Introduction

If you’ve ever wondered why your perfectly tuned SQL query suddenly starts crawling at a snail’s pace, the culprit might be stale statistics. In SQL Server, statistics are like the GPS system for your query optimizer – they help it choose the fastest route to your data. When they’re outdated, your database might be taking the scenic route when it should be on the highway.

In this post, we’ll explore why updating statistics is crucial, how often you should do it, and various approaches to keep them fresh, with a special focus on schema-specific maintenance.

What Are SQL Server Statistics?

Statistics in SQL Server are objects that contain information about the distribution of values in one or more columns of a table or indexed view. Think of them as a summary of your data that helps the query optimizer make smart decisions about how to execute queries.

When the optimizer knows that a column contains mostly unique values versus many duplicates, or that most values fall within a certain range, it can choose more efficient execution plans. Without accurate statistics, it’s like trying to navigate a city with an outdated map.

Why Should You Update Statistics?

1. Query Performance

The most immediate benefit is improved query performance. When statistics are current, the optimizer can accurately estimate how many rows will be returned by different operations, leading to better execution plans.

2. Resource Efficiency

Accurate statistics help SQL Server allocate resources appropriately. This means less memory waste, reduced CPU usage, and fewer I/O operations.

3. Preventing Parameter Sniffing Issues

While not a complete solution, fresh statistics can help mitigate some parameter sniffing problems by ensuring the optimizer has current data distribution information.

4. Avoiding Query Timeouts

Queries that normally run in seconds can timeout when statistics are severely outdated, as the optimizer might choose nested loops when hash joins would be more appropriate.

How Often Should You Update Statistics?

The frequency depends on several factors:

High-Transaction Tables

• Daily updates for tables with more than 10-20% daily data changes
• Consider multiple daily updates for extremely volatile tables

Moderate-Activity Tables

• Weekly updates for tables with 5-10% weekly changes
• Good for most OLTP systems with regular business hours

Static or Slow-Changing Tables

• Monthly or quarterly updates for reference tables
• Configuration or lookup tables that rarely change

After Major Data Operations

Always update statistics after:

• Bulk inserts or updates
• Archive operations
• Major DELETE operations
• Index rebuilds (though REBUILD operations update statistics automatically)

Different Approaches to Updating Statistics

1. The Sledgehammer: Update All Statistics

The simplest approach is to update all statistics in your database or schema:

-- Update all statistics in a specific schema
EXEC sp_MSforeachtable 
    @command1 = 'UPDATE STATISTICS ? WITH FULLSCAN',
    @whereand = 'AND SCHEMA_NAME(schema_id) = ''dyn''';

Pros:

• Simple to implement
• Ensures nothing is missed
• Good for smaller databases

Cons:

• Resource intensive
• May update statistics that don’t need it
• Can cause blocking on busy systems

2. The Surgeon: Targeted Updates Based on Modifications

A more refined approach updates only statistics that have changed significantly:

-- Update only stale statistics (>10% modifications)
SELECT 'UPDATE STATISTICS [' + SCHEMA_NAME(t.schema_id) + '].[' + t.name + '] WITH FULLSCAN;'
FROM sys.tables t
INNER JOIN sys.stats s ON t.object_id = s.object_id
CROSS APPLY sys.dm_db_stats_properties(s.object_id, s.stats_id) sp
WHERE sp.modification_counter > 0.1 * sp.rows

Pros:

• Efficient resource usage
• Minimal system impact
• Can run more frequently

Cons:

• More complex to implement
• Might miss slowly degrading statistics

3. The Optimizer: Adaptive Sampling

For large databases, use different sampling rates based on table size:

-- FULLSCAN for small tables, SAMPLE for large ones
CASE 
    WHEN rows < 1000000 THEN 'WITH FULLSCAN'
    ELSE 'WITH SAMPLE 30 PERCENT'
END

Pros:

• Balances accuracy with performance
• Scales well with database size
• Reasonable execution times

Cons:

• Sampled statistics less accurate
• Requires size threshold tuning

4. The Scheduler: Time-Based Updates

Update statistics based on age rather than modifications:

-- Update statistics older than 7 days
WHERE DATEDIFF(DAY, last_updated, GETDATE()) > 7

Pros:

• Predictable maintenance windows
• Catches slowly changing tables
• Simple to schedule

Cons:

• May update unnecessarily
• Might miss rapid changes

5. The Hybrid: Multi-Criteria Approach

Combine multiple criteria for intelligent updates:

WHERE 
    last_updated IS NULL -- Never updated
    OR DATEDIFF(DAY, last_updated, GETDATE()) > 7 -- Age threshold
    OR modification_counter > 0.1 * rows -- Change threshold

Pros:

• Comprehensive coverage
• Flexible and adaptable
• Catches edge cases

Cons:

• More complex logic
• Requires careful tuning

Best Practices and Recommendations

1. Create a Maintenance Strategy

• Document your statistics update policy
• Different schemas may need different approaches
• Consider business cycles and peak times

2. Monitor and Adjust

• Track query performance over time
• Monitor statistics age and modification counters
• Adjust thresholds based on observations

3. Use SQL Agent Jobs

• Automate statistics updates
• Schedule during low-activity periods
• Include error handling and notifications

4. Consider Index Maintenance Together

• Index rebuilds update statistics automatically
• Reorganize operations do not
• Plan these operations together

5. Test in Non-Production First

• Verify the impact of your maintenance strategy
• Test different sampling rates
• Measure execution times

Common Pitfalls to Avoid

1. Over-updating: Running updates too frequently wastes resources
2. Under-sampling: Using sample rates too low for accurate statistics
3. Peak-time updates: Causing blocking during business hours
4. Ignoring small tables: Even small tables benefit from accurate statistics
5. Set and forget: Not monitoring the effectiveness of your strategy

Conclusion

Maintaining fresh statistics is like regular oil changes for your car – it’s preventive maintenance that keeps everything running smoothly. While SQL Server does some automatic statistics maintenance, taking control of this process ensures optimal performance for your specific workload.

Start with a simple approach and refine it based on your observations. Monitor query performance, track statistics age, and adjust your strategy as needed. Remember, the best statistics maintenance plan is one that balances accuracy with resource usage while meeting your performance requirements.

Whether you’re managing a single schema or an entire database server, regular statistics updates should be a cornerstone of your maintenance strategy. Your queries will thank you with consistent, predictable performance.

Quick Reference: Statistics Update Decision Tree

1. Is your table static? → Monthly updates with FULLSCAN
2. Does your table change >20% daily? → Daily updates, consider SAMPLE for large tables
3. Is your table between 1-20% daily change? → Weekly updates with adaptive sampling
4. Did you just perform bulk operations? → Immediate update with FULLSCAN
5. Are queries timing out? → Check statistics age immediately

Remember: When in doubt, measure and adjust. Your data patterns are unique, and your statistics strategy should reflect that.