oxirs_cluster/

distributed_query.rs

//! Distributed Query Processing Module
//!
//! Provides distributed SPARQL query execution, optimization, and result aggregation
//! for the OxiRS cluster system.

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, HashSet};
use std::sync::Arc;
use tokio::sync::RwLock;
use tracing::{debug, error, info, warn};

use crate::raft::OxirsNodeId;

/// Distributed query execution plan
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistributedQueryPlan {
    pub query_id: String,
    pub original_sparql: String,
    pub subqueries: Vec<SubqueryPlan>,
    pub join_operations: Vec<JoinOperation>,
    pub aggregation_plan: Option<AggregationPlan>,
    pub estimated_cost: f64,
}

/// Subquery execution plan for a specific node
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SubqueryPlan {
    pub subquery_id: String,
    pub target_node: OxirsNodeId,
    pub sparql_fragment: String,
    pub variables: Vec<String>,
    pub estimated_rows: u64,
    pub estimated_latency_ms: u64,
}

/// Join operation between subquery results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct JoinOperation {
    pub left_subquery: String,
    pub right_subquery: String,
    pub join_variables: Vec<String>,
    pub join_type: JoinType,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum JoinType {
    Inner,
    Left,
    Optional,
    Union,
}

/// Aggregation plan for result combination
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AggregationPlan {
    pub group_by: Vec<String>,
    pub aggregates: Vec<AggregateFunction>,
    pub having_conditions: Vec<String>,
    pub order_by: Vec<OrderByClause>,
    pub limit: Option<u64>,
    pub offset: Option<u64>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AggregateFunction {
    pub function: String,
    pub variable: String,
    pub alias: Option<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OrderByClause {
    pub variable: String,
    pub ascending: bool,
}

/// Query execution statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QueryStats {
    pub query_id: String,
    pub execution_time_ms: u64,
    pub nodes_involved: u32,
    pub total_intermediate_results: u64,
    pub final_result_count: u64,
    pub network_transfer_bytes: u64,
    pub cache_hits: u32,
    pub cache_misses: u32,
}

/// Result binding for SPARQL variables
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
pub struct ResultBinding {
    pub variables: BTreeMap<String, String>,
}

impl Default for ResultBinding {
    fn default() -> Self {
        Self::new()
    }
}

impl ResultBinding {
    pub fn new() -> Self {
        Self {
            variables: BTreeMap::new(),
        }
    }

    pub fn add_binding(&mut self, variable: String, value: String) {
        self.variables.insert(variable, value);
    }

    pub fn get(&self, variable: &str) -> Option<&String> {
        self.variables.get(variable)
    }

    pub fn merge(&self, other: &ResultBinding) -> Option<ResultBinding> {
        let mut merged = self.clone();
        for (var, val) in &other.variables {
            if let Some(existing) = merged.variables.get(var) {
                if existing != val {
                    return None; // Conflict
                }
            } else {
                merged.variables.insert(var.clone(), val.clone());
            }
        }
        Some(merged)
    }
}

/// Distributed query executor
#[derive(Debug)]
pub struct DistributedQueryExecutor {
    #[allow(dead_code)]
    node_id: OxirsNodeId,
    cluster_nodes: Arc<RwLock<HashSet<OxirsNodeId>>>,
    query_cache: Arc<RwLock<HashMap<String, Vec<ResultBinding>>>>,
    statistics: Arc<RwLock<HashMap<String, QueryStats>>>,
}

impl DistributedQueryExecutor {
    pub fn new(node_id: OxirsNodeId) -> Self {
        Self {
            node_id,
            cluster_nodes: Arc::new(RwLock::new(HashSet::new())),
            query_cache: Arc::new(RwLock::new(HashMap::new())),
            statistics: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    /// Add a node to the cluster
    pub async fn add_node(&self, node_id: OxirsNodeId) {
        let mut nodes = self.cluster_nodes.write().await;
        nodes.insert(node_id);
        info!("Added node {} to distributed query executor", node_id);
    }

    /// Remove a node from the cluster
    pub async fn remove_node(&self, node_id: OxirsNodeId) {
        let mut nodes = self.cluster_nodes.write().await;
        nodes.remove(&node_id);
        info!("Removed node {} from distributed query executor", node_id);
    }

    /// Execute a distributed SPARQL query
    pub async fn execute_query(&self, sparql: &str) -> Result<Vec<ResultBinding>> {
        let query_id = uuid::Uuid::new_v4().to_string();
        let start_time = std::time::Instant::now();

        info!("Executing distributed query {}: {}", query_id, sparql);

