Optimize performance patterns

Choose efficient implementation patterns that improve performance. Apply these optimizations throughout your code:

1. Avoid redundant operations: Store results of expensive lookups rather than repeating them. ```go // Less efficient: searching for time field twice for _, f := range frame.Fields { if f.Type() == data.FieldTypeTime { timeField = f break } } // …later… for _, f := range frame.Fields { if f.Type() == data.FieldTypeTime { timeField = f break } }

// More efficient: store the reference timeField := timeFields[frame]

2. **Pre-allocate when size is known**: For collections with predictable sizes, pre-allocate to avoid resizing.
```go
// Pre-allocate with known capacity
rows := make([]row, 0, totalRows)

1. Properly benchmark code: Use b.ResetTimer() before the actual code being benchmarked to exclude setup time.

   func BenchmarkOperation(b *testing.B) {
    // Setup code
    data := prepareTestData()
       
    // Start measuring only the operation we care about
    b.ResetTimer()
    for i := 0; i < b.N; i++ {
        performOperation(data)
    }
   }
   

2. Optimize database operations: Choose the most appropriate database operation for your context. For example, after truncating a table, use regular inserts instead of upserts: ```go // Less efficient - using upsert after truncation if _, err := sess.Exec(“DELETE FROM alert_instance”); err != nil { return err } // Then using upserts

// More efficient - using regular inserts after truncation if _, err := sess.Exec(“DELETE FROM alert_instance”); err != nil { return err } // Then using regular inserts ```

Regularly profile your code with realistic data volumes to identify and address bottlenecks.