Ever seen this pattern?

It “works”… until it doesn’t. Pulling every row to the app and filtering in memory is a silent performance killer.

A safer, faster approach is to let the database do the work:

Why the query wins 🏆

Performance & scalability: Filtering happens server-side. You transfer only matching rows over the network and avoid loading the entire table into JVM memory.

Indexes: The DB can leverage indexes for fast lookups. In-memory filtering can’t.

Correctness: Case-insensitive matching and SQL semantics (LIKE/ILIKE, collations) are consistent and locale-aware; equalsIgnoreCase() isn’t.

Security: Parameters are bound, preventing SQL injection.

Observability: DB plans are explainable and optimizable; streams hide the cost until prod traffic hits.

Transactions: Reads participate in DB isolation levels; client-side filters don’t.

Make it even better ✅

Prefer Spring Data method names when possible:

If you’re on PostgreSQL, use ILIKE (case-insensitive) or CITEXT for index-friendly case-insensitive equality:

Avoid LOWER(column) unless you have a functional index—it can block index usage. Prefer ILIKE/collations or a functional index:

Don’t SELECT * in production—choose explicit columns.

If email is unique, return Optional and enforce a unique constraint on the column.

TL;DR 🎯

Filtering in Java wastes CPU, memory, and bandwidth. Push filters to the database for correctness and speed, and lean on Spring Data’s derived queries or DB-native features for clean, maintainable code.

#java #springboot #springdata #jpa #performance #database #postgresql #cleanarchitecture #scalability

https://vladmihalcea.com/spring-data-findall-anti-pattern/

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