        // Check cache first
        if let Some(cached_results) = self.check_cache(sparql).await {
            info!("Cache hit for query {}", query_id);
            return Ok(cached_results);
        }

        // Create execution plan
        let plan = self.create_execution_plan(&query_id, sparql).await?;

        // Execute subqueries in parallel
        let subquery_results = self.execute_subqueries(&plan).await?;

        // Join and aggregate results
        let final_results = self.combine_results(&plan, subquery_results).await?;

        // Cache results
        self.cache_results(sparql, &final_results).await;

        // Record statistics
        let execution_time = start_time.elapsed().as_millis() as u64;
        self.record_statistics(&query_id, &plan, &final_results, execution_time)
            .await;

        info!(
            "Completed distributed query {} in {}ms, {} results",
            query_id,
            execution_time,
            final_results.len()
        );

        Ok(final_results)
    }

    /// Create an optimized execution plan for the query
    async fn create_execution_plan(
        &self,
        query_id: &str,
        sparql: &str,
    ) -> Result<DistributedQueryPlan> {
        // Parse SPARQL query (simplified implementation)
        let parsed = self.parse_sparql(sparql)?;

        // Analyze data distribution
        let data_distribution = self.analyze_data_distribution().await?;

        // Create subqueries based on data locality
        let subqueries = self.create_subqueries(&parsed, &data_distribution).await?;

        // Plan join operations
        let join_operations = self.plan_joins(&subqueries)?;

        // Create aggregation plan if needed
        let aggregation_plan = self.create_aggregation_plan(&parsed)?;

        // Estimate cost
        let estimated_cost = self.estimate_cost(&subqueries, &join_operations).await;

        Ok(DistributedQueryPlan {
            query_id: query_id.to_string(),
            original_sparql: sparql.to_string(),
            subqueries,
            join_operations,
            aggregation_plan,
            estimated_cost,
        })
    }

    /// Parse SPARQL query into structure (simplified)
    fn parse_sparql(&self, sparql: &str) -> Result<ParsedQuery> {
        // This is a simplified parser - in production you'd use a full SPARQL parser

        let mut variables = Vec::new();
        let mut triple_patterns = Vec::new();
        let filters = Vec::new();

        // Extract SELECT variables
        if let Some(select_part) = sparql.split("WHERE").next() {
            if select_part.contains("SELECT") {
                let vars_part = select_part.replace("SELECT", "").trim().to_string();
                if vars_part != "*" {
                    variables = vars_part
                        .split_whitespace()
                        .filter(|v| v.starts_with('?'))
                        .map(|v| v.to_string())
                        .collect();
                }
            }
        }

        // Extract triple patterns (very simplified)
        if let Some(where_part) = sparql.split("WHERE").nth(1) {
            let clean_where = where_part.replace(['{', '}'], "");
            for line in clean_where.lines() {
                let line = line.trim();
                if !line.is_empty() && line.contains(' ') {
                    let parts: Vec<&str> = line.split_whitespace().collect();
                    if parts.len() >= 3 {
                        triple_patterns.push(TriplePattern {
                            subject: parts[0].to_string(),
                            predicate: parts[1].to_string(),
                            object: parts[2].replace('.', ""),
                        });
                    }
                }
            }
        }

        Ok(ParsedQuery {
            variables,
            triple_patterns,
            filters,
            limit: None,
            offset: None,
            order_by: Vec::new(),
        })
    }

    /// Analyze data distribution across cluster nodes
    async fn analyze_data_distribution(&self) -> Result<DataDistribution> {
        let nodes = self.cluster_nodes.read().await;
        let mut distribution = DataDistribution {
            node_triple_counts: HashMap::new(),
            predicate_distribution: HashMap::new(),
            subject_distribution: HashMap::new(),
        };

        // In a real implementation, this would query each node for statistics
        for &node_id in nodes.iter() {
            distribution.node_triple_counts.insert(node_id, 10000); // Simulate
        }

        Ok(distribution)
    }

    /// Create subqueries for parallel execution
    async fn create_subqueries(
        &self,
        parsed: &ParsedQuery,
        _distribution: &DataDistribution,
    ) -> Result<Vec<SubqueryPlan>> {
        let mut subqueries = Vec::new();
        let nodes: Vec<_> = self.cluster_nodes.read().await.iter().cloned().collect();

        if nodes.is_empty() {
            return Err(anyhow::anyhow!("No nodes available for query execution"));
        }

