Learn PostgreSQL in Action

Kaufman-style skill map for mastering PostgreSQL as a Java software engineer: mental models, sub-skills, practice plan, evaluation rubric, and production-grade learning path.

Build a repeatable PostgreSQL 18 lab with Docker, psql, pgbench, pg_stat_statements, logging, metrics queries, and Java/Testcontainers readiness.

Deep mental model of PostgreSQL architecture: postmaster, backend processes, auxiliary processes, memory areas, shared buffers, WAL buffers, checkpoints, storage layout, and Java production implications.

Deep production model of PostgreSQL cluster, database, schema, role, ownership, privileges, search_path, namespace boundaries, tenant design, and Java application implications.

Deep mental model of PostgreSQL type system semantics for Java engineers: numeric, text, collation, time, UUID/UUIDv7, JSONB, arrays, ranges, domains, enums, composite types, and production mapping decisions.

DDL as application contract: constraints, identity, generated columns, defaults, foreign keys, exclusion constraints, temporal constraints, deferrable constraints, and safe schema evolution for Java production systems.

Deep mental model of PostgreSQL physical storage: relation forks, heap pages, tuple headers, TOAST, fillfactor, visibility map, free space map, HOT updates, and bloat failure modes.

Deep mental model of PostgreSQL MVCC and snapshot semantics: tuple visibility, xmin/xmax, snapshots, read consistency, update chains, anomalies, Java transaction boundaries, and production diagnostics.

Production-grade transaction engineering, PostgreSQL locking, blocking, deadlock prevention, lock observability, Java retry boundaries, and safe concurrency patterns.

Deep guide to PostgreSQL isolation levels, anomaly modeling, serializable retries, application invariants, Java transaction semantics, and correctness-oriented database design.

Query planner mental model for Java engineers: cost model, cardinality, statistics, selectivity, correlation, extended statistics, and plan instability.

Practical EXPLAIN ANALYZE diagnostics: reading plans, actual vs estimated rows, buffers, timing, loops, JIT, parallelism, and safe production workflow.

B-tree index deep dive for Java engineers: physical ordering, composite indexes, left-prefix behavior, skip scan, covering indexes, partial and expression indexes, and production failure modes.

Specialized PostgreSQL indexes for advanced workloads: GIN, GiST, SP-GiST, BRIN, Hash, JSONB, arrays, full-text search, trigram search, ranges, and time-series access patterns.

Index selection strategy for PostgreSQL production systems: workload inventory, index budgets, composite vs separate indexes, partial indexes, covering indexes, redundant index cleanup, and migration-safe index governance for Java applications.

Query shape engineering for PostgreSQL: sargability, predicate design, CTE materialization, EXISTS, IN, LATERAL, pagination, batching, optional filters, JSONB predicates, and Java/Hibernate query patterns that help or hurt the planner.

Join strategies and execution behavior in PostgreSQL: nested loop, hash join, merge join, join order, join cardinality, skew, memory pressure, outer joins, semi/anti joins, Java ORM implications, and production diagnostics.

Aggregation, sorting, and memory engineering in PostgreSQL: sort methods, hash aggregate, group aggregate, DISTINCT, window functions, work_mem, temp files, parallel aggregation, reporting workloads, and Java production implications.

JSONB dan hybrid relational-document modeling di PostgreSQL untuk engineer Java yang perlu menyeimbangkan fleksibilitas schema, queryability, integrity, performance, migration, dan operasional production.