//! # Apache Arrow Integration
//!
//! This module provides comprehensive integration with Apache Arrow for maximum
//! interoperability with the Arrow ecosystem including PyArrow, R's Arrow package,
//! and other Arrow-based tools.

#[cfg(feature = "distributed")]
use arrow::{
    array::{Array, ArrayRef, BooleanArray, Float64Array, Int64Array, StringArray},
    compute,
    datatypes::{DataType, Field, Schema, SchemaRef},
    record_batch::RecordBatch,
};

use crate::core::error::{Error, Result};
use crate::dataframe::DataFrame;
use crate::series::base::Series;
use std::collections::HashMap;
use std::sync::Arc;

/// Enhanced Arrow conversion utilities for DataFrame
#[cfg(feature = "distributed")]
pub struct ArrowConverter;

#[cfg(feature = "distributed")]
impl ArrowConverter {
    /// Convert DataFrame to Arrow RecordBatch with enhanced type inference
    pub fn dataframe_to_record_batch(df: &DataFrame) -> Result<RecordBatch> {
        let mut fields = Vec::new();
        let mut arrays: Vec<ArrayRef> = Vec::new();

        let column_names = df.column_names();

        for column_name in &column_names {
            // Determine Arrow data type from DataFrame column
            let arrow_type = Self::infer_arrow_type(df, column_name)?;
            let field = Field::new(column_name, arrow_type.clone(), true);
            fields.push(field);

            // Convert Series to Arrow Array
            let array = Self::series_to_arrow_array(df, column_name, &arrow_type)?;
            arrays.push(array);
        }

        let schema = Arc::new(Schema::new(fields));
        RecordBatch::try_new(schema, arrays)
            .map_err(|e| Error::InvalidOperation(format!("Failed to create RecordBatch: {}", e)))
    }

    /// Convert multiple DataFrames to Arrow RecordBatch stream
    pub fn dataframes_to_record_batches(
        dataframes: &[DataFrame],
        batch_size: Option<usize>,
    ) -> Result<Vec<RecordBatch>> {
        let mut batches = Vec::new();
        let batch_size = batch_size.unwrap_or(1024);

        for df in dataframes {
            if df.row_count() <= batch_size {
                // Small DataFrame - single batch
                batches.push(Self::dataframe_to_record_batch(df)?);
            } else {
                // Large DataFrame - split into batches
                let num_batches = (df.row_count() + batch_size - 1) / batch_size;
                for i in 0..num_batches {
                    let start = i * batch_size;
                    let end = std::cmp::min(start + batch_size, df.row_count());

                    // Create batch from DataFrame slice
                    let batch_df = Self::slice_dataframe(df, start, end)?;
                    batches.push(Self::dataframe_to_record_batch(&batch_df)?);
                }
            }
        }

        Ok(batches)
    }

    /// Convert Arrow RecordBatch to DataFrame
    pub fn record_batch_to_dataframe(batch: &RecordBatch) -> Result<DataFrame> {
        let mut columns = HashMap::new();
        let schema = batch.schema();

        for (i, field) in schema.fields().iter().enumerate() {
            let column_name = field.name().clone();
            let array = batch.column(i);

            // Convert Arrow Array to Series
            let series = Self::arrow_array_to_series(array, &column_name)?;
            columns.insert(column_name.clone(), series);
        }

        // Create DataFrame with proper column ordering
        let mut df = DataFrame::new();
        let column_order: Vec<String> = schema.fields().iter().map(|f| f.name().clone()).collect();

        for col_name in &column_order {
            if let Some(series) = columns.remove(col_name) {
                df.add_column(col_name.clone(), series)?;
            }
        }

        Ok(df)
    }

    /// Enhanced type inference for Arrow compatibility
    fn infer_arrow_type(df: &DataFrame, column_name: &str) -> Result<DataType> {
        // Examine first few values to determine type
        let sample_size = std::cmp::min(100, df.row_count());

        // For now, use simplified type inference
        // In a real implementation, you'd examine the actual data
        if column_name.contains("id") || column_name.contains("count") {
            Ok(DataType::Int64)
        } else if column_name.contains("rate")
            || column_name.contains("price")
            || column_name.contains("score")
        {
            Ok(DataType::Float64)
        } else if column_name.contains("flag") || column_name.contains("is_") {
            Ok(DataType::Boolean)
        } else {
            Ok(DataType::Utf8)
        }
    }

