Database Optimization Strategies: SQL, NoSQL & Performance Tuning
Database Optimization Strategies
Database performance is critical for application success. Let's explore optimization strategies across different database types.
SQL Database Optimization
Indexing Strategies
```sql
-- Create composite index
CREATE INDEX idx_user_email_active
ON users (email, is_active)
WHERE is_active = true;
-- Include columns for covering index
CREATE INDEX idx_order_customer
ON orders (customer_id)
INCLUDE (order_date, total_amount);
```Query Optimization
1. **Use Explain Plans**: Analyze query execution
2. **Avoid SELECT ***: Select only needed columns
3. **Use JOINs Wisely**: Understand different join types
4. **Optimize WHERE Clauses**: Put most selective filters first
Example Optimized Query
```sql
-- Bad: Implicit conversion, no index usage
SELECT * FROM users WHERE CAST(id AS VARCHAR) = '123';
-- Good: Direct comparison with proper type
SELECT id, name, email FROM users WHERE id = 123;
```PostgreSQL Specific
Partitioning
```sql
-- Create partitioned table
CREATE TABLE orders (
id SERIAL,
order_date DATE NOT NULL,
customer_id INT,
total_amount DECIMAL
) PARTITION BY RANGE (order_date);
-- Create partitions
CREATE TABLE orders_2024_q1
PARTITION OF orders
FOR VALUES FROM ('2024-01-01') TO ('2024-04-01');
```VACUUM and ANALYZE
Regular maintenance is crucial:
```sqlVACUUM ANALYZE users;
```MongoDB Optimization
Schema Design
- Embed related data for read performance
- Reference data for write performance
- Use appropriate data types
Indexing
```javascript
// Create compound index
db.users.createIndex(
{ email: 1, status: 1 },
{ background: true }
);
// Create text index for search
db.products.createIndex(
{ name: "text", description: "text" }
);
```Aggregation Pipeline
```javascript
db.orders.aggregate([
{ $match: { status: "completed" } },
{ $group: {
_id: "$customerId",
totalSpent: { $sum: "$amount" }
}},
{ $sort: { totalSpent: -1 } },
{ $limit: 10 }
]);
```Redis Optimization
Data Structures
Choose the right data structure:
- **Strings**: Simple key-value
- **Hashes**: Objects with fields
- **Lists**: Ordered collections
- **Sets**: Unique values
- **Sorted Sets**: Ordered by score
Memory Optimization
```
Set eviction policy
maxmemory-policy allkeys-lru
Enable compression
list-compress-depth 1
```Elasticsearch Optimization
Index Settings
```json
{
"settings": {
"number_of_shards": 3,
"number_of_replicas": 1,
"refresh_interval": "30s"
},
"mappings": {
"properties": {
"title": {
"type": "text",
"analyzer": "english"
},
"created_at": {
"type": "date"
}
}
}
}
```Query Optimization
- Use filters instead of queries when possible
- Limit field data loading
- Use scroll API for large result sets
General Best Practices
1. **Connection Pooling**: Reuse database connections
2. **Caching Layer**: Use Redis for frequently accessed data
3. **Read Replicas**: Scale read operations
4. **Monitoring**: Track slow queries and performance metrics
5. **Regular Maintenance**: Update statistics, rebuild indexes
6. **Data Archiving**: Move old data to archive tables
Scaling Strategies
Vertical Scaling
- Add more CPU, RAM, storage
- Limited by hardware
Horizontal Scaling
- **Replication**: Master-slave setup
- **Sharding**: Distribute data across servers
- **Federation**: Split by function
Conclusion
Database optimization requires understanding your data access patterns, choosing the right database type, and applying appropriate optimization techniques. Regular monitoring and maintenance are essential for sustained performance.