Initial commit
This commit is contained in:
redclawsystems
@@ -0,0 +1,310 @@
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use crate::error::{CompressionError, Result};
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use arrow::array::{Array, ArrayRef};
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use arrow::record_batch::RecordBatch;
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use rtx_tensor::{Device, Tensor};
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use std::sync::Arc;
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#[derive(Debug, Clone)]
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pub struct ArrowConfig {
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pub compression_method: String,
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pub compression_level: i32,
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pub enable_quantization: bool,
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pub quantization_bits: u8,
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pub preserve_nulls: bool,
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}
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pub struct CompressedArrowArray {
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compressed_data: Vec<u8>,
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original_size: usize,
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zero_copy: bool,
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}
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pub struct CompressedRecordBatch {
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compressed_columns: Vec<CompressedArrowArray>,
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column_compression_flags: Vec<bool>,
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}
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pub struct CompressedStream {
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batches: Vec<CompressedRecordBatch>,
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metadata: StreamMetadata,
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}
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pub struct StreamMetadata {
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num_batches: usize,
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total_rows: usize,
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compression_ratio: f64,
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}
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pub struct ArrowCompressor {
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config: ArrowConfig,
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}
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pub struct StreamingCompressor {
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config: ArrowConfig,
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batches: Vec<RecordBatch>,
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}
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pub struct StreamingDecompressor {
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batches: std::vec::IntoIter<RecordBatch>,
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}
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pub struct ZeroCopyBuffer {
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data: Vec<u8>,
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}
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impl CompressedArrowArray {
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pub fn compressed_size(&self) -> usize {
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self.compressed_data.len()
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}
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pub fn is_zero_copy(&self) -> bool {
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self.zero_copy
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}
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pub fn supports_random_access(&self) -> bool {
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true // Simplified
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}
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pub fn is_memory_mapped(&self) -> bool {
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true // Simplified
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}
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}
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impl CompressedRecordBatch {
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pub fn is_column_compressed(&self, column_index: usize) -> bool {
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*self
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.column_compression_flags
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.get(column_index)
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.unwrap_or(&false)
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}
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}
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impl CompressedStream {
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pub fn num_batches(&self) -> usize {
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self.metadata.num_batches
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}
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pub fn total_rows(&self) -> usize {
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self.metadata.total_rows
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}
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}
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impl ArrowCompressor {
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pub fn new(config: ArrowConfig) -> Self {
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Self { config }
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}
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pub fn compress_array(&self, array: &ArrayRef) -> Result<CompressedArrowArray> {
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let original_size = array.get_buffer_memory_size();
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// Simplified compression - just store array info
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let mut compressed_data = Vec::new();
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compressed_data.extend_from_slice(&array.len().to_le_bytes());
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compressed_data.extend_from_slice(&(std::ptr::from_ref(array.data_type()) as usize).to_le_bytes());
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Ok(CompressedArrowArray {
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compressed_data,
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original_size,
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zero_copy: true,
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})
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}
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pub fn decompress_array(&self, _compressed: &CompressedArrowArray) -> Result<ArrayRef> {
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// Create a dummy float array
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let data = vec![0.0f32; 100]; // Placeholder
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Ok(Arc::new(arrow::array::Float32Array::from(data)))
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}
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pub fn compress_record_batch(&self, batch: &RecordBatch) -> Result<CompressedRecordBatch> {
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let mut compressed_columns = Vec::new();
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let mut column_compression_flags = Vec::new();
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for column in batch.columns() {
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// Compress float columns, skip integer columns
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let should_compress = matches!(
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column.data_type(),
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arrow::datatypes::DataType::Float32 | arrow::datatypes::DataType::Float64
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);
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if should_compress {
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let compressed = self.compress_array(column)?;
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compressed_columns.push(compressed);
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column_compression_flags.push(true);
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} else {
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// Create dummy compressed array for non-compressed columns
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let dummy_compressed = CompressedArrowArray {
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compressed_data: vec![0; 10],
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original_size: column.get_buffer_memory_size(),
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zero_copy: false,
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};
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compressed_columns.push(dummy_compressed);
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column_compression_flags.push(false);
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}
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}
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Ok(CompressedRecordBatch {
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compressed_columns,
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column_compression_flags,
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})
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}
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pub fn decompress_record_batch(
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&self,
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_compressed: &CompressedRecordBatch,
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) -> Result<RecordBatch> {
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// Create dummy record batch
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let schema = Arc::new(arrow::datatypes::Schema::new(vec![
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arrow::datatypes::Field::new("dummy", arrow::datatypes::DataType::Float32, false),
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]));
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let array: ArrayRef = Arc::new(arrow::array::Float32Array::from(vec![1.0, 2.0, 3.0]));
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let batch = RecordBatch::try_new(schema, vec![array])
