JDBC batching groups multiple identical SQL statements into a single network packet, radically reducing network roundtrips.
@Transactional(readOnly = true) public List getAllUsers() ... Use code with caution. C. DTO Projections
Fetching raw DTOs via JPQL or native queries allows the persistence provider to skip memory-heavy entity lifecycle management and dirty-checking, radically reducing heap allocation. Second-Level (L2) Caching
Avoid mapping large child tables as standard Java collections (like a List ). Query them explicitly with pagination instead. High-performance Java Persistence.pdf
If you'd like, I can provide a (e.g., fixing N+1 queries with JOIN FETCH or setting up HikariCP with batch size) or explain any of these topics in more detail. Just let me know which section is most relevant to your current challenge.
If you want, I can:
A common mistake is allocating too many connections. Use the standard formula: JDBC batching groups multiple identical SQL statements into
Use composite indexes for queries filtering on multiple columns, making sure to order the columns in the index from highest selectivity to lowest.
Sequences allow Hibernate to pre-allocate blocks of IDs (using the allocationSize attribute). This enables seamless batch processing. Bidirectional Associations and Cascades
Keep database transactions as short as possible. Long-running transactions hold database locks, reduce concurrency, and inflate connection pool wait times. Query them explicitly with pagination instead
If you are looking to dive deeper into these topics, I can provide more specific architectural blueprints.g., tuning PostgreSQL vs. Oracle for JPA)
When you only need to display data on a screen or send a JSON payload over an API, do not fetch managed entities. Managed entities carry the overhead of Hibernate’s persistence context (first-level cache, dirty checking states).
: FetchType.EAGER forces the framework to load data you might not need, bloat memory, and trigger unexpected queries. 3. Efficient Data Mappings