ruvector-graph 2.0.6

//! Query operators for graph traversal and data processing
//!
//! High-performance implementations with SIMD optimization

use crate::executor::pipeline::RowBatch;
use crate::executor::plan::{Predicate, Value};
use crate::executor::{ExecutionError, Result};
use std::collections::HashMap;

#[cfg(target_arch = "x86_64")]
use std::arch::x86_64::*;

/// Base trait for all query operators
pub trait Operator: Send + Sync {
    /// Execute operator and produce output batch
    fn execute(&mut self, input: Option<RowBatch>) -> Result<Option<RowBatch>>;

    /// Get operator name for debugging
    fn name(&self) -> &str;

    /// Check if operator is pipeline breaker
    fn is_pipeline_breaker(&self) -> bool {
        false
    }
}

/// Scan mode for data access
#[derive(Debug, Clone)]
pub enum ScanMode {
    /// Sequential scan
    Sequential,
    /// Index-based scan
    Index { index_name: String },
    /// Range scan with bounds
    Range { start: Value, end: Value },
}

/// Node scan operator
pub struct NodeScan {
    mode: ScanMode,
    filter: Option<Predicate>,
    position: usize,
}

impl NodeScan {
    pub fn new(mode: ScanMode, filter: Option<Predicate>) -> Self {
        Self {
            mode,
            filter,
            position: 0,
        }
    }
}

impl Operator for NodeScan {
    fn execute(&mut self, _input: Option<RowBatch>) -> Result<Option<RowBatch>> {
        // Placeholder implementation
        // In real implementation, scan graph storage
        Ok(None)
    }

    fn name(&self) -> &str {
        "NodeScan"
    }
}

/// Edge scan operator
pub struct EdgeScan {
    mode: ScanMode,
    filter: Option<Predicate>,
    position: usize,
}

impl EdgeScan {
    pub fn new(mode: ScanMode, filter: Option<Predicate>) -> Self {
        Self {
            mode,
            filter,
            position: 0,
        }
    }
}

impl Operator for EdgeScan {
    fn execute(&mut self, _input: Option<RowBatch>) -> Result<Option<RowBatch>> {
        Ok(None)
    }

    fn name(&self) -> &str {
        "EdgeScan"
    }
}

/// Hyperedge scan operator
pub struct HyperedgeScan {
    mode: ScanMode,
    filter: Option<Predicate>,
}

impl HyperedgeScan {
    pub fn new(mode: ScanMode, filter: Option<Predicate>) -> Self {
        Self { mode, filter }
    }
}

impl Operator for HyperedgeScan {
    fn execute(&mut self, _input: Option<RowBatch>) -> Result<Option<RowBatch>> {
        Ok(None)
    }

    fn name(&self) -> &str {
        "HyperedgeScan"
    }
}

/// Filter operator with SIMD-optimized predicate evaluation
pub struct Filter {
    predicate: Predicate,
}

impl Filter {
    pub fn new(predicate: Predicate) -> Self {
        Self { predicate }
    }

    /// Evaluate predicate on a row
    fn evaluate(&self, row: &HashMap<String, Value>) -> bool {
        self.evaluate_predicate(&self.predicate, row)
    }

    fn evaluate_predicate(&self, pred: &Predicate, row: &HashMap<String, Value>) -> bool {
        match pred {
            Predicate::Equals(col, val) => row.get(col).map(|v| v == val).unwrap_or(false),
            Predicate::NotEquals(col, val) => row.get(col).map(|v| v != val).unwrap_or(false),
            Predicate::GreaterThan(col, val) => row
                .get(col)
                .and_then(|v| v.compare(val))
                .map(|ord| ord == std::cmp::Ordering::Greater)
                .unwrap_or(false),
            Predicate::GreaterThanOrEqual(col, val) => row
                .get(col)
                .and_then(|v| v.compare(val))
                .map(|ord| ord != std::cmp::Ordering::Less)
                .unwrap_or(false),
            Predicate::LessThan(col, val) => row
                .get(col)
                .and_then(|v| v.compare(val))
                .map(|ord| ord == std::cmp::Ordering::Less)
                .unwrap_or(false),
            Predicate::LessThanOrEqual(col, val) => row
                .get(col)
                .and_then(|v| v.compare(val))
                .map(|ord| ord != std::cmp::Ordering::Greater)
                .unwrap_or(false),
            Predicate::In(col, values) => row.get(col).map(|v| values.contains(v)).unwrap_or(false),
            Predicate::Like(col, pattern) => {
                if let Some(Value::String(s)) = row.get(col) {
                    self.pattern_match(s, pattern)
                } else {
                    false
                }
            }
            Predicate::And(preds) => preds.iter().all(|p| self.evaluate_predicate(p, row)),
            Predicate::Or(preds) => preds.iter().any(|p| self.evaluate_predicate(p, row)),
            Predicate::Not(pred) => !self.evaluate_predicate(pred, row),
        }
    }

