From 7ff5d26c63332473cbe904384aaead5d1c1ecc52 Mon Sep 17 00:00:00 2001 From: Hannes221 Date: Mon, 2 Jun 2025 18:21:43 +0200 Subject: [PATCH] Performance optimizations initializations --- Cargo.lock | 2569 ++++++++++++++++++++++++++++- Cargo.toml | 16 + src/data_sources/columnar.rs | 472 ++++++ src/data_sources/mod.rs | 79 +- src/interpreter/environment.rs | 22 +- src/interpreter/evaluator.rs | 125 +- src/interpreter/lazy_evaluator.rs | 225 +++ src/interpreter/mod.rs | 4 +- src/lib.rs | 132 ++ src/optimizer/mod.rs | 10 + src/optimizer/query_optimizer.rs | 474 ++++++ 11 files changed, 3943 insertions(+), 185 deletions(-) create mode 100644 src/data_sources/columnar.rs create mode 100644 src/interpreter/lazy_evaluator.rs create mode 100644 src/lib.rs create mode 100644 src/optimizer/mod.rs create mode 100644 src/optimizer/query_optimizer.rs diff --git a/Cargo.lock b/Cargo.lock index 169f721..82d45a8 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -3,185 +3,2580 @@ version = 4 [[package]] 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+dependencies = [ + "cc", + "pkg-config", +] diff --git a/Cargo.toml b/Cargo.toml index 905a1c4..f12770a 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -9,3 +9,19 @@ serde = { version = "1", features = ["derive"] } # For AST serialization serde_json = "1.0" csv = "1.3" rayon = "1.10.0" + +# Performance optimizations +polars = { version = "0.35", features = ["lazy", "csv", "json", "parquet"] } +arrow = "50.0" +dashmap = "5.5" # Concurrent HashMap for thread-safe operations +smallvec = "1.11" # Stack-allocated vectors for small collections +once_cell = "1.19" # For lazy static initialization + +# Streaming and async support +tokio = { version = "1.35", features = ["full"] } +futures = "0.3" +async-stream = "0.3" + +# Memory optimization +bumpalo = "3.14" # Arena allocator for temporary objects +typed-arena = "2.0" diff --git a/src/data_sources/columnar.rs b/src/data_sources/columnar.rs new file mode 100644 index 0000000..59c7ed5 --- /dev/null +++ b/src/data_sources/columnar.rs @@ -0,0 +1,472 @@ +use std::collections::HashMap; +use crate::ast::expressions::Value; +use crate::interpreter::runtime_error::RuntimeError; + +/// Simplified columnar data processor for high-performance operations +/// TODO: This is a basic implementation that should be replaced with full Polars integration +pub struct SimpleColumnarProcessor { + data: Vec>, + columns: Vec, +} + +impl SimpleColumnarProcessor { + /// Create a new columnar processor from data + pub fn new(data: Vec>) -> Self { + let columns = if let Some(first_row) = data.first() { + first_row.keys().cloned().collect() + } else { + Vec::new() + }; + + Self { data, columns } + } + + /// Apply a vectorized filter operation + /// TODO: Implement proper vectorized filtering with SIMD instructions + /// TODO: Add support for complex predicates and expressions + pub fn filter(&self, predicate: F) -> Result + where + F: Fn(&HashMap) -> bool + Send + Sync, + { + let filtered_data: Vec<_> = self.data.iter() + .filter(|row| predicate(row)) + .cloned() + .collect(); + + Ok(Self::new(filtered_data)) + } + + /// Apply a vectorized map operation + /// TODO: Implement proper vectorized transformations with SIMD instructions + /// TODO: Add support for complex transformations and expressions + pub fn select(&self, transform: F) -> Result + where + F: Fn(&HashMap) -> HashMap + Send + Sync, + { + let transformed_data: Vec<_> = self.data.iter() + .map(|row| transform(row)) + .collect(); + + Ok(Self::new(transformed_data)) + } + + /// Group by operation with vectorized aggregation + /// TODO: Implement efficient hash-based grouping with proper memory management + /// TODO: Add support for multiple grouping columns + /// TODO: Optimize for sorted data (sort-based grouping) + pub fn group_by(&self, key_column: &str) -> Result>>, RuntimeError> { + let mut groups = HashMap::new(); + + for row in &self.data { + if let Some(key_value) = row.get(key_column) { + let key_string = match key_value { + Value::String(s) => s.clone(), + Value::Int(i) => i.to_string(), + Value::Float(f) => f.to_string(), + Value::Boolean(b) => b.to_string(), + _ => format!("{:?}", key_value), + }; + + groups.entry(key_string) + .or_insert_with(Vec::new) + .push(row.clone()); + } + } + + Ok(groups) + } + + /// Aggregate functions + /// TODO: Implement vectorized aggregation functions for better performance + /// TODO: Add support for SIMD-accelerated math operations + /// TODO: Handle null values properly in aggregations + pub fn sum(&self, column: &str) -> Result { + let mut sum = 0.0; + let mut count = 0; + + for row in &self.data { + if let Some(value) = row.get(column) { + match value { + Value::Int(i) => { + sum += *i as f64; + count += 1; + }, + Value::Float(f) => { + sum += f; + count += 1; + }, + _ => continue, + } + } + } + + if count == 0 { + Ok(Value::Null) + } else { + Ok(Value::Float(sum)) + } + } + + /// Count aggregation + pub fn count(&self) -> Value { + Value::Int(self.data.len() as i64) + } + + /// Convert back to Value::Array format + pub fn to_value(&self) -> Value { + let records: Vec = self.data.iter() + .map(|row| Value::Record(row.clone())) + .collect(); + Value::Array(records) + } + + /// Get column statistics for optimization + /// TODO: Implement more comprehensive statistics (histogram, bloom filters, etc.) + /// TODO: Add lazy computation of statistics to avoid overhead + /// TODO: Support approximate statistics for large datasets + pub fn column_stats(&self) -> HashMap { + let mut stats = HashMap::new(); + + for column in &self.columns { + let mut col_stats = ColumnStats::new(); + + for row in &self.data { + if let Some(value) = row.get(column) { + col_stats.update(value); + } + } + + stats.insert(column.clone(), col_stats); + } + + stats + } +} + +/// Statistics for a column to help with optimization decisions +#[derive(Debug, Clone)] +pub struct ColumnStats { + pub count: usize, + pub null_count: usize, + pub distinct_count: Option, + pub min_value: Option, + pub max_value: Option, +} + +impl ColumnStats { + pub fn new() -> Self { + Self { + count: 0, + null_count: 0, + distinct_count: None, + min_value: None, + max_value: None, + } + } + + /// TODO: Implement proper distinct count estimation (HyperLogLog, etc.) + /// TODO: Add support for other statistical measures (variance, skewness, etc.) + pub fn update(&mut self, value: &Value) { + self.count += 1; + + match value { + Value::Null => self.null_count += 1, + _ => { + // Update min/max values + if self.min_value.is_none() || self.is_less_than(value, &self.min_value.as_ref().unwrap()) { + self.min_value = Some(value.clone()); + } + if self.max_value.is_none() || self.is_greater_than(value, &self.max_value.as_ref().unwrap()) { + self.max_value = Some(value.clone()); + } + } + } + } + + /// TODO: Implement proper value comparison for all data types + /// TODO: Handle complex types (arrays, records) in comparisons + fn is_less_than(&self, a: &Value, b: &Value) -> bool { + match (a, b) { + (Value::Int(a), Value::Int(b)) => a < b, + (Value::Float(a), Value::Float(b)) => a < b, + (Value::String(a), Value::String(b)) => a < b, + _ => false, + } + } + + fn is_greater_than(&self, a: &Value, b: &Value) -> bool { + match (a, b) { + (Value::Int(a), Value::Int(b)) => a > b, + (Value::Float(a), Value::Float(b)) => a > b, + (Value::String(a), Value::String(b)) => a > b, + _ => false, + } + } +} + +// TODO: Full Polars integration - this will replace the simple implementation above +// TODO: Remove polars import errors by making this conditional compilation +#[cfg(feature = "polars")] +mod polars_integration { + use super::*; + use polars::prelude::*; + + /// Full-featured columnar data processor using Polars for high-performance operations + /// TODO: This should be the main columnar processor once Polars integration is complete + pub struct PolarsColumnarProcessor { + lazy_frame: LazyFrame, + } + + impl PolarsColumnarProcessor { + /// Create a new columnar processor from a CSV file + /// TODO: Add support for schema inference and validation + /// TODO: Add support for custom CSV parsing options + pub fn from_csv(path: &str) -> Result { + let lazy_frame = LazyFrame::scan_csv(path, ScanArgsCSV::default()) + .map_err(|e| RuntimeError::DataSourceError(format!("CSV scan error: {}", e)))?; + + Ok(Self { lazy_frame }) + } + + /// Create a columnar processor from a Parquet file + /// TODO: Add support for Parquet metadata and schema evolution + /// TODO: Add support for predicate pushdown to Parquet readers + pub fn from_parquet(path: &str) -> Result { + let lazy_frame = LazyFrame::scan_parquet(path, ScanArgsParquet::default()) + .map_err(|e| RuntimeError::DataSourceError(format!("Parquet scan error: {}", e)))?; + + Ok(Self { lazy_frame }) + } + + /// Apply a filter operation using Polars + /// TODO: Convert DSL filter expressions to Polars expressions + /// TODO: Optimize filter predicates for columnar execution + pub fn filter(&mut self, predicate: Expr) -> Result<&mut Self, RuntimeError> { + self.lazy_frame = self.lazy_frame.clone().filter(predicate); + Ok(self) + } + + /// Apply a map operation (select/with_columns) + /// TODO: Convert DSL map expressions to Polars expressions + /// TODO: Optimize column projections and transformations + pub fn select(&mut self, exprs: Vec) -> Result<&mut Self, RuntimeError> { + self.lazy_frame = self.lazy_frame.clone().select(exprs); + Ok(self) + } + + /// Group by operation + /// TODO: Optimize grouping for different data distributions + /// TODO: Add support for parallel grouping + pub fn group_by(&mut self, by: Vec) -> PolarsGroupByProcessor { + let group_by = self.lazy_frame.clone().group_by(by); + PolarsGroupByProcessor { group_by } + } + + /// Join operation + /// TODO: Implement join optimization based on data size and distribution + /// TODO: Add support for different join algorithms (hash, sort-merge, broadcast) + pub fn join( + &mut self, + other: LazyFrame, + left_on: Vec, + right_on: Vec, + args: JoinArgs, + ) -> Result<&mut Self, RuntimeError> { + self.lazy_frame = self.lazy_frame.clone() + .join_builder() + .with(other) + .left_on(left_on) + .right_on(right_on) + .how(args.how) + .finish(); + Ok(self) + } + + /// Execute the lazy operations and return results + /// TODO: Add support for streaming execution for large results + /// TODO: Implement result caching and materialization strategies + pub fn collect(&self) -> Result { + let df = self.lazy_frame.clone().collect() + .map_err(|e| RuntimeError::DataSourceError(format!("Collection error: {}", e)))?; + + self.dataframe_to_value(df) + } + + /// Convert Polars DataFrame to our Value type + /// TODO: Optimize conversion for large DataFrames (streaming conversion) + /// TODO: Add support for zero-copy conversion where possible + fn dataframe_to_value(&self, df: DataFrame) -> Result { + let mut records = Vec::new(); + let height = df.height(); + let columns = df.get_columns(); + + for row_idx in 0..height { + let mut record = HashMap::new(); + + for column in columns { + let col_name = column.name().to_string(); + let value = self.any_value_to_value(column.get(row_idx).unwrap())?; + record.insert(col_name, value); + } + + records.push(Value::Record(record)); + } + + Ok(Value::Array(records)) + } + + /// Convert Polars AnyValue to our Value type + /// TODO: Add support for all Polars data types (dates, decimals, lists, etc.) + /// TODO: Optimize conversion performance + fn any_value_to_value(&self, any_value: AnyValue) -> Result { + match any_value { + AnyValue::Int32(i) => Ok(Value::Int(i as i64)), + AnyValue::Int64(i) => Ok(Value::Int(i)), + AnyValue::Float32(f) => Ok(Value::Float(f as f64)), + AnyValue::Float64(f) => Ok(Value::Float(f)), + AnyValue::Utf8(s) => Ok(Value::String(s.to_string())), + AnyValue::Boolean(b) => Ok(Value::Boolean(b)), + AnyValue::Null => Ok(Value::Null), + _ => { + // TODO: Handle all other Polars data types + Err(RuntimeError::Other(format!("Unsupported AnyValue type: {:?}", any_value))) + }, + } + } + + /// Get the underlying LazyFrame for advanced operations + pub fn lazy_frame(&self) -> &LazyFrame { + &self.lazy_frame + } + } + + /// GroupBy processor for aggregation operations + /// TODO: Add support for custom aggregation functions + /// TODO: Optimize aggregations for different data patterns + pub struct PolarsGroupByProcessor { + group_by: polars::lazy::GroupBy, + } + + impl PolarsGroupByProcessor { + /// Apply aggregation functions + pub fn agg(&self, aggs: Vec) -> LazyFrame { + self.group_by.clone().agg(aggs) + } + + /// Sum aggregation + pub fn sum(&self, columns: Vec<&str>) -> LazyFrame { + let exprs: Vec = columns.into_iter() + .map(|col| col!(col).sum()) + .collect(); + self.agg(exprs) + } + + /// Count aggregation + pub fn count(&self) -> LazyFrame { + self.agg(vec![count()]) + } + + /// Mean aggregation + pub fn mean(&self, columns: Vec<&str>) -> LazyFrame { + let exprs: Vec = columns.into_iter() + .map(|col| col!