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  📊 Understanding Table Statistics in PostgreSQL (With Simple Examples) Efficient queries depend on knowing your data . PostgreSQL uses table statistics to build better query plans, and you can use them too, to diagnose slow queries , analyze distributions , and optimize performance . Let’s explore how to gather and use these statistics using tools like: ANALYZE pg_stat_statements auto_explain pg_stats 📦 Why Do Table Statistics Matter? PostgreSQL keeps metadata about: 🔢 Number of rows in each table 💾 Storage usage 🔄 Value frequency (e.g., most common values) 📉 Data distribution (e.g., histograms, skew) ⚖️ Cardinality (how many distinct values a column has) This info helps the query planner decide how to execute your queries efficiently. ⚙️ 1. Collecting Stats with ANALYZE Run ANALYZE to gather statistics on tables: ANALYZE your_table; You can also combine it with VACUUM to clean and analyze at the same time: VACUUM ANALYZE your...

  🚀 Boost Query Speed with PostgreSQL Materialized Views When performance matters and you're running expensive queries repeatedly , there's a simple trick in PostgreSQL that can save you time: Materialized Views . Let’s break it down, step by step. 🧠 What is a Materialized View? A Materialized View is like a snapshot of a query . It stores the result of a query physically on disk , so you can reuse it without re-executing the original query every time. Think of it like: 📋 A view = live feed 🧊 A materialized view = frozen snapshot (until refreshed) ✅ Why Use Materialized Views? 💨 Faster query responses (no need to rejoin large tables every time) 🔁 Reuse precomputed results 💾 Trade storage for speed 📘 Example: Creating a Materialized View Let’s say you frequently run a report showing staff and their region: CREATE MATERIALIZED VIEW mv_staff AS SELECT s.last_name, s.department, s.job_title, c.region AS company_region FROM staf...