes to diagnosing logical issues tied to the business. In those cases, we need a deeper understanding of business metrics and the connections between various components.
Unfortunately, most monitoring tools can’t provide this. Instead, they create the illusion of useful insights while flooding us with irrelevant data.
Take a moment to reflect: how often this year did you need metrics about your L2 cache hit ratio? Sure, there are scenarios where optimizing down to bare metal is necessary, but for most of us, these details are rarely relevant. Despite this, monitoring tools excel at delivering CPU metrics and similar data.
Now consider how often you’ve needed to determine if a failing request was linked to a specific customer cohort or product line. These are the types of metrics we actually need - and they’re precisely what most monitoring systems fail to deliver automatically.
Datapoints Must Be Data-Oriented
If you feed your monitoring solutions with generic data points, it’s no surprise you won’t get meaningful insights. What we need are solutions that provide reasoning, coherent explanations, and actionable insights. To achieve this, monitoring systems must be enriched with database-oriented and business-focused metrics.
Rather than focusing solely on raw infrastructure metrics, we should prioritize capturing details related to our databases. This includes transactions, queries, execution plans, indexes, and other database-specific activities. Additionally, we must integrate with development environments to track deployments, code changes, schema migrations, and configuration updates.
With these enriched data points, we can build clear narratives that explain the issues we observe. For instance, we can determine that a slowdown in the database is caused by a code change deployed last week, which inadvertently prevented an index from being used. There’s no need to sift through low-level infrastructure metrics for such insights. Instead, leveraging business context and development history enables a more effective approach to problem-solving.
Metis Leads the Way
Metis stands out as the only solution on the market that leverages database-specific metrics while seamlessly integrating data from developers’ environments. It analyzes queries, tracks schema migrations, and identifies potential issues even before code is committed to the repository.
Metis focuses on database-related metrics around your databases:
Among many other features, Metis uses AI to optimize your queries and suggest schema optimizations:
Use Metis With Database Observability
Monitoring becomes far more effective when it understands your ecosystem. Avoid flooding it with low-level metrics that provide little value. Instead, choose solutions that can interpret your applications and deliver actionable insights. Metis is the only solution that offers peace of mind by automatically optimizing everything related to your databases.