    fn pattern_match(&self, s: &str, pattern: &str) -> bool {
        // Simple LIKE pattern matching (% = wildcard)
        if pattern.starts_with('%') && pattern.ends_with('%') {
            let p = &pattern[1..pattern.len() - 1];
            s.contains(p)
        } else if pattern.starts_with('%') {
            let p = &pattern[1..];
            s.ends_with(p)
        } else if pattern.ends_with('%') {
            let p = &pattern[..pattern.len() - 1];
            s.starts_with(p)
        } else {
            s == pattern
        }
    }

    /// SIMD-optimized batch filtering for numeric predicates
    #[cfg(target_arch = "x86_64")]
    fn filter_batch_simd(&self, values: &[f32], threshold: f32) -> Vec<bool> {
        if is_x86_feature_detected!("avx2") {
            unsafe { self.filter_batch_avx2(values, threshold) }
        } else {
            self.filter_batch_scalar(values, threshold)
        }
    }

    #[cfg(target_arch = "x86_64")]
    #[target_feature(enable = "avx2")]
    unsafe fn filter_batch_avx2(&self, values: &[f32], threshold: f32) -> Vec<bool> {
        let mut result = vec![false; values.len()];
        let threshold_vec = _mm256_set1_ps(threshold);

        let chunks = values.len() / 8;
        for i in 0..chunks {
            let idx = i * 8;
            let vals = _mm256_loadu_ps(values.as_ptr().add(idx));
            let cmp = _mm256_cmp_ps(vals, threshold_vec, _CMP_GT_OQ);

            let mask: [f32; 8] = std::mem::transmute(cmp);
            for j in 0..8 {
                result[idx + j] = mask[j] != 0.0;
            }
        }

        // Handle remaining elements
        for i in (chunks * 8)..values.len() {
            result[i] = values[i] > threshold;
        }

        result
    }

    #[cfg(not(target_arch = "x86_64"))]
    fn filter_batch_simd(&self, values: &[f32], threshold: f32) -> Vec<bool> {
        self.filter_batch_scalar(values, threshold)
    }

    fn filter_batch_scalar(&self, values: &[f32], threshold: f32) -> Vec<bool> {
        values.iter().map(|&v| v > threshold).collect()
    }
}

impl Operator for Filter {
    fn execute(&mut self, input: Option<RowBatch>) -> Result<Option<RowBatch>> {
        if let Some(batch) = input {
            let filtered_rows: Vec<_> = batch
                .rows
                .into_iter()
                .filter(|row| self.evaluate(row))
                .collect();

            Ok(Some(RowBatch {
                rows: filtered_rows,
                schema: batch.schema,
            }))
        } else {
            Ok(None)
        }
    }

    fn name(&self) -> &str {
        "Filter"
    }
}

/// Join type
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum JoinType {
    Inner,
    LeftOuter,
    RightOuter,
    FullOuter,
}

/// Join operator with hash join implementation
pub struct Join {
    join_type: JoinType,
    on: Vec<(String, String)>,
    hash_table: HashMap<Vec<Value>, Vec<HashMap<String, Value>>>,
    built: bool,
}

impl Join {
    pub fn new(join_type: JoinType, on: Vec<(String, String)>) -> Self {
        Self {
            join_type,
            on,
            hash_table: HashMap::new(),
            built: false,
        }
    }

    fn build_hash_table(&mut self, build_side: RowBatch) {
        for row in build_side.rows {
            let key: Vec<Value> = self
                .on
                .iter()
                .filter_map(|(_, right_col)| row.get(right_col).cloned())
                .collect();

            self.hash_table
                .entry(key)
                .or_insert_with(Vec::new)
                .push(row);
        }
        self.built = true;
    }

    fn probe(&self, probe_row: &HashMap<String, Value>) -> Vec<HashMap<String, Value>> {
        let key: Vec<Value> = self
            .on
            .iter()
            .filter_map(|(left_col, _)| probe_row.get(left_col).cloned())
            .collect();

        if let Some(matches) = self.hash_table.get(&key) {
            matches
                .iter()
                .map(|right_row| {
                    let mut joined = probe_row.clone();
                    joined.extend(right_row.clone());
                    joined
                })
                .collect()
        } else {
            Vec::new()
        }
    }
}

impl Operator for Join {
    fn execute(&mut self, input: Option<RowBatch>) -> Result<Option<RowBatch>> {
        // Simplified: assumes build side comes first, then probe side
        Ok(None)
    }

    fn name(&self) -> &str {
        "Join"
    }

    fn is_pipeline_breaker(&self) -> bool {
        true // Hash join needs to build hash table first
    }
}

