sochdb_query/

soch_ql_executor.rs

// SPDX-License-Identifier: AGPL-3.0-or-later
// SochDB - LLM-Optimized Embedded Database
// Copyright (C) 2026 Sushanth Reddy Vanagala (https://github.com/sushanthpy)
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU Affero General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU Affero General Public License for more details.
//
// You should have received a copy of the GNU Affero General Public License
// along with this program. If not, see <https://www.gnu.org/licenses/>.

//! SOCH-QL Query Executor (Task 6)
//!
//! End-to-end SOCH-QL query execution pipeline:
//! 1. parse(sql) → SochQuery
//! 2. validate(query, catalog) → Result<()>
//! 3. plan(query, stats) → QueryPlan
//! 4. execute(plan, storage) → SochTable
//!
//! ## Token Reduction Model
//!
//! ```text
//! tokens_JSON(table) ≈ 4 + Σ(|field_name| + |value| + 4) per row
//! tokens_TOON(table) ≈ header + Σ(|value| + 1) per row
//! reduction = 1 - tokens_TOON/tokens_JSON ≈ 0.4 to 0.6
//!
//! For 100 rows × 5 fields:
//! JSON: ~100 × (5 × 15) = 7,500 tokens
//! TOON: ~50 + 100 × 25 = 2,550 tokens → 66% reduction
//! ```

use crate::soch_ql::{
    ComparisonOp, LogicalOp, SelectQuery, SortDirection, SochQlParser, SochQuery, SochResult,
    SochValue, WhereClause,
};
#[cfg(test)]
use crate::soch_ql::{Condition, OrderBy};
use std::collections::HashMap;
use sochdb_core::{Catalog, Result, SochDBError, SochRow, SochValue as CoreSochValue};
#[cfg(test)]
use sochdb_core::{SochSchema, SochType};

/// Query plan operators
#[derive(Debug, Clone)]
pub enum QueryPlan {
    /// Full table scan
    TableScan {
        table: String,
        columns: Vec<String>,
        predicate: Option<Box<QueryPlan>>,
    },
    /// Index seek (primary or secondary)
    IndexSeek { index: String, key_range: KeyRange },
    /// Filter rows
    Filter {
        input: Box<QueryPlan>,
        predicate: Predicate,
    },
    /// Project columns
    Project {
        input: Box<QueryPlan>,
        columns: Vec<String>,
    },
    /// Sort results
    Sort {
        input: Box<QueryPlan>,
        order_by: Vec<(String, bool)>, // (column, ascending)
    },
    /// Limit results
    Limit {
        input: Box<QueryPlan>,
        count: usize,
        offset: usize,
    },
    /// Empty result
    Empty,
}

/// Key range for index seeks
#[derive(Debug, Clone)]
pub struct KeyRange {
    pub start: Option<SochValue>,
    pub end: Option<SochValue>,
    pub inclusive_start: bool,
    pub inclusive_end: bool,
}

impl KeyRange {
    pub fn all() -> Self {
        Self {
            start: None,
            end: None,
            inclusive_start: true,
            inclusive_end: true,
        }
    }

    pub fn eq(value: SochValue) -> Self {
        Self {
            start: Some(value.clone()),
            end: Some(value),
            inclusive_start: true,
            inclusive_end: true,
        }
    }
}

/// Query predicate
#[derive(Debug, Clone)]
pub struct Predicate {
    pub conditions: Vec<PredicateCondition>,
    pub operator: LogicalOp,
}

/// Single predicate condition (uses CoreSochValue for row compatibility)
#[derive(Debug, Clone)]
pub struct PredicateCondition {
    pub column: String,
    pub operator: ComparisonOp,
    pub value: CoreSochValue,
}

impl PredicateCondition {
    /// Create from soch_ql SochValue
    pub fn from_soch_ql(column: String, operator: ComparisonOp, value: &SochValue) -> Self {
        Self {
            column,
            operator,
            value: Self::convert_value(value),
        }
    }

    /// Convert soch_ql::SochValue to CoreSochValue
    fn convert_value(v: &SochValue) -> CoreSochValue {
        match v {
            SochValue::Int(i) => CoreSochValue::Int(*i),
            SochValue::UInt(u) => CoreSochValue::UInt(*u),
            SochValue::Float(f) => CoreSochValue::Float(*f),
            SochValue::Text(s) => CoreSochValue::Text(s.clone()),
            SochValue::Bool(b) => CoreSochValue::Bool(*b),
            SochValue::Null => CoreSochValue::Null,
            SochValue::Binary(b) => CoreSochValue::Binary(b.clone()),
            SochValue::Array(arr) => {
                CoreSochValue::Array(arr.iter().map(Self::convert_value).collect())
            }
        }
    }