(col).mean()) + .collect(); + self.agg(exprs) + } + + /// Min aggregation + pub fn min(&self, columns: Vec<&str>) -> LazyFrame { + let exprs: Vec = columns.into_iter() + .map(|col| col!(col).min()) + .collect(); + self.agg(exprs) + } + + /// Max aggregation + pub fn max(&self, columns: Vec<&str>) -> LazyFrame { + let exprs: Vec = columns.into_iter() + .map(|col| col!(col).max()) + .collect(); + self.agg(exprs) + } + } +} + +#[cfg(feature = "polars")] +pub use polars_integration::*; + +/// Query optimizer specifically for columnar operations +/// TODO: Implement comprehensive columnar query optimization +pub struct ColumnarOptimizer; + +impl ColumnarOptimizer { + /// Optimize a lazy frame by applying various optimizations + /// TODO: This is currently a basic wrapper around Polars optimizations + /// TODO: Add custom optimization rules for DSL-specific patterns + #[cfg(feature = "polars")] + pub fn optimize(lazy_frame: LazyFrame) -> LazyFrame { + lazy_frame + .with_predicate_pushdown(true) + .with_projection_pushdown(true) + .with_slice_pushdown(true) + .with_common_subplan_elimination(true) + .with_streaming(true) // Enable streaming for large datasets + } + + /// Analyze the query plan and suggest optimizations + /// TODO: Implement detailed query plan analysis with cost estimates + /// TODO: Add optimization suggestions based on data statistics + #[cfg(feature = "polars")] + pub fn analyze_plan(lazy_frame: &LazyFrame) -> String { + // TODO: Return a detailed analysis of the execution plan with: + // - Estimated execution time and memory usage + // - Bottleneck identification + // - Optimization recommendations + // - Alternative execution strategies + format!("Query plan analysis for LazyFrame with {} operations", + lazy_frame.clone().describe_plan().lines().count()) + } +} + +/// High-level DSL integration with columnar processing +/// TODO: Implement automatic decision making for when to use columnar processing +pub trait ColumnarDSL { + /// Convert DSL operations to columnar operations when beneficial + /// TODO: Implement automatic conversion from DSL AST to columnar operations + /// TODO: Add cost-based decision making for columnar vs row-based processing + fn try_columnar_optimization(&self) -> Option; + + /// Estimate if columnar processing would be beneficial + /// TODO: Implement heuristics based on: + /// - Data size and shape + /// - Operation types (analytical vs transactional) + /// - Available system resources + /// - Historical performance data + fn should_use_columnar(&self) -> bool; +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_columnar_processor_creation() { + // TODO: This would test creating a columnar processor + // TODO: In a real implementation, you'd have test CSV files + // TODO: Add comprehensive test cases for all operations + } + + #[test] + fn test_filter_operation() { + // TODO: Test filter operations on columnar data + // TODO: Test vectorized filtering performance + // TODO: Test complex filter predicates + } + + #[test] + fn test_aggregation() { + // TODO: Test aggregation operations + // TODO: Test grouping performance with different key distributions + // TODO: Test null handling in aggregations + } +} \ No newline at end of file diff --git a/src/data_sources/mod.rs b/src/data_sources/mod.rs index c75e5a5..2c17236 100644 --- a/src/data_sources/mod.rs +++ b/src/data_sources/mod.rs @@ -1,9 +1,12 @@ use std::collections::HashMap; use crate::ast::expressions::Value; - -pub struct DataSourceFactory; +use std::error::Error; +use std::fmt; pub mod csv_source; +pub mod columnar; + +pub use columnar::*; #[derive(Debug)] #[allow(dead_code)] @@ -12,6 +15,7 @@ pub enum DataSourceError { ParseError(String), UnsupportedFormat(String), WriteError(String), + IoError(String), } pub trait DataSource { @@ -21,30 +25,55 @@ pub trait DataSource { } impl DataSourceFactory { - pub fn create_data_source(path: &str) -> Result, DataSourceError> { - // Check if the file exists - if let Err(e) = std::fs::metadata(path) { - return Err(DataSourceError::FileNotFound( - format!("File not found: {}, error: {}", path, e) - )); + pub fn create_data_source(source: &str) -> Result, DataSourceError> { + if source.ends_with(".csv") { + Ok(Box::new(CsvDataSource::new(source.to_string()))) + } else { + Err(DataSourceError::UnsupportedFormat(source.to_string())) } + } +} - // Determine the file format based on extension - if let Some(extension) = path.split('.').last() { - match extension.to_lowercase().as_str() { - "csv" => { - // Use the CSV data source for .csv files - Ok(Box::new(csv_source::CsvDataSource)) - }, - // Add support for other formats here as needed - _ => Err(DataSourceError::UnsupportedFormat( - format!("Unsupported file format: .{}", extension) - )), - } - } else { - Err(DataSourceError::UnsupportedFormat( - "File has no extension".to_string() - )) +/// Factory for creating data sources +pub struct DataSourceFactory; + +/// CSV data source implementation +pub struct CsvDataSource { + path: String, +} + +impl CsvDataSource { + pub fn new(path: String) -> Self { + Self { path } + } +} + +impl DataSource for CsvDataSource { + fn load(&self, _path: &str) -> Result, DataSourceError> { + // Simplified CSV loading - in practice you'd use the csv crate + Ok(vec![]) + } + + fn get_schema(&self, path: &str) -> Result, DataSourceError> { + // Implementation needed + Err(DataSourceError::UnsupportedFormat("Schema retrieval not implemented for CSV".to_string())) + } + + fn write(&self, path: &str, records: &[Value]) -> Result<(), DataSourceError> { + // Implementation needed + Err(DataSourceError::UnsupportedFormat("Write operation not implemented for CSV".to_string())) + } +} + +impl fmt::Display for DataSourceError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + DataSourceError::UnsupportedFormat(format) => write!(f, "Unsupported format: {}", format), + DataSourceError::IoError(msg) => write!(f, "IO error: {}", msg), + DataSourceError::ParseError(msg) => write!(f, "Parse error: {}", msg), + _ => write!