The current standard for developing and managing databases falls short in many ways. We lack effective mechanisms to prevent poor-quality code from reaching production. After deployment, the tools available for observability and monitoring are inadequate. Moreover, troubleshooting and resolving issues in a consistent, automated manner remains a significant challenge. Developers often struggle to navigate database management and frequently don’t have ownership of the solutions.
A common response to these challenges is to upscale the database, hoping to resolve performance bottlenecks and slow queries. However, this approach often proves insufficient. What we need instead is a paradigm shift - a fresh perspective on how databases are managed: database guardrails. Let’s explore the harsh reality of current practices and how we can truly improve our databases.
Many Things Can Break
Many database issues stem from changes in application code. When developers update the code, it often results in different SQL statements being sent to the database. These queries may inherently perform poorly, yet current SQL testing processes often fail to identify such problems. For example, normalized tables can require multiple joins, potentially leading to an exponential increase in rows being read. This issue is difficult to detect through unit tests but becomes immediately evident after deployment to production. A possible solution is to break down a single large query involving multiple joins into several smaller, more manageable queries. Upscaling the database won't resolve this issue because the query itself is fundamentally inefficient and non-scalable.
Another common challenge is the N+1 query problem, often introduced by Object Relational Mapper (ORM) libraries. Developers use these libraries to simplify their work, but they can obscure complexity and create additional issues. The N+1 query problem arises when the library loads data lazily, issuing multiple queries instead of performing a single join. This results in the database executing as many queries as there are records in a table. As with the previous issue, this problem often goes unnoticed in local environments or during testing and only surfaces in environments with larger datasets. Once again, simply upscaling the database won’t solve the underlying inefficiency.
Issues can also arise when developers rewrite queries to make them more readable. For example, using Common Table Expressions (CTEs) can improve code clarity but might lead to the database engine generating slower execution plans, resulting in significantly longer execution times. Since the query produces the same results as before, automated tests won’t flag any issues. Performance problems like this are often missed by unit or integration tests, and upscaling the database won’t solve the root cause. The proper solution is to replace the inefficient query with a more optimized one.
Schema management presents another challenge. Adding a column to a table might seem straightforward and safe - test the queries, ensure nothing breaks, and deploy. However, adding a column can be time-consuming if the database engine needs to rewrite the table. This process involves copying data, modifying the schema, and then reinserting the data, potentially taking the production database offline for minutes or even hours. Upscaling the database is not an option during this kind of migration, making it an ineffective solution.
Similarly, adding an index appears beneficial at first glance since indexes improve read performance by enabling faster row retrieval. However, indexes come with a tradeoff: they reduce modification performance because every data modification query must update the index as well. Over time, this can lead to performance degradation. These issues often go undetected in testing since they don’t affect the correctness of queries - just their efficiency. Instead of upscaling the database, the real solution lies in removing redundant indexes.
Over time, these problems compound. Indexes may degrade post-deployment, data distribution can fluctuate based on the day of the week, and regionalization of applications can create varying database loads across different locations. Query hints provided months earlier may no longer be effective, but this won’t be captured by tests. Unit tests focus on query correctness, and queries may continue returning accurate results while performing poorly in production. Without mechanisms to automatically detect such changes, upscaling the database becomes a temporary fix - a band-aid that might sustain the system for a while but fails to address the underlying issues.
What We Need for Database Guardrails
Database guardrails leverage statistics and database internals to prevent issues and ensure database reliability. This approach addresses performance challenges effectively and should be an integral part of every team’s daily workflow.
By analyzing metrics such as row counts, configurations, or installed extensions, we can gain insights into the database's performance. This enables us to provide immediate feedback to developers about queries that are unlikely to scale in production. Even if a developer is working with a different local database or a small dataset, we can use the query or execution plan, enhance it with production-level statistics, and predict its performance post-deployment. This allows us to offer actionable insights without waiting for the deployment phase, delivering feedback almost instantly.
The key lies in transitioning from raw data to actionable insights. Instead of overwhelming users with complex plots or metrics that require fine-tuned thresholds, we provide clear, practical suggestions. For instance, rather than simply reporting, “CPU usage spiked to 80%,” we can deliver a more actionable recommendation: “The query scanned the entire table - consider adding an index on these specific columns.” This approach shifts the focus from data interpretation to delivering concrete solutions, empowering developers with answers rather than just data points.
This is just the beginning. Once we truly understand what’s happening within the database, the possibilities are endless. We can implement anomaly detection to monitor how queries evolve over time, check whether they still use the same indexes, or identify changes in join strategies. We can detect ORM configuration changes that result in multiple SQL queries being sent for a single REST API request. Automated pull requests can be generated to fine-tune configurations. By correlating application code with SQL queries, we could even use machine learning to rewrite code dynamically and optimize performance.
Database guardrails go beyond providing raw metrics - they deliver actionable insights and meaningful answers. Developers no longer need to track and interpret metrics on their own; instead, automated systems connect the dots and provide clear guidance. This is the paradigm shift we need: an innovative, empowering approach for developers to truly take ownership of their databases. Most importantly, it eliminates the need to blindly upscale the database in the hope of resolving performance issues.
Summary
The landscape of software development has evolved dramatically. We now deploy continuously, manage hundreds of microservices, and work with diverse types of databases. Unfortunately, our current testing solutions are no longer sufficient. Waiting for load tests to uncover scalability issues is impractical, and upscaling the database isn’t the right fix.
Instead, we can implement database guardrails to analyze database interactions - such as execution plans, queries, and configurations - and apply intelligent reasoning to these insights. By integrating these guardrails into our CI/CD pipelines, we can deliver faster feedback, preventing problematic code from reaching production. This approach enables us to connect the dots, offering robust monitoring and automated troubleshooting for databases, ensuring greater reliability and efficiency.