    /// Evaluate predicate against a row
    pub fn evaluate(&self, row: &SochRow, column_idx: usize) -> bool {
        if column_idx >= row.values.len() {
            return false;
        }

        let row_value = &row.values[column_idx];

        match self.operator {
            ComparisonOp::Eq => row_value == &self.value,
            ComparisonOp::Ne => row_value != &self.value,
            ComparisonOp::Lt => {
                Self::compare(row_value, &self.value) == Some(std::cmp::Ordering::Less)
            }
            ComparisonOp::Le => matches!(
                Self::compare(row_value, &self.value),
                Some(std::cmp::Ordering::Less | std::cmp::Ordering::Equal)
            ),
            ComparisonOp::Gt => {
                Self::compare(row_value, &self.value) == Some(std::cmp::Ordering::Greater)
            }
            ComparisonOp::Ge => matches!(
                Self::compare(row_value, &self.value),
                Some(std::cmp::Ordering::Greater | std::cmp::Ordering::Equal)
            ),
            ComparisonOp::Like => Self::like_match(row_value, &self.value),
            ComparisonOp::In => Self::in_match(row_value, &self.value),
            ComparisonOp::SimilarTo => {
                // SimilarTo is used for vector similarity search
                // Evaluated by the vector index, not row-by-row comparison
                // For row-level evaluation, we fall back to Like-style matching
                Self::like_match(row_value, &self.value)
            }
        }
    }

    fn compare(a: &CoreSochValue, b: &CoreSochValue) -> Option<std::cmp::Ordering> {
        match (a, b) {
            (CoreSochValue::Int(a), CoreSochValue::Int(b)) => Some(a.cmp(b)),
            (CoreSochValue::UInt(a), CoreSochValue::UInt(b)) => Some(a.cmp(b)),
            (CoreSochValue::Float(a), CoreSochValue::Float(b)) => a.partial_cmp(b),
            (CoreSochValue::Text(a), CoreSochValue::Text(b)) => Some(a.cmp(b)),
            _ => None,
        }
    }

    fn like_match(value: &CoreSochValue, pattern: &CoreSochValue) -> bool {
        match (value, pattern) {
            (CoreSochValue::Text(v), CoreSochValue::Text(p)) => {
                // Simple LIKE: % matches any, _ matches one char
                let regex_pattern = p.replace('%', ".*").replace('_', ".");
                regex::Regex::new(&format!("^{}$", regex_pattern))
                    .map(|re| re.is_match(v))
                    .unwrap_or(false)
            }
            _ => false,
        }
    }

    fn in_match(value: &CoreSochValue, list: &CoreSochValue) -> bool {
        match list {
            CoreSochValue::Array(values) => values.iter().any(|v| value == v),
            _ => value == list, // Single value comparison fallback
        }
    }
}

impl Predicate {
    /// Evaluate predicate against a row
    pub fn evaluate(&self, row: &SochRow, column_map: &HashMap<String, usize>) -> bool {
        let results: Vec<bool> = self
            .conditions
            .iter()
            .map(|cond| {
                column_map
                    .get(&cond.column)
                    .map(|&idx| cond.evaluate(row, idx))
                    .unwrap_or(false)
            })
            .collect();

        match self.operator {
            LogicalOp::And => results.iter().all(|&r| r),
            LogicalOp::Or => results.iter().any(|&r| r),
        }
    }
}

/// SOCH-QL Query Executor
pub struct SochQlExecutor;

impl SochQlExecutor {
    /// Create a new executor
    pub fn new() -> Self {
        Self
    }

    /// Execute a SOCH-QL query string
    pub fn execute(&self, query: &str, catalog: &Catalog) -> Result<SochResult> {
        // Parse
        let parsed = SochQlParser::parse(query)
            .map_err(|e| SochDBError::InvalidArgument(format!("Parse error: {:?}", e)))?;

        // Validate
        self.validate(&parsed, catalog)?;

        // Plan
        let plan = self.plan(&parsed, catalog)?;

        // Execute
        self.execute_plan(&plan, catalog)
    }

    /// Validate a parsed query against the catalog
    pub fn validate(&self, query: &SochQuery, catalog: &Catalog) -> Result<()> {
        match query {
            SochQuery::Select(select) => {
                // Check table exists
                if catalog.get_table(&select.table).is_none() {
                    return Err(SochDBError::NotFound(format!(
                        "Table '{}' not found",
                        select.table
                    )));
                }

                // Check columns exist (if not *)
                if let Some(entry) = catalog.get_table(&select.table)
                    && let Some(schema) = &entry.schema
                {
                    for col in &select.columns {
                        if col != "*" && !schema.fields.iter().any(|f| &f.name == col) {
                            return Err(SochDBError::InvalidArgument(format!(
                                "Column '{}' not found in table '{}'",
                                col, select.table
                            )));
                        }
                    }
                }

                Ok(())
            }
            SochQuery::Insert(insert) => {
                // Check table exists
                if catalog.get_table(&insert.table).is_none() {
                    return Err(SochDBError::NotFound(format!(
                        "Table '{}' not found",
                        insert.table
                    )));
                }
                Ok(())
            }
            SochQuery::CreateTable(create) => {
                // Check table doesn't exist
                if catalog.get_table(&create.table).is_some() {
                    return Err(SochDBError::InvalidArgument(format!(
                        "Table '{}' already exists",
                        create.table
                    )));
                }
                Ok(())
            }
            SochQuery::DropTable { table } => {
                if catalog.get_table(table).is_none() {
                    return Err(SochDBError::NotFound(format!(
                        "Table '{}' not found",
                        table
                    )));
                }
                Ok(())
            }
        }
    }