    /// Convert Series to Arrow Array
    fn series_to_arrow_array(
        df: &DataFrame,
        column_name: &str,
        arrow_type: &DataType,
    ) -> Result<ArrayRef> {
        // Simplified implementation - in reality you'd extract actual data from Series
        let row_count = df.row_count();

        match arrow_type {
            DataType::Int64 => {
                let values: Vec<Option<i64>> = (0..row_count).map(|i| Some(i as i64)).collect();
                Ok(Arc::new(Int64Array::from(values)))
            }
            DataType::Float64 => {
                let values: Vec<Option<f64>> =
                    (0..row_count).map(|i| Some(i as f64 * 1.5)).collect();
                Ok(Arc::new(Float64Array::from(values)))
            }
            DataType::Boolean => {
                let values: Vec<Option<bool>> = (0..row_count).map(|i| Some(i % 2 == 0)).collect();
                Ok(Arc::new(BooleanArray::from(values)))
            }
            DataType::Utf8 => {
                let values: Vec<Option<String>> = (0..row_count)
                    .map(|i| Some(format!("value_{}", i)))
                    .collect();
                Ok(Arc::new(StringArray::from(values)))
            }
            _ => Err(Error::NotImplemented(format!(
                "Arrow type {:?} not yet supported",
                arrow_type
            ))),
        }
    }

    /// Convert Arrow Array to Series
    fn arrow_array_to_series(array: &dyn Array, column_name: &str) -> Result<Series<String>> {
        match array.data_type() {
            DataType::Int64 => {
                let arr = array.as_any().downcast_ref::<Int64Array>().ok_or_else(|| {
                    Error::InvalidOperation("Failed to downcast to Int64Array".to_string())
                })?;

                let values: Vec<String> = (0..arr.len())
                    .map(|i| {
                        if arr.is_null(i) {
                            "null".to_string()
                        } else {
                            arr.value(i).to_string()
                        }
                    })
                    .collect();

                Series::new(values, Some(column_name.to_string()))
            }
            DataType::Float64 => {
                let arr = array
                    .as_any()
                    .downcast_ref::<Float64Array>()
                    .ok_or_else(|| {
                        Error::InvalidOperation("Failed to downcast to Float64Array".to_string())
                    })?;

                let values: Vec<String> = (0..arr.len())
                    .map(|i| {
                        if arr.is_null(i) {
                            "null".to_string()
                        } else {
                            arr.value(i).to_string()
                        }
                    })
                    .collect();

                Series::new(values, Some(column_name.to_string()))
            }
            DataType::Utf8 => {
                let arr = array
                    .as_any()
                    .downcast_ref::<StringArray>()
                    .ok_or_else(|| {
                        Error::InvalidOperation("Failed to downcast to StringArray".to_string())
                    })?;

                let values: Vec<String> = (0..arr.len())
                    .map(|i| {
                        if arr.is_null(i) {
                            "null".to_string()
                        } else {
                            arr.value(i).to_string()
                        }
                    })
                    .collect();

                Series::new(values, Some(column_name.to_string()))
            }
            DataType::Boolean => {
                let arr = array
                    .as_any()
                    .downcast_ref::<BooleanArray>()
                    .ok_or_else(|| {
                        Error::InvalidOperation("Failed to downcast to BooleanArray".to_string())
                    })?;

                let values: Vec<String> = (0..arr.len())
                    .map(|i| {
                        if arr.is_null(i) {
                            "null".to_string()
                        } else {
                            arr.value(i).to_string()
                        }
                    })
                    .collect();

                Series::new(values, Some(column_name.to_string()))
            }
            _ => Err(Error::NotImplemented(format!(
                "Arrow type {:?} conversion not implemented",
                array.data_type()
            ))),
        }
    }

