Mastering Java Persistence: Best Practices for Cloud-Native...
summary
Argues that cloud native Java needs polyglot persistence and shows concrete tradeoffs: Redis for caching and pubsub, Cassandra for denormalized wide columns with TTL and multi‑DC replication, MongoDB for document embeds and sharding, Neo4j for directional graph queries. Explains JDBC and JPA costs, N+1 and mapping overhead, and contrasts six patterns with their costs and benefits. Recommends denormalize for read‑heavy stores, use snapshots and partitioning for scale, and notes repositories hide DB details but add adapter complexity. driver, data mapper, repository
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chapters (13)
- 0:00Introduction and motivation
- 3:47Why databases matter today
- 7:04Persistence layer challenges
- 12:11NoSQL paradigms overview
- 19:41Key-value and wide-column databases
- 22:35Document and graph databases
- 27:40Application-database impedance
- 31:10Persistence layer patterns
- 34:01Driver and mapper patterns
- 36:53Data Access Object and Active Record
- 38:41Repository pattern explained
- 41:08Tradeoffs in persistence layers
- 43:16Beyond patterns and Q&A
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