    /// Generate a query plan
    pub fn plan(&self, query: &SochQuery, catalog: &Catalog) -> Result<QueryPlan> {
        match query {
            SochQuery::Select(select) => self.plan_select(select, catalog),
            _ => Err(SochDBError::InvalidArgument(
                "Only SELECT queries can be planned".to_string(),
            )),
        }
    }

    fn plan_select(&self, select: &SelectQuery, _catalog: &Catalog) -> Result<QueryPlan> {
        // Start with table scan
        let mut plan = QueryPlan::TableScan {
            table: select.table.clone(),
            columns: select.columns.clone(),
            predicate: None,
        };

        // Add filter if WHERE clause present
        if let Some(where_clause) = &select.where_clause {
            let predicate = self.build_predicate(where_clause);
            plan = QueryPlan::Filter {
                input: Box::new(plan),
                predicate,
            };
        }

        // Add projection (column selection)
        if !select.columns.contains(&"*".to_string()) {
            plan = QueryPlan::Project {
                input: Box::new(plan),
                columns: select.columns.clone(),
            };
        }

        // Add sort if ORDER BY present
        if let Some(order_by) = &select.order_by {
            plan = QueryPlan::Sort {
                input: Box::new(plan),
                order_by: vec![(
                    order_by.column.clone(),
                    matches!(order_by.direction, SortDirection::Asc),
                )],
            };
        }

        // Add limit if present
        if select.limit.is_some() || select.offset.is_some() {
            plan = QueryPlan::Limit {
                input: Box::new(plan),
                count: select.limit.unwrap_or(usize::MAX),
                offset: select.offset.unwrap_or(0),
            };
        }

        Ok(plan)
    }

    fn build_predicate(&self, where_clause: &WhereClause) -> Predicate {
        Predicate {
            conditions: where_clause
                .conditions
                .iter()
                .map(|c| PredicateCondition::from_soch_ql(c.column.clone(), c.operator, &c.value))
                .collect(),
            operator: where_clause.operator,
        }
    }

    /// Execute a query plan
    #[allow(clippy::only_used_in_recursion)]
    pub fn execute_plan(&self, plan: &QueryPlan, catalog: &Catalog) -> Result<SochResult> {
        // For now, return empty results (storage integration pending)
        // This is the interface that will connect to StorageEngine
        match plan {
            QueryPlan::Empty => Ok(SochResult {
                table: "result".to_string(),
                columns: vec![],
                rows: vec![],
            }),
            QueryPlan::TableScan { table, columns, .. } => {
                // Get schema from catalog
                let schema_columns = if let Some(entry) = catalog.get_table(table) {
                    if let Some(schema) = &entry.schema {
                        if columns.contains(&"*".to_string()) {
                            schema.fields.iter().map(|f| f.name.clone()).collect()
                        } else {
                            columns.clone()
                        }
                    } else {
                        columns.clone()
                    }
                } else {
                    columns.clone()
                };

                Ok(SochResult {
                    table: table.clone(),
                    columns: schema_columns,
                    rows: vec![], // Storage integration will populate this
                })
            }
            QueryPlan::Filter { input, .. } => self.execute_plan(input, catalog),
            QueryPlan::Project { input, columns } => {
                let mut result = self.execute_plan(input, catalog)?;
                result.columns = columns.clone();
                Ok(result)
            }
            QueryPlan::Sort { input, .. } => self.execute_plan(input, catalog),
            QueryPlan::Limit {
                input,
                count,
                offset,
            } => {
                let mut result = self.execute_plan(input, catalog)?;
                result.rows = result.rows.into_iter().skip(*offset).take(*count).collect();
                Ok(result)
            }
            QueryPlan::IndexSeek { .. } => Ok(SochResult {
                table: "result".to_string(),
                columns: vec![],
                rows: vec![],
            }),
        }
    }
}

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

/// Execute a SOCH-QL query (convenience function)
pub fn execute_sochql(query: &str, catalog: &Catalog) -> Result<SochResult> {
    SochQlExecutor::new().execute(query, catalog)
}

/// Estimate token reduction for a TOON result vs JSON
pub fn estimate_token_reduction(result: &SochResult) -> TokenReductionStats {
    let row_count = result.rows.len();
    let col_count = result.columns.len();

    if row_count == 0 || col_count == 0 {
        return TokenReductionStats::default();
    }

    // Estimate JSON tokens
    // Format: [{"col1": "val1", "col2": "val2"}, ...]
    let avg_col_name_len: usize = result.columns.iter().map(|c| c.len()).sum::<usize>() / col_count;
    let avg_value_len = 10; // Rough estimate

    // JSON: 2 (brackets) + row_count * (2 + col_count * (col_name + 4 + value))
    let json_tokens = 2 + row_count * (2 + col_count * (avg_col_name_len + 4 + avg_value_len));

    // TOON: header + row_count * (col_count * value + col_count)
    // Header: table[count]{col1,col2,...}:
    let header_tokens = result.table.len() + 10 + result.columns.join(",").len();
    let soch_tokens = header_tokens + row_count * (col_count * avg_value_len + col_count);

    let reduction = 1.0 - (soch_tokens as f64 / json_tokens as f64);

