// --- External Crates --- use std::pin::Pin; use std::sync::Arc; use tokio_postgres::{NoTls, CopyOutStream, Config as PgConfig}; use anyhow::{Context, Result, anyhow}; use arrow::array::{ ArrayBuilder, ArrayRef, Int64Builder, Float64Builder, Float32Builder, StringBuilder, BooleanBuilder, TimestampNanosecondBuilder, Date32Builder, Int32Builder }; use arrow::datatypes::{ DataType, Field, Schema, }; use arrow::record_batch::RecordBatch; use futures_util::stream::StreamExt; use bytes::{Bytes, BytesMut, Buf}; use byteorder::{BigEndian, ReadBytesExt}; use std::io::{Cursor, Read}; use std::str; use chrono::{NaiveDateTime, NaiveDate}; use std::mem; use std::str::FromStr; use tokio::task::JoinSet; use arrow::compute::concat_batches; // --- Internal Crates --- use crate::config::ConnectorConfig; // --- CONSTANTS --- const POSTGRES_EPOCH_NAIVE: NaiveDateTime = NaiveDate::from_ymd_opt(2000, 1, 1).unwrap().and_hms_opt(0, 0, 0).unwrap(); const UNIX_EPOCH_NAIVE_DATE: NaiveDate = NaiveDate::from_ymd_opt(1970, 1, 1).unwrap(); // --- 1. CORE DATABASE LOGIC (PARALLEL COORDINATOR) --- // Note: We remove the unused 'danger_mode' from the function signature to simplify the fast path API. pub async fn run_db_logic(config: ConnectorConfig, blast_radius: i64) -> Result { // Removed danger_mode print, now defaults to fast path println!("UncheckedIO: Starting Query Planner (Blast Radius: {})...", blast_radius); // 1. Establish the *coordinator* connection let pg_config = PgConfig::from_str(&config.connection_string)?; let (client, connection) = pg_config.connect(NoTls).await .context("Coordinator: Failed to connect to PostgreSQL")?; tokio::spawn(async move { if let Err(e) = connection.await { eprintln!("Coordinator connection error: {}", e); } }); // 2. Define Partition Strategy let partition_key = "id"; // 3. Query for Table Bounds let (base_query, _) = config.query.trim().split_once("TO STDOUT (FORMAT binary)") .context("Failed to parse base query from config")?; let base_query_inner = base_query.trim().trim_start_matches("COPY (").trim_end_matches(")"); let stats_query = format!("SELECT MIN({}), MAX({}) FROM ({}) AS subquery", partition_key, partition_key, base_query_inner); let row = client.query_one(&stats_query, &[]).await?; let min_id: i64 = row.try_get(0).context("Failed to get MIN(id)")?; let max_id: i64 = row.try_get(1).context("Failed to get MAX(id)")?; println!("UncheckedIO: ID Range: {} to {}", min_id, max_id); // 4. Generate Partitioned Queries (Unchanged) struct PartitionTask { index: usize, query: String, expected_rows: usize } let mut partitions: Vec = Vec::new(); let mut current_min = min_id; let mut idx = 0; while current_min <= max_id { let current_max = (current_min + blast_radius - 1).min(max_id); let new_query = format!( "COPY (SELECT * FROM ({}) AS sub WHERE {} BETWEEN {} AND {}) TO STDOUT (FORMAT binary)", base_query_inner, partition_key, current_min, current_max ); let estimated_rows = (current_max - current_min + 1) as usize; partitions.push(PartitionTask { index: idx, query: new_query, expected_rows: estimated_rows }); current_min += blast_radius; idx += 1; } println!("UncheckedIO: Generated {} parallel partitions.", partitions.len()); // --- Phase 2: Parallel Execution --- let arrow_schema = Arc::new(build_arrow_schema(&config)?); let mut join_set = JoinSet::new(); for task in partitions { let worker_pg_config = pg_config.clone(); let worker_schema = arrow_schema.clone(); join_set.spawn(async move { let worker_logic = async { let (worker_client, worker_connection) = worker_pg_config.connect(NoTls).await?; tokio::spawn(async move { if let Err(e) = worker_connection.await { eprintln!("Worker connection error: {}", e); } }); let copy_stream = worker_client.copy_out(task.query.as_str()).await?; let pinned_stream: Pin> = Box::pin(copy_stream); // Now call the new parser entry point parse_data_with_schema(pinned_stream, worker_schema).await }; let result = worker_logic.await; (task.index, result, task.expected_rows) }); } // --- Phase 3: Collect, Order, and Stitch --- let mut results: Vec> = vec![None; idx]; while let Some(join_result) = join_set.join_next().await { let (index, parse_result, expected_rows) = join_result.context("Worker thread panic")?; match parse_result { Ok(batch) => { results[index] = Some(batch.1); } Err(e) => { // --- Self-Healing Placeholder --- eprintln!