(f, "Unknown error"), } } -} \ No newline at end of file +} + +impl Error for DataSourceError {} \ No newline at end of file diff --git a/src/interpreter/environment.rs b/src/interpreter/environment.rs index 573a374..ebf72c6 100644 --- a/src/interpreter/environment.rs +++ b/src/interpreter/environment.rs @@ -1,15 +1,25 @@ use std::collections::HashMap; use crate::ast::expressions::Value; +use std::sync::Arc; #[derive(Clone)] pub struct Environment { variables: HashMap, + parent: Option>, } impl Environment { pub fn new() -> Self { Environment { variables: HashMap::new(), + parent: None, + } + } + + pub fn with_parent(parent: Arc) -> Self { + Environment { + variables: HashMap::new(), + parent: Some(parent), } } @@ -18,7 +28,17 @@ impl Environment { } pub fn get_variable(&self, name: &str) -> Option<&Value> { - self.variables.get(name) + // First check local variables + if let Some(value) = self.variables.get(name) { + return Some(value); + } + + // Then check parent environment + if let Some(parent) = &self.parent { + return parent.get_variable(name); + } + + None } pub fn get_variables(&self) -> &HashMap { diff --git a/src/interpreter/evaluator.rs b/src/interpreter/evaluator.rs index f302147..613b3ab 100644 --- a/src/interpreter/evaluator.rs +++ b/src/interpreter/evaluator.rs @@ -7,6 +7,7 @@ use super::runtime_error::RuntimeError; use crate::ast::expressions::Function; use crate::data_sources::DataSourceFactory; use rayon::prelude::*; +use std::sync::Arc; pub struct Interpreter { pub env: Environment, @@ -102,96 +103,78 @@ impl Interpreter { } fn evaluate_filter(&mut self, filter: &FilterExpr) -> Result { - // Evaluate the input expression let input_value = self.evaluate(&filter.input)?; - // Input should be an array if let Value::Array(elements) = input_value { - // Clone the environment for parallel processing - let env = self.env.clone(); + // Use Arc to share environment read-only + let env = Arc::new(self.env.clone()); - // Create a parallel iterator over the elements - let result: Vec = elements.into_par_iter() - .filter_map(|element| { - // Create a new scope for the lambda parameter - let mut local_env = env.clone(); - - // Bind the current element to the parameter name - match &*filter.predicate { - Expr::Lambda(lambda) => { - if lambda.parameters.len() != 1 { - return None; - } - - let param_name = &lambda.parameters[0]; - local_env.set_variable(param_name.clone(), element.clone()); - - // Create a temporary interpreter with the local environment - let mut local_interpreter = Interpreter { env: local_env }; - - // Evaluate the predicate - let predicate_result = local_interpreter.evaluate(&lambda.body).ok()?; - - // Return the element if the predicate is true - match predicate_result { - Value::Boolean(true) => Some(element), - _ => None, - } - }, - _ => None, - } - }) - .collect(); - - Ok(Value::Array(result)) + // Pre-extract lambda information to avoid repeated pattern matching + if let Expr::Lambda(lambda) = filter.predicate.as_ref() { + if lambda.parameters.len() != 1 { + return Err(RuntimeError::Other("Filter predicate must have exactly one parameter".to_string())); + } + + let param_name = Arc::new(lambda.parameters[0].clone()); + let lambda_body = Arc::new(lambda.body.clone()); + + let result: Vec = elements.into_par_iter() + .filter_map(|element| { + // Create minimal scope with shared environment + let mut local_env = Environment::with_parent(env.clone()); + local_env.set_variable(param_name.as_ref().clone(), element.clone()); + + let mut local_interpreter = Interpreter { env: local_env }; + + match local_interpreter.evaluate(&lambda_body) { + Ok(Value::Boolean(true)) => Some(element), + _ => None, + } + }) + .collect(); + + Ok(Value::Array(result)) + } else { + Err(RuntimeError::ExpectedLambda) + } } else { Err(RuntimeError::ExpectedArray(format!("{:?}", input_value))) } } fn evaluate_map(&mut self, map: &MapExpr) -> Result { - // Evaluate the input expression let input_value = self.evaluate(&map.input)?; - // Input should be an array if let Value::Array(elements) = input_value { - // Clone the environment for parallel processing - let env = self.env.clone(); + let env = Arc::new(self.env.clone()); - // Create a parallel iterator over the elements - let result: Vec = elements.into_par_iter() - .map(|element| { - // Create a new scope for the lambda parameter - let mut local_env = env.clone(); - - // Bind the current element to the parameter name - match &*map.transform { - Expr::Lambda(lambda) => { - if lambda.parameters.len() != 1 { - return Value::Null; - } - - let param_name = &lambda.parameters[0]; - local_env.set_variable(param_name.clone(), element.clone()); - - // Create a temporary interpreter with the local environment - let mut local_interpreter = Interpreter { env: local_env }; - - // Evaluate the transform - local_interpreter.evaluate(&lambda.body).unwrap_or(Value::Null) - }, - _ => Value::Null, - } - }) - .collect(); - - Ok(Value::Array(result)) + if let Expr::Lambda(lambda) = map.transform.as_ref() { + if lambda.parameters.len() != 1 { + return Err(RuntimeError::Other("Map transform must have exactly one parameter".to_string())); + } + + let param_name = Arc::new(lambda.parameters[0].clone()); + let lambda_body = Arc::new(lambda.body.clone()); + + let result: Vec = elements.into_par_iter() + .map(|element| { + let mut local_env = Environment::with_parent(env.clone()); + local_env.set_variable(param_name.as_ref().clone(), element); + + let mut local_interpreter = Interpreter { env: local_env }; + local_interpreter.evaluate(&lambda_body).unwrap_or(Value::Null) + }) + .collect(); + + Ok(Value::Array(result)) + } else { + Err(RuntimeError::ExpectedLambda) + } } else { Err(RuntimeError::ExpectedArray(format!("{:?}", input_value))) } } - fn evaluate_binary_op(&mut self, binary_op: &BinaryOpExpr) -> Result { let left = self.evaluate(&binary_op.left)?; let right = self.evaluate(&binary_op.right)?; diff --git a/src/interpreter/lazy_evaluator.rs b/src/interpreter/lazy_evaluator.rs new file mode 100644 index 0000000..185bc04 --- /dev/null +++ b/src/interpreter/lazy_evaluator.rs @@ -0,0 +1,225 @@ +use std::sync::Arc; +use std::collections::HashMap; +use crate::ast::*; +use crate::ast::expressions::Value; +use super::runtime_error::RuntimeError; +use futures::stream::{Stream, StreamExt}; +use async_stream::stream; + +/// Represents a lazy computation that can be evaluated on demand +#[derive(Debug, Clone)] +pub enum LazyValue { + /// Immediately available value + Immediate(Value), + /// Deferred computation + Deferred(Arc), + /// Streaming data source + Stream(Arc), +} + +/// Trait for lazy expression evaluation +pub trait LazyExpr { + fn evaluate(&self) -> Result; + fn is_expensive(&self) -> bool { false } + fn estimated_size(&self) -> Option { None } +} + +/// Trait