oxirs-arq 0.2.4

//! Distributed Query Processing Module
//!
//! Provides distributed SPARQL query execution across multiple nodes with
//! intelligent query decomposition, workload distribution, and result aggregation.

use crate::algebra::{Algebra, Term, TriplePattern, Variable};
use crate::optimizer::{IndexType, Statistics};
use anyhow::{anyhow, Result};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::{mpsc, RwLock};
use uuid::Uuid;

/// Distributed query execution configuration
#[derive(Debug, Clone)]
pub struct DistributedConfig {
    /// Maximum number of parallel subqueries
    pub max_parallel_queries: usize,
    /// Timeout for individual subquery execution
    pub subquery_timeout: Duration,
    /// Result transfer batch size
    pub result_batch_size: usize,
    /// Enable result caching across nodes
    pub enable_result_caching: bool,
    /// Load balancing strategy
    pub load_balancing: LoadBalancingStrategy,
    /// Fault tolerance configuration
    pub fault_tolerance: FaultToleranceConfig,
}

impl Default for DistributedConfig {
    fn default() -> Self {
        Self {
            max_parallel_queries: 16,
            subquery_timeout: Duration::from_secs(300), // 5 minutes
            result_batch_size: 10000,
            enable_result_caching: true,
            load_balancing: LoadBalancingStrategy::RoundRobin,
            fault_tolerance: FaultToleranceConfig::default(),
        }
    }
}

/// Load balancing strategies for distributed execution
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum LoadBalancingStrategy {
    /// Simple round-robin distribution
    RoundRobin,
    /// Distribution based on node capacity and current load
    LoadAware,
    /// Distribution based on data locality
    DataAware,
    /// Adaptive strategy that learns from execution patterns
    Adaptive,
}

/// Fault tolerance configuration
#[derive(Debug, Clone)]
pub struct FaultToleranceConfig {
    /// Maximum number of retry attempts
    pub max_retries: usize,
    /// Retry delay with exponential backoff
    pub retry_delay: Duration,
    /// Enable automatic failover to backup nodes
    pub enable_failover: bool,
    /// Minimum number of successful nodes required
    pub min_success_threshold: f64,
}

impl Default for FaultToleranceConfig {
    fn default() -> Self {
        Self {
            max_retries: 3,
            retry_delay: Duration::from_millis(500),
            enable_failover: true,
            min_success_threshold: 0.7, // 70% of nodes must succeed
        }
    }
}

/// Distributed query execution plan
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistributedPlan {
    /// Unique plan identifier
    pub plan_id: Uuid,
    /// Subqueries to execute on different nodes
    pub subqueries: Vec<SubqueryPlan>,
    /// Result aggregation strategy
    pub aggregation_strategy: AggregationStrategy,
    /// Estimated execution time
    pub estimated_time: Duration,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
}

/// Individual subquery plan for distributed execution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SubqueryPlan {
    /// Subquery identifier
    pub subquery_id: Uuid,
    /// Target node(s) for execution
    pub target_nodes: Vec<NodeId>,
    /// SPARQL algebra to execute
    pub algebra: Algebra,
    /// Expected result cardinality
    pub expected_cardinality: usize,
    /// Priority level (higher = more important)
    pub priority: u8,
    /// Dependencies on other subqueries
    pub dependencies: Vec<Uuid>,
}

/// Result aggregation strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AggregationStrategy {
    /// Simple union of all results
    Union,
    /// Join results based on shared variables
    Join(Vec<Variable>),
    /// Apply aggregation functions (COUNT, SUM, etc.)
    Aggregate(AggregationFunction),
    /// Custom aggregation with user-defined logic
    Custom(String),
}

/// Aggregation function types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AggregationFunction {
    Count,
    Sum(Variable),
    Avg(Variable),
    Min(Variable),
    Max(Variable),
    GroupConcat(Variable, Option<String>),
}

/// Node identifier in the distributed system
pub type NodeId = String;

/// Resource requirements for query execution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequirements {
    /// Estimated CPU usage (cores)
    pub cpu_cores: f64,
    /// Estimated memory usage (bytes)
    pub memory_bytes: usize,
    /// Estimated network bandwidth (bytes/sec)
    pub network_bandwidth: usize,
    /// Expected I/O operations
    pub io_operations: usize,
}

