leptos-helios 0.8.1

//! Data processing pipeline with Polars integration

use polars::prelude::*;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Data processing error types
#[derive(Debug, thiserror::Error)]
pub enum DataError {
    #[error("Polars error: {0}")]
    Polars(#[from] PolarsError),

    #[error("Data format error: {0}")]
    Format(String),

    #[error("Data validation error: {0}")]
    Validation(String),

    #[error("Data source error: {0}")]
    Source(String),

    #[error("Processing error: {0}")]
    Processing(String),
}

/// Data processing strategy selection
#[derive(Debug, Clone)]
pub enum ProcessingStrategy {
    CPU(RayonConfig),
    GPU(ComputeConfig),
    Streaming(StreamConfig),
    Hybrid(HybridConfig),
}

#[derive(Debug, Clone)]
pub struct RayonConfig {
    pub num_threads: Option<usize>,
    pub chunk_size: Option<usize>,
}

#[derive(Debug, Clone)]
pub struct ComputeConfig {
    pub workgroup_size: u32,
    pub memory_budget: usize,
}

#[derive(Debug, Clone)]
pub struct StreamConfig {
    pub buffer_size: usize,
    pub batch_size: usize,
    pub enable_backpressure: bool,
}

#[derive(Debug, Clone)]
pub struct HybridConfig {
    pub cpu_threshold: usize,
    pub gpu_threshold: usize,
    pub cpu_config: RayonConfig,
    pub gpu_config: ComputeConfig,
}

/// Strategy selector for optimal data processing
pub struct StrategySelector {
    benchmarks: HashMap<String, f64>,
    device_capabilities: DeviceCapabilities,
}

impl StrategySelector {
    pub fn new() -> Self {
        Self {
            benchmarks: HashMap::new(),
            device_capabilities: DeviceCapabilities::detect(),
        }
    }

    pub fn select(&self, spec: &DataSpec) -> ProcessingStrategy {
        let data_size = spec.estimated_size();
        let complexity = spec.complexity();
        let is_streaming = spec.is_streaming();

        if is_streaming {
            return ProcessingStrategy::Streaming(StreamConfig {
                buffer_size: 10_000,
                batch_size: 1_000,
                enable_backpressure: true,
            });
        }

        if data_size > 1_000_000 && complexity > 0.7 && self.device_capabilities.gpu_available {
            ProcessingStrategy::GPU(ComputeConfig {
                workgroup_size: 64,
                memory_budget: 100 * 1024 * 1024, // 100MB
            })
        } else {
            ProcessingStrategy::CPU(RayonConfig {
                num_threads: None, // Use all available cores
                chunk_size: Some(10_000),
            })
        }
    }

    pub fn benchmark(&mut self, name: &str, duration: f64) {
        self.benchmarks.insert(name.to_string(), duration);
    }
}

#[derive(Debug, Clone)]
pub struct DeviceCapabilities {
    pub gpu_available: bool,
    pub cpu_cores: usize,
    pub memory_gb: f64,
    pub simd_available: bool,
}

impl DeviceCapabilities {
    pub fn detect() -> Self {
        Self {
            gpu_available: Self::detect_gpu(),
            cpu_cores: num_cpus::get(),
            memory_gb: Self::detect_memory(),
            simd_available: Self::detect_simd(),
        }
    }

    fn detect_gpu() -> bool {
        // Check for WebGPU support
        #[cfg(target_arch = "wasm32")]
        {
            // This would be implemented with web-sys
            false // Placeholder
        }
        #[cfg(not(target_arch = "wasm32"))]
        {
            // Check for native GPU support
            false // Placeholder
        }
    }

    fn detect_memory() -> f64 {
        // Get available memory in GB
        8.0 // Placeholder
    }

    fn detect_simd() -> bool {
        if cfg!(target_feature = "simd128") {
            true
        } else {
            false
        }
    }
}

