ryankert01 commented on code in PR #753: URL: https://github.com/apache/mahout/pull/753#discussion_r2650078534
########## qdp/qdp-core/src/readers/parquet.rs: ########## @@ -0,0 +1,486 @@ +// +// Licensed to the Apache Software Foundation (ASF) under one or more +// contributor license agreements. See the NOTICE file distributed with +// this work for additional information regarding copyright ownership. +// The ASF licenses this file to You under the Apache License, Version 2.0 +// (the "License"); you may not use this file except in compliance with +// the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +//! Parquet format reader implementation. + +use std::fs::File; +use std::path::Path; + +use arrow::array::{Array, Float64Array, FixedSizeListArray, ListArray}; +use arrow::datatypes::DataType; +use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder; + +use crate::error::{MahoutError, Result}; +use crate::reader::{DataReader, StreamingDataReader}; + +/// Reader for Parquet files containing List<Float64> or FixedSizeList<Float64> columns. +pub struct ParquetReader { + reader: Option<parquet::arrow::arrow_reader::ParquetRecordBatchReader>, + sample_size: Option<usize>, + total_rows: usize, +} + +impl ParquetReader { + /// Create a new Parquet reader. + /// + /// # Arguments + /// * `path` - Path to the Parquet file + /// * `batch_size` - Optional batch size for reading (defaults to entire file) + pub fn new<P: AsRef<Path>>(path: P, batch_size: Option<usize>) -> Result<Self> { + let file = File::open(path.as_ref()).map_err(|e| { + MahoutError::Io(format!("Failed to open Parquet file: {}", e)) + })?; + + let builder = ParquetRecordBatchReaderBuilder::try_new(file).map_err(|e| { + MahoutError::Io(format!("Failed to create Parquet reader: {}", e)) + })?; + + let schema = builder.schema(); + if schema.fields().len() != 1 { + return Err(MahoutError::InvalidInput(format!( + "Expected exactly one column, got {}", + schema.fields().len() + ))); + } + + let field = &schema.fields()[0]; + match field.data_type() { + DataType::List(child_field) => { + if !matches!(child_field.data_type(), DataType::Float64) { + return Err(MahoutError::InvalidInput(format!( + "Expected List<Float64> column, got List<{:?}>", + child_field.data_type() + ))); + } + } + DataType::FixedSizeList(child_field, _) => { + if !matches!(child_field.data_type(), DataType::Float64) { + return Err(MahoutError::InvalidInput(format!( + "Expected FixedSizeList<Float64> column, got FixedSizeList<{:?}>", + child_field.data_type() + ))); + } + } + _ => { + return Err(MahoutError::InvalidInput(format!( + "Expected List<Float64> or FixedSizeList<Float64> column, got {:?}", + field.data_type() + ))); + } + } + + let total_rows = builder.metadata().file_metadata().num_rows() as usize; + + let reader = if let Some(batch_size) = batch_size { + builder.with_batch_size(batch_size).build() + } else { + builder.build() + } + .map_err(|e| MahoutError::Io(format!("Failed to build Parquet reader: {}", e)))?; + + Ok(Self { + reader: Some(reader), + sample_size: None, + total_rows, + }) + } +} + +impl DataReader for ParquetReader { + fn read_batch(&mut self) -> Result<(Vec<f64>, usize, usize)> { + let reader = self.reader.take().ok_or_else(|| { + MahoutError::InvalidInput("Reader already consumed".to_string()) + })?; + + let mut all_data = Vec::new(); + let mut num_samples = 0; + let mut sample_size = None; + + for batch_result in reader { + let batch = batch_result.map_err(|e| { + MahoutError::Io(format!