NeuralBlitz What I do
- Design efficient indexes
- Choose index types
- Optimize query performance
When to use me
When creating database indexes.
Index Types
B-Tree Index
class BTreeIndex:
"""B-Tree index implementation"""
def __init__(self, order: int = 3):
self.root = BTreeNode(order)
self.order = order
def insert(self, key: any, value: any):
"""Insert key-value pair"""
root = self.root
if root.is_full():
new_root = BTreeNode(self.order)
new_root.children.append(self.root)
new_root.split_child(0)
self.root = new_root
self.root.insert_non_full(key, value)
def search(self, key: any) -> any:
"""Search for key"""
return self.root.search(key)
Hash Index
class HashIndex:
"""Hash-based index"""
def __init__(self, size: int = 100):
self.buckets = [[] for _ in range(size)]
self.size = size
def _hash(self, key: any) -> int:
return hash(key) % self.size
def insert(self, key: any, value: any):
"""Insert with hash collision handling"""
bucket = self._hash(key)
self.buckets[bucket].append((key, value))
def search(self, key: any) -> any:
"""Search for key"""
bucket = self._hash(key)
for k, v in self.buckets[bucket]:
if k == key:
return v
return None
Composite Index
class CompositeIndex:
"""Multi-column index"""
def __init__(self, columns: List[str]):
self.columns = columns
self.index = {}
def create_key(self, row: dict) -> tuple:
"""Create composite key from row"""
return tuple(row.get(col) for col in self.columns)
def insert(self, row: dict):
"""Insert row into index"""
key = self.create_key(row)
self.index[key] = row
def search(self, **criteria) -> List[dict]:
"""Search using partial key"""
partial = tuple(criteria.get(col) for col in self.columns)
return [row for key, row in self.index.items()
if key[:len(partial)] == partial]
Index Selection
class IndexSelector:
"""Choose appropriate indexes"""
@staticmethod
def recommend_indexes(queries: List[dict]) -> List[dict]:
"""Recommend indexes based on queries"""
recommendations = []
for query in queries:
if "WHERE" in query:
recommendations.append({
"columns": query["where_columns"],
"type": "btree",
"reason": "Equality/range query"
})
if "ORDER BY" in query:
recommendations.append({
"columns": query["order_columns"],
"type": "btree",
"reason": "Sort optimization"
})
return recommendations