/// Data specification for processing
#[derive(Debug, Clone)]
pub struct DataSpec {
    pub source: DataSource,
    pub transforms: Vec<DataTransform>,
    pub filters: Vec<Filter>,
    pub aggregations: Vec<Aggregation>,
    pub output_format: OutputFormat,
}

impl DataSpec {
    pub fn estimated_size(&self) -> usize {
        // Estimate data size based on source and transforms
        match &self.source {
            DataSource::DataFrame(df) => df.height(),
            DataSource::Url { .. } => 100_000,     // Estimate
            DataSource::Query { .. } => 1_000_000, // Estimate
            DataSource::Stream { .. } => 10_000,   // Streaming estimate
        }
    }

    pub fn complexity(&self) -> f64 {
        let base_complexity = 1.0;
        let transform_complexity = self.transforms.len() as f64 * 0.5;
        let filter_complexity = self.filters.len() as f64 * 0.3;
        let aggregation_complexity = self.aggregations.len() as f64 * 1.0;

        base_complexity + transform_complexity + filter_complexity + aggregation_complexity
    }

    pub fn is_streaming(&self) -> bool {
        matches!(self.source, DataSource::Stream { .. })
    }

    pub fn hash(&self) -> u64 {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        self.source.hash(&mut hasher);
        self.transforms.hash(&mut hasher);
        self.filters.hash(&mut hasher);
        self.aggregations.hash(&mut hasher);
        hasher.finish()
    }
}

#[derive(Debug, Clone)]
pub enum DataSource {
    DataFrame(crate::DataFrame),
    Url { url: String, format: DataFormat },
    Query { sql: String, dataset: String },
    Stream { stream_id: String },
}

#[derive(Debug, Clone)]
pub enum DataTransform {
    Select { columns: Vec<String> },
    Rename { mappings: HashMap<String, String> },
    Cast { column: String, data_type: DataType },
    FillNull { column: String, value: FillValue },
    DropNulls { columns: Option<Vec<String>> },
}

#[derive(Debug, Clone)]
pub enum Filter {
    Expression {
        expr: String,
    },
    Range {
        column: String,
        min: Option<f64>,
        max: Option<f64>,
    },
    Values {
        column: String,
        values: Vec<serde_json::Value>,
    },
    Null {
        column: String,
        keep_nulls: bool,
    },
}

#[derive(Debug, Clone, Hash)]
pub enum Aggregation {
    GroupBy {
        columns: Vec<String>,
    },
    Aggregate {
        operations: Vec<AggOp>,
    },
    Pivot {
        index: String,
        columns: String,
        values: String,
    },
    Window {
        operations: Vec<WindowOp>,
    },
}

#[derive(Debug, Clone, Hash)]
pub enum AggOp {
    Sum {
        column: String,
        alias: Option<String>,
    },
    Mean {
        column: String,
        alias: Option<String>,
    },
    Count {
        column: String,
        alias: Option<String>,
    },
    Min {
        column: String,
        alias: Option<String>,
    },
    Max {
        column: String,
        alias: Option<String>,
    },
    Std {
        column: String,
        alias: Option<String>,
    },
    Var {
        column: String,
        alias: Option<String>,
    },
}

#[derive(Debug, Clone, Hash)]
pub enum WindowOp {
    RowNumber {
        alias: String,
    },
    Rank {
        column: String,
        alias: String,
    },
    Lag {
        column: String,
        offset: i64,
        alias: String,
    },
    Lead {
        column: String,
        offset: i64,
        alias: String,
    },
    RollingMean {
        column: String,
        window: usize,
        alias: String,
    },
    RollingSum {
        column: String,
        window: usize,
        alias: String,
    },
}

#[derive(Debug, Clone, Hash)]
pub enum OutputFormat {
    DataFrame,
    Json,
    Csv,
    Parquet,
    Arrow,
}

#[derive(Debug, Clone, Hash)]
pub enum DataFormat {
    Csv,
    Json,
    Parquet,
    Arrow,
}

#[derive(Debug, Clone, Hash)]
pub enum DataType {
    Int32,
    Int64,
    Float32,
    Float64,
    String,
    Boolean,
    Date,
    DateTime,
}

#[derive(Debug, Clone, Hash)]
pub enum FillValue {
    Zero,
    Mean,
    Median,
    Mode,
    Forward,
    Backward,
    Custom(serde_json::Value),
}