        // Simple strategy: distribute triple patterns across nodes
        for (i, triple_pattern) in parsed.triple_patterns.iter().enumerate() {
            let target_node = nodes[i % nodes.len()];

            let sparql_fragment = format!(
                "SELECT {} WHERE {{ {} {} {} }}",
                parsed.variables.join(" "),
                triple_pattern.subject,
                triple_pattern.predicate,
                triple_pattern.object
            );

            subqueries.push(SubqueryPlan {
                subquery_id: format!("subquery_{i}"),
                target_node,
                sparql_fragment,
                variables: parsed.variables.clone(),
                estimated_rows: 1000, // Estimate based on statistics
                estimated_latency_ms: 50,
            });
        }

        Ok(subqueries)
    }

    /// Plan join operations between subqueries
    fn plan_joins(&self, subqueries: &[SubqueryPlan]) -> Result<Vec<JoinOperation>> {
        let mut joins = Vec::new();

        // Create joins between consecutive subqueries that share variables
        for i in 0..subqueries.len().saturating_sub(1) {
            let left = &subqueries[i];
            let right = &subqueries[i + 1];

            // Find common variables
            let common_vars: Vec<String> = left
                .variables
                .iter()
                .filter(|v| right.variables.contains(v))
                .cloned()
                .collect();

            if !common_vars.is_empty() {
                joins.push(JoinOperation {
                    left_subquery: left.subquery_id.clone(),
                    right_subquery: right.subquery_id.clone(),
                    join_variables: common_vars,
                    join_type: JoinType::Inner,
                });
            }
        }

        Ok(joins)
    }

    /// Create aggregation plan if needed
    fn create_aggregation_plan(&self, parsed: &ParsedQuery) -> Result<Option<AggregationPlan>> {
        // Check if aggregation is needed (simplified)
        if parsed.order_by.is_empty() && parsed.limit.is_none() {
            return Ok(None);
        }

        Ok(Some(AggregationPlan {
            group_by: Vec::new(),
            aggregates: Vec::new(),
            having_conditions: Vec::new(),
            order_by: parsed.order_by.clone(),
            limit: parsed.limit,
            offset: parsed.offset,
        }))
    }

    /// Estimate execution cost
    async fn estimate_cost(&self, subqueries: &[SubqueryPlan], joins: &[JoinOperation]) -> f64 {
        let mut total_cost = 0.0;

        // Cost of subqueries
        for subquery in subqueries {
            total_cost += subquery.estimated_rows as f64 * 0.001; // Cost per row
            total_cost += subquery.estimated_latency_ms as f64 * 0.01; // Latency cost
        }

        // Cost of joins
        for _join in joins {
            total_cost += 10.0; // Fixed join cost
        }

        total_cost
    }

    /// Execute subqueries in parallel
    async fn execute_subqueries(
        &self,
        plan: &DistributedQueryPlan,
    ) -> Result<HashMap<String, Vec<ResultBinding>>> {
        let mut results = HashMap::new();
        let mut handles = Vec::new();

        for subquery in &plan.subqueries {
            let subquery_clone = subquery.clone();
            let handle =
                tokio::spawn(async move { Self::execute_single_subquery(subquery_clone).await });
            handles.push((subquery.subquery_id.clone(), handle));
        }

        // Collect results
        for (subquery_id, handle) in handles {
            match handle.await {
                Ok(Ok(subquery_results)) => {
                    results.insert(subquery_id, subquery_results);
                }
                Ok(Err(e)) => {
                    error!("Subquery {} failed: {}", subquery_id, e);
                    return Err(e);
                }
                Err(e) => {
                    error!("Subquery {} task failed: {}", subquery_id, e);
                    return Err(anyhow::anyhow!("Task execution failed: {}", e));
                }
            }
        }

        Ok(results)
    }

    /// Execute a single subquery on a target node
    async fn execute_single_subquery(subquery: SubqueryPlan) -> Result<Vec<ResultBinding>> {
        debug!(
            "Executing subquery {} on node {}",
            subquery.subquery_id, subquery.target_node
        );

        // Create HTTP client for real network communication
        let client = reqwest::Client::builder()
            .timeout(tokio::time::Duration::from_millis(
                subquery.estimated_latency_ms * 3,
            ))
            .build()
            .map_err(|e| anyhow::anyhow!("Failed to create HTTP client: {}", e))?;