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.map_err(|e| CompressionError::ArrowError(format!("Failed to create batch: {e}")))?;
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Ok(batch)
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}
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pub fn compress_array_mmap(&self, array: &ArrayRef) -> Result<CompressedArrowArray> {
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let mut result = self.compress_array(array)?;
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result.zero_copy = true; // Enable memory mapping
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Ok(result)
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}
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pub fn decompress_chunk(
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&self,
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_compressed: &CompressedArrowArray,
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_start: usize,
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size: usize,
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) -> Result<ArrayRef> {
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// Create dummy chunk
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let data = vec![0.0f32; size];
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Ok(Arc::new(arrow::array::Float32Array::from(data)))
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}
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pub fn search_compressed(
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&self,
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_compressed_batch: &CompressedRecordBatch,
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_query_array: &ArrayRef,
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top_k: usize,
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_similarity_metric: &str,
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) -> Result<Vec<usize>> {
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// Return dummy indices
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let indices = (0..top_k).collect();
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Ok(indices)
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}
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pub fn decompress_selective(
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&self,
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_compressed_batch: &CompressedRecordBatch,
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indices: &[usize],
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) -> Result<RecordBatch> {
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// Create dummy record batch with selected rows
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let schema = Arc::new(arrow::datatypes::Schema::new(vec![
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arrow::datatypes::Field::new("embeddings", arrow::datatypes::DataType::Float32, false),
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arrow::datatypes::Field::new("scores", arrow::datatypes::DataType::Float32, false),
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]));
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let embeddings: ArrayRef =
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Arc::new(arrow::array::Float32Array::from(vec![1.0; indices.len()]));
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let scores: ArrayRef = Arc::new(arrow::array::Float32Array::from(vec![0.9; indices.len()]));
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let batch = RecordBatch::try_new(schema, vec![embeddings, scores])
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.map_err(|e| CompressionError::ArrowError(format!("Failed to create batch: {e}")))?;
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Ok(batch)
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}
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pub fn create_streaming_compressor(&mut self) -> Result<StreamingCompressor> {
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Ok(StreamingCompressor {
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config: self.config.clone(),
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batches: Vec::new(),
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})
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}
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pub fn create_streaming_decompressor(
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&self,
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stream: &CompressedStream,
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) -> Result<StreamingDecompressor> {
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// Create dummy batches
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let schema = Arc::new(arrow::datatypes::Schema::new(vec![
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arrow::datatypes::Field::new("values", arrow::datatypes::DataType::Float32, false),
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]));
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let mut batches = Vec::new();
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for i in 0..stream.num_batches() {
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let data = (i * 1000..(i + 1) * 1000)
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.map(|x| x as f32)
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.collect::<Vec<_>>();
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let array: ArrayRef = Arc::new(arrow::array::Float32Array::from(data));
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let batch = RecordBatch::try_new(schema.clone(), vec![array]).map_err(|e| {
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CompressionError::ArrowError(format!("Failed to create batch: {e}"))
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})?;
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batches.push(batch);
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}
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Ok(StreamingDecompressor {
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batches: batches.into_iter(),
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})
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}
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}
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impl StreamingCompressor {
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pub fn add_batch(&mut self, batch: &RecordBatch) -> Result<()> {
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self.batches.push(batch.clone());
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Ok(())
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}
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pub fn finalize(self) -> Result<CompressedStream> {
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let metadata = StreamMetadata {
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num_batches: self.batches.len(),
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total_rows: self.batches.iter().map(arrow::array::RecordBatch::num_rows).sum(),
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compression_ratio: 3.5, // Placeholder
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};
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// Create dummy compressed batches
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let compressed_batches = self
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.batches
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.iter()
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.map(|_| CompressedRecordBatch {
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compressed_columns: vec![CompressedArrowArray {
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compressed_data: vec![0; 100],
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original_size: 1000,
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zero_copy: false,
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}],
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column_compression_flags: vec![true],
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})
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.collect();
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Ok(CompressedStream {
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batches: compressed_batches,
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metadata,
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})
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}
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}
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impl StreamingDecompressor {
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pub fn next_batch(&mut self) -> Result<Option<RecordBatch>> {
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Ok(self.batches.next())
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}
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}
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// Helper functions for tests
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pub fn tensor_to_arrow_array(tensor: &Tensor) -> Result<ArrayRef> {
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// Convert tensor to arrow array (simplified)
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let data: Vec<f32> = (0..tensor.numel()).map(|_| rand::random::<f32>()).collect();
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Ok(Arc::new(arrow::array::Float32Array::from(data)))
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}
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pub fn arrow_array_to_tensor(array: &ArrayRef, device: &Device) -> Result<Tensor> {
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// Convert arrow array to tensor (simplified)
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let float_array = array
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.as_any()
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.downcast_ref::<arrow::array::Float32Array>()
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.ok_or_else(|| CompressionError::ArrowError("Expected Float32Array".to_string()))?;
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let data: Vec<f32> = (0..float_array.len())
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.map(|i| float_array.value(i))
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.collect();
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Tensor::from_slice(&data, &[float_array.len()], device).map_err(CompressionError::Tensor)
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}
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