/// Execution result from a distributed subquery
#[derive(Debug, Clone)]
pub struct SubqueryResult {
    /// Subquery identifier
    pub subquery_id: Uuid,
    /// Node that executed the subquery
    pub executing_node: NodeId,
    /// Execution status
    pub status: ExecutionStatus,
    /// Result bindings
    pub bindings: Vec<HashMap<Variable, Term>>,
    /// Execution metrics
    pub metrics: ExecutionMetrics,
}

/// Execution status for subqueries
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ExecutionStatus {
    Success,
    Failed(String),
    Timeout,
    Cancelled,
}

/// Execution metrics for performance monitoring
#[derive(Debug, Clone)]
pub struct ExecutionMetrics {
    /// Total execution time
    pub execution_time: Duration,
    /// Number of results returned
    pub result_count: usize,
    /// Memory peak usage
    pub memory_peak: usize,
    /// CPU time consumed
    pub cpu_time: Duration,
    /// Network bytes transferred
    pub network_bytes: usize,
}

/// Node information and capabilities
#[derive(Debug, Clone)]
pub struct NodeInfo {
    /// Node identifier
    pub node_id: NodeId,
    /// Node endpoint URL
    pub endpoint: String,
    /// Available CPU cores
    pub cpu_cores: u32,
    /// Available memory (bytes)
    pub memory_bytes: usize,
    /// Current load factor (0.0 to 1.0)
    pub load_factor: f64,
    /// Supported features and capabilities
    pub capabilities: NodeCapabilities,
    /// Last heartbeat timestamp
    pub last_heartbeat: Instant,
}

/// Node capabilities and supported features
#[derive(Debug, Clone)]
pub struct NodeCapabilities {
    /// Supported SPARQL features
    pub sparql_features: HashSet<String>,
    /// Available indexes
    pub available_indexes: HashSet<IndexType>,
    /// Maximum query complexity score
    pub max_query_complexity: f64,
    /// Specialized data types supported
    pub data_specializations: Vec<DataSpecialization>,
}

/// Data specialization types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DataSpecialization {
    Temporal,
    Spatial,
    FullText,
    Numerical,
    Graph,
}

/// Main distributed query processor
pub struct DistributedQueryProcessor {
    config: DistributedConfig,
    nodes: Arc<RwLock<HashMap<NodeId, NodeInfo>>>,
    statistics: Arc<RwLock<Statistics>>,
    active_queries: Arc<RwLock<HashMap<Uuid, DistributedExecution>>>,
    #[allow(dead_code)]
    load_balancer: LoadBalancer,
}

/// Active distributed query execution state
#[derive(Debug)]
#[allow(dead_code)]
struct DistributedExecution {
    plan: DistributedPlan,
    start_time: Instant,
    completed_subqueries: HashSet<Uuid>,
    results: HashMap<Uuid, SubqueryResult>,
    execution_context: ExecutionContext,
}

/// Execution context for distributed queries
#[derive(Debug)]
#[allow(dead_code)]
struct ExecutionContext {
    query_id: Uuid,
    user_context: HashMap<String, String>,
    timeout: Instant,
    cancellation_token: mpsc::UnboundedSender<()>,
}

/// Load balancer for distributing subqueries across nodes
#[allow(dead_code)]
struct LoadBalancer {
    strategy: LoadBalancingStrategy,
    node_loads: HashMap<NodeId, f64>,
    historical_performance: HashMap<NodeId, Vec<Duration>>,
}

impl DistributedQueryProcessor {
    /// Create a new distributed query processor
    pub fn new(config: DistributedConfig) -> Self {
        Self {
            load_balancer: LoadBalancer::new(config.load_balancing.clone()),
            config,
            nodes: Arc::new(RwLock::new(HashMap::new())),
            statistics: Arc::new(RwLock::new(Statistics::new())),
            active_queries: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    /// Register a new node in the distributed system
    pub async fn register_node(&self, node_info: NodeInfo) -> Result<()> {
        let mut nodes = self.nodes.write().await;
        nodes.insert(node_info.node_id.clone(), node_info);
        Ok(())
    }

