book

Just Use Postgres!

Name: Just Use Postgres!
Author: Denis Magda
ISBN: 9781633435698

by Denis Magda

November 2025

Beginner to intermediate

400 pages

11h 43m

English

Manning Publications

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Just use Postgres! All the database you need
copyright
contents
dedication
foreword
preface
acknowledgments
about this book
about the author
about the cover illustration

Part 1 Postgres as a relational database
1 Meeting Postgres
1.1 Why is Postgres so popular?1.2 “Just use Postgres!” explained1.3 Starting Postgres in Docker1.4 Connecting with psql1.5 Generating mock data1.6 Running basic queries
2 Standard RDBMS capabilities
2.1 Creating the database structure2.1.1 Creating databases2.1.2 Creating schemas2.1.3 Creating tables2.2 Querying and manipulating data2.3 Data integrity2.3.1 Constraints2.3.2 Foreign keys2.4 Transactions2.4.1 Implicit transactions2.4.2 Explicit transactions2.4.3 Multiversion concurrence control2.5 Joins2.6 Functions and triggers2.6.1 Functions: A practical example2.6.2 Triggers: A practical example2.7 Views2.8 Roles and access control
3 Modern SQL
3.1 What is modern SQL?3.2 Loading the music service dataset3.3 Common table expressions3.3.1 Selecting data with CTEs3.3.2 Using multiple CTEs in a query3.3.3 Modifying data with CTEs3.4 Recursive queries3.4.1 Querying hierarchical data3.4.2 Using arguments in recursion3.5 Window functions
4 Indexes
4.1 Why are indexes so popular?4.2 Overview of Postgres index types4.3 Loading the multiplayer game dataset4.4 Learning to use the EXPLAIN statement4.5 Single-column indexes4.5.1 Single-column B-tree indexes4.5.2 Single-column hash indexes4.6 Composite indexes4.6.1 Considering an additional single-column index4.6.2 Creating a composite index4.6.3 Caveats of composite indexes4.7 Covering indexes4.8 Partial indexes4.9 Functional and expression indexes
Part 2 Core Postgres beyond relational
5 Postgres and JSON
5.1 Storing JSON data5.2 Loading the pizza order dataset5.3 JSON in Postgres: Striking the balance5.4 Querying JSON data5.4.1 Extracting fields with the -> and ->> operators5.4.2 Using the ? operator to check for the presence of a key5.4.3 Comparing objects with the @> operator5.4.4 Using JSON path expressions5.5 Modifying JSON data5.6 Indexing JSON data5.6.1 Using an expression index with a B-tree5.6.2 Using GIN indexes
6 Postgres for full-text search
6.1 Basics of full-text search in Postgres6.1.1 Tokenization and normalization6.1.2 Full-text search configurations6.2 Preparing data for text search6.2.1 Generating lexemes with the to_tsvector function6.2.2 Storing tsvector lexemes in the database6.3 Performing full-text search6.3.1 Using plainto_tsquery for simple queries6.3.2 Using to_tsquery for advanced filtering6.4 Ranking search results6.5 Highlighting search results6.6 Indexing lexemes6.6.1 Using GIN indexes6.6.2 Using GiST indexes
Part 3 Extensions and the broader ecosystem
7 Postgres extensions
7.1 The roots of Postgres extensibility7.2 Getting started with extensions7.2.1 Exploring available extensions7.2.2 Installing and using extensions7.3 Essential extensions for developers7.4 Postgres-compatible solutions
8 Postgres for generative AI
8.1 How to use Postgres with gen AI8.1.1 Postgres and LLMs8.1.2 Postgres and embedding models8.2 Starting Postgres with pgvector8.3 Generating embeddings8.3.1 Generating embeddings for movies8.3.2 Loading the final dataset into Postgres8.4 Performing vector similarity search8.4.1 Using cosine distance for similarity search8.4.2 Changing the search phrase for better results8.5 Indexing embeddings8.5.1 Using IVFFlat indexes8.5.2 Using the HNSW index8.6 Implementing RAG8.6.1 Preparing the environment for the prototype8.6.2 Interacting with the LLM8.6.3 Retrieving context for LLM8.6.4 Using RAG to answer questions
9 Postgres for time series
9.1 How Postgres works with time-series data9.2 Starting Postgres with TimescaleDB9.3 Loading the time-series data9.4 Exploring TimescaleDB hypertables9.5 Analyzing time-series data9.5.1 Using the time_bucket function9.5.2 Using the time_bucket_gapfill function9.6 Using continuous aggregates9.6.1 Creating and using aggregates9.6.2 Refreshing aggregates9.7 Indexing time-series data9.7.1 Using a B-tree index9.7.2 Using BRIN indexes
10 Postgres for geospatial data
10.1 How Postgres works with geodata10.1.1 Built-in Postgres capabilities10.1.2 Extended capabilities with PostGIS10.2 Starting Postgres with PostGIS10.3 Loading the OpenStreetMap dataset10.3.1 Exploring tables with points10.3.2 Exploring tables with ways10.3.3 Exploring table with polygons10.4 Visualizing geospatial data10.5 Querying geospatial data10.5.1 Working with points10.5.2 Working with polygons10.5.3 Working with line segments10.6 Indexing geospatial data10.6.1 Understanding GiST structure10.6.2 Using a GiST index
11 Postgres as a message queue
11.1 When to use Postgres as a message queue11.2 Building a custom message queue11.3 Using a custom queue11.4 Using LISTEN and NOTIFY11.5 Queue implementation considerations11.5.1 LISTEN and NOTIFY considerations11.5.2 Indexing considerations11.5.3 Partitioning considerations11.5.4 Messages processing failover considerations11.6 Starting Postgres with pgmq11.7 Using pgmq11.7.1 Creating and using a visitors queue11.7.2 Using message visibility timeouts
appendix A Five optimization tips
A.1 Master using EXPLAINA.2 Know when to use indexesA.3 Use connection poolingA.4 Query only what you really needA.4.1 General caseA.4.2 The count(*) caseA.5 Use the computational capabilities of the database
appendix B When not to use Postgres
afterword

Content preview from Just Use Postgres!

6 Postgres for full-text search

This chapter covers

Understanding how Postgres supports full-text search
Converting textual data into lexemes
Storing lexemes in the database
Performing full-text search using built-in functions and operators
Optimizing full-text search performance with GIN and GiST indexes

Postgres has supported various text data types and search operators for years. For instance, we can store text values in a column of the TEXT type and then use the equality operator (=), pattern-matching operators (LIKE and ILIKE), regular-expression-matching operators (~ and ~*), and other operators to query the text data. Although these capabilities work well for simple text data and queries, they are less effective when searching ...

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Just Use Postgres!

by Denis Magda