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Thinking in PostgreSQL

A field guide to PostgreSQL for MySQL engineers

Stop treating PostgreSQL like slightly broken MySQL and start using it the way it was designed.

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About

About

About the Book

You already know how to keep MySQL alive in production. You’ve tuned InnoDB, chased deadlocks, and survived your fair share of late-night incident calls. Now you’ve been handed PostgreSQL—and you’d like to avoid relearning everything the hard way.

Thinking in PostgreSQL is a field guide for engineers who grew up on MySQL and now have to run Postgres in anger. It doesn’t start from “what is a database?” and it doesn’t pretend PostgreSQL is just a drop-in replacement. Instead, it focuses on the mental model shift:

  • From threads in one big daemon to one process per connection and mandatory connection pooling.
  • From InnoDB’s clustered primary key to Postgres’s heap + separate indexes and real MVCC tuples.
  • From “purge will sort it out” to autovacuum, freezing, and transaction age as first-class concerns.
  • From AUTO_INCREMENT and REPLACE INTO to sequences, identity columns, and ON CONFLICT.
  • From hoping the slow query log tells you something useful to EXPLAIN (ANALYZE, BUFFERS) and pg_stat_* views.

The book is organized as a practical, production-oriented journey:

  • Architecture & MVCC – how Postgres actually lays out data on disk, how MVCC works, why bloat happens, and how to keep autovacuum on your side.
  • Concurrency & Indexing – reading pg_stat_activity, understanding isolation levels, locks, and deadlocks, and designing indexes using partial, expression, covering, GIN, BRIN, and more.
  • Monitoring & High Availability – using the built-in statistics views, pg_stat_statements, streaming replication, WAL, replication slots, and point-in-time recovery instead of hoping backups work.
  • Advanced Features & Configuration – JSONB, window functions, partitioning, extensions, key configuration knobs, and the migration “gotchas” that surprise MySQL veterans most.

Throughout, the focus is vendor-neutral and operational: short SQL snippets you can paste into psql, checklists you can run weekly, and mental models that still make sense whether your Postgres is on-prem, in the cloud, behind a proxy, or sitting next to ClickHouse and MongoDB.

If you’ve ever caught yourself treating PostgreSQL like “MySQL with slightly different syntax,” this book is your gentle intervention.

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Author

About the Author

Nick Vyzas

Nikolaos (Nick) Vyzas is a senior data platform engineer with more than two decades of experience designing, scaling, and operating mission-critical data systems. He has worked across ad-tech, fintech, gaming, telecoms, and other high-throughput environments where millions to billions of events per day, strict SLAs, and tight latency budgets are the norm.

Nick’s work spans OLTP and analytics: large MySQL and MariaDB deployments (including Percona, Galera, Aurora, and ProxySQL), massive ClickHouse clusters, PostgreSQL, MongoDB, Cassandra, and distributed data pipelines built on Kafka and Python. He has architected multi-region clusters, blue-green and zero-downtime migrations, high-volume ingestion and compaction frameworks, and observability stacks using Prometheus, Grafana, and OpenTelemetry.

He spent five years as a core maintainer and release manager for ProxySQL, used in thousands of production environments, where he focused on performance, reliability, and safe rollout practices. Earlier roles at consulting and managed services companies put him on the front lines of incident response for hundreds of customer environments, from finance and e-commerce to utilities and large online platforms.

Nick is deliberately vendor-neutral: he cares more about patterns than specific products, and about designs that survive cloud migrations, engine changes, and new AI/ML workloads. He speaks regularly at community conferences, contributes to open source, and has served as a technical reviewer for database and Linux books.

Thinking in PostgreSQL continues the same theme as his day-to-day work: take the hard lessons from real systems under real load, and turn them into practical mental models, checklists, and queries that engineers can reuse—whether they’re running Postgres next to ClickHouse, behind ProxySQL, inside Kubernetes, or in whatever stack comes next.

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