    /// Execute a query in distributed mode
    pub async fn execute_distributed(
        &self,
        algebra: Algebra,
        user_context: HashMap<String, String>,
    ) -> Result<Vec<HashMap<Variable, Term>>> {
        let query_id = Uuid::new_v4();

        // Step 1: Create distributed execution plan
        let plan = self.create_distributed_plan(&algebra).await?;

        // Step 2: Execute subqueries in parallel
        let results = self.execute_plan(query_id, plan, user_context).await?;

        Ok(results)
    }

    /// Create a distributed execution plan from algebra
    async fn create_distributed_plan(&self, algebra: &Algebra) -> Result<DistributedPlan> {
        let plan_id = Uuid::new_v4();

        // Analyze query for distribution opportunities
        let analysis = self.analyze_for_distribution(algebra).await?;

        // Decompose query into subqueries
        let subqueries = self.decompose_query(algebra, &analysis).await?;

        // Determine aggregation strategy
        let aggregation_strategy = self.determine_aggregation_strategy(algebra, &subqueries);

        // Estimate resource requirements
        let resource_requirements = self.estimate_resource_requirements(&subqueries).await?;

        // Estimate execution time
        let estimated_time = self.estimate_execution_time(&subqueries).await?;

        Ok(DistributedPlan {
            plan_id,
            subqueries,
            aggregation_strategy,
            estimated_time,
            resource_requirements,
        })
    }

    /// Analyze query for distribution opportunities
    async fn analyze_for_distribution(&self, algebra: &Algebra) -> Result<DistributionAnalysis> {
        match algebra {
            Algebra::Join { left, right } => {
                let left_analysis = Box::pin(self.analyze_for_distribution(left)).await?;
                let right_analysis = Box::pin(self.analyze_for_distribution(right)).await?;

                Ok(DistributionAnalysis {
                    is_distributable: true,
                    join_variables: self.find_join_variables(left, right),
                    subquery_candidates: vec![left.as_ref().clone(), right.as_ref().clone()],
                    estimated_cardinality: left_analysis.estimated_cardinality
                        + right_analysis.estimated_cardinality,
                    complexity_score: left_analysis.complexity_score
                        + right_analysis.complexity_score
                        + 1.0,
                })
            }
            Algebra::Union { left, right } => {
                let left_analysis = Box::pin(self.analyze_for_distribution(left)).await?;
                let right_analysis = Box::pin(self.analyze_for_distribution(right)).await?;

                Ok(DistributionAnalysis {
                    is_distributable: true,
                    join_variables: Vec::new(),
                    subquery_candidates: vec![left.as_ref().clone(), right.as_ref().clone()],
                    estimated_cardinality: left_analysis.estimated_cardinality
                        + right_analysis.estimated_cardinality,
                    complexity_score: left_analysis.complexity_score
                        + right_analysis.complexity_score,
                })
            }
            Algebra::Bgp(patterns) if patterns.len() > 1 => {
                Ok(DistributionAnalysis {
                    is_distributable: true,
                    join_variables: self.extract_bgp_variables(patterns),
                    subquery_candidates: patterns
                        .iter()
                        .map(|p| Algebra::Bgp(vec![p.clone()]))
                        .collect(),
                    estimated_cardinality: patterns.len() * 1000, // Rough estimate
                    complexity_score: patterns.len() as f64,
                })
            }
            _ => Ok(DistributionAnalysis {
                is_distributable: false,
                join_variables: Vec::new(),
                subquery_candidates: vec![algebra.clone()],
                estimated_cardinality: 1000,
                complexity_score: 1.0,
            }),
        }
    }

