We’re all familiar with the principles of DevOps: building small, well-tested increments, deploying frequently, and automating pipelines to eliminate the need for manual steps. We monitor our applications closely, set up alerts, roll back problematic changes, and receive notifications when issues arise. However, when it comes to databases, we often lack the same level of control and visibility. Debugging performance issues can be challenging, and we might struggle to understand why databases slow down. Schema migrations and modifications can spiral out of control, leading to significant challenges. Overcoming these obstacles requires strategies that streamline schema migration and adaptation, enabling efficient database structure changes with minimal downtime or performance impact. It’s essential to test all changes cohesively throughout the pipeline. Let’s explore how this can be achieved.
Automate Your Tests
Databases are prone to many types of failures, yet they often don’t receive the same rigorous testing as applications. While developers typically test whether applications can read and write the correct data, they often overlook how this is achieved. Key aspects like ensuring the proper use of indexes, avoiding unnecessary lazy loading, or verifying query efficiency often go unchecked. For example, we focus on how many rows the database returns but neglect to analyze how many rows it had to read. Similarly, rollback procedures are rarely tested, leaving us vulnerable to potential data loss with every change. To address these gaps, we need comprehensive automated tests that detect issues proactively, minimizing the need for manual intervention.
We often rely on load tests to identify performance issues, and while they can reveal whether our queries are fast enough for production, they come with significant drawbacks. First, load tests are expensive to build and maintain, requiring careful handling of GDPR compliance, data anonymization, and stateful applications. Moreover, they occur too late in the development pipeline. When load tests uncover issues, the changes are already implemented, reviewed, and merged, forcing us to go back to the drawing board and potentially start over. Finally, load tests are time-consuming, often requiring hours to fill caches and validate application reliability, making them less practical for catching issues early.
Schema migrations often fall outside the scope of our tests. Typically, we only run test suites after migrations are completed, meaning we don’t evaluate how long they took, whether they triggered table rewrites, or whether they caused performance bottlenecks. These issues often go unnoticed during testing and only become apparent when deployed to production.
Another challenge is that we test with databases that are too small to uncover performance problems early. This reliance on inadequate testing can lead to wasted time on load tests and leaves critical aspects, like schema migrations, entirely untested. This lack of coverage reduces our development velocity, introduces application-breaking issues, and hinders agility.
The solution to these challenges lies in implementing database guardrails. Database guardrails evaluate queries, schema migrations, configurations, and database designs as we write code. Instead of relying on pipeline runs or lengthy load tests, these checks can be performed directly in the IDE or developer environment. By leveraging observability and projections of the production database, guardrails assess execution plans, statistics, and configurations, ensuring everything will function smoothly post-deployment.
Build Observability Around Databases
When we deploy to production, system dynamics can change over time. CPU load may spike, memory usage might grow, data volumes could expand, and data distribution patterns may shift. Identifying these issues quickly is essential, but it's not enough. Current monitoring tools overwhelm us with raw signals, leaving us to piece together the reasoning. For example, they might indicate an increase in CPU load but fail to explain why it happened. The burden of investigating and identifying root causes falls entirely on us. This approach is outdated and inefficient.
To truly move fast, we need to shift from traditional monitoring to full observability. Instead of being inundated with raw data, we need actionable insights that help us understand the root cause of issues. Database guardrails offer this transformation. They connect the dots, showing how various factors interrelate, pinpointing the problem, and suggesting solutions. Instead of simply observing a spike in CPU usage, guardrails help us understand that a recent deployment altered a query, causing an index to be bypassed, which led to the increased CPU load. With this clarity, we can act decisively, fixing the query or index to resolve the issue. This shift from "seeing" to "understanding" is key to maintaining speed and reliability.
The next evolution in database management is transitioning from automated issue investigation to automated resolution. Many problems can be fixed automatically with well-integrated systems. Observability tools can analyze performance and reliability issues and generate the necessary code or configuration changes to resolve them. These fixes can either be applied automatically or require explicit approval, ensuring that issues are addressed immediately with minimal effort on your part.
Beyond fixing problems quickly, the ultimate goal is to prevent issues from occurring in the first place. Frequent rollbacks or failures hinder progress and agility. True agility is achieved not by rapidly resolving issues but by designing systems where issues rarely arise. While this vision may require incremental steps to reach, it represents the ultimate direction for innovation.
Metis empowers you to overcome these challenges. It evaluates your changes before they’re even committed to the repository, analyzing queries, schema migrations, execution plans, performance, and correctness throughout your pipelines. Metis integrates seamlessly with CI/CD workflows, preventing flawed changes from reaching production. But it goes further - offering deep observability into your production database by analyzing metrics and tracking deployments, extensions, and configurations. It automatically fixes issues when possible and alerts you when manual intervention is required. With Metis, you can move faster and automate every aspect of your CI/CD pipeline, ensuring smoother and more reliable database management.
Everyone Needs to Participate
Database observability is about proactively preventing issues, advancing toward automated understanding and resolution, and incorporating database-specific checks throughout the development process. Relying on outdated tools and workflows is no longer sufficient; we need modern solutions that adapt to today’s complexities. Database guardrails provide this support. They help developers avoid creating inefficient code, analyze schemas and configurations, and validate every step of the software development lifecycle within our pipelines.
Guardrails also transform raw monitoring data into actionable insights, explaining not just what went wrong but how to fix it. This capability is essential across all industries, as the complexity of systems will only continue to grow. To stay ahead, we must embrace innovative tools and processes that enable us to move faster and more efficiently.