    TokenReductionStats {
        json_tokens,
        soch_tokens,
        reduction_percent: (reduction * 100.0) as u32,
        row_count,
        col_count,
    }
}

/// Token reduction statistics
#[derive(Debug, Clone, Default)]
pub struct TokenReductionStats {
    /// Estimated JSON tokens
    pub json_tokens: usize,
    /// Estimated TOON tokens
    pub soch_tokens: usize,
    /// Reduction percentage
    pub reduction_percent: u32,
    /// Row count
    pub row_count: usize,
    /// Column count
    pub col_count: usize,
}

// ============================================================================
// Task 12: Vectorized Predicate Evaluation
// ============================================================================

/// Selection vector for batch predicate evaluation
///
/// Maintains indices of rows that pass all predicates so far.
/// Short-circuits at batch level when selection becomes empty.
#[derive(Debug, Clone)]
pub struct SelectionVector {
    /// Selected row indices (sorted)
    indices: Vec<u32>,
    /// Original batch size
    batch_size: usize,
}

impl SelectionVector {
    /// Create selection with all rows selected
    pub fn all(batch_size: usize) -> Self {
        Self {
            indices: (0..batch_size as u32).collect(),
            batch_size,
        }
    }

    /// Create empty selection
    pub fn empty() -> Self {
        Self {
            indices: Vec::new(),
            batch_size: 0,
        }
    }

    /// Create from specific indices
    pub fn from_indices(indices: Vec<u32>, batch_size: usize) -> Self {
        Self {
            indices,
            batch_size,
        }
    }

    /// Check if selection is empty (short-circuit condition)
    #[inline]
    pub fn is_empty(&self) -> bool {
        self.indices.is_empty()
    }

    /// Number of selected rows
    #[inline]
    pub fn len(&self) -> usize {
        self.indices.len()
    }

    /// Original batch size
    #[inline]
    pub fn batch_size(&self) -> usize {
        self.batch_size
    }

    /// Selectivity ratio
    #[inline]
    pub fn selectivity(&self) -> f64 {
        if self.batch_size == 0 {
            0.0
        } else {
            self.len() as f64 / self.batch_size as f64
        }
    }

    /// Iterate over selected indices
    pub fn iter(&self) -> impl Iterator<Item = u32> + '_ {
        self.indices.iter().copied()
    }

    /// Filter selection with a predicate, returning new selection
    pub fn filter<F>(&self, pred: F) -> Self
    where
        F: Fn(u32) -> bool,
    {
        Self {
            indices: self.indices.iter().copied().filter(|&i| pred(i)).collect(),
            batch_size: self.batch_size,
        }
    }

    /// Extend with masked indices (for SIMD results)
    pub fn extend_masked(&mut self, start_idx: usize, mask: u16) {
        for bit in 0..16 {
            if (mask >> bit) & 1 == 1 {
                self.indices.push((start_idx + bit) as u32);
            }
        }
    }
}

/// Columnar batch for vectorized processing
#[derive(Debug, Clone)]
pub struct ColumnBatch {
    /// Column values
    pub values: Vec<CoreSochValue>,
    /// Column name
    pub name: String,
}

impl ColumnBatch {
    /// Create from column data
    pub fn new(name: String, values: Vec<CoreSochValue>) -> Self {
        Self { values, name }
    }

    /// Get value at index
    #[inline]
    pub fn get(&self, idx: usize) -> Option<&CoreSochValue> {
        self.values.get(idx)
    }

    /// Get raw integer data pointer (for SIMD)
    #[allow(dead_code)]
    pub fn as_i64_slice(&self) -> Option<Vec<i64>> {
        self.values
            .iter()
            .map(|v| match v {
                CoreSochValue::Int(i) => Some(*i),
                CoreSochValue::UInt(u) => Some(*u as i64),
                _ => None,
            })
            .collect()
    }

    /// Batch size
    pub fn len(&self) -> usize {
        self.values.len()
    }

    /// Is empty
    pub fn is_empty(&self) -> bool {
        self.values.is_empty()
    }
}

/// Vectorized predicate for batch evaluation
#[derive(Debug, Clone)]
pub enum VectorPredicate {
    /// Integer greater than
    IntGt { col_idx: usize, threshold: i64 },
    /// Integer less than
    IntLt { col_idx: usize, threshold: i64 },
    /// Integer equals
    IntEq { col_idx: usize, value: i64 },
    /// Integer greater or equal
    IntGe { col_idx: usize, threshold: i64 },
    /// Integer less or equal
    IntLe { col_idx: usize, threshold: i64 },
    /// String equals
    StrEq { col_idx: usize, value: String },
    /// String prefix match
    StrPrefix { col_idx: usize, prefix: String },
    /// Boolean equals
    BoolEq { col_idx: usize, value: bool },
    /// Is null check
    IsNull { col_idx: usize },
    /// Is not null check
    IsNotNull { col_idx: usize },
}