("Failed to read Parquet batch: {}", e)) + })?; + + if batch.num_columns() == 0 { + return Err(MahoutError::Io("Parquet file has no columns".to_string())); + } + + let column = batch.column(0); + + match column.data_type() { + DataType::List(_) => { + let list_array = column + .as_any() + .downcast_ref::<ListArray>() + .ok_or_else(|| MahoutError::Io("Failed to downcast to ListArray".to_string()))?; + + for i in 0..list_array.len() { + let value_array = list_array.value(i); + let float_array = value_array + .as_any() + .downcast_ref::<Float64Array>() + .ok_or_else(|| MahoutError::Io("List values must be Float64".to_string()))?; + + let current_size = float_array.len(); + + if let Some(expected_size) = sample_size { + if current_size != expected_size { + return Err(MahoutError::InvalidInput(format!( + "Inconsistent sample sizes: expected {}, got {}", + expected_size, current_size + ))); + } + } else { + sample_size = Some(current_size); + all_data.reserve(current_size * self.total_rows); + } + + if float_array.null_count() == 0 { + all_data.extend_from_slice(float_array.values()); + } else { + all_data.extend(float_array.iter().map(|opt| opt.unwrap_or(0.0))); + } + + num_samples += 1; + } + } + DataType::FixedSizeList(_, size) => { + let list_array = column + .as_any() + .downcast_ref::<FixedSizeListArray>() + .ok_or_else(|| MahoutError::Io("Failed to downcast to FixedSizeListArray".to_string()))?; + + let current_size = *size as usize; + + if sample_size.is_none() { + sample_size = Some(current_size); + all_data.reserve(current_size * batch.num_rows()); + } + + let values = list_array.values(); + let float_array = values + .as_any() + .downcast_ref::<Float64Array>() + .ok_or_else(|| MahoutError::Io("Values must be Float64".to_string()))?; + + if float_array.null_count() == 0 { + all_data.extend_from_slice(float_array.values()); + } else { + all_data.extend(float_array.iter().map(|opt| opt.unwrap_or(0.0))); + } + + num_samples += list_array.len(); + } + _ => { + return Err(MahoutError::Io(format!( + "Expected List<Float64> or FixedSizeList<Float64>, got {:?}", + column.data_type() + ))); + } + } + } + + let sample_size = sample_size.ok_or_else(|| { + MahoutError::Io("Parquet file contains no data".to_string()) + })?; + + self.sample_size = Some(sample_size); + + Ok((all_data, num_samples, sample_size)) + } + + fn get_sample_size(&self) -> Option<usize> { + self.sample_size + } + + fn get_num_samples(&self) -> Option<usize> { + Some(self.total_rows) + } +} + +/// Streaming Parquet reader for List<Float64> and FixedSizeList<Float64> columns. +/// +/// Reads Parquet files in chunks without loading entire file into memory. +/// Supports efficient streaming for large files via Producer-Consumer pattern. +pub struct ParquetStreamingReader { + reader: parquet::arrow::arrow_reader::ParquetRecordBatchReader, + sample_size: Option<usize>, + leftover_data: Vec<f64>, + leftover_cursor: usize, + pub total_rows: usize, +} + +impl ParquetStreamingReader { + /// Create a new streaming Parquet reader. + /// + /// # Arguments + /// * `path` - Path to the Parquet file + /// * `batch_size` - Optional batch size (defaults to 2048) + pub fn new<P: AsRef<Path>>(path: P, batch_size: Option<usize>) -> Result<Self> { + let file = File::open(path.as_ref()).map_err(|e| { + MahoutError::Io(format!("Failed to open Parquet file: {}", e)) + })?; + + let builder = ParquetRecordBatchReaderBuilder::try_new(file).map_err(|e| { + MahoutError::Io(format!("Failed to create Parquet reader: {}", e)) + })?; + + let schema = builder.schema(); + if schema.fields().len() != 1 { + return Err(MahoutError::InvalidInput(format!( + "Expected exactly one column, got {}", + schema.fields().len() + ))); + } + + let field = &schema.fields()[0]; + match field.data_type() { + DataType::List(child_field) => { + if !matches!(child_field.data_type(), DataType::Float64) { + return Err(MahoutError::InvalidInput(format!( + "Expected List<Float64> column, got List<{:?}>", + child_field.data_type() + ))); + } + } + DataType::FixedSizeList(child_field, _) => { + if !matches!(child_field.data_type(), DataType::Float64) { + return Err(MahoutError::InvalidInput(format!