    /// Decompose query into distributable subqueries
    async fn decompose_query(
        &self,
        algebra: &Algebra,
        analysis: &DistributionAnalysis,
    ) -> Result<Vec<SubqueryPlan>> {
        if !analysis.is_distributable {
            // Single subquery for non-distributable queries
            return Ok(vec![SubqueryPlan {
                subquery_id: Uuid::new_v4(),
                target_nodes: self.select_nodes_for_query(algebra, 1).await?,
                algebra: algebra.clone(),
                expected_cardinality: analysis.estimated_cardinality,
                priority: 100, // High priority for single queries
                dependencies: Vec::new(),
            }]);
        }

        let mut subqueries = Vec::new();

        for (i, candidate) in analysis.subquery_candidates.iter().enumerate() {
            let target_nodes = self.select_nodes_for_query(candidate, 1).await?;

            subqueries.push(SubqueryPlan {
                subquery_id: Uuid::new_v4(),
                target_nodes,
                algebra: candidate.clone(),
                expected_cardinality: analysis.estimated_cardinality
                    / analysis.subquery_candidates.len(),
                priority: (100 - i * 10) as u8, // Decreasing priority
                dependencies: Vec::new(),
            });
        }

        Ok(subqueries)
    }

    /// Select optimal nodes for executing a query
    async fn select_nodes_for_query(
        &self,
        _algebra: &Algebra,
        count: usize,
    ) -> Result<Vec<NodeId>> {
        let nodes = self.nodes.read().await;

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

        // For now, use simple round-robin selection
        let available_nodes: Vec<_> = nodes.keys().cloned().collect();
        let selected = available_nodes.into_iter().take(count).collect();

        Ok(selected)
    }

    /// Execute the distributed plan
    async fn execute_plan(
        &self,
        query_id: Uuid,
        plan: DistributedPlan,
        user_context: HashMap<String, String>,
    ) -> Result<Vec<HashMap<Variable, Term>>> {
        let (cancel_tx, mut cancel_rx) = mpsc::unbounded_channel();

        let execution = DistributedExecution {
            plan: plan.clone(),
            start_time: Instant::now(),
            completed_subqueries: HashSet::new(),
            results: HashMap::new(),
            execution_context: ExecutionContext {
                query_id,
                user_context,
                timeout: Instant::now() + self.config.subquery_timeout,
                cancellation_token: cancel_tx,
            },
        };

        // Store active execution
        {
            let mut active = self.active_queries.write().await;
            active.insert(query_id, execution);
        }

        // Execute subqueries in parallel
        let mut handles = Vec::new();

        for subquery in plan.subqueries {
            let processor = self.clone();
            let handle =
                tokio::spawn(async move { processor.execute_subquery(query_id, subquery).await });
            handles.push(handle);
        }

        // Wait for all subqueries to complete or timeout
        let mut subquery_results = Vec::new();

        for handle in handles {
            tokio::select! {
                result = handle => {
                    match result? {
                        Ok(subresult) => subquery_results.push(subresult),
                        Err(e) => return Err(e),
                    }
                }
                _ = cancel_rx.recv() => {
                    return Err(anyhow!("Query execution cancelled"));
                }
            }
        }

        // Aggregate results
        let final_results = self
            .aggregate_results(subquery_results, &plan.aggregation_strategy)
            .await?;

        // Clean up active execution
        {
            let mut active = self.active_queries.write().await;
            active.remove(&query_id);
        }

        Ok(final_results)
    }

    /// Execute a single subquery
    async fn execute_subquery(
        &self,
        _query_id: Uuid,
        subquery: SubqueryPlan,
    ) -> Result<SubqueryResult> {
        let start_time = Instant::now();

        // For now, simulate subquery execution
        // In a real implementation, this would send the subquery to the target node
        tokio::time::sleep(Duration::from_millis(100)).await;

        let execution_time = start_time.elapsed();

        Ok(SubqueryResult {
            subquery_id: subquery.subquery_id,
            executing_node: subquery
                .target_nodes
                .first()
                .unwrap_or(&"unknown".to_string())
                .clone(),
            status: ExecutionStatus::Success,
            bindings: Vec::new(), // Simulated empty results
            metrics: ExecutionMetrics {
                execution_time,
                result_count: 0,
                memory_peak: 1024 * 1024, // 1MB
                cpu_time: execution_time,
                network_bytes: 0,
            },
        })
    }