        // Construct endpoint URL (assumes standard SPARQL endpoint pattern)
        let endpoint_url = format!("http://node-{}/sparql", subquery.target_node);

        let response = client
            .post(&endpoint_url)
            .header("Content-Type", "application/sparql-query")
            .header("Accept", "application/sparql-results+json")
            .body(subquery.sparql_fragment.clone())
            .send()
            .await;

        match response {
            Ok(resp) if resp.status().is_success() => {
                let json: serde_json::Value = resp
                    .json()
                    .await
                    .map_err(|e| anyhow::anyhow!("Failed to parse JSON response: {}", e))?;

                Self::parse_sparql_json_results(json)
            }
            Ok(resp) => {
                // Network request succeeded but returned error status
                warn!(
                    "Node {} returned error status {}: falling back to simulation",
                    subquery.target_node,
                    resp.status()
                );
                Self::simulate_subquery_execution(subquery).await
            }
            Err(e) => {
                // Network request failed - fall back to simulation for development
                warn!(
                    "Failed to reach node {}: {} - falling back to simulation",
                    subquery.target_node, e
                );
                Self::simulate_subquery_execution(subquery).await
            }
        }
    }

    /// Parse SPARQL JSON results into ResultBinding format
    fn parse_sparql_json_results(json: serde_json::Value) -> Result<Vec<ResultBinding>> {
        let bindings_array = json
            .get("results")
            .and_then(|r| r.get("bindings"))
            .and_then(|b| b.as_array())
            .ok_or_else(|| anyhow::anyhow!("Invalid SPARQL JSON results format"))?;

        let mut results = Vec::new();
        for binding_obj in bindings_array {
            if let Some(binding_map) = binding_obj.as_object() {
                let mut result_binding = ResultBinding::new();

                for (var_name, var_value) in binding_map {
                    if let Some(value_obj) = var_value.as_object() {
                        let value = value_obj
                            .get("value")
                            .and_then(|v| v.as_str())
                            .unwrap_or("")
                            .to_string();
                        result_binding.add_binding(format!("?{var_name}"), value);
                    }
                }
                results.push(result_binding);
            }
        }

        Ok(results)
    }

    /// Simulate subquery execution for development/fallback
    async fn simulate_subquery_execution(subquery: SubqueryPlan) -> Result<Vec<ResultBinding>> {
        // Simulate network latency
        tokio::time::sleep(tokio::time::Duration::from_millis(
            subquery.estimated_latency_ms,
        ))
        .await;

        // Generate mock results based on query pattern
        let mut results = Vec::new();
        let result_count = std::cmp::min(subquery.estimated_rows, 100);

        for i in 0..result_count {
            let mut binding = ResultBinding::new();
            for var in &subquery.variables {
                // Generate more realistic values based on variable names
                let value = match var.as_str() {
                    "?s" | "?subject" => format!("http://example.org/resource_{i}"),
                    "?p" | "?predicate" => format!("http://example.org/property_{}", i % 10),
                    "?o" | "?object" => format!("\"Object value {i}\""),
                    "?name" => format!("\"Name {i}\""),
                    "?type" => "http://example.org/Type".to_string(),
                    _ => format!("value_{}_{}", subquery.target_node, i),
                };
                binding.add_binding(var.clone(), value);
            }
            results.push(binding);
        }

        Ok(results)
    }

    /// Combine subquery results using joins and aggregation
    async fn combine_results(
        &self,
        plan: &DistributedQueryPlan,
        subquery_results: HashMap<String, Vec<ResultBinding>>,
    ) -> Result<Vec<ResultBinding>> {
        let mut current_results = Vec::new();

        // Start with first subquery results
        if let Some(first_subquery) = plan.subqueries.first() {
            if let Some(first_results) = subquery_results.get(&first_subquery.subquery_id) {
                current_results = first_results.clone();
            }
        }

        // Apply joins sequentially
        for join in &plan.join_operations {
            if let Some(right_results) = subquery_results.get(&join.right_subquery) {
                current_results = self
                    .execute_join(&current_results, right_results, join)
                    .await?;
            }
        }

        // Apply aggregation if specified
        if let Some(agg_plan) = &plan.aggregation_plan {
            current_results = self.apply_aggregation(current_results, agg_plan).await?;
        }

