When Database Indexing Makes Things Worse: RDS Performance Pitfalls

While database indexing is often the go-to solution for performance optimization, it's not always the silver bullet we expect it to be. In fact, there are scenarios where indexing can actually degrade your RDS performance. Let's explore when and why this happens.

Understanding Index Limitations

Think of database indexes like the index in a book - they're great for finding specific information quickly, but maintaining them comes at a cost. Just as you wouldn't create an index for every word in a book, you shouldn't index every column in your database.

When Indexes Become the Problem

1. High-Write Environments

In write-heavy applications, indexes can become your worst enemy. Here's why:

-- Example of a write-heavy table with multiple indexes
CREATE TABLE user_activities (
    id SERIAL PRIMARY KEY,
    user_id INTEGER,
    activity_type VARCHAR(50),
    timestamp TIMESTAMP,
    metadata JSONB,
    INDEX idx_user_id (user_id),
    INDEX idx_activity_type (activity_type),
    INDEX idx_timestamp (timestamp)
);

Every INSERT operation now needs to update:

• The main table
• Three separate indexes
• The PRIMARY KEY index

This can turn a simple insert into a performance nightmare, especially under high concurrency.

2. Low Cardinality Trap

Consider a table with a status column:

-- Index on a low cardinality column
CREATE INDEX idx_status ON orders(status);

-- When most queries look like this
SELECT * FROM orders 
WHERE status = 'PENDING' -- Returns 40% of the table

When your column only has a few distinct values (like 'PENDING', 'COMPLETED', 'FAILED'), an index scan might be more expensive than a simple table scan.

3. Data Skewness Problems

When data distribution is heavily skewed:

-- Example of skewed data query
SELECT * FROM products 
WHERE category = 'Electronics' -- Contains 80% of all products

The index becomes less effective because:

• The optimizer might still choose a full table scan
• Index maintenance costs remain high
• Storage overhead doesn't justify the performance gain

4. Text Column Challenges

Large text fields pose unique challenges:

-- Problematic index on large text field
CREATE INDEX idx_description ON products(description);

-- Better alternative using specialized indexing
CREATE INDEX idx_description_gin ON products 
USING gin(to_tsvector('english', description));

Better Alternatives

1. For High-Write Scenarios:

   • Consider periodic batch indexing
   • Use materialized views
   • Implement caching strategies

2. For Low Cardinality:

   • Partition tables instead of indexing
   • Use bitmap indexes where supported
   • Consider column-oriented storage

3. For Skewed Data:

-- Using partitioning instead of indexing
CREATE TABLE products (
    id SERIAL,
    name VARCHAR(100),
    category VARCHAR(50)
) PARTITION BY LIST (category);

4. For Text Search:
   • Utilize full-text search capabilities
   • Consider specialized search engines like Elasticsearch
   • Use appropriate text-specific index types

Monitoring Index Impact

Always measure the impact of your indexes:

-- Check index usage
SELECT 
    schemaname,
    tablename,
    indexname,
    idx_scan,
    idx_tup_read,
    idx_tup_fetch
FROM pg_stat_user_indexes;

Conclusion

Before automatically reaching for indexes as a performance solution:

• Analyze your workload patterns
• Consider the write/read ratio
• Measure actual performance impact
• Look at data distribution

Sometimes, the best index is no index at all. Focus on understanding your data access patterns and choose optimization strategies that align with your specific use case.

Additional Resources

• AWS RDS Performance Insights Documentation
• PostgreSQL Index Types
• RDS Best Practices Guide