    /// Aggregate results from multiple subqueries
    async fn aggregate_results(
        &self,
        results: Vec<SubqueryResult>,
        strategy: &AggregationStrategy,
    ) -> Result<Vec<HashMap<Variable, Term>>> {
        match strategy {
            AggregationStrategy::Union => {
                let mut all_bindings = Vec::new();
                for result in results {
                    all_bindings.extend(result.bindings);
                }
                Ok(all_bindings)
            }
            AggregationStrategy::Join(join_vars) => {
                self.perform_distributed_join(results, join_vars).await
            }
            AggregationStrategy::Aggregate(func) => {
                self.perform_distributed_aggregation(results, func).await
            }
            AggregationStrategy::Custom(_) => {
                // For now, fall back to union
                Box::pin(self.aggregate_results(results, &AggregationStrategy::Union)).await
            }
        }
    }

    /// Perform distributed join on subquery results
    async fn perform_distributed_join(
        &self,
        results: Vec<SubqueryResult>,
        join_vars: &[Variable],
    ) -> Result<Vec<HashMap<Variable, Term>>> {
        if results.len() < 2 {
            return Ok(results.into_iter().flat_map(|r| r.bindings).collect());
        }

        let mut joined_results = results[0].bindings.clone();

        for result in results.into_iter().skip(1) {
            joined_results = self.join_binding_sets(joined_results, result.bindings, join_vars)?;
        }

        Ok(joined_results)
    }

    /// Join two sets of variable bindings
    fn join_binding_sets(
        &self,
        left: Vec<HashMap<Variable, Term>>,
        right: Vec<HashMap<Variable, Term>>,
        join_vars: &[Variable],
    ) -> Result<Vec<HashMap<Variable, Term>>> {
        let mut results = Vec::new();

        for left_binding in &left {
            for right_binding in &right {
                // Check if join variables match
                let mut compatible = true;
                for var in join_vars {
                    if let (Some(left_val), Some(right_val)) =
                        (left_binding.get(var), right_binding.get(var))
                    {
                        if left_val != right_val {
                            compatible = false;
                            break;
                        }
                    }
                }

                if compatible {
                    // Merge bindings
                    let mut merged = left_binding.clone();
                    for (var, term) in right_binding {
                        merged.insert(var.clone(), term.clone());
                    }
                    results.push(merged);
                }
            }
        }

        Ok(results)
    }

    /// Perform distributed aggregation
    async fn perform_distributed_aggregation(
        &self,
        results: Vec<SubqueryResult>,
        func: &AggregationFunction,
    ) -> Result<Vec<HashMap<Variable, Term>>> {
        match func {
            AggregationFunction::Count => {
                let _total_count: usize = results.iter().map(|r| r.bindings.len()).sum();
                // Return single binding with count
                Ok(vec![HashMap::new()]) // Simplified implementation
            }
            _ => {
                // For other aggregation functions, implement as needed
                Ok(Vec::new())
            }
        }
    }

    // Helper methods

    /// Find join variables between two algebra expressions
    fn find_join_variables(&self, left: &Algebra, right: &Algebra) -> Vec<Variable> {
        let left_vars: HashSet<_> = left.variables().into_iter().collect();
        let right_vars: HashSet<_> = right.variables().into_iter().collect();
        left_vars.intersection(&right_vars).cloned().collect()
    }

    /// Extract variables from BGP patterns
    fn extract_bgp_variables(&self, patterns: &[TriplePattern]) -> Vec<Variable> {
        let mut variables = HashSet::new();
        for pattern in patterns {
            variables.extend(pattern.variables());
        }
        variables.into_iter().collect()
    }

    /// Determine aggregation strategy based on query structure
    fn determine_aggregation_strategy(
        &self,
        algebra: &Algebra,
        _subqueries: &[SubqueryPlan],
    ) -> AggregationStrategy {
        match algebra {
            Algebra::Join { left, right } => {
                let join_vars = self.find_join_variables(left, right);
                if !join_vars.is_empty() {
                    AggregationStrategy::Join(join_vars)
                } else {
                    AggregationStrategy::Union
                }
            }
            Algebra::Union { .. } => AggregationStrategy::Union,
            _ => AggregationStrategy::Union,
        }
    }

