You don’t need to test your databases to keep them in shape. There is a better way to achieve database reliability. Let’s see how.
Where Tests Fail
Tests are designed to determine whether the service and database are fast and reliable enough for production deployment. While they fulfill this purpose effectively, their numerous drawbacks make them less desirable in practice.
One major issue is the time they consume. Load testing requires running thousands of transactions over several hours to yield meaningful insights. This involves filling caches, drives, and networks, and maintaining operations for extended periods. As a result, quick feedback is impossible. Tests often need to run overnight or be triggered in the CI/CD pipeline without waiting for immediate results.
Writing tests is inherently challenging. Unit tests often fail to capture real-world interactions, leaving significant issues undetected. Integration tests are even more complex - they require more than just sending random requests to the service. To be effective, they must replicate production data distribution and ensure the data used is contextually valid.
Handling stateful services adds another layer of complexity. Preparing databases, managing states, and covering all possible code paths in the application is a demanding task. This becomes especially difficult when the service exhibits region-specific behavior or evolves rapidly over time. Achieving meaningful and reliable results in such scenarios is far from straightforward.
Maintaining tests is another significant challenge. As data evolves and services undergo implementation changes, test data must be continuously updated to remain relevant. While replaying production traffic might seem like a straightforward solution, it carries the risk of encountering invalid states or missing critical code paths, undermining the reliability of the results.
Another challenge is compliance with regulations like GDPR, CCPA, and similar privacy laws. Using production data in non-production environments is often prohibited due to security policies and the inherent risks involved. To mitigate these issues, data must be anonymized, sensitive information such as social security numbers must be removed, and safeguards must be in place to prevent customer data leaks. This process is complex and fraught with potential risks.
Finally, unit tests often fail to catch critical issues, while load tests come into play too late in the development cycle. By the time load tests reveal problems, the code has already been written, reviewed, merged, and deployed to some environments. Addressing these issues at this stage is costly and time-consuming, often requiring a return to the drawing board, which demands significant effort.
What should we do instead? Read on to understand.
Observability Is The Key
Rather than relying on unit tests or load tests for databases, we should focus on identifying issues early in the development process. Leveraging observability techniques allows us to monitor activity behind the scenes and detect potential problems within developers' environments.
For example, telemetry can capture database queries. These queries can then be projected onto the production database to analyze their execution plans. This approach enables immediate insights into whether queries will perform efficiently in production.
Similarly, schemas and configurations can be validated immediately. We can check if indexes are properly configured, if queries are utilizing indexes effectively, and whether data access patterns can be optimized. Importantly, these checks can be automated and compared directly with the production database, providing developers with instant feedback within their development environment - eliminating the need for code reviews or staging deployments.
Observability goes even further. By tracking changes and their effects, we can pinpoint production issues and correlate them to specific code changes. This enables automated pull requests to address problems, optimizing configurations, schemas, indexes, extensions, and more. The result is improved database reliability and automated self-healing capabilities.
Metis Saves Time and Money
Metis analyzes the queries right in the developers’ environments:
In the same way, Metis analyzes schema migrations and protects your databases from performance degradation and data loss:
Metis keeps your whole database under control:
Summary
Tests are costly, complex to create and maintain, and time-consuming to run. Moreover, load tests often occur only after the code has been reviewed and merged. By leveraging observability, we can sidestep these challenges and detect issues much earlier in the development pipeline. Metis automates this process, providing robust database reliability effortlessly.