( + "Expected FixedSizeList<Float64> column, got FixedSizeList<{:?}>", + child_field.data_type() + ))); + } + } + _ => { + return Err(MahoutError::InvalidInput(format!( + "Expected List<Float64> or FixedSizeList<Float64> column, got {:?}", + field.data_type() + ))); + } + } + + let total_rows = builder.metadata().file_metadata().num_rows() as usize; + + let batch_size = batch_size.unwrap_or(2048); + let reader = builder + .with_batch_size(batch_size) + .build() + .map_err(|e| { + MahoutError::Io(format!("Failed to build Parquet reader: {}", e)) + })?; + + Ok(Self { + reader, + sample_size: None, + leftover_data: Vec::new(), + leftover_cursor: 0, + total_rows, + }) + } + + /// Get the sample size (number of elements per sample). + pub fn get_sample_size(&self) -> Option<usize> { + self.sample_size + } +} + +impl DataReader for ParquetStreamingReader { + fn read_batch(&mut self) -> Result<(Vec<f64>, usize, usize)> { + let mut all_data = Vec::new(); + let mut num_samples = 0; + + loop { + let mut buffer = vec![0.0; 1024 * 1024]; // 1M elements buffer + let written = self.read_chunk(&mut buffer)?; + if written == 0 { + break; + } + all_data.extend_from_slice(&buffer[..written]); + num_samples += written / self.sample_size.unwrap_or(1); + } + + let sample_size = self.sample_size.ok_or_else(|| { + MahoutError::Io("No data read from Parquet file".to_string()) + })?; + + Ok((all_data, num_samples, sample_size)) + } + + fn get_sample_size(&self) -> Option<usize> { + self.sample_size + } + + fn get_num_samples(&self) -> Option<usize> { + Some(self.total_rows) + } +} + +impl StreamingDataReader for ParquetStreamingReader { + fn read_chunk(&mut self, buffer: &mut [f64]) -> Result<usize> { + let mut written = 0; + let buf_cap = buffer.len(); + let calc_limit = |ss: usize| -> usize { + if ss == 0 { + buf_cap + } else { + (buf_cap / ss) * ss + } + }; + let mut limit = self.sample_size.map_or(buf_cap, calc_limit); + + if self.sample_size.is_some() { + while self.leftover_cursor < self.leftover_data.len() && written < limit { + let available = self.leftover_data.len() - self.leftover_cursor; + let space_left = limit - written; + let to_copy = std::cmp::min(available, space_left); + + if to_copy > 0 { + buffer[written..written+to_copy].copy_from_slice( + &self.leftover_data[self.leftover_cursor..self.leftover_cursor+to_copy] + ); + written += to_copy; + self.leftover_cursor += to_copy; + + if self.leftover_cursor == self.leftover_data.len() { + self.leftover_data.clear(); + self.leftover_cursor = 0; + break; + } + } else { + break; + } + } + } + + while written < limit { + match self.reader.next() { + Some(Ok(batch)) => { + if batch.num_columns() == 0 { + continue; + } + let column = batch.column(0); + + let (current_sample_size, batch_values) = match column.data_type() { + DataType::List(_) => { + let list_array = column + .as_any() + .downcast_ref::<ListArray>() + .ok_or_else(|| MahoutError::Io("Failed to downcast to ListArray".to_string()))?; + + if list_array.len() == 0 { + continue; + } + + let mut batch_values = Vec::new(); + let mut current_sample_size = None; + for i in 0..list_array.len() { + let value_array = list_array.value(i); + let float_array = value_array + .as_any() + .downcast_ref::<Float64Array>() + .ok_or_else(|| MahoutError::Io("List values must be Float64".to_string()))?; + + if i == 0 { + current_sample_size = Some(float_array.len()); + } + + if float_array.null_count() == 0 { + batch_values.extend_from_slice(float_array.values()); + } else { + return Err(MahoutError::Io("Null value encountered in Float64Array during quantum encoding. Please check data quality at the source.".to_string())); Review Comment: Let's make it a followup! I think it requires some thinking -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