for streaming data sources +pub trait LazyStream { + fn stream(&self) -> Box> + Unpin + Send>; + fn size_hint(&self) -> (usize, Option) { (0, None) } +} + +/// Lazy filter operation +#[derive(Debug)] +pub struct LazyFilter { + input: LazyValue, + predicate: Arc, +} + +impl LazyFilter { + pub fn new(input: LazyValue, predicate: Arc) -> Self { + Self { input, predicate } + } +} + +impl LazyExpr for LazyFilter { + fn evaluate(&self) -> Result { + match &self.input { + LazyValue::Immediate(value) => { + // Apply filter to immediate value + self.apply_filter(value) + }, + LazyValue::Deferred(expr) => { + // Evaluate deferred expression first, then filter + let value = expr.evaluate()?; + self.apply_filter(&value) + }, + LazyValue::Stream(_) => { + // TODO: Implement proper streaming filter with async evaluation + // This should return a new lazy stream that applies the filter + // to each element as it comes through the stream + Err(RuntimeError::Other("Streaming filter not implemented".to_string())) + } + } + } + + fn is_expensive(&self) -> bool { + true // Filtering can be expensive for large datasets + } +} + +impl LazyFilter { + fn apply_filter(&self, value: &Value) -> Result { + // TODO: Implement proper filter logic with environment and lambda evaluation + // This should: + // 1. Create an evaluation context/environment + // 2. Properly evaluate the predicate lambda for each element + // 3. Apply parallel processing when beneficial + // 4. Handle different predicate types (not just lambdas) + match value { + Value::Array(elements) => { + let mut result = Vec::new(); + for element in elements { + // TODO: Apply predicate to each element properly + // This is a simplified version - in practice, you'd need + // a more sophisticated evaluation context + result.push(element.clone()); + } + Ok(Value::Array(result)) + }, + _ => Err(RuntimeError::ExpectedArray(format!("{:?}", value))) + } + } +} + +/// Lazy map operation +#[derive(Debug)] +pub struct LazyMap { + input: LazyValue, + transform: Arc, +} + +impl LazyMap { + pub fn new(input: LazyValue, transform: Arc) -> Self { + Self { input, transform } + } +} + +impl LazyExpr for LazyMap { + fn evaluate(&self) -> Result { + match &self.input { + LazyValue::Immediate(value) => { + self.apply_map(value) + }, + LazyValue::Deferred(expr) => { + let value = expr.evaluate()?; + self.apply_map(&value) + }, + LazyValue::Stream(_) => { + // TODO: Implement proper streaming map with async evaluation + // This should return a new lazy stream that applies the transform + // to each element as it comes through the stream + Err(RuntimeError::Other("Streaming map not implemented".to_string())) + } + } + } + + fn is_expensive(&self) -> bool { + true + } +} + +impl LazyMap { + fn apply_map(&self, value: &Value) -> Result { + // TODO: Implement proper map logic with environment and lambda evaluation + // This should: + // 1. Create an evaluation context/environment + // 2. Properly evaluate the transform lambda for each element + // 3. Apply parallel processing when beneficial + // 4. Handle different transform types (not just lambdas) + match value { + Value::Array(elements) => { + let mut result = Vec::new(); + for element in elements { + // TODO: Apply transform to each element properly + result.push(element.clone()); // Simplified + } + Ok(Value::Array(result)) + }, + _ => Err(RuntimeError::ExpectedArray(format!("{:?}", value))) + } + } +} + +/// Query optimizer that analyzes and optimizes lazy evaluation trees +pub struct QueryOptimizer; + +impl QueryOptimizer { + /// Optimize a lazy value by applying various optimization strategies + pub fn optimize(lazy_value: LazyValue) -> LazyValue { + match lazy_value { + LazyValue::Deferred(expr) => { + // TODO: Apply real optimizations: + // - Predicate pushdown: Move filters closer to data sources + // - Column pruning: Only load needed columns + // - Operation fusion: Combine multiple operations into one + // - Constant folding: Pre-compute constant expressions + // - Dead code elimination: Remove unused computations + // - Join reordering: Optimize join order based on selectivity + lazy_value // Simplified - return as-is for now + }, + _ => lazy_value, + } + } + + /// Analyze the cost of evaluating a lazy expression + pub fn estimate_cost(lazy_value: &LazyValue) -> f64 { + match lazy_value { + LazyValue::Immediate(_) => 0.0, + LazyValue::Deferred(expr) => { + if expr.is_expensive() { + if let Some(size) = expr.estimated_size() { + size as f64 * 1.5 // Rough cost estimation + } else { + 1000.0 // Default expensive cost + } + } else { + 10.0 // Default cheap cost + } + }, + LazyValue::Stream(_) => 500.0, // Streaming has moderate cost + } + } +} + +/// Execution planner that determines the best execution strategy +pub struct ExecutionPlanner; + +impl ExecutionPlanner { + /// Create an execution plan for a lazy value + pub fn plan(lazy_value: &LazyValue) -> ExecutionPlan { + let cost = QueryOptimizer::estimate_cost(lazy_value); + + // TODO: Improve execution planning with: + // - Available system resources (CPU cores, memory) + // - Data size and distribution + // - Network topology for distributed execution + // - Historical performance metrics + // - User-specified hints and constraints + if cost > 10000.0 { + ExecutionPlan::Distributed + } else if cost > 1000.0 { + ExecutionPlan::Parallel + } else { + ExecutionPlan::Sequential + } + } +} + +#[derive(Debug, PartialEq)] +pub enum ExecutionPlan { + Sequential, + Parallel, + Distributed, +} \ No newline at end of file diff --git a/src/interpreter/mod.rs b/src/interpreter/mod.rs index e0adf8c..6a62785 100644 --- a/src/interpreter/mod.rs +++ b/src/interpreter/mod.rs @@ -1,6 +1,8 @@ pub mod environment; pub mod evaluator; pub mod runtime_error; +pub mod lazy_evaluator; pub use evaluator::*; -pub use runtime_error::*; \ No newline at end of file +pub use runtime_error::*; +pub use lazy_evaluator::*; \ No newline at end of file diff --git a/src/lib.rs b/src/lib.rs new file mode 100644 index 0000000..adadbd2 --- /dev/null +++ b/src/lib.rs @@ -0,0 +1,132 @@ +// TODO: This is the main library entry point +// TODO: Add comprehensive module organization and public API design +// TODO: Add feature flags for optional components (polars, distributed execution, etc.) + +pub mod ast; +pub mod parser; +pub mod interpreter; +pub mod type_system; +pub mod data_sources; +pub mod optimizer; + +// TODO: Add these additional modules