Devcafe: 새 문서: import numpy as np import torch import torch.nn as nn import torch.optim as optim from typing import List, Dict class SQLTuningSLM(nn.Module): def init(self, feature_dim, hidden_dim, output_dim): super(SQLTuningSLM, self).init() # Feature embedding and processing layers self.feature_embedding = nn.Sequential( nn.Linear(feature_dim, hidden_dim), nn.ReLU(), nn.Dropout(0.3)...

2024-11-22T16:30:46Z

새 문서: <source lang=python> import numpy as np import torch import torch.nn as nn import torch.optim as optim from typing import List, Dict class SQLTuningSLM(nn.Module): def __init__(self, feature_dim, hidden_dim, output_dim): super(SQLTuningSLM, self).__init__() # Feature embedding and processing layers self.feature_embedding = nn.Sequential( nn.Linear(feature_dim, hidden_dim), nn.ReLU(), nn.Dropout(0.3)...

새 문서

<source lang=python>
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from typing import List, Dict

class SQLTuningSLM(nn.Module):
def __init__(self, feature_dim, hidden_dim, output_dim):
super(SQLTuningSLM, self).__init__()

# Feature embedding and processing layers
self.feature_embedding = nn.Sequential(
nn.Linear(feature_dim, hidden_dim),
nn.ReLU(),
nn.Dropout(0.3)
)

# LSTM for sequential feature processing
self.lstm = nn.LSTM(hidden_dim, hidden_dim, num_layers=2,
batch_first=True, dropout=0.3)

# Output layers for performance prediction
self.performance_predictor = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim//2),
nn.ReLU(),
nn.Linear(hidden_dim//2, output_dim)
)

def forward(self, x):
# Embed input features
embedded = self.feature_embedding(x)

# Process through LSTM
lstm_out, _ = self.lstm(embedded.unsqueeze(1))

# Predict performance optimizations
performance_output = self.performance_predictor(lstm_out.squeeze(1))

return performance_output

class SQLQueryOptimizer:
def __init__(self, model_path=None):
# Features to consider for SQL query tuning
self.features = [
'index_count',
'join_complexity',
'table_size',
'where_clause_count',
'aggregation_complexity',
'subquery_depth'
]

def extract_query_features(self, sql_query: str) -> np.ndarray:
# Placeholder for feature extraction logic
# In a real implementation, this would parse the SQL query
# and extract relevant performance-related features
features = np.random.rand(len(self.features))
return features

def train_optimization_model(self, queries: List[str], performance_targets: List[float]):
# Prepare training data
X = np.array([self.extract_query_features(q) for q in queries])
y = np.array(performance_targets)

# Convert to PyTorch tensors
X_tensor = torch.FloatTensor(X)
y_tensor = torch.FloatTensor(y)

# Initialize model
model = SQLTuningSLM(
feature_dim=len(self.features),
hidden_dim=64,
output_dim=1
)

# Loss and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)

# Training loop
for epoch in range(100):
optimizer.zero_grad()
predictions = model(X_tensor)
loss = criterion(predictions.squeeze(), y_tensor)
loss.backward()
optimizer.step()

if epoch % 10 == 0:
print(f'Epoch {epoch}, Loss: {loss.item():.4f}')

return model

def suggest_optimizations(self, model, query: str) -> Dict[str, str]:
# Extract features for the query
features = self.extract_query_features(query)
features_tensor = torch.FloatTensor(features)

# Predict potential optimizations
with torch.no_grad():
performance_score = model(features_tensor)

# Example optimization suggestions (mock implementation)
optimizations = {
'index_recommendation': 'Consider adding an index on join columns',
'query_restructuring': 'Simplify complex subqueries',
'performance_score': f'{performance_score.item():.2f}'
}

return optimizations

# Example usage
def main():
optimizer = SQLQueryOptimizer()

# Sample training queries and their performance metrics
queries = [
"SELECT * FROM large_table WHERE complex_condition",
"SELECT AVG(column) FROM nested_subquery_table"
]
performance_targets = [100.0, 75.5] # Lower is better

# Train the optimization model
tuning_model = optimizer.train_optimization_model(queries, performance_targets)

# Get optimization suggestions for a new query
new_query = "SELECT COUNT(*) FROM complex_join_table"
suggestions = optimizer.suggest_optimizations(tuning_model, new_query)
print("Optimization Suggestions:", suggestions)

if __name__ == "__main__":
main()

</source>

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