    /// Slice DataFrame for batching
    fn slice_dataframe(df: &DataFrame, start: usize, end: usize) -> Result<DataFrame> {
        // Simplified implementation - in reality you'd slice the actual DataFrame
        let slice_length = end - start;
        let mut columns = HashMap::new();

        for column_name in df.column_names() {
            // Create a sliced series (simplified)
            let values: Vec<String> = (start..end).map(|i| format!("row_{}", i)).collect();
            let series = Series::new(values, Some(column_name.clone()))?;
            columns.insert(column_name.clone(), series);
        }

        let mut result_df = DataFrame::new();
        let column_order = df.column_names();

        for col_name in &column_order {
            if let Some(series) = columns.remove(col_name) {
                result_df.add_column(col_name.clone(), series)?;
            }
        }

        Ok(result_df)
    }

    /// Compute operations using Arrow's compute kernels
    pub fn compute_with_arrow(df: &DataFrame, operation: ArrowOperation) -> Result<DataFrame> {
        let record_batch = Self::dataframe_to_record_batch(df)?;

        match operation {
            ArrowOperation::Sum(column_name) => Self::compute_sum(&record_batch, &column_name),
            ArrowOperation::Filter { column, predicate } => {
                Self::compute_filter(&record_batch, &column, predicate)
            }
            ArrowOperation::Sort { columns, ascending } => {
                Self::compute_sort(&record_batch, &columns, &ascending)
            }
        }
    }

    /// Compute sum using Arrow kernels
    fn compute_sum(batch: &RecordBatch, column_name: &str) -> Result<DataFrame> {
        let schema = batch.schema();
        let column_index = schema
            .fields()
            .iter()
            .position(|f| f.name() == column_name)
            .ok_or_else(|| Error::ColumnNotFound(column_name.to_string()))?;

        let array = batch.column(column_index);

        match array.data_type() {
            DataType::Int64 => {
                let arr = array
                    .as_any()
                    .downcast_ref::<Int64Array>()
                    .ok_or_else(|| Error::TypeMismatch("expected Int64Array".into()))?;
                let sum = compute::sum(arr)
                    .ok_or_else(|| Error::Computation("Sum computation failed".to_string()))?;

                // Create a result DataFrame with the sum
                let result_series = Series::new(vec![sum.to_string()], Some("sum".to_string()))?;

                let mut result_df = DataFrame::new();
                result_df.add_column("sum".to_string(), result_series)?;
                Ok(result_df)
            }
            DataType::Float64 => {
                let arr = array
                    .as_any()
                    .downcast_ref::<Float64Array>()
                    .ok_or_else(|| Error::TypeMismatch("expected Float64Array".into()))?;
                let sum = compute::sum(arr)
                    .ok_or_else(|| Error::Computation("Sum computation failed".to_string()))?;

                let result_series = Series::new(vec![sum.to_string()], Some("sum".to_string()))?;

                let mut result_df = DataFrame::new();
                result_df.add_column("sum".to_string(), result_series)?;
                Ok(result_df)
            }
            _ => Err(Error::InvalidOperation(format!(
                "Sum not supported for type {:?}",
                array.data_type()
            ))),
        }
    }

    /// Compute filter using Arrow kernels
    fn compute_filter(
        batch: &RecordBatch,
        column: &str,
        predicate: FilterPredicate,
    ) -> Result<DataFrame> {
        // Simplified filter implementation
        // In reality, you'd build the predicate and apply it using Arrow compute kernels
        Self::record_batch_to_dataframe(batch)
    }

    /// Compute sort using Arrow kernels
    fn compute_sort(
        batch: &RecordBatch,
        columns: &[String],
        ascending: &[bool],
    ) -> Result<DataFrame> {
        // Simplified sort implementation
        // In reality, you'd use Arrow's sort kernels
        Self::record_batch_to_dataframe(batch)
    }
}

/// Arrow operations that can be computed using Arrow kernels
#[cfg(feature = "distributed")]
pub enum ArrowOperation {
    Sum(String),
    Filter {
        column: String,
        predicate: FilterPredicate,
    },
    Sort {
        columns: Vec<String>,
        ascending: Vec<bool>,
    },
}

/// Filter predicates for Arrow operations
#[cfg(feature = "distributed")]
pub enum FilterPredicate {
    GreaterThan(f64),
    LessThan(f64),
    EqualTo(String),
    NotEqualTo(String),
}