/// Main data processor
pub struct DataProcessor {
    strategy_selector: StrategySelector,
    cache: HashMap<u64, ProcessedData>,
    stream_buffers: HashMap<String, StreamBuffer>,
}

impl DataProcessor {
    pub fn new() -> Result<Self, DataError> {
        Ok(Self {
            strategy_selector: StrategySelector::new(),
            cache: HashMap::new(),
            stream_buffers: HashMap::new(),
        })
    }

    pub async fn process(&mut self, spec: &DataSpec) -> Result<ProcessedData, DataError> {
        // Check cache first
        let spec_hash = spec.hash();
        if let Some(cached) = self.cache.get(&spec_hash) {
            return Ok(cached.clone());
        }

        // Select optimal processing strategy
        let strategy = self.strategy_selector.select(spec);

        // Process data based on strategy
        let result = match strategy {
            ProcessingStrategy::CPU(config) => self.process_cpu(spec, &config).await,
            ProcessingStrategy::GPU(config) => self.process_gpu(spec, &config).await,
            ProcessingStrategy::Streaming(config) => self.process_streaming(spec, &config).await,
            ProcessingStrategy::Hybrid(config) => self.process_hybrid(spec, &config).await,
        }?;

        // Cache result
        self.cache.insert(spec_hash, result.clone());

        Ok(result)
    }

    async fn process_cpu(
        &self,
        spec: &DataSpec,
        config: &RayonConfig,
    ) -> Result<ProcessedData, DataError> {
        // Load data
        let mut df = self.load_data(&spec.source).await?;

        // Apply filters
        for filter in &spec.filters {
            df = self.apply_filter(df, filter)?;
        }

        // Apply transforms
        for transform in &spec.transforms {
            df = self.apply_transform(df, transform)?;
        }

        // Apply aggregations
        for aggregation in &spec.aggregations {
            df = self.apply_aggregation(df, aggregation)?;
        }

        Ok(ProcessedData {
            data: df,
            metadata: DataMetadata::from_dataframe(&df),
            processing_time: std::time::Duration::from_millis(0), // Placeholder
        })
    }

    async fn process_gpu(
        &self,
        spec: &DataSpec,
        config: &ComputeConfig,
    ) -> Result<ProcessedData, DataError> {
        // GPU processing would be implemented here
        // For now, fall back to CPU processing
        self.process_cpu(
            spec,
            &RayonConfig {
                num_threads: None,
                chunk_size: None,
            },
        )
        .await
    }

    async fn process_streaming(
        &mut self,
        spec: &DataSpec,
        config: &StreamConfig,
    ) -> Result<ProcessedData, DataError> {
        // Streaming processing implementation
        if let DataSource::Stream { stream_id } = &spec.source {
            let buffer = self
                .stream_buffers
                .entry(stream_id.clone())
                .or_insert_with(|| StreamBuffer::new(config.buffer_size));

            // Process streaming data
            buffer.process_batch(config.batch_size)
        } else {
            Err(DataError::Processing(
                "Streaming strategy requires stream source".to_string(),
            ))
        }
    }

    async fn process_hybrid(
        &self,
        spec: &DataSpec,
        config: &HybridConfig,
    ) -> Result<ProcessedData, DataError> {
        let data_size = spec.estimated_size();

        if data_size < config.cpu_threshold {
            self.process_cpu(spec, &config.cpu_config).await
        } else if data_size < config.gpu_threshold {
            self.process_gpu(spec, &config.gpu_config).await
        } else {
            // Split processing between CPU and GPU
            self.process_cpu(spec, &config.cpu_config).await
        }
    }

    async fn load_data(&self, source: &DataSource) -> Result<DataFrame, DataError> {
        match source {
            DataSource::DataFrame(df) => Ok(df.clone()),
            DataSource::Url { url, format } => match format {
                DataFormat::Csv => {
                    let df = LazyFrame::scan_csv(url, ScanArgsIo::default())
                        .collect()
                        .map_err(DataError::Polars)?;
                    Ok(df)
                }
                DataFormat::Json => {
                    let df = LazyFrame::scan_ndjson(url, ScanArgsNdJson::default())
                        .collect()
                        .map_err(DataError::Polars)?;
                    Ok(df)
                }
                DataFormat::Parquet => {
                    let df = LazyFrame::scan_parquet(url, ScanArgsParquet::default())
                        .collect()
                        .map_err(DataError::Polars)?;
                    Ok(df)
                }
                _ => Err(DataError::Format("Unsupported format".to_string())),
            },
            DataSource::Query { sql, dataset } => {
                // Execute SQL query - would integrate with DataFusion
                Err(DataError::Processing(
                    "SQL queries not yet implemented".to_string(),
                ))
            }
            DataSource::Stream { stream_id } => {
                // Get data from stream buffer
                Err(DataError::Processing(
                    "Stream processing not yet implemented".to_string(),
                ))
            }
        }
    }