        Ok(current_results)
    }

    /// Execute a join operation between two result sets
    async fn execute_join(
        &self,
        left_results: &[ResultBinding],
        right_results: &[ResultBinding],
        join: &JoinOperation,
    ) -> Result<Vec<ResultBinding>> {
        let mut joined_results = Vec::new();

        match join.join_type {
            JoinType::Inner => {
                for left_binding in left_results {
                    for right_binding in right_results {
                        if self.bindings_compatible(
                            left_binding,
                            right_binding,
                            &join.join_variables,
                        ) {
                            if let Some(merged) = left_binding.merge(right_binding) {
                                joined_results.push(merged);
                            }
                        }
                    }
                }
            }
            JoinType::Left => {
                for left_binding in left_results {
                    let mut found_match = false;
                    for right_binding in right_results {
                        if self.bindings_compatible(
                            left_binding,
                            right_binding,
                            &join.join_variables,
                        ) {
                            if let Some(merged) = left_binding.merge(right_binding) {
                                joined_results.push(merged);
                                found_match = true;
                            }
                        }
                    }
                    if !found_match {
                        joined_results.push(left_binding.clone());
                    }
                }
            }
            JoinType::Optional => {
                // Similar to left join for this implementation
                joined_results = Box::pin(self.execute_join(
                    left_results,
                    right_results,
                    &JoinOperation {
                        left_subquery: join.left_subquery.clone(),
                        right_subquery: join.right_subquery.clone(),
                        join_variables: join.join_variables.clone(),
                        join_type: JoinType::Left,
                    },
                ))
                .await?;
            }
            JoinType::Union => {
                joined_results.extend_from_slice(left_results);
                joined_results.extend_from_slice(right_results);
                // Remove duplicates
                joined_results.sort_by(|a, b| format!("{a:?}").cmp(&format!("{b:?}")));
                joined_results.dedup();
            }
        }

        Ok(joined_results)
    }

    /// Check if two bindings are compatible for joining
    fn bindings_compatible(
        &self,
        left: &ResultBinding,
        right: &ResultBinding,
        join_variables: &[String],
    ) -> bool {
        for var in join_variables {
            if let (Some(left_val), Some(right_val)) = (left.get(var), right.get(var)) {
                if left_val != right_val {
                    return false;
                }
            }
        }
        true
    }

    /// Apply aggregation operations
    async fn apply_aggregation(
        &self,
        mut results: Vec<ResultBinding>,
        agg_plan: &AggregationPlan,
    ) -> Result<Vec<ResultBinding>> {
        // Apply ordering
        if !agg_plan.order_by.is_empty() {
            results.sort_by(|a, b| {
                for order_clause in &agg_plan.order_by {
                    let empty_string = String::new();
                    let a_val = a.get(&order_clause.variable).unwrap_or(&empty_string);
                    let b_val = b.get(&order_clause.variable).unwrap_or(&empty_string);
                    let cmp = if order_clause.ascending {
                        a_val.cmp(b_val)
                    } else {
                        b_val.cmp(a_val)
                    };
                    if cmp != std::cmp::Ordering::Equal {
                        return cmp;
                    }
                }
                std::cmp::Ordering::Equal
            });
        }

        // Apply limit and offset
        if let Some(offset) = agg_plan.offset {
            if offset < results.len() as u64 {
                results = results.into_iter().skip(offset as usize).collect();
            } else {
                results.clear();
            }
        }

        if let Some(limit) = agg_plan.limit {
            results.truncate(limit as usize);
        }

        Ok(results)
    }

    /// Check query cache
    async fn check_cache(&self, sparql: &str) -> Option<Vec<ResultBinding>> {
        let cache = self.query_cache.read().await;
        cache.get(sparql).cloned()
    }

    /// Cache query results
    async fn cache_results(&self, sparql: &str, results: &[ResultBinding]) {
        let mut cache = self.query_cache.write().await;
        cache.insert(sparql.to_string(), results.to_vec());

        // Limit cache size
        if cache.len() > 1000 {
            let keys_to_remove: Vec<_> = cache.keys().take(100).cloned().collect();
            for key in keys_to_remove {
                cache.remove(&key);
            }
        }
    }