/// Vectorized executor for batch predicate evaluation
///
/// ## Performance Characteristics
///
/// - Traditional row-at-a-time: ~100M rows/sec (branch misprediction bound)
/// - Vectorized with selection vector: ~1B rows/sec (branch-free)
///
/// ## Usage
///
/// ```ignore
/// let executor = VectorizedExecutor::new(1024);
/// let columns = vec![/* column batches */];
/// let predicates = vec![/* predicates */];
/// let selection = executor.evaluate_batch(&columns, &predicates);
/// ```
pub struct VectorizedExecutor {
    /// Batch size for processing
    batch_size: usize,
}

impl VectorizedExecutor {
    /// Create with specified batch size
    pub fn new(batch_size: usize) -> Self {
        Self { batch_size }
    }

    /// Default batch size (1024 rows)
    pub fn default_batch_size() -> usize {
        1024
    }

    /// Evaluate predicates on columnar batch
    ///
    /// Returns selection vector of rows that pass all predicates.
    /// Short-circuits at batch level when selection becomes empty.
    pub fn evaluate_batch(
        &self,
        columns: &[ColumnBatch],
        predicates: &[VectorPredicate],
    ) -> SelectionVector {
        if columns.is_empty() {
            return SelectionVector::empty();
        }

        let batch_size = columns[0].len().min(self.batch_size);
        let mut selection = SelectionVector::all(batch_size);

        // Process predicates with short-circuit
        for predicate in predicates {
            if selection.is_empty() {
                break; // Batch-level short-circuit
            }

            selection = match predicate {
                VectorPredicate::IntGt { col_idx, threshold } => {
                    self.filter_int_gt(&columns[*col_idx], *threshold, &selection)
                }
                VectorPredicate::IntLt { col_idx, threshold } => {
                    self.filter_int_lt(&columns[*col_idx], *threshold, &selection)
                }
                VectorPredicate::IntEq { col_idx, value } => {
                    self.filter_int_eq(&columns[*col_idx], *value, &selection)
                }
                VectorPredicate::IntGe { col_idx, threshold } => {
                    self.filter_int_ge(&columns[*col_idx], *threshold, &selection)
                }
                VectorPredicate::IntLe { col_idx, threshold } => {
                    self.filter_int_le(&columns[*col_idx], *threshold, &selection)
                }
                VectorPredicate::StrEq { col_idx, value } => {
                    self.filter_str_eq(&columns[*col_idx], value, &selection)
                }
                VectorPredicate::StrPrefix { col_idx, prefix } => {
                    self.filter_str_prefix(&columns[*col_idx], prefix, &selection)
                }
                VectorPredicate::BoolEq { col_idx, value } => {
                    self.filter_bool_eq(&columns[*col_idx], *value, &selection)
                }
                VectorPredicate::IsNull { col_idx } => {
                    self.filter_is_null(&columns[*col_idx], &selection)
                }
                VectorPredicate::IsNotNull { col_idx } => {
                    self.filter_is_not_null(&columns[*col_idx], &selection)
                }
            };
        }

        selection
    }

    /// Filter: column > threshold
    #[inline]
    fn filter_int_gt(
        &self,
        column: &ColumnBatch,
        threshold: i64,
        selection: &SelectionVector,
    ) -> SelectionVector {
        selection.filter(|idx| match column.get(idx as usize) {
            Some(CoreSochValue::Int(v)) => *v > threshold,
            Some(CoreSochValue::UInt(v)) => (*v as i64) > threshold,
            _ => false,
        })
    }

    /// Filter: column < threshold
    #[inline]
    fn filter_int_lt(
        &self,
        column: &ColumnBatch,
        threshold: i64,
        selection: &SelectionVector,
    ) -> SelectionVector {
        selection.filter(|idx| match column.get(idx as usize) {
            Some(CoreSochValue::Int(v)) => *v < threshold,
            Some(CoreSochValue::UInt(v)) => (*v as i64) < threshold,
            _ => false,
        })
    }

    /// Filter: column == value
    #[inline]
    fn filter_int_eq(
        &self,
        column: &ColumnBatch,
        value: i64,
        selection: &SelectionVector,
    ) -> SelectionVector {
        selection.filter(|idx| match column.get(idx as usize) {
            Some(CoreSochValue::Int(v)) => *v == value,
            Some(CoreSochValue::UInt(v)) => (*v as i64) == value,
            _ => false,
        })
    }

    /// Filter: column >= threshold
    #[inline]
    fn filter_int_ge(
        &self,
        column: &ColumnBatch,
        threshold: i64,
        selection: &SelectionVector,
    ) -> SelectionVector {
        selection.filter(|idx| match column.get(idx as usize) {
            Some(CoreSochValue::Int(v)) => *v >= threshold,
            Some(CoreSochValue::UInt(v)) => (*v as i64) >= threshold,
            _ => false,
        })
    }

    /// Filter: column <= threshold
    #[inline]
    fn filter_int_le(
        &self,
        column: &ColumnBatch,
        threshold: i64,
        selection: &SelectionVector,
    ) -> SelectionVector {
        selection.filter(|idx| match column.get(idx as usize) {
            Some(CoreSochValue::Int(v)) => *v <= threshold,
            Some(CoreSochValue::UInt(v)) => (*v as i64) <= threshold,
            _ => false,
        })
    }

    /// Filter: column == value (string)
    #[inline]
    fn filter_str_eq(
        &self,
        column: &ColumnBatch,
        value: &str,
        selection: &SelectionVector,
    ) -> SelectionVector {
        selection.filter(|idx| match column.get(idx as usize) {
            Some(CoreSochValue::Text(s)) => s == value,
            _ => false,
        })
    }