    fn apply_filter(&self, df: DataFrame, filter: &Filter) -> Result<DataFrame, DataError> {
        match filter {
            Filter::Expression { expr } => {
                let lazy_df = df.lazy().filter(col(expr));
                lazy_df.collect().map_err(DataError::Polars)
            }
            Filter::Range { column, min, max } => {
                let mut lazy_df = df.lazy();
                if let Some(min_val) = min {
                    lazy_df = lazy_df.filter(col(column).gt(lit(*min_val)));
                }
                if let Some(max_val) = max {
                    lazy_df = lazy_df.filter(col(column).lt(lit(*max_val)));
                }
                lazy_df.collect().map_err(DataError::Polars)
            }
            Filter::Values { column, values } => {
                // Convert values to appropriate type and filter
                let lazy_df = df.lazy().filter(col(column).is_in(values));
                lazy_df.collect().map_err(DataError::Polars)
            }
            Filter::Null { column, keep_nulls } => {
                if *keep_nulls {
                    df.lazy()
                        .filter(col(column).is_null())
                        .collect()
                        .map_err(DataError::Polars)
                } else {
                    df.lazy()
                        .filter(col(column).is_not_null())
                        .collect()
                        .map_err(DataError::Polars)
                }
            }
        }
    }

    fn apply_transform(
        &self,
        df: DataFrame,
        transform: &DataTransform,
    ) -> Result<DataFrame, DataError> {
        match transform {
            DataTransform::Select { columns } => {
                let lazy_df = df.lazy().select(columns.iter().map(|c| col(c)));
                lazy_df.collect().map_err(DataError::Polars)
            }
            DataTransform::Rename { mappings } => {
                let mut lazy_df = df.lazy();
                for (old_name, new_name) in mappings {
                    lazy_df = lazy_df.rename([old_name], [new_name]);
                }
                lazy_df.collect().map_err(DataError::Polars)
            }
            DataTransform::Cast { column, data_type } => {
                let polars_type = match data_type {
                    DataType::Int32 => DataType::Int32,
                    DataType::Int64 => DataType::Int64,
                    DataType::Float32 => DataType::Float32,
                    DataType::Float64 => DataType::Float64,
                    DataType::String => DataType::String,
                    DataType::Boolean => DataType::Boolean,
                    DataType::Date => DataType::Date,
                    DataType::DateTime => DataType::Datetime(TimeUnit::Milliseconds, None),
                };
                let lazy_df = df.lazy().with_columns([col(column).cast(polars_type)]);
                lazy_df.collect().map_err(DataError::Polars)
            }
            DataTransform::FillNull { column, value } => {
                let fill_value = match value {
                    FillValue::Zero => lit(0),
                    FillValue::Mean => col(column).mean(),
                    FillValue::Median => col(column).median(),
                    FillValue::Mode => col(column).mode().first(),
                    FillValue::Forward => col(column).forward_fill(),
                    FillValue::Backward => col(column).backward_fill(),
                    FillValue::Custom(val) => lit(val),
                };
                let lazy_df = df.lazy().with_columns([col(column).fill_null(fill_value)]);
                lazy_df.collect().map_err(DataError::Polars)
            }
            DataTransform::DropNulls { columns } => {
                let lazy_df = if let Some(cols) = columns {
                    df.lazy().drop_nulls(Some(cols))
                } else {
                    df.lazy().drop_nulls(None)
                };
                lazy_df.collect().map_err(DataError::Polars)
            }
        }
    }