    /// Estimate resource requirements for subqueries
    async fn estimate_resource_requirements(
        &self,
        subqueries: &[SubqueryPlan],
    ) -> Result<ResourceRequirements> {
        let total_cardinality: usize = subqueries.iter().map(|sq| sq.expected_cardinality).sum();

        Ok(ResourceRequirements {
            cpu_cores: subqueries.len() as f64 * 0.5, // 0.5 cores per subquery
            memory_bytes: total_cardinality * 100,    // 100 bytes per result
            network_bandwidth: total_cardinality * 50, // 50 bytes/sec per result
            io_operations: total_cardinality / 100,   // 1 I/O per 100 results
        })
    }

    /// Estimate execution time for subqueries
    async fn estimate_execution_time(&self, subqueries: &[SubqueryPlan]) -> Result<Duration> {
        // Simple estimation based on expected cardinality
        let max_cardinality = subqueries
            .iter()
            .map(|sq| sq.expected_cardinality)
            .max()
            .unwrap_or(1000);
        let base_time = Duration::from_millis(100); // Base execution time
        let cardinality_factor = (max_cardinality as f64).log10();

        Ok(base_time + Duration::from_millis((cardinality_factor * 50.0) as u64))
    }
}

impl Clone for DistributedQueryProcessor {
    fn clone(&self) -> Self {
        Self {
            config: self.config.clone(),
            nodes: Arc::clone(&self.nodes),
            statistics: Arc::clone(&self.statistics),
            active_queries: Arc::clone(&self.active_queries),
            load_balancer: LoadBalancer::new(self.config.load_balancing.clone()),
        }
    }
}

/// Analysis result for distribution opportunities
#[derive(Debug)]
struct DistributionAnalysis {
    is_distributable: bool,
    #[allow(dead_code)]
    join_variables: Vec<Variable>,
    subquery_candidates: Vec<Algebra>,
    estimated_cardinality: usize,
    complexity_score: f64,
}

impl LoadBalancer {
    fn new(strategy: LoadBalancingStrategy) -> Self {
        Self {
            strategy,
            node_loads: HashMap::new(),
            historical_performance: HashMap::new(),
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::algebra::Variable;
    use oxirs_core::model::NamedNode;

    #[tokio::test]
    async fn test_distributed_processor_creation() {
        let config = DistributedConfig::default();
        let processor = DistributedQueryProcessor::new(config);

        // Test node registration
        let node_info = NodeInfo {
            node_id: "node1".to_string(),
            endpoint: "http://localhost:8080".to_string(),
            cpu_cores: 4,
            memory_bytes: 8 * 1024 * 1024 * 1024, // 8GB
            load_factor: 0.5,
            capabilities: NodeCapabilities {
                sparql_features: HashSet::new(),
                available_indexes: HashSet::new(),
                max_query_complexity: 100.0,
                data_specializations: Vec::new(),
            },
            last_heartbeat: Instant::now(),
        };

        assert!(processor.register_node(node_info).await.is_ok());
    }

    #[tokio::test]
    async fn test_query_decomposition() {
        let processor = DistributedQueryProcessor::new(DistributedConfig::default());

        // Create a simple join query
        let left = Algebra::Bgp(vec![TriplePattern {
            subject: Term::Variable(Variable::new("s").unwrap()),
            predicate: Term::Iri(NamedNode::new_unchecked("http://example.org/name")),
            object: Term::Variable(Variable::new("name").unwrap()),
        }]);

        let right = Algebra::Bgp(vec![TriplePattern {
            subject: Term::Variable(Variable::new("s").unwrap()),
            predicate: Term::Iri(NamedNode::new_unchecked("http://example.org/age")),
            object: Term::Variable(Variable::new("age").unwrap()),
        }]);

        let join = Algebra::Join {
            left: Box::new(left),
            right: Box::new(right),
        };

        let analysis = processor.analyze_for_distribution(&join).await.unwrap();
        assert!(analysis.is_distributable);
        assert_eq!(analysis.subquery_candidates.len(), 2);
    }
}