Database Testing
SQL is often the least-tested part of an application stack. Business logic lives in stored procedures nobody touches, queries run against production data, and "it works in dev" becomes the only verification. Then a constraint violation or a missing index bites you in production at 2 AM.
Database Testing
Database migrations are a deployment risk. A bad migration can corrupt data, block a release, or cause hours of downtime while you figure out how to roll back. Flyway is the most popular Java database migration tool, but most teams only run migrations — they don't test them. This
Testing Strategy
Database changes are the highest-risk category of production deployments. A bad migration can corrupt data, lock tables, and cause downtime that no amount of application-level testing can prevent. Shifting database testing left means validating schema changes, migrations, and data contracts in CI before a single line of application
Test Automation
Traffic shadowing is straightforward for read-only APIs: duplicate the request, compare responses, discard shadow output. Write operations are harder. Shadow writes that hit a real database can corrupt data, trigger emails, charge payment methods, or create duplicate records. Yet skipping write validation means your database migration or ORM refactor
Testing
Your local-first app feels instant. Writes land immediately, the UI updates without a spinner, and sync happens invisibly in the background. Until it doesn't. A conflict resolver fires incorrectly, a shape subscription misses rows, or the client state diverges from Postgres after a partition. Nobody sees it
Database Testing
Java applications that talk to a database face a persistent testing problem: how do you test database interactions without making tests slow, fragile, or dependent on production data? DbUnit solves this by providing a framework for managing test datasets — populating tables before tests and verifying their state afterward. This guide
Database Testing
Schema drift is one of the most dangerous forms of technical debt in database-driven applications. It happens gradually: a developer adds a column directly to staging but forgets to write a migration, another runs a manual ALTER TABLE on production to fix an emergency, and a third adds an
Deployment Testing
Database migration bugs are among the most damaging deployment failures. A bad migration can corrupt data, lock tables during peak traffic, or make rollback impossible. Most teams test their application code thoroughly and test their migrations barely at all. This guide covers how to build database migration testing into your
Database Testing
A bad database migration is one of the worst production incidents you can have. Unlike a bad code deploy, you can't just roll back — the data is already mutated. A migration that drops a column, truncates a table, or corrupts data during a lock-heavy ALTER can take
Testing
Convex is a backend platform that bundles your database, backend functions (queries, mutations, actions), and real-time subscriptions together. The testing story is distinct from traditional backends: you're not testing a REST API — you're testing TypeScript functions that run inside the Convex runtime, with reactive query
Database Testing
PostgreSQL performance problems are rarely obvious until they're in production. A query that runs fine on 10,000 rows starts doing sequential scans on 10 million. An index that covers the happy path misses the query your new feature generates. Connection pool exhaustion looks like application errors, not
Database Testing
Database testing has its own equivalence partition structure driven by database constraints, SQL semantics, and the specific behaviors of NULL, type boundaries, and referential integrity. This post covers how to apply BVA and EP to database layer testing specifically. Database Column Constraints as Partition Sources Every column constraint defines partitions: