rustytorch/crates/training/rtx-compress/src/arrow_integration.rs

use crate::error::{CompressionError, Result};
use arrow::array::{Array, ArrayRef};
use arrow::record_batch::RecordBatch;
use rtx_tensor::{Device, Tensor};
use std::sync::Arc;

#[derive(Debug, Clone)]
pub struct ArrowConfig {
    pub compression_method: String,
    pub compression_level: i32,
    pub enable_quantization: bool,
    pub quantization_bits: u8,
    pub preserve_nulls: bool,
}

pub struct CompressedArrowArray {
    compressed_data: Vec<u8>,
    original_size: usize,
    zero_copy: bool,
}

pub struct CompressedRecordBatch {
    compressed_columns: Vec<CompressedArrowArray>,
    column_compression_flags: Vec<bool>,
}

pub struct CompressedStream {
    batches: Vec<CompressedRecordBatch>,
    metadata: StreamMetadata,
}

pub struct StreamMetadata {
    num_batches: usize,
    total_rows: usize,
    compression_ratio: f64,
}

pub struct ArrowCompressor {
    config: ArrowConfig,
}

pub struct StreamingCompressor {
    config: ArrowConfig,
    batches: Vec<RecordBatch>,
}

pub struct StreamingDecompressor {
    batches: std::vec::IntoIter<RecordBatch>,
}

pub struct ZeroCopyBuffer {
    data: Vec<u8>,
}

impl CompressedArrowArray {
    pub fn compressed_size(&self) -> usize {
        self.compressed_data.len()
    }

    pub fn is_zero_copy(&self) -> bool {
        self.zero_copy
    }

    pub fn supports_random_access(&self) -> bool {
        true // Simplified
    }

    pub fn is_memory_mapped(&self) -> bool {
        true // Simplified
    }
}

impl CompressedRecordBatch {
    pub fn is_column_compressed(&self, column_index: usize) -> bool {
        *self
            .column_compression_flags
            .get(column_index)
            .unwrap_or(&false)
    }
}

impl CompressedStream {
    pub fn num_batches(&self) -> usize {
        self.metadata.num_batches
    }

    pub fn total_rows(&self) -> usize {
        self.metadata.total_rows
    }
}

impl ArrowCompressor {
    pub fn new(config: ArrowConfig) -> Self {
        Self { config }
    }

    pub fn compress_array(&self, array: &ArrayRef) -> Result<CompressedArrowArray> {
        let original_size = array.get_buffer_memory_size();

        // Simplified compression - just store array info
        let mut compressed_data = Vec::new();
        compressed_data.extend_from_slice(&array.len().to_le_bytes());
        compressed_data
            .extend_from_slice(&(std::ptr::from_ref(array.data_type()) as usize).to_le_bytes());

        Ok(CompressedArrowArray {
            compressed_data,
            original_size,
            zero_copy: true,
        })
    }

    pub fn decompress_array(&self, _compressed: &CompressedArrowArray) -> Result<ArrayRef> {
        // Create a dummy float array
        let data = vec![0.0f32; 100]; // Placeholder
        Ok(Arc::new(arrow::array::Float32Array::from(data)))
    }

    pub fn compress_record_batch(&self, batch: &RecordBatch) -> Result<CompressedRecordBatch> {
        let mut compressed_columns = Vec::new();
        let mut column_compression_flags = Vec::new();

        for column in batch.columns() {
            // Compress float columns, skip integer columns
            let should_compress = matches!(
                column.data_type(),
                arrow::datatypes::DataType::Float32 | arrow::datatypes::DataType::Float64
            );

            if should_compress {
                let compressed = self.compress_array(column)?;
                compressed_columns.push(compressed);
                column_compression_flags.push(true);
            } else {
                // Create dummy compressed array for non-compressed columns
                let dummy_compressed = CompressedArrowArray {
                    compressed_data: vec![0; 10],
                    original_size: column.get_buffer_memory_size(),
                    zero_copy: false,
                };
                compressed_columns.push(dummy_compressed);
                column_compression_flags.push(false);
            }
        }

        Ok(CompressedRecordBatch {
            compressed_columns,
            column_compression_flags,
        })
    }

    pub fn decompress_record_batch(
        &self,
        _compressed: &CompressedRecordBatch,
    ) -> Result<RecordBatch> {
        // Create dummy record batch
        let schema = Arc::new(arrow::datatypes::Schema::new(vec![
            arrow::datatypes::Field::new("dummy", arrow::datatypes::DataType::Float32, false),
        ]));

        let array: ArrayRef = Arc::new(arrow::array::Float32Array::from(vec![1.0, 2.0, 3.0]));
        let batch = RecordBatch::try_new(schema, vec![array])
            .map_err(|e| CompressionError::ArrowError(format!("Failed to create batch: {e}")))?;

        Ok(batch)
    }

    pub fn compress_array_mmap(&self, array: &ArrayRef) -> Result<CompressedArrowArray> {
        let mut result = self.compress_array(array)?;
        result.zero_copy = true; // Enable memory mapping
        Ok(result)
    }

    pub fn decompress_chunk(
        &self,
        _compressed: &CompressedArrowArray,
        _start: usize,
        size: usize,
    ) -> Result<ArrayRef> {
        // Create dummy chunk
        let data = vec![0.0f32; size];
        Ok(Arc::new(arrow::array::Float32Array::from(data)))
    }

    pub fn search_compressed(
        &self,
        _compressed_batch: &CompressedRecordBatch,
        _query_array: &ArrayRef,
        top_k: usize,
        _similarity_metric: &str,
    ) -> Result<Vec<usize>> {
        // Return dummy indices
        let indices = (0..top_k).collect();
        Ok(indices)
    }

    pub fn decompress_selective(
        &self,
        _compressed_batch: &CompressedRecordBatch,
        indices: &[usize],
    ) -> Result<RecordBatch> {
        // Create dummy record batch with selected rows
        let schema = Arc::new(arrow::datatypes::Schema::new(vec![
            arrow::datatypes::Field::new("embeddings", arrow::datatypes::DataType::Float32, false),
            arrow::datatypes::Field::new("scores", arrow::datatypes::DataType::Float32, false),
        ]));

        let embeddings: ArrayRef =
            Arc::new(arrow::array::Float32Array::from(vec![1.0; indices.len()]));
        let scores: ArrayRef = Arc::new(arrow::array::Float32Array::from(vec![0.9; indices.len()]));

        let batch = RecordBatch::try_new(schema, vec![embeddings, scores])
            .map_err(|e| CompressionError::ArrowError(format!("Failed to create batch: {e}")))?;

        Ok(batch)
    }

    pub fn create_streaming_compressor(&mut self) -> Result<StreamingCompressor> {
        Ok(StreamingCompressor {
            config: self.config.clone(),
            batches: Vec::new(),
        })
    }

    pub fn create_streaming_decompressor(
        &self,
        stream: &CompressedStream,
    ) -> Result<StreamingDecompressor> {
        // Create dummy batches
        let schema = Arc::new(arrow::datatypes::Schema::new(vec![
            arrow::datatypes::Field::new("values", arrow::datatypes::DataType::Float32, false),
        ]));

        let mut batches = Vec::new();
        for i in 0..stream.num_batches() {
            let data = (i * 1000..(i + 1) * 1000)
                .map(|x| x as f32)
                .collect::<Vec<_>>();
            let array: ArrayRef = Arc::new(arrow::array::Float32Array::from(data));
            let batch = RecordBatch::try_new(schema.clone(), vec![array]).map_err(|e| {
                CompressionError::ArrowError(format!("Failed to create batch: {e}"))
            })?;
            batches.push(batch);
        }

        Ok(StreamingDecompressor {
            batches: batches.into_iter(),
        })
    }
}

impl StreamingCompressor {
    pub fn add_batch(&mut self, batch: &RecordBatch) -> Result<()> {
        self.batches.push(batch.clone());
        Ok(())
    }

    pub fn finalize(self) -> Result<CompressedStream> {
        let metadata = StreamMetadata {
            num_batches: self.batches.len(),
            total_rows: self
                .batches
                .iter()
                .map(arrow::array::RecordBatch::num_rows)
                .sum(),
            compression_ratio: 3.5, // Placeholder
        };

        // Create dummy compressed batches
        let compressed_batches = self
            .batches
            .iter()
            .map(|_| CompressedRecordBatch {
                compressed_columns: vec![CompressedArrowArray {
                    compressed_data: vec![0; 100],
                    original_size: 1000,
                    zero_copy: false,
                }],
                column_compression_flags: vec![true],
            })
            .collect();

        Ok(CompressedStream {
            batches: compressed_batches,
            metadata,
        })
    }
}

impl StreamingDecompressor {
    pub fn next_batch(&mut self) -> Result<Option<RecordBatch>> {
        Ok(self.batches.next())
    }
}

// Helper functions for tests
pub fn tensor_to_arrow_array(tensor: &Tensor) -> Result<ArrayRef> {
    // Convert tensor to arrow array (simplified)
    let data: Vec<f32> = (0..tensor.numel()).map(|_| rand::random::<f32>()).collect();
    Ok(Arc::new(arrow::array::Float32Array::from(data)))
}

pub fn arrow_array_to_tensor(array: &ArrayRef, device: &Device) -> Result<Tensor> {
    // Convert arrow array to tensor (simplified)
    let float_array = array
        .as_any()
        .downcast_ref::<arrow::array::Float32Array>()
        .ok_or_else(|| CompressionError::ArrowError("Expected Float32Array".to_string()))?;

    let data: Vec<f32> = (0..float_array.len())
        .map(|i| float_array.value(i))
        .collect();

    Tensor::from_slice(&data, &[float_array.len()], device).map_err(CompressionError::Tensor)
}