High-performance Java Persistence Pdf 20 Jun 2026

: Understanding when the Second-Level Cache helps versus when it causes stale data nightmares. The Connection Pool Mystery

Prepared statement caching allows the database to skip parsing. For high-throughput systems, setting hibernate.jdbc.batch_size to is often the magic number—large enough to amortize network round trips, small enough to avoid memory bloat.

A unidirectional @OneToMany collection mapping results in Hibernate generating an unnecessary join table and executing redundant UPDATE statements to manage foreign keys. Always make the collection bidirectional, using the mappedBy attribute on the parent side to hand foreign key ownership to the child ( @ManyToOne ). high-performance java persistence pdf 20

(e.g., how to configure a HikariCP pool or map a Many-to-Many relationship), let me know and I can provide more technical detail! High-Performance Java Persistence - Leanpub

Hibernate tracks changes to managed entities by keeping a snapshot copy of the entity state in the persistence context (First-Level Cache). When loading tens of thousands of entities in a single transaction, this mechanism degrades memory performance and increases garbage collection overhead. : Understanding when the Second-Level Cache helps versus

Excellent for distributed systems where ID generation must happen disconnected from the database. Use binary formats ( UUID-binary or string optimized) rather than standard text strings to save index page space. Relationship Optimizations

: Configuring robust connection pools like HikariCP with optimal maximum pool sizes and timeouts. Track metrics: latency percentiles

Choose the lowest safe isolation level to increase concurrency. 🔗 Resources and Tools Official Site: You can find the book and video courses at Vlad Mihalcea's official store Code Examples:

high-performance-java-persistence/README.md at master - GitHub

Monitoring, profiling, and benchmarking (≈500 words) Measure before optimizing. Use application profilers (YourKit, VisualVM), APMs (New Relic, Datadog), and database monitoring (pg_stat_statements, Performance Schema). Benchmark realistic workloads with tools like JMH for microbenchmarks and Gatling or k6 for end-to-end tests. Track metrics: latency percentiles, query counts, cache hit ratios, connection pool metrics.

Design indexes to match the exact filter and sort criteria of your application's most critical read queries. Transaction Management and Connection Pooling