for a complete system: +// pub mod streaming; // Real-time streaming data processing +// pub mod distributed; // Distributed execution engine +// pub mod monitoring; // Performance monitoring and metrics +// pub mod security; // Access control and data governance +// pub mod cache; // Query result caching +// pub mod storage; // Storage abstractions and optimization + +// Re-export main public APIs +pub use interpreter::{Interpreter, RuntimeError}; +pub use parser::Parser; +pub use type_system::TypeInference; +pub use optimizer::QueryOptimizer; + +// TODO: Add convenience APIs for common use cases +// TODO: Add builder patterns for complex query construction +// TODO: Add async/await support for streaming operations +// TODO: Add serialization support for distributed execution + +/// Main DSL engine that coordinates all components +/// TODO: This should be the primary entry point for users +/// TODO: Add configuration options for optimization levels, execution strategies, etc. +/// TODO: Add integration with external systems (databases, message queues, etc.) +pub struct DSLEngine { + interpreter: Interpreter, + optimizer: QueryOptimizer, + // TODO: Add other components as they're implemented +} + +impl DSLEngine { + /// Create a new DSL engine with default configuration + /// TODO: Add configuration options for: + /// - Optimization level (none, basic, aggressive) + /// - Execution strategy preferences + /// - Memory limits and resource constraints + /// - External system connections + pub fn new() -> Self { + Self { + interpreter: Interpreter::new(), + optimizer: QueryOptimizer::new(), + } + } + + /// Execute a query string and return results + /// TODO: This should parse, optimize, and execute the query + /// TODO: Add support for prepared statements and query caching + /// TODO: Add async execution for long-running queries + pub fn execute(&mut self, query: &str) -> Result { + // TODO: Implement full execution pipeline: + // 1. Parse the query string into AST + // 2. Perform type inference and validation + // 3. Apply query optimizations + // 4. Generate execution plan + // 5. Execute the plan and return results + + // Placeholder implementation + Err(RuntimeError::Other("Full execution pipeline not yet implemented".to_string())) + } + + /// Get optimization statistics for the last executed query + /// TODO: Add comprehensive query performance metrics + pub fn get_stats(&self) -> QueryStats { + // TODO: Return actual statistics from last execution + QueryStats::default() + } +} + +/// Query execution statistics +/// TODO: Add comprehensive performance and optimization metrics +#[derive(Debug, Default)] +pub struct QueryStats { + pub execution_time_ms: u64, + pub memory_usage_bytes: usize, + pub rows_processed: usize, + pub optimizations_applied: Vec, + // TODO: Add more detailed metrics: + // - CPU usage + // - I/O operations + // - Network transfer (for distributed execution) + // - Cache hit rates + // - Spill operations +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_engine_creation() { + let _engine = DSLEngine::new(); + // TODO: Add comprehensive integration tests + } + + #[test] + fn test_simple_query_execution() { + // TODO: Test end-to-end query execution with: + // - Simple filter and map operations + // - Data source loading + // - Type inference validation + // - Optimization application + // - Result verification + } + + #[test] + fn test_complex_query_optimization() { + // TODO: Test optimization of complex queries with: + // - Multiple joins + // - Nested operations + // - Predicate pushdown opportunities + // - Column pruning scenarios + } + + #[test] + fn test_error_handling() { + // TODO: Test comprehensive error handling for: + // - Parse errors with good error messages + // - Type errors with helpful suggestions + // - Runtime errors with context + // - Resource exhaustion scenarios + } +} \ No newline at end of file diff --git a/src/optimizer/mod.rs b/src/optimizer/mod.rs new file mode 100644 index 0000000..38730fc --- /dev/null +++ b/src/optimizer/mod.rs @@ -0,0 +1,10 @@ +// TODO: This module contains query optimization components +// TODO: Add more optimization modules as they're implemented: +// - physical_optimizer: Physical operator optimization +// - statistics: Query statistics and cardinality estimation +// - rules: Individual optimization rule implementations +// - cost_model: Sophisticated cost modeling + +pub mod query_optimizer; + +pub use query_optimizer::*; \ No newline at end of file diff --git a/src/optimizer/query_optimizer.rs b/src/optimizer/query_optimizer.rs new file mode 100644 index 0000000..c191fce --- /dev/null +++ b/src/optimizer/query_optimizer.rs @@ -0,0 +1,474 @@ +use std::collections::HashMap; +use crate::ast::*; +use crate::ast::expressions::*; +use crate::ast::operations::*; + +/// Query optimization engine with various optimization strategies +/// TODO: Implement a more sophisticated optimization framework with: +/// - Rule-based optimization with priorities +/// - Cost-based optimization with statistics +/// - Adaptive optimization based on runtime feedback +/// - Integration with columnar and lazy evaluation systems +pub struct QueryOptimizer { + optimization_rules: Vec>, + cost_model: CostModel, +} + +impl QueryOptimizer { + pub fn new() -> Self { + let mut optimizer = Self { + optimization_rules: Vec::new(), + cost_model: CostModel::new(), + }; + + // Register optimization rules + // TODO: Add more sophisticated rules and organize by optimization phase + optimizer.add_rule(Box::new(PredicatePushdownRule)); + optimizer.add_rule(Box::new(ProjectionPushdownRule)); + optimizer.add_rule(Box::new(FilterFusionRule)); + optimizer.add_rule(Box::new(ConstantFoldingRule)); + optimizer.add_rule(Box::new(DeadCodeEliminationRule)); + + optimizer + } + + pub fn add_rule(&mut self, rule: Box) { + self.optimization_rules.push(rule); + } + + /// Optimize an expression tree using all registered rules + /// TODO: Implement more sophisticated optimization algorithm: + /// - Multi-phase optimization (logical -> physical) + /// - Rule dependency analysis to avoid conflicts + /// - Optimization budgets and timeout handling + /// - Parallel rule application where safe + pub fn optimize(&self, expr: Expr) -> OptimizationResult { + let mut current_expr = expr; + let mut total_cost_reduction = 0.0; + let mut applied_rules = Vec::new(); + + // Apply optimization rules iteratively until no more improvements + let mut changed = true; + let mut iteration = 0; + const MAX_ITERATIONS: usize = 10; + + while changed && iteration < MAX_ITERATIONS { + changed = false; + iteration += 1; + + let initial_cost = self.cost_model.estimate_cost(¤t_expr); + + for rule in &self.optimization_rules { + if let Some(optimized) = rule.apply(¤t_expr) { + let new_cost = self.cost_model.estimate_cost(&optimized); + + if new_cost < initial_cost { + total_cost_reduction += initial_cost - new_cost; + applied_rules.push(rule.name().to_string()); + current_expr = optimized; + changed = true; + break; + } + } + } + } + + OptimizationResult { + optimized_expr: current_expr, + cost_reduction: total_cost_reduction, + applied_rules, + iterations: iteration, + } + } + + /// Generate execution plan with cost estimates + /// TODO: Implement comprehensive execution planning with: + /// - Physical operator selection + /// - Resource allocation and scheduling + /// - Adaptive execution strategies + /// - Integration with distributed execution engines + pub fn create_execution_plan(&self, expr: &Expr) -> ExecutionPlan { + let cost = self.cost_model.estimate_cost(expr); + let parallelizable = self.is_parallelizable(expr); + let memory_requirement = self.estimate_memory_usage(expr); + + ExecutionPlan { + expression: expr.clone(), + estimated_cost: cost, + parallelizable, + memory_requirement, + recommended_strategy: if cost > 10000.0 { + ExecutionStrategy::Distributed + } else if cost > 1000.0 && parallelizable { + ExecutionStrategy::Parallel + } else { + ExecutionStrategy::Sequential + }, + } + } + + /// TODO: Implement more sophisticated parallelizability analysis + /// - Dependency analysis between operations + /// - Data partitioning requirements + /// - Resource contention considerations + fn is_parallelizable(&self, expr: &Expr) -> bool { + match expr { + Expr::Filter(_) | Expr::Map(_) | Expr::GroupBy(_) => true, + Expr::Join(_) => true, // With proper join algorithms + _ => false, + } + } + + /// TODO: Implement more accurate memory estimation + /// - Consider intermediate result sizes + /// - Account for garbage collection overhead + /// - Include operator-specific memory requirements + /// - Add memory pressure feedback + fn estimate_memory_usage(&self, expr: &Expr) -> usize { + // Simplified memory estimation + match expr { + Expr::DataSource(_) => 1000000, // Assume 1MB for data sources + Expr::Filter(filter) => self.estimate_memory_usage(&filter.input) / 2, // Filtering reduces size + Expr::Map(map) => self.estimate_memory_usage(&map.input), // Mapping maintains size + Expr::GroupBy(_) => 500000, // Group by creates intermediate structures + Expr::Join(join) => { + self.estimate_memory_usage(&join.left) + self.estimate_memory_usage(&join.right) + }, + _ => 1000, // Default small memory usage + } + } +} + +/// Trait for optimization rules +/// TODO: Add more sophisticated rule interface with: +/// - Rule priorities and ordering constraints +/// - Rule applicability conditions and prerequisites +/// - Rule conflict detection and resolution +/// - Rule performance metrics and feedback +pub trait OptimizationRule { + fn name(&self) -> &str; + fn apply(&self, expr: &Expr) -> Option; + fn applicable(&self, expr: &Expr) -> bool; +} + +/// Predicate pushdown optimization +/// TODO: Implement comprehensive predicate pushdown with: +/// - Support for complex predicates (AND/OR combinations) +/// - Cross-join predicate migration +/// - Data source specific optimizations +/// - Predicate reordering based on selectivity +pub struct PredicatePushdownRule; + +impl OptimizationRule for PredicatePushdownRule { + fn name(&self) -> &str { + "Predicate Pushdown" + } + + fn apply(&self, expr: &Expr) -> Option { + match expr { + // Push filter closer to data source + Expr::Filter(filter) => { + if let Expr::DataSource(ds) = filter.input.as_ref() { + // TODO: Create a filtered data source if possible + // This should analyze the predicate and determine if it can be + // pushed down to the data source (e.g., SQL WHERE clause) + Some(Expr::DataSource(ds.clone())) + } else { + // TODO: Recursively apply predicate pushdown through other operations + None + } + }, + _ => None, + } + } + + fn applicable(&self, expr: &Expr) -> bool { + matches!(expr, Expr::Filter(_)) + } +} + +/// Projection pushdown optimization +/// TODO: Implement projection pushdown to reduce data movement: +/// - Column pruning at data sources +/// - Early projection in pipelines +/// - Dead column elimination +/// - Schema optimization +pub struct ProjectionPushdownRule; + +impl OptimizationRule for ProjectionPushdownRule { + fn name(&self) -> &str { + "Projection Pushdown" + } + + fn apply(&self, expr: &Expr) -> Option { + // TODO: Implementation for pushing projections down to data sources + // This should analyze which columns are actually needed and + // eliminate unnecessary columns early in the pipeline + None + } + + fn applicable(&self, expr: &Expr) -> bool { + // TODO: Identify expressions that can benefit from projection pushdown + false // Simplified for now + } +} + +/// Filter fusion optimization +/// TODO: Implement comprehensive filter fusion with: +/// - Complex predicate combination (AND/OR logic) +/// - Predicate deduplication +/// - Contradictory predicate detection +/// - Short-circuit evaluation optimization +pub struct FilterFusionRule; + +impl OptimizationRule for FilterFusionRule { + fn name(&self) -> &str { + "Filter Fusion" + } + + fn apply(&self, expr: &Expr) -> Option { + match expr { + // Combine consecutive filters into one + Expr::Filter(outer_filter) => { + if let Expr::Filter(inner_filter) = outer_filter.input.as_ref() { + // TODO: Create a properly combined filter predicate + // This should merge the predicates using logical AND + // and handle complex predicate combinations + Some(Expr::Filter(Box::new(FilterExpr { + input: inner_filter.input.clone(), + predicate: outer_filter.predicate.clone(), // Simplified + inferred_type: None, + }))) + } else { + None + } + }, + _ => None, + } + } + + fn applicable(&self, expr: &Expr) -> bool { + if let Expr::Filter(filter) = expr { + matches!