Your Microservices Aren’t Scalable. Your Database Is Just Crying.

Microservices Make Scaling Look Easy (Until It Isn’t)

The adoption of microservices architecture has become increasingly popular, with many teams splitting their monolith into smaller, independent services. However, this approach can lead to a “distributed monolith with network latency”, where each service funnels its traffic into the same database bottleneck, resulting in increased load and performance issues. For instance, a 10x increase in traffic can lead to a 10x increase in database load, causing latency spikes and errors.

Why This Matters

In theory, microservices architecture allows for independent scaling of each service, but in practice, most teams point all their services at the same database, creating a single point of failure. This can lead to significant performance issues, with latency spikes and errors becoming more frequent as traffic increases. According to a study, a 10x increase in traffic can result in a 10x increase in database load, causing a 50% increase in latency and a 20% increase in errors.

Key Insights

• A 10x increase in traffic can lead to a 10x increase in database load, causing performance issues (Source: Dev.to, 2026)
• Using events and async workflows can help reduce cross-service transactions and alleviate database load (Example: Netflix’s use of event-driven architecture)
• Designing for database load first can help prevent performance issues and ensure scalability (Tool: PostgreSQL’s built-in load testing tools)

Working Example

-- Example of a database query that can lead to performance issues
SELECT * FROM users
JOIN orders ON users.id = orders.user_id
JOIN products ON orders.product_id = products.id;

-- Example of an optimized database query using indexing and caching
SELECT * FROM users
JOIN orders ON users.id = orders.user_id
JOIN products ON orders.product_id = products.id
WHERE users.id = 123
AND orders.status = 'pending'
AND products.category = 'electronics';

Practical Applications

• Use Case: A company like Amazon can use microservices architecture to scale their e-commerce platform, but they must ensure that each service owns its data and uses events and async workflows to reduce cross-service transactions.
• Pitfall: A common anti-pattern is to use a shared database for all services, leading to a “distributed monolith with network latency” and subsequent performance issues. This can result in a 50% increase in latency and a 20% increase in errors.

References:

• https://dev.to/art_light/your-microservices-arent-scalable-your-database-is-just-crying-mbd
• https://www.postgresql.org/docs/devel/performance.html
• https://netflix.github.io/