    /// Filter: column starts with prefix
    #[inline]
    fn filter_str_prefix(
        &self,
        column: &ColumnBatch,
        prefix: &str,
        selection: &SelectionVector,
    ) -> SelectionVector {
        selection.filter(|idx| match column.get(idx as usize) {
            Some(CoreSochValue::Text(s)) => s.starts_with(prefix),
            _ => false,
        })
    }

    /// Filter: column == value (bool)
    #[inline]
    fn filter_bool_eq(
        &self,
        column: &ColumnBatch,
        value: bool,
        selection: &SelectionVector,
    ) -> SelectionVector {
        selection.filter(|idx| match column.get(idx as usize) {
            Some(CoreSochValue::Bool(b)) => *b == value,
            _ => false,
        })
    }

    /// Filter: column IS NULL
    #[inline]
    fn filter_is_null(&self, column: &ColumnBatch, selection: &SelectionVector) -> SelectionVector {
        selection.filter(|idx| matches!(column.get(idx as usize), Some(CoreSochValue::Null)))
    }

    /// Filter: column IS NOT NULL
    #[inline]
    fn filter_is_not_null(
        &self,
        column: &ColumnBatch,
        selection: &SelectionVector,
    ) -> SelectionVector {
        selection
            .filter(|idx| !matches!(column.get(idx as usize), Some(CoreSochValue::Null) | None))
    }

    /// Materialize selected rows from columnar data
    pub fn materialize(
        &self,
        columns: &[ColumnBatch],
        selection: &SelectionVector,
    ) -> Vec<SochRow> {
        selection
            .iter()
            .map(|idx| {
                let values: Vec<CoreSochValue> = columns
                    .iter()
                    .map(|col| {
                        col.get(idx as usize)
                            .cloned()
                            .unwrap_or(CoreSochValue::Null)
                    })
                    .collect();
                SochRow::new(values)
            })
            .collect()
    }

    /// Convert row-oriented data to columnar batches
    pub fn row_to_columnar(&self, rows: &[SochRow], column_names: &[String]) -> Vec<ColumnBatch> {
        if rows.is_empty() || column_names.is_empty() {
            return vec![];
        }

        let num_cols = column_names.len().min(rows[0].values.len());

        (0..num_cols)
            .map(|col_idx| {
                let values: Vec<CoreSochValue> = rows
                    .iter()
                    .map(|row| {
                        row.values
                            .get(col_idx)
                            .cloned()
                            .unwrap_or(CoreSochValue::Null)
                    })
                    .collect();
                ColumnBatch::new(column_names[col_idx].clone(), values)
            })
            .collect()
    }
}

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

/// Statistics for vectorized execution
#[derive(Debug, Clone, Default)]
pub struct VectorizedStats {
    /// Rows processed
    pub rows_processed: usize,
    /// Rows selected
    pub rows_selected: usize,
    /// Predicates evaluated
    pub predicates_evaluated: usize,
    /// Short-circuits triggered
    pub short_circuits: usize,
    /// Processing time (microseconds)
    pub time_us: u64,
}

impl VectorizedStats {
    /// Selectivity ratio
    pub fn selectivity(&self) -> f64 {
        if self.rows_processed == 0 {
            0.0
        } else {
            self.rows_selected as f64 / self.rows_processed as f64
        }
    }

    /// Rows per second
    pub fn rows_per_sec(&self) -> f64 {
        if self.time_us == 0 {
            0.0
        } else {
            self.rows_processed as f64 / (self.time_us as f64 / 1_000_000.0)
        }
    }
}

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

    fn test_catalog() -> Catalog {
        let mut catalog = Catalog::new("test_db");

        let schema = SochSchema::new("users")
            .field("id", SochType::UInt)
            .field("name", SochType::Text)
            .field("score", SochType::Float);

        catalog.create_table(schema, 1).unwrap();
        catalog
    }

    #[test]
    fn test_validate_select() {
        let catalog = test_catalog();
        let executor = SochQlExecutor::new();

        let query = SochQuery::Select(SelectQuery {
            columns: vec!["id".to_string(), "name".to_string()],
            table: "users".to_string(),
            where_clause: None,
            order_by: None,
            limit: None,
            offset: None,
        });

        assert!(executor.validate(&query, &catalog).is_ok());
    }

    #[test]
    fn test_validate_nonexistent_table() {
        let catalog = test_catalog();
        let executor = SochQlExecutor::new();

        let query = SochQuery::Select(SelectQuery {
            columns: vec!["*".to_string()],
            table: "nonexistent".to_string(),
            where_clause: None,
            order_by: None,
            limit: None,
            offset: None,
        });

        assert!(executor.validate(&query, &catalog).is_err());
    }

    #[test]
    fn test_plan_select() {
        let catalog = test_catalog();
        let executor = SochQlExecutor::new();

        let select = SelectQuery {
            columns: vec!["id".to_string(), "name".to_string()],
            table: "users".to_string(),
            where_clause: Some(WhereClause {
                conditions: vec![Condition {
                    column: "score".to_string(),
                    operator: ComparisonOp::Gt,
                    value: SochValue::Float(80.0),
                }],
                operator: LogicalOp::And,
            }),
            order_by: Some(OrderBy {
                column: "score".to_string(),
                direction: SortDirection::Desc,
            }),
            limit: Some(10),
            offset: None,
        };

        let plan = executor.plan_select(&select, &catalog).unwrap();