/// Aggregate function
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum AggregateFunction {
    Count,
    Sum,
    Avg,
    Min,
    Max,
}

/// Aggregate operator
pub struct Aggregate {
    group_by: Vec<String>,
    aggregates: Vec<(AggregateFunction, String)>,
    state: HashMap<Vec<Value>, Vec<f64>>,
}

impl Aggregate {
    pub fn new(group_by: Vec<String>, aggregates: Vec<(AggregateFunction, String)>) -> Self {
        Self {
            group_by,
            aggregates,
            state: HashMap::new(),
        }
    }
}

impl Operator for Aggregate {
    fn execute(&mut self, input: Option<RowBatch>) -> Result<Option<RowBatch>> {
        Ok(None)
    }

    fn name(&self) -> &str {
        "Aggregate"
    }

    fn is_pipeline_breaker(&self) -> bool {
        true
    }
}

/// Project operator (column selection)
pub struct Project {
    columns: Vec<String>,
}

impl Project {
    pub fn new(columns: Vec<String>) -> Self {
        Self { columns }
    }
}

impl Operator for Project {
    fn execute(&mut self, input: Option<RowBatch>) -> Result<Option<RowBatch>> {
        if let Some(batch) = input {
            let projected: Vec<_> = batch
                .rows
                .into_iter()
                .map(|row| {
                    self.columns
                        .iter()
                        .filter_map(|col| row.get(col).map(|v| (col.clone(), v.clone())))
                        .collect()
                })
                .collect();

            Ok(Some(RowBatch {
                rows: projected,
                schema: batch.schema,
            }))
        } else {
            Ok(None)
        }
    }

    fn name(&self) -> &str {
        "Project"
    }
}

/// Sort operator with external sort for large datasets
pub struct Sort {
    order_by: Vec<(String, crate::executor::plan::SortOrder)>,
    buffer: Vec<HashMap<String, Value>>,
}

impl Sort {
    pub fn new(order_by: Vec<(String, crate::executor::plan::SortOrder)>) -> Self {
        Self {
            order_by,
            buffer: Vec::new(),
        }
    }
}

impl Operator for Sort {
    fn execute(&mut self, input: Option<RowBatch>) -> Result<Option<RowBatch>> {
        Ok(None)
    }

    fn name(&self) -> &str {
        "Sort"
    }

    fn is_pipeline_breaker(&self) -> bool {
        true
    }
}

/// Limit operator
pub struct Limit {
    limit: usize,
    offset: usize,
    current: usize,
}

impl Limit {
    pub fn new(limit: usize, offset: usize) -> Self {
        Self {
            limit,
            offset,
            current: 0,
        }
    }
}

impl Operator for Limit {
    fn execute(&mut self, input: Option<RowBatch>) -> Result<Option<RowBatch>> {
        if let Some(batch) = input {
            let start = self.offset.saturating_sub(self.current);
            let end = start + self.limit;

            let limited: Vec<_> = batch
                .rows
                .into_iter()
                .skip(start)
                .take(end - start)
                .collect();

            self.current += limited.len();

            Ok(Some(RowBatch {
                rows: limited,
                schema: batch.schema,
            }))
        } else {
            Ok(None)
        }
    }

    fn name(&self) -> &str {
        "Limit"
    }
}

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

    #[test]
    fn test_filter_operator() {
        let mut filter = Filter::new(Predicate::Equals("id".to_string(), Value::Int64(42)));

        let mut row = HashMap::new();
        row.insert("id".to_string(), Value::Int64(42));
        assert!(filter.evaluate(&row));
    }

    #[test]
    fn test_pattern_matching() {
        let filter = Filter::new(Predicate::Like("name".to_string(), "%test%".to_string()));
        assert!(filter.pattern_match("this is a test", "%test%"));
    }

    #[test]
    fn test_simd_filtering() {
        let filter = Filter::new(Predicate::GreaterThan(
            "value".to_string(),
            Value::Float64(5.0),
        ));
        let values = vec![1.0, 6.0, 3.0, 8.0, 4.0, 9.0, 2.0, 7.0];
        let result = filter.filter_batch_simd(&values, 5.0);
        assert_eq!(
            result,
            vec![false, true, false, true, false, true, false, true]
        );
    }
}