    fn apply_aggregation(
        &self,
        df: DataFrame,
        aggregation: &Aggregation,
    ) -> Result<DataFrame, DataError> {
        match aggregation {
            Aggregation::GroupBy { columns } => {
                let lazy_df = df.lazy().group_by(columns.iter().map(|c| col(c)));
                lazy_df.collect().map_err(DataError::Polars)
            }
            Aggregation::Aggregate { operations } => {
                let mut lazy_df = df.lazy();
                let agg_exprs: Vec<Expr> = operations
                    .iter()
                    .map(|op| {
                        match op {
                            AggOp::Sum { column, alias } => {
                                let expr = col(column).sum();
                                if let Some(alias) = alias {
                                    expr.alias(alias)
                                } else {
                                    expr
                                }
                            }
                            AggOp::Mean { column, alias } => {
                                let expr = col(column).mean();
                                if let Some(alias) = alias {
                                    expr.alias(alias)
                                } else {
                                    expr
                                }
                            }
                            AggOp::Count { column, alias } => {
                                let expr = col(column).count();
                                if let Some(alias) = alias {
                                    expr.alias(alias)
                                } else {
                                    expr
                                }
                            }
                            _ => col(column).sum(), // Placeholder
                        }
                    })
                    .collect();

                lazy_df.agg(agg_exprs).collect().map_err(DataError::Polars)
            }
            _ => Ok(df), // Placeholder for other aggregation types
        }
    }
}

/// Processed data result
#[derive(Debug, Clone)]
pub struct ProcessedData {
    pub data: DataFrame,
    pub metadata: DataMetadata,
    pub processing_time: std::time::Duration,
}

/// Data metadata
#[derive(Debug, Clone)]
pub struct DataMetadata {
    pub row_count: usize,
    pub column_count: usize,
    pub column_types: HashMap<String, String>,
    pub memory_usage: usize,
    pub processing_stats: ProcessingStats,
}

impl DataMetadata {
    pub fn from_dataframe(df: &DataFrame) -> Self {
        let column_types: HashMap<String, String> = df
            .get_column_names()
            .iter()
            .map(|name| {
                let dtype = df.column(name).unwrap().dtype();
                (name.clone(), format!("{:?}", dtype))
            })
            .collect();

        Self {
            row_count: df.height(),
            column_count: df.width(),
            column_types,
            memory_usage: df.estimated_size(),
            processing_stats: ProcessingStats::default(),
        }
    }
}

#[derive(Debug, Clone, Default)]
pub struct ProcessingStats {
    pub cpu_time: std::time::Duration,
    pub gpu_time: std::time::Duration,
    pub memory_peak: usize,
    pub cache_hits: u64,
    pub cache_misses: u64,
}

/// Streaming data buffer
pub struct StreamBuffer {
    buffer: Vec<DataFrame>,
    max_size: usize,
    dropped_count: u64,
}

impl StreamBuffer {
    pub fn new(max_size: usize) -> Self {
        Self {
            buffer: Vec::new(),
            max_size,
            dropped_count: 0,
        }
    }

    pub fn push(&mut self, data: DataFrame) {
        if self.buffer.len() >= self.max_size {
            self.buffer.remove(0);
            self.dropped_count += 1;
        }
        self.buffer.push(data);
    }

    pub fn process_batch(&mut self, batch_size: usize) -> Result<ProcessedData, DataError> {
        if self.buffer.is_empty() {
            return Ok(ProcessedData {
                data: DataFrame::empty(),
                metadata: DataMetadata::from_dataframe(&DataFrame::empty()),
                processing_time: std::time::Duration::from_millis(0),
            });
        }

        let combined_df = self.buffer.iter().fold(DataFrame::empty(), |acc, df| {
            if acc.is_empty() {
                df.clone()
            } else {
                concat(&[acc, df.clone()], Default::default()).unwrap_or(df.clone())
            }
        });

        Ok(ProcessedData {
            data: combined_df,
            metadata: DataMetadata::from_dataframe(&combined_df),
            processing_time: std::time::Duration::from_millis(0),
        })
    }

    pub fn health_metrics(&self) -> StreamHealth {
        StreamHealth {
            buffer_utilization: self.buffer.len() as f32 / self.max_size as f32,
            dropped_messages: self.dropped_count,
            current_size: self.buffer.len(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct StreamHealth {
    pub buffer_utilization: f32,
    pub dropped_messages: u64,
    pub current_size: usize,
}