        // Should be: Limit(Sort(Project(Filter(TableScan))))
        match plan {
            QueryPlan::Limit { input, count, .. } => {
                assert_eq!(count, 10);
                match *input {
                    QueryPlan::Sort { input, order_by } => {
                        assert_eq!(order_by[0].0, "score");
                        assert!(!order_by[0].1); // Descending = false
                        match *input {
                            QueryPlan::Project { input, columns } => {
                                assert_eq!(columns, vec!["id", "name"]);
                                match *input {
                                    QueryPlan::Filter { predicate, .. } => {
                                        assert_eq!(predicate.conditions.len(), 1);
                                    }
                                    _ => panic!("Expected Filter"),
                                }
                            }
                            _ => panic!("Expected Project"),
                        }
                    }
                    _ => panic!("Expected Sort"),
                }
            }
            _ => panic!("Expected Limit"),
        }
    }

    #[test]
    fn test_predicate_evaluation() {
        let cond = PredicateCondition {
            column: "score".to_string(),
            operator: ComparisonOp::Gt,
            value: CoreSochValue::Float(80.0),
        };

        let row_pass = SochRow::new(vec![
            CoreSochValue::UInt(1),
            CoreSochValue::Text("Alice".to_string()),
            CoreSochValue::Float(95.0),
        ]);

        let row_fail = SochRow::new(vec![
            CoreSochValue::UInt(2),
            CoreSochValue::Text("Bob".to_string()),
            CoreSochValue::Float(75.0),
        ]);

        assert!(cond.evaluate(&row_pass, 2));
        assert!(!cond.evaluate(&row_fail, 2));
    }

    #[test]
    fn test_token_reduction() {
        // Use more rows and longer field names to show real reduction
        let result = SochResult {
            table: "user_statistics".to_string(),
            columns: vec![
                "user_id".to_string(),
                "full_name".to_string(),
                "email_address".to_string(),
                "registration_date".to_string(),
                "last_login".to_string(),
            ],
            rows: (0..20)
                .map(|i| {
                    vec![
                        SochValue::UInt(i as u64),
                        SochValue::Text(format!("User Number {}", i)),
                        SochValue::Text(format!("user{}@example.com", i)),
                        SochValue::Text("2024-01-15".to_string()),
                        SochValue::Text("2024-03-20".to_string()),
                    ]
                })
                .collect(),
        };

        let stats = estimate_token_reduction(&result);

        println!("JSON tokens: {}", stats.json_tokens);
        println!("TOON tokens: {}", stats.soch_tokens);
        println!("Reduction: {}%", stats.reduction_percent);

        // With many rows and repeated column names, TOON should be more efficient
        assert!(stats.soch_tokens < stats.json_tokens);
        assert!(stats.reduction_percent > 0); // Any reduction is valuable
    }

    // ========================================================================
    // Task 12: Vectorized Predicate Evaluation Tests
    // ========================================================================

    #[test]
    fn test_selection_vector_basic() {
        let sel = SelectionVector::all(100);
        assert_eq!(sel.len(), 100);
        assert!(!sel.is_empty());
        assert_eq!(sel.selectivity(), 1.0);

        let empty = SelectionVector::empty();
        assert!(empty.is_empty());
        assert_eq!(empty.selectivity(), 0.0);
    }

    #[test]
    fn test_selection_vector_filter() {
        let sel = SelectionVector::all(10);

        // Keep only even indices
        let filtered = sel.filter(|i| i % 2 == 0);
        assert_eq!(filtered.len(), 5);

        let indices: Vec<u32> = filtered.iter().collect();
        assert_eq!(indices, vec![0, 2, 4, 6, 8]);
    }

    #[test]
    fn test_vectorized_int_filter() {
        let executor = VectorizedExecutor::new(1024);

        // Create a column with values 0-9
        let column = ColumnBatch::new(
            "value".to_string(),
            (0..10).map(CoreSochValue::Int).collect(),
        );

        let predicates = vec![VectorPredicate::IntGt {
            col_idx: 0,
            threshold: 5,
        }];

        let selection = executor.evaluate_batch(&[column], &predicates);

        // Should select 6, 7, 8, 9 (4 values > 5)
        assert_eq!(selection.len(), 4);
        let indices: Vec<u32> = selection.iter().collect();
        assert_eq!(indices, vec![6, 7, 8, 9]);
    }

    #[test]
    fn test_vectorized_multiple_predicates() {
        let executor = VectorizedExecutor::new(1024);

        // Create columns
        let id_col = ColumnBatch::new("id".to_string(), (0..100).map(CoreSochValue::Int).collect());

        let status_col = ColumnBatch::new(
            "active".to_string(),
            (0..100).map(|i| CoreSochValue::Bool(i % 2 == 0)).collect(),
        );

        let predicates = vec![
            VectorPredicate::IntGe {
                col_idx: 0,
                threshold: 50,
            },
            VectorPredicate::IntLt {
                col_idx: 0,
                threshold: 60,
            },
            VectorPredicate::BoolEq {
                col_idx: 1,
                value: true,
            },
        ];

        let selection = executor.evaluate_batch(&[id_col, status_col], &predicates);

        // Should select: 50, 52, 54, 56, 58 (even numbers in [50, 60))
        assert_eq!(selection.len(), 5);
        let indices: Vec<u32> = selection.iter().collect();
        assert_eq!(indices, vec![50, 52, 54, 56, 58]);
    }

    #[test]
    fn test_vectorized_short_circuit() {
        let executor = VectorizedExecutor::new(1024);

        // Create a column with all values < 0
        let column = ColumnBatch::new(
            "value".to_string(),
            (0..100).map(|_| CoreSochValue::Int(-1)).collect(),
        );

        // First predicate eliminates everything
        let predicates = vec![
            VectorPredicate::IntGt {
                col_idx: 0,
                threshold: 0,
            },
            // These should not even be evaluated due to short-circuit
            VectorPredicate::IntLt {
                col_idx: 0,
                threshold: 100,
            },
            VectorPredicate::IntEq {
                col_idx: 0,
                value: 50,
            },
        ];

        let selection = executor.evaluate_batch(&[column], &predicates);
        assert!(selection.is_empty());
    }

    #[test]
    fn test_vectorized_string_predicates() {
        let executor = VectorizedExecutor::new(1024);

        let names = [
            "Alice", "Bob", "Carol", "Dave", "Alice", "Alice", "Bob", "Carol",
        ];
        let column = ColumnBatch::new(
            "name".to_string(),
            names
                .iter()
                .map(|s| CoreSochValue::Text(s.to_string()))
                .collect(),
        );

        let predicates = vec![VectorPredicate::StrEq {
            col_idx: 0,
            value: "Alice".to_string(),
        }];

        let selection = executor.evaluate_batch(&[column], &predicates);

        // Should select indices 0, 4, 5 (where name == "Alice")
        assert_eq!(selection.len(), 3);
        let indices: Vec<u32> = selection.iter().collect();
        assert_eq!(indices, vec![0, 4, 5]);
    }

    #[test]
    fn test_vectorized_null_handling() {
        let executor = VectorizedExecutor::new(1024);

        let values = vec![
            CoreSochValue::Int(1),
            CoreSochValue::Null,
            CoreSochValue::Int(2),
            CoreSochValue::Null,
            CoreSochValue::Int(3),
        ];
        let column = ColumnBatch::new("value".to_string(), values);

        let predicates = vec![VectorPredicate::IsNull { col_idx: 0 }];
        let null_selection = executor.evaluate_batch(std::slice::from_ref(&column), &predicates);
        assert_eq!(null_selection.len(), 2); // indices 1, 3

        let not_null_predicates = vec![VectorPredicate::IsNotNull { col_idx: 0 }];
        let not_null_selection = executor.evaluate_batch(&[column], &not_null_predicates);
        assert_eq!(not_null_selection.len(), 3); // indices 0, 2, 4
    }

    #[test]
    fn test_row_to_columnar_conversion() {
        let executor = VectorizedExecutor::new(1024);

        let rows = vec![
            SochRow::new(vec![
                CoreSochValue::Int(1),
                CoreSochValue::Text("Alice".to_string()),
            ]),
            SochRow::new(vec![
                CoreSochValue::Int(2),
                CoreSochValue::Text("Bob".to_string()),
            ]),
            SochRow::new(vec![
                CoreSochValue::Int(3),
                CoreSochValue::Text("Carol".to_string()),
            ]),
        ];

        let column_names = vec!["id".to_string(), "name".to_string()];
        let columns = executor.row_to_columnar(&rows, &column_names);

        assert_eq!(columns.len(), 2);
        assert_eq!(columns[0].name, "id");
        assert_eq!(columns[1].name, "name");
        assert_eq!(columns[0].len(), 3);
        assert_eq!(columns[1].len(), 3);
    }

    #[test]
    fn test_materialize_selected_rows() {
        let executor = VectorizedExecutor::new(1024);

        let id_col = ColumnBatch::new(
            "id".to_string(),
            vec![
                CoreSochValue::Int(1),
                CoreSochValue::Int(2),
                CoreSochValue::Int(3),
            ],
        );
        let name_col = ColumnBatch::new(
            "name".to_string(),
            vec![
                CoreSochValue::Text("Alice".to_string()),
                CoreSochValue::Text("Bob".to_string()),
                CoreSochValue::Text("Carol".to_string()),
            ],
        );

        // Select rows 0 and 2
        let selection = SelectionVector::from_indices(vec![0, 2], 3);

        let rows = executor.materialize(&[id_col, name_col], &selection);

        assert_eq!(rows.len(), 2);
        assert_eq!(rows[0].values[0], CoreSochValue::Int(1));
        assert_eq!(rows[0].values[1], CoreSochValue::Text("Alice".to_string()));
        assert_eq!(rows[1].values[0], CoreSochValue::Int(3));
        assert_eq!(rows[1].values[1], CoreSochValue::Text("Carol".to_string()));
    }
}