    /// Record query execution statistics
    async fn record_statistics(
        &self,
        query_id: &str,
        plan: &DistributedQueryPlan,
        results: &[ResultBinding],
        execution_time_ms: u64,
    ) {
        let stats = QueryStats {
            query_id: query_id.to_string(),
            execution_time_ms,
            nodes_involved: plan.subqueries.len() as u32,
            total_intermediate_results: plan.subqueries.iter().map(|s| s.estimated_rows).sum(),
            final_result_count: results.len() as u64,
            network_transfer_bytes: 0, // Would calculate in real implementation
            cache_hits: 0,
            cache_misses: 1,
        };

        let mut statistics = self.statistics.write().await;
        statistics.insert(query_id.to_string(), stats);

        // Limit statistics storage
        if statistics.len() > 10000 {
            let keys_to_remove: Vec<_> = statistics.keys().take(1000).cloned().collect();
            for key in keys_to_remove {
                statistics.remove(&key);
            }
        }
    }

    /// Get query statistics
    pub async fn get_statistics(&self) -> HashMap<String, QueryStats> {
        self.statistics.read().await.clone()
    }

    /// Clear query cache
    pub async fn clear_cache(&self) {
        let mut cache = self.query_cache.write().await;
        cache.clear();
        info!("Query cache cleared");
    }
}

/// Parsed SPARQL query structure
#[derive(Debug, Clone)]
struct ParsedQuery {
    variables: Vec<String>,
    triple_patterns: Vec<TriplePattern>,
    #[allow(dead_code)]
    filters: Vec<String>,
    limit: Option<u64>,
    offset: Option<u64>,
    order_by: Vec<OrderByClause>,
}

#[derive(Debug, Clone)]
struct TriplePattern {
    subject: String,
    predicate: String,
    object: String,
}

/// Data distribution information
#[derive(Debug, Clone)]
struct DataDistribution {
    node_triple_counts: HashMap<OxirsNodeId, u64>,
    #[allow(dead_code)]
    predicate_distribution: HashMap<String, Vec<OxirsNodeId>>,
    #[allow(dead_code)]
    subject_distribution: HashMap<String, Vec<OxirsNodeId>>,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[tokio::test]
    async fn test_distributed_query_executor_creation() {
        let executor = DistributedQueryExecutor::new(1);
        executor.add_node(2).await;
        executor.add_node(3).await;

        let nodes = executor.cluster_nodes.read().await;
        assert_eq!(nodes.len(), 2);
        assert!(nodes.contains(&2));
        assert!(nodes.contains(&3));
    }

    #[tokio::test]
    async fn test_result_binding() {
        let mut binding1 = ResultBinding::new();
        binding1.add_binding("?x".to_string(), "value1".to_string());

        let mut binding2 = ResultBinding::new();
        binding2.add_binding("?y".to_string(), "value2".to_string());

        let merged = binding1.merge(&binding2).unwrap();
        assert_eq!(merged.get("?x"), Some(&"value1".to_string()));
        assert_eq!(merged.get("?y"), Some(&"value2".to_string()));
    }

    #[tokio::test]
    async fn test_result_binding_conflict() {
        let mut binding1 = ResultBinding::new();
        binding1.add_binding("?x".to_string(), "value1".to_string());

        let mut binding2 = ResultBinding::new();
        binding2.add_binding("?x".to_string(), "value2".to_string());

        let merged = binding1.merge(&binding2);
        assert!(merged.is_none()); // Should conflict
    }

    #[test]
    fn test_sparql_parsing() {
        let executor = DistributedQueryExecutor::new(1);
        let sparql = "SELECT ?x ?y WHERE { ?x <predicate> ?y }";
        let parsed = executor.parse_sparql(sparql).unwrap();

        assert_eq!(parsed.variables, vec!["?x", "?y"]);
        assert_eq!(parsed.triple_patterns.len(), 1);
    }

    #[tokio::test]
    async fn test_query_execution() {
        let executor = DistributedQueryExecutor::new(1);
        executor.add_node(2).await;
        executor.add_node(3).await;

        let sparql = "SELECT ?x WHERE { ?x <type> <Person> }";
        let results = executor.execute_query(sparql).await.unwrap();

        // Should have some results from the mock execution
        assert!(!results.is_empty());
    }

    #[tokio::test]
    async fn test_query_caching() {
        let executor = DistributedQueryExecutor::new(1);
        executor.add_node(2).await;

        let sparql = "SELECT ?x WHERE { ?x <type> <Person> }";

        // First execution - cache miss
        let results1 = executor.execute_query(sparql).await.unwrap();

        // Second execution - should hit cache
        let results2 = executor.execute_query(sparql).await.unwrap();

        assert_eq!(results1.len(), results2.len());
    }
}