veloxx 0.4.0

//! Lazy evaluation module for Veloxx
//!
//! This module implements lazy evaluation for DataFrames, allowing for query optimization
//! and improved performance through techniques like predicate pushdown and projection pushdown.

use crate::dataframe::DataFrame;
use crate::series::Series;
use crate::types::Value;
use crate::VeloxxError;
use indexmap::IndexMap;

pub mod optimizer;

/// Represents a logical plan for lazy evaluation
#[derive(Debug, Clone)]
pub enum LogicalPlan {
    /// Scan a DataFrame from a source
    DataFrameScan {
        schema: IndexMap<String, String>, // column name -> data type
        dataframe: DataFrame,
        projection: Option<Vec<String>>,
        filters: Vec<Expr>,
    },
    /// Filter operation
    Filter {
        input: Box<LogicalPlan>,
        predicate: Expr,
    },
    /// Projection operation (select columns)
    Projection {
        input: Box<LogicalPlan>,
        expr: Vec<Expr>,
        schema: IndexMap<String, String>,
    },
    /// Group by operation
    GroupBy {
        input: Box<LogicalPlan>,
        keys: Vec<String>,
        aggregations: Vec<Aggregation>,
        schema: IndexMap<String, String>,
    },
}

/// Represents an expression in a logical plan
#[derive(Debug, Clone)]
pub enum Expr {
    /// Column reference
    Column(String),
    /// Literal value
    Literal(Value),
    /// Binary operation
    BinaryOp {
        left: Box<Expr>,
        op: BinaryOperator,
        right: Box<Expr>,
    },
}

/// Represents a binary operator
#[derive(Debug, Clone)]
pub enum BinaryOperator {
    Eq,
    Neq,
    Lt,
    LtEq,
    Gt,
    GtEq,
    And,
    Or,
    Add,
    Subtract,
    Multiply,
    Divide,
}

/// Represents an aggregation function
#[derive(Debug, Clone)]
pub enum Aggregation {
    Sum(String),
    Mean(String),
    Count(String),
    Min(String),
    Max(String),
}

/// Lazy DataFrame structure
#[derive(Debug, Clone)]
pub struct LazyDataFrame {
    pub(crate) logical_plan: LogicalPlan,
}

impl LazyDataFrame {
    /// Create a new LazyDataFrame from a regular DataFrame
    pub fn from_dataframe(df: DataFrame) -> Self {
        // Infer schema from DataFrame
        let mut schema = IndexMap::new();
        for (name, series) in &df.columns {
            let dtype = match series {
                Series::I32(_, _, _) => "i32".to_string(),
                Series::F64(_, _, _) => "f64".to_string(),
                Series::Bool(_, _, _) => "bool".to_string(),
                Series::String(_, _, _) => "string".to_string(),
                Series::DateTime(_, _, _) => "datetime".to_string(),
            };
            schema.insert(name.clone(), dtype);
        }

        let logical_plan = LogicalPlan::DataFrameScan {
            schema,
            dataframe: df,
            projection: None,
            filters: vec![],
        };

        LazyDataFrame { logical_plan }
    }

    /// Filter the DataFrame based on a predicate
    pub fn filter(self, predicate: Expr) -> Self {
        let logical_plan = LogicalPlan::Filter {
            input: Box::new(self.logical_plan),
            predicate,
        };
        LazyDataFrame { logical_plan }
    }

    /// Select specific columns
    pub fn select(self, expr: Vec<Expr>) -> Self {
        // For simplicity, we'll just extract column names from the expressions
        let mut schema = IndexMap::new();
        let mut column_names = Vec::new();

        // Extract column names from expressions (simplified)
        for e in &expr {
            if let Expr::Column(name) = e {
                column_names.push(name.clone());
            }
        }

        // Create a simplified schema for selected columns
        if let LogicalPlan::DataFrameScan {
            schema: original_schema,
            ..
        } = &self.logical_plan
        {
            for name in &column_names {
                if let Some(dtype) = original_schema.get(name) {
                    schema.insert(name.clone(), dtype.clone());
                }
            }
        }

        let logical_plan = LogicalPlan::Projection {
            input: Box::new(self.logical_plan),
            expr,
            schema,
        };
        LazyDataFrame { logical_plan }
    }

    /// Group by specific columns
    pub fn group_by(self, keys: Vec<String>) -> LazyGroupBy {
        LazyGroupBy { input: self, keys }
    }

    /// Collect and execute the lazy plan
    pub fn collect(self) -> Result<DataFrame, VeloxxError> {
        // Optimize the plan before execution
        let optimizer = optimizer::QueryOptimizer::new();
        let optimized_plan = optimizer.optimize(self.logical_plan);

        // Execute the optimized plan
        Self::execute_plan_static(&optimized_plan)
    }

    /// Collect and execute the lazy plan without optimization
    pub fn collect_unoptimized(self) -> Result<DataFrame, VeloxxError> {
        // Execute the plan as-is without optimization
        Self::execute_plan_static(&self.logical_plan)
    }

    /// Execute a logical plan (static method to avoid borrow issues)
    /// Evaluate an expression against a DataFrame
    #[allow(unreachable_patterns)]
    fn evaluate_expr(df: &DataFrame, expr: &Expr) -> Result<Series, VeloxxError> {
        match expr {
            Expr::Column(name) => df
                .get_column(name)
                .cloned()
                .ok_or_else(|| VeloxxError::ColumnNotFound(format!("Column '{}' not found", name))),
            Expr::Literal(value) => {
                let len = df.row_count();
                // Create constant series
                match value {
                    Value::I32(v) => Ok(Series::new_i32("literal", vec![Some(*v); len])),
                    Value::F64(v) => Ok(Series::new_f64("literal", vec![Some(*v); len])),
                    Value::Bool(v) => Ok(Series::new_bool("literal", vec![Some(*v); len])),
                    Value::String(v) => {
                        Ok(Series::new_string("literal", vec![Some(v.clone()); len]))
                    }
                    Value::DateTime(v) => Ok(Series::new_datetime("literal", vec![Some(*v); len])),
                    Value::Null => Err(VeloxxError::Unsupported(
                        "Literal Null not fully supported in evaluation yet".to_string(),
                    )),
                }
            }
            Expr::BinaryOp { left, op, right } => {
                let left_series = Self::evaluate_expr(df, left)?;
                let right_series = Self::evaluate_expr(df, right)?;

                match op {
                    BinaryOperator::Eq => left_series.equal(&right_series),
                    BinaryOperator::Neq => left_series.equal(&right_series)?.not(),
                    BinaryOperator::Gt => left_series.gt(&right_series),
                    BinaryOperator::Lt => right_series.gt(&left_series),
                    BinaryOperator::GtEq => right_series.gt(&left_series)?.not(),
                    BinaryOperator::LtEq => left_series.gt(&right_series)?.not(),
                    BinaryOperator::And => left_series.and(&right_series),
                    BinaryOperator::Or => left_series.or(&right_series),
                    BinaryOperator::Add => {
                        use crate::performance::series_ext::SeriesPerformanceExt;
                        left_series.simd_add(&right_series)
                    }
                    BinaryOperator::Subtract => left_series.arrow_sub(&right_series),
                    BinaryOperator::Multiply => left_series.arrow_mul(&right_series),
                    BinaryOperator::Divide => left_series.arrow_div(&right_series),
                    _ => Err(VeloxxError::Unsupported(format!(
                        "Binary operator {:?} not implemented",
                        op
                    ))),
                }
            }
        }
    }

    fn execute_plan_static(plan: &LogicalPlan) -> Result<DataFrame, VeloxxError> {
        match plan {
            LogicalPlan::DataFrameScan {
                dataframe,
                projection,
                filters,
                ..
            } => {
                let mut df = dataframe.clone();

                // Apply filters
                for filter_expr in filters {
                    let mask = Self::evaluate_expr(&df, filter_expr)?;
                    df = df.filter_by_mask(&mask)?;
                }

                // Apply projection
                if let Some(columns) = projection {
                    df = df.select_columns(columns.clone())?;
                }

                Ok(df)
            }
            LogicalPlan::Filter { input, predicate } => {
                let df = Self::execute_plan_static(input)?;

                let mask = Self::evaluate_expr(&df, predicate)?;
                let df = df.filter_by_mask(&mask)?;
                // Filter applied
                // In a real implementation, we would evaluate the predicate expression
                // For now, we'll just return the DataFrame as-is
                Ok(df)
            }
            LogicalPlan::Projection { input, expr, .. } => {
                let df = Self::execute_plan_static(input)?;

                // Extract column names from expressions and select them
                let mut column_names = Vec::new();
                for e in expr {
                    if let Expr::Column(name) = e {
                        column_names.push(name.clone());
                    }
                }

                if !column_names.is_empty() {
                    df.select_columns(column_names)
                } else {
                    Ok(df)
                }
            }
            LogicalPlan::GroupBy { input, .. } => {
                let df = Self::execute_plan_static(input)?;

                // For now, we'll just return the original DataFrame
                // A full implementation would perform group-by and aggregation operations
                Ok(df)
            }
        }
    }
}

/// Lazy GroupBy structure
pub struct LazyGroupBy {
    input: LazyDataFrame,
    keys: Vec<String>,
}

impl LazyGroupBy {
    /// Apply aggregations
    pub fn agg(self, aggregations: Vec<Aggregation>) -> LazyDataFrame {
        // Compute the resulting schema
        let mut schema = IndexMap::new();

        // Add group keys to schema
        if let LogicalPlan::DataFrameScan {
            schema: original_schema,
            ..
        } = &self.input.logical_plan
        {
            for key in &self.keys {
                if let Some(dtype) = original_schema.get(key) {
                    schema.insert(key.clone(), dtype.clone());
                }
            }
        }

        // Add aggregation columns to schema (simplified)
        for agg in &aggregations {
            match agg {
                Aggregation::Sum(col)
                | Aggregation::Mean(col)
                | Aggregation::Min(col)
                | Aggregation::Max(col) => {
                    schema.insert(format!("{}_agg", col), "f64".to_string()); // Simplified
                }
                Aggregation::Count(col) => {
                    schema.insert(format!("{}_count", col), "i32".to_string());
                }
            }
        }

        let logical_plan = LogicalPlan::GroupBy {
            input: Box::new(self.input.logical_plan),
            keys: self.keys,
            aggregations,
            schema,
        };

        LazyDataFrame { logical_plan }
    }
}

/// Helper function to create a column expression
pub fn col(name: &str) -> Expr {
    Expr::Column(name.to_string())
}

/// Helper function to create a literal expression
pub fn lit(value: Value) -> Expr {
    Expr::Literal(value)
}

/// Helper function to create a binary operation expression
pub fn binary_op(left: Expr, op: BinaryOperator, right: Expr) -> Expr {
    Expr::BinaryOp {
        left: Box::new(left),
        op,
        right: Box::new(right),
    }
}