How to Build Ultra-fast Java Microservices. Overcome Pitfalls of Data Persistence
summary
Talk claims in-memory approaches and EclipseStore can cut query latency dramatically — benchmarks shown claim up to 1000x faster than traditional relational stacks. Covers specific tech: Redis key-value topologies, Cassandra column-family patterns, MongoDB document limits, graph use cases, and EclipseStore as a Java-embedded database that removes ORM translation. Shows common pitfalls (over‑normalization, heavy joins, migration pain), pattern tradeoffs (driver, DAO, repository, active record), and architecture sketches for sharding, replication and where each choice actually improves or hurts performance.
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chapters (6)
- 0:00Introduction to data persistence
- 8:07Why persistence matters
- 12:03Polyglot persistence philosophy
- 26:27Object-relational mismatch
- 31:10Persistence layer patterns
- 39:06Summary and Q&A
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