📊 SQL Statistics Explained – Usage, Updates, and Best Practices

🧲 Introduction – Why SQL Statistics Matter

SQL Statistics are critical for powering the query optimizer’s decisions. Whether you’re tuning a slow query or designing indexes, knowing how your database uses statistics helps you write faster, more efficient SQL. Statistics describe the distribution, density, and cardinality of column values. Without them, even the best indexes may go unused.

🎯 In this guide, you’ll learn:

• What SQL statistics are and how they work
• How to view and update statistics
• Platform-specific syntax for MySQL, PostgreSQL, and SQL Server
• Best practices for statistics maintenance
• Real-world performance impacts

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📚 1. What Are SQL Statistics?

SQL Statistics are metadata the query planner uses to estimate:

• How many rows will match a filter
• Whether to scan or seek via index
• Optimal join order and join methods

🧠 Key Concepts:

• Histograms – Frequency of column values
• Density – Selectivity of data (duplicates vs unique)
• Cardinality – Estimated result size of a filter or join
• Null counts – Help estimate rows skipped in filters

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🛠️ 2. View & Update Statistics by Platform

✅ MySQL

ANALYZE TABLE employees;
SHOW INDEX FROM employees;

✅ PostgreSQL

ANALYZE employees;
SELECT * FROM pg_stats WHERE tablename = 'employees';

✅ SQL Server

UPDATE STATISTICS employees;
EXEC sp_helpstats 'employees', 'ALL';

💡 Outdated statistics lead to bad execution plans—even with proper indexing.

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⚖️ 3. Auto vs Manual Statistics Collection

Platform	Auto Enabled?	Manual Needed When…
MySQL	✅ (InnoDB)	After bulk inserts or schema changes
PostgreSQL	✅	After large DELETE/INSERT/UPDATE
SQL Server	✅	ETL processes, after index rebuild

✅ Schedule manual ANALYZE/UPDATE STATISTICS for better performance in batch systems.

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🧪 4. Real-World Use Case – Bad Plan from Outdated Stats

SELECT * FROM orders WHERE order_date = '2023-01-01';

With outdated stats, the optimizer may:

• Underestimate rows → pick nested loop instead of hash join
• Overestimate → ignore an existing index

📉 The result? Slower query time, higher CPU/IO usage.

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💡 Best Practices

✅ Do This	⚠️ Avoid This
Update stats after data changes	Ignoring stats after schema migration
Enable auto-analyze in prod systems	Relying only on default thresholds
Set higher stats target for skewed data	Using same target for all tables/columns
Monitor actual vs estimated rows	Trusting the plan blindly

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📌 Summary – Recap & Relevance

SQL Statistics are the foundation of query performance. They influence everything from join type to index usage. Keep them up to date for smarter execution plans and faster queries.

🔍 Key Takeaways:

• Statistics describe column value distributions
• Optimizers rely on them for row estimates and plan decisions
• Always analyze after data or schema changes

⚙️ Real-World Relevance:
Accurate stats = optimal plans. Without them, the optimizer is blind.

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❓ FAQ – SQL Statistics

❓ How do I update SQL statistics?

✅ Use:

• ANALYZE TABLE (MySQL)
• ANALYZE (PostgreSQL)
• UPDATE STATISTICS (SQL Server)

❓ When should I manually update stats?

✅ After:

• Bulk data loads
• Index creation
• Schema modifications

❓ Where can I see PostgreSQL statistics?

✅ Query pg_stats, pg_stat_user_tables, and pg_stat_statements.

❓ What’s the default auto update behavior?

✅ Triggers when a percentage of rows are modified, but thresholds vary by DBMS.

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