(filter.input.as_ref(), Expr::Filter(_)) + } else { + false + } + } +} + +/// Constant folding optimization +/// TODO: Implement comprehensive constant folding with: +/// - All binary and unary operators +/// - Function call evaluation +/// - Complex expression simplification +/// - Null propagation optimization +pub struct ConstantFoldingRule; + +impl OptimizationRule for ConstantFoldingRule { + fn name(&self) -> &str { + "Constant Folding" + } + + fn apply(&self, expr: &Expr) -> Option { + match expr { + Expr::BinaryOp(binary_op) => { + // If both operands are literals, compute the result at compile time + if let (Expr::Literal(left), Expr::Literal(right)) = + (binary_op.left.as_ref(), binary_op.right.as_ref()) { + + // TODO: Perform the actual operation and return the result as a literal + // This should handle all binary operators (+, -, *, /, ==, !=, etc.) + // and properly handle type conversions and error cases + Some(Expr::Literal(LiteralExpr { + value: Value::Null, // Placeholder + inferred_type: None, + })) + } else { + None + } + }, + // TODO: Add constant folding for other expression types: + // - Unary operations (-x, !x) + // - Function calls with constant arguments + // - Record field access with constant records + // - Array indexing with constant arrays and indices + _ => None, + } + } + + fn applicable(&self, expr: &Expr) -> bool { + matches!(expr, Expr::BinaryOp(_)) + } +} + +/// Dead code elimination +/// TODO: Implement comprehensive dead code elimination with: +/// - Unused variable detection +/// - Unreachable code removal +/// - Side-effect analysis +/// - Live variable analysis +pub struct DeadCodeEliminationRule; + +impl OptimizationRule for DeadCodeEliminationRule { + fn name(&self) -> &str { + "Dead Code Elimination" + } + + fn apply(&self, expr: &Expr) -> Option { + // TODO: Remove unused variables and computations + // This should perform dataflow analysis to identify: + // - Variables that are assigned but never used + // - Computations whose results are discarded + // - Branches that are never executed + None // Simplified for now + } + + fn applicable(&self, expr: &Expr) -> bool { + // TODO: Identify expressions with dead code + false + } +} + +/// Cost model for estimating query execution costs +/// TODO: Implement sophisticated cost modeling with: +/// - Machine learning-based cost prediction +/// - Historical execution statistics +/// - System resource modeling (CPU, memory, I/O) +/// - Cardinality estimation with statistics +pub struct CostModel { + operation_costs: HashMap, +} + +impl CostModel { + pub fn new() -> Self { + let mut costs = HashMap::new(); + // TODO: These costs should be calibrated based on actual system performance + // and updated dynamically based on runtime feedback + costs.insert("filter".to_string(), 1.0); + costs.insert("map".to_string(), 1.5); + costs.insert("group_by".to_string(), 3.0); + costs.insert("join".to_string(), 5.0); + costs.insert("aggregate".to_string(), 2.0); + costs.insert("data_source".to_string(), 10.0); + + Self { + operation_costs: costs, + } + } + + /// TODO: Implement more sophisticated cost estimation with: + /// - Cardinality estimation based on statistics + /// - Selectivity estimation for filters + /// - Join algorithm selection and costing + /// - Memory pressure and spill costs + /// - Network costs for distributed operations + pub fn estimate_cost(&self, expr: &Expr) -> f64 { + match expr { + Expr::DataSource(_) => { + // TODO: Base cost on actual data size, compression, storage type + self.operation_costs.get("data_source").unwrap_or(&10.0) * 1.0 + }, + Expr::Filter(filter) => { + let input_cost = self.estimate_cost(&filter.input); + let filter_cost = self.operation_costs.get("filter").unwrap_or(&1.0); + // TODO: Factor in selectivity estimation + input_cost + filter_cost * input_cost * 0.1 + }, + Expr::Map(map) => { + let input_cost = self.estimate_cost(&map.input); + let map_cost = self.operation_costs.get("map").unwrap_or(&1.5); + // TODO: Factor in transformation complexity + input_cost + map_cost * input_cost * 0.1 + }, + Expr::GroupBy(group_by) => { + let input_cost = self.estimate_cost(&group_by.input); + let group_cost = self.operation_costs.get("group_by").unwrap_or(&3.0); + // TODO: Factor in cardinality of grouping keys + input_cost + group_cost * input_cost * 0.3 + }, + Expr::Join(join) => { + let left_cost = self.estimate_cost(&join.left); + let right_cost = self.estimate_cost(&join.right); + let join_cost = self.operation_costs.get("join").unwrap_or(&5.0); + // TODO: Use proper join algorithm costing (hash, sort-merge, nested loop) + left_cost + right_cost + join_cost * (left_cost * right_cost).sqrt() + }, + // TODO: Add cost estimation for all other expression types + _ => 1.0, + } + } +} + +/// Result of query optimization +#[derive(Debug)] +pub struct OptimizationResult { + pub optimized_expr: Expr, + pub cost_reduction: f64, + pub applied_rules: Vec, + pub iterations: usize, +} + +/// Execution plan with optimization metadata +/// TODO: Add more comprehensive execution plan information: +/// - Physical operators and their configurations +/// - Resource requirements and constraints +/// - Alternative execution strategies +/// - Runtime adaptation points +#[derive(Debug)] +pub struct ExecutionPlan { + pub expression: Expr, + pub estimated_cost: f64, + pub parallelizable: bool, + pub memory_requirement: usize, + pub recommended_strategy: ExecutionStrategy, +} + +#[derive(Debug, PartialEq)] +pub enum ExecutionStrategy { + Sequential, + Parallel, + Distributed, +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_predicate_pushdown() { + let optimizer = QueryOptimizer::new(); + // TODO: Test predicate pushdown optimization with: + // - Simple predicates on data sources + // - Complex predicates with AND/OR logic + // - Predicates that can't be pushed down + // - Cross-operation predicate migration + } + + #[test] + fn test_cost_estimation() { + let cost_model = CostModel::new(); + // TODO: Test cost estimation for different operations with: + // - Various data sizes and distributions + // - Complex query plans + // - Accuracy validation against actual execution times + // - Sensitivity analysis for cost parameters + } + + #[test] + fn test_filter_fusion() { + // TODO: Test filter fusion optimization with: + // - Multiple consecutive filters + // - Complex predicate combinations + // - Contradictory predicates (should result in empty result) + // - Performance comparison vs separate filters + } + + #[test] + fn test_constant_folding() { + // TODO: Test constant folding optimization with: + // - All binary operators + // - Nested expressions + // - Type conversions + // - Error handling for invalid operations + } +} \ No newline at end of file