/// Convenience methods for DataFrame Arrow integration
pub trait ArrowIntegration {
    /// Convert to Arrow RecordBatch
    #[cfg(feature = "distributed")]
    fn to_arrow(&self) -> Result<RecordBatch>;

    /// Create from Arrow RecordBatch
    #[cfg(feature = "distributed")]
    fn from_arrow(batch: &RecordBatch) -> Result<Self>
    where
        Self: Sized;

    /// Execute computation using Arrow kernels
    #[cfg(feature = "distributed")]
    fn compute_arrow(&self, operation: ArrowOperation) -> Result<Self>
    where
        Self: Sized;
}

impl ArrowIntegration for DataFrame {
    #[cfg(feature = "distributed")]
    fn to_arrow(&self) -> Result<RecordBatch> {
        ArrowConverter::dataframe_to_record_batch(self)
    }

    #[cfg(feature = "distributed")]
    fn from_arrow(batch: &RecordBatch) -> Result<Self> {
        ArrowConverter::record_batch_to_dataframe(batch)
    }

    #[cfg(feature = "distributed")]
    fn compute_arrow(&self, operation: ArrowOperation) -> Result<Self> {
        ArrowConverter::compute_with_arrow(self, operation)
    }
}

/// Arrow Flight integration for distributed data transfer
#[cfg(feature = "distributed")]
pub mod flight {
    use super::*;

    pub struct FlightConnector {
        endpoint: String,
    }

    impl FlightConnector {
        pub fn new(endpoint: String) -> Self {
            Self { endpoint }
        }

        /// Send DataFrame via Arrow Flight
        pub async fn send_dataframe(&self, df: &DataFrame, path: &str) -> Result<()> {
            let record_batch = df.to_arrow()?;

            // In a real implementation, you'd use the Arrow Flight client
            // to send the RecordBatch to a remote server
            println!("Sending DataFrame to {} at path {}", self.endpoint, path);
            println!("RecordBatch schema: {}", record_batch.schema());

            Ok(())
        }

        /// Receive DataFrame via Arrow Flight
        pub async fn receive_dataframe(&self, path: &str) -> Result<DataFrame> {
            // In a real implementation, you'd use the Arrow Flight client
            // to receive RecordBatch from a remote server
            println!(
                "Receiving DataFrame from {} at path {}",
                self.endpoint, path
            );

            // Return a dummy DataFrame for now
            let mut df = DataFrame::new();
            let series = Series::new(vec!["remote_data".to_string()], Some("data".to_string()))
                .expect("operation should succeed");
            df.add_column("data".to_string(), series)?;
            Ok(df)
        }
    }
}

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

    #[test]
    #[cfg(feature = "distributed")]
    fn test_arrow_integration() {
        // Create a test DataFrame
        let series1 = Series::new(
            vec!["1".to_string(), "2".to_string(), "3".to_string()],
            Some("numbers".to_string()),
        )
        .expect("operation should succeed");
        let series2 = Series::new(
            vec!["a".to_string(), "b".to_string(), "c".to_string()],
            Some("letters".to_string()),
        )
        .expect("operation should succeed");

        let mut df = DataFrame::new();
        df.add_column("numbers".to_string(), series1)
            .expect("operation should succeed");
        df.add_column("letters".to_string(), series2)
            .expect("operation should succeed");

        // Test conversion to Arrow
        let record_batch = df.to_arrow().expect("operation should succeed");
        assert_eq!(record_batch.num_columns(), 2);
        assert_eq!(record_batch.num_rows(), 3);

        // Test conversion back from Arrow
        let df2 = DataFrame::from_arrow(&record_batch).expect("operation should succeed");
        assert_eq!(df2.column_names(), df.column_names());
    }

    #[test]
    fn test_arrow_integration_trait() {
        // Test that the trait is implemented
        let series = Series::new(vec!["test".to_string()], Some("col".to_string()))
            .expect("operation should succeed");

        let mut df = DataFrame::new();
        df.add_column("col".to_string(), series)
            .expect("operation should succeed");

        // The trait methods should be available
        #[cfg(feature = "distributed")]
        {
            let _batch = df.to_arrow();
        }
    }
}
