optimize data structures

Choose appropriate data structures and algorithms to optimize computational complexity and performance. Consider the specific use case and access patterns when selecting between different approaches.

Key optimization opportunities:

1. Use efficient data structures for lookups: Replace linear searches with constant-time operations where possible. For example, convert ArrayContains operations on literal arrays to InSet operations for O(1) lookup instead of O(n).

2. Choose appropriate collection types: Use Set[String] with contains() for simple string matching instead of Set[Regex] when regex functionality isn’t needed. This avoids unnecessary regex compilation overhead.

3. Leverage built-in collection methods: Use Array.tabulate(size)(constructor) instead of manual loops for array initialization, and collection.zip(other).toMap for efficient map construction.

4. Consider algorithmic properties: Be aware of algorithm limitations like XOR checksums having issues with duplicate values. Consider alternatives like combining sum + XOR or using hash functions like Fowler–Noll–Vo when order-independence and duplicate-handling are required.

5. Select semantic-appropriate data structures: Use ExpressionSet for deduplicating expressions by semantics rather than object equality when the logical meaning matters more than object identity.

Example transformation:

// Instead of O(n) array search:
case ArrayContains(arrayParam: Literal, col) =>
  // Linear search through array elements

// Use O(1) set lookup:
case ArrayContains(arrayParam: Literal, col) if arrayParam.value != null =>
  InSet(col, arrayParam.value.asInstanceOf[GenericArrayData].array.toSet)

This approach reduces computational complexity and improves performance, especially for frequently executed operations or large datasets.