use ruvector_dag::dag::{OperatorNode, OperatorType, QueryDag};
fn main() {
println!("=== Neural DAG Learning - Basic Usage ===\n");
let mut dag = QueryDag::new();
println!("Building query DAG...");
let scan = dag.add_node(OperatorNode::seq_scan(0, "users"));
println!(" Added SeqScan on 'users' (id: {})", scan);
let filter = dag.add_node(OperatorNode::filter(1, "age > 18"));
println!(" Added Filter 'age > 18' (id: {})", filter);
let sort = dag.add_node(OperatorNode::sort(2, vec!["name".to_string()]));
println!(" Added Sort by 'name' (id: {})", sort);
let limit = dag.add_node(OperatorNode::limit(3, 10));
println!(" Added Limit 10 (id: {})", limit);
let result = dag.add_node(OperatorNode::new(4, OperatorType::Result));
println!(" Added Result (id: {})", result);
dag.add_edge(scan, filter).unwrap();
dag.add_edge(filter, sort).unwrap();
dag.add_edge(sort, limit).unwrap();
dag.add_edge(limit, result).unwrap();
println!("\nDAG Statistics:");
println!(" Nodes: {}", dag.node_count());
println!(" Edges: {}", dag.edge_count());
let order = dag.topological_sort().unwrap();
println!("\nTopological Order: {:?}", order);
let depths = dag.compute_depths();
println!("\nNode Depths:");
for (id, depth) in &depths {
println!(" Node {}: depth {}", id, depth);
}
println!("\nNode Children:");
for node_id in 0..5 {
let children = dag.children(node_id);
println!(" Node {}: {:?}", node_id, children);
}
println!("\nDFS Traversal:");
for (i, node_id) in dag.dfs_iter(scan).enumerate() {
if i < 10 {
println!(" Visit: {}", node_id);
}
}
println!("\nBFS Traversal:");
for (i, node_id) in dag.bfs_iter(scan).enumerate() {
if i < 10 {
println!(" Visit: {}", node_id);
}
}
println!("\n=== Example Complete ===");
}