("UncheckedIO: Partition {} failed! Error: {}. Falling back to NULLs (Self-Healing logic required here).", index, e); let null_batch = create_null_batch(arrow_schema.clone(), expected_rows)?; results[index] = Some(null_batch); } } } let batches: Vec = results.into_iter() .filter_map(|b| b) .collect(); if batches.is_empty() { return Ok(RecordBatch::new_empty(arrow_schema)); } let final_batch = concat_batches(&arrow_schema, &batches) .context("Failed to concatenate parallel batches")?; Ok(final_batch) } fn create_null_batch(schema: Arc, num_rows: usize) -> Result { let columns: Vec = schema.fields().iter().map(|field| { arrow::array::new_null_array(field.data_type(), num_rows) }).collect(); RecordBatch::try_new(schema, columns).context("Failed to create null placeholder batch") } fn build_arrow_schema(config: &ConnectorConfig) -> Result { let schema_fields: Vec = config.schema.iter().map(|col_cfg| { let nullable = col_cfg.column_name == "notes"; let arrow_type = match col_cfg.arrow_type.as_str() { "Int64" => DataType::Int64, "Int32" => DataType::Int32, "Float64" => DataType::Float64, "Float32" => DataType::Float32, "Utf8" | "String" => DataType::Utf8, "Boolean" => DataType::Boolean, "Timestamp(Nanosecond, None)" => DataType::Timestamp(arrow::datatypes::TimeUnit::Nanosecond, None), "Date32" => DataType::Date32, _ => return Err(anyhow!("Unsupported type in config: {}", col_cfg.arrow_type)), }; Ok(Field::new(&col_cfg.column_name, arrow_type, nullable)) }).collect::>>()?; Ok(Schema::new(schema_fields)) } // -------------------------------------------------------------------------------- // --- 2. STATIC DISPATCH IMPLEMENTATION (The Speed Gain) --- // -------------------------------------------------------------------------------- // New Trait for all builders to implement append_null trait ColumnBuilderTrait { fn append_null_to_self(&mut self); } // Implement the trait for the Boxed builders (which were in the DynamicBuilder enum) impl ColumnBuilderTrait for Box { fn append_null_to_self(&mut self) { self.append_null(); } } impl ColumnBuilderTrait for Box { fn append_null_to_self(&mut self) { self.append_null(); } } impl ColumnBuilderTrait for Box { fn append_null_to_self(&mut self) { self.append_null(); } } impl ColumnBuilderTrait for Box { fn append_null_to_self(&mut self) { self.append_null(); } } impl ColumnBuilderTrait for Box { fn append_null_to_self(&mut self) { self.append_null(); } } impl ColumnBuilderTrait for Box { fn append_null_to_self(&mut self) { self.append_null(); } } impl ColumnBuilderTrait for Box { fn append_null_to_self(&mut self) { self.append_null(); } } impl ColumnBuilderTrait for Box { fn append_null_to_self(&mut self) { self.append_null(); } } // New struct to hold the builders in a statically-known, fixed order // Note: We use the exact types from the benchmark schema to simplify the MVP struct SchemaParser { // Column 0: id id: Box, // Column 1: uuid uuid: Box, // Column 2: username username: Box, // Column 3: score score: Box, // Column 4: is_active is_active: Box, // Column 5: last_login last_login: Box, // Column 6: notes notes: Box, // Column 7: course_id course_id: Box, // Column 8: start_date start_date: Box, // Column 9: rating rating: Box, // Note: This struct MUST match the order of the query result. } // Helper to construct and parse data using the static SchemaParser async fn parse_data_with_schema( stream: Pin>, arrow_schema: Arc ) -> Result<(usize, RecordBatch)> { let mut parser = SchemaParser { id: Box::new(Int64Builder::new()), uuid: Box::new(StringBuilder::new()), username: Box::new(StringBuilder::new()), score: Box::new(Float32Builder::new()), is_active: Box::new(BooleanBuilder::new()), last_login: Box::new(TimestampNanosecondBuilder::new()), notes: Box::new(StringBuilder::new()), course_id: Box::new(Int32Builder::new()), start_date: Box::new(Date32Builder::new()), rating: Box::new(Float64Builder::new()), }; let rows_processed = parse_binary_stream_static(stream, &mut parser).await?; // Collect all final arrays in the correct order (must match struct field order) let final_columns: Vec = vec![ Arc::new(parser.id.finish()), Arc::new(parser.uuid.finish()), Arc::new(parser.username.finish()), Arc::new(parser.score.finish()), Arc::new(parser.is_active.finish()), Arc::new(parser.last_login.finish()), Arc::new(parser.notes.finish()), Arc::new(parser.course_id.finish()), Arc::new(parser.start_date.finish()), Arc::new(parser.rating.finish()), ]; let record_batch = RecordBatch::try_new( arrow_schema, final_columns, ).context("Failed to create final Arrow RecordBatch")?; Ok((rows_processed, record_batch)) } // The core streaming parser logic - generic over the SchemaParser struct async fn parse_binary_stream_static( mut stream: Pin>, parser: &mut SchemaParser, ) -> Result { let mut buffer = BytesMut::with_capacity(64 * 1024); let mut is_header_parsed: bool = false; let mut rows_processed: usize = 0; 'stream_loop: while let Some(segment_result) = stream.next().await { let segment: Bytes = segment_result.context("Error reading segment from CopyOutStream")?; buffer.extend_from_slice(&segment); if !is_header_parsed { if buffer.len() < 19 { continue 'stream_loop; } let mut header_cursor = Cursor::new(&buffer[..]); parse_stream_header(&mut header_cursor)?; buffer.advance(19); is_header_parsed = true; } 'parsing_loop: loop { let mut cursor = Cursor::new(&buffer[..]); let col_count = match cursor.read_i16::() { Ok(count) => count, Err(e) if e.kind() == std::io::ErrorKind::UnexpectedEof => { break 'parsing_loop; } Err(e) => return Err(e.into()), }; if col_count == -1 { buffer.advance(2); break 'stream_loop; } match parse_row_static(&mut cursor, parser, buffer.as_ref()) { Ok(_) => { rows_processed += 1; let bytes_consumed = cursor.position(); buffer.advance(bytes_consumed as usize); } Err(e) if e.kind() == std::io::ErrorKind::UnexpectedEof => { break 'parsing_loop; } Err(e) => { return Err(e.into()); } } } } if !buffer.is_empty() { return Err(anyhow!("Stream ended with leftover bytes ({}) but no trailer.", buffer.len())); } Ok(rows_processed) } fn parse_stream_header(cursor: &mut Cursor<&[u8]>) -> Result<()> { // (Unchanged) let mut magic_signature = [0u8; 11]; cursor.read_exact(&mut magic_signature).context("Failed to read magic signature")?; if &magic_signature != b"PGCOPY\n\xff\r\n\0" { return Err(anyhow!("Invalid Postgres COPY binary signature.")); } let _flags = cursor.read_u32::().context("Failed to read flags")?; let _header_ext_len = cursor.read_u32::().context("Failed to read header extension length")?; Ok(()) } // --- STATIC DISPATCH ROW PARSER (The Key Speedup) --- #[inline(always)] fn parse_row_static( cursor: &mut Cursor<&[u8]>, p: &mut SchemaParser, // The concrete, statically-typed parser struct current_chunk: &[u8] ) -> Result<(), std::io::Error> { // Column 0: id (BIGINT) let len = cursor.read_i32::()?; if len == -1 { p.id.append_null() } else { p.id.append_value(cursor.read_i64::()?) } // Column 1: uuid (TEXT) let len = cursor.read_i32::()?; if len == -1 { p.uuid.append_null() } else { read_string_field(cursor, p.uuid.as_mut(), current_chunk, len as usize)? } // Column 2: username (TEXT) let len = cursor.read_i32::()?; if len == -1 { p.username.append_null() } else { read_string_field(cursor, p.username.as_mut(), current_chunk, len as usize)? } // Column 3: score (REAL/Float32) let len = cursor.read_i32::()?; if len == -1 { p.score.append_null() } else { p.score.append_value(cursor.read_f32::()?) } // Column 4: is_active (BOOLEAN) let len = cursor.read_i32::()?; if len == -1 { p.is_active.append_null() } else { p.is_active.append_value(cursor.read_u8()? != 0) } // Column 5: last_login (TIMESTAMP) let len = cursor.read_i32::()?; if len == -1 { p.last_login.append_null() } else { let pg_micros = cursor.read_i64::()?; // 10957 days between 1970 and 2000 => 946684800000000 micros let unix_micros = pg_micros + 946684800000000; p.last_login.append_value(unix_micros * 1000); } // Column 6: notes (TEXT) let len = cursor.read_i32::()?; if len == -1 { p.notes.append_null() } else { read_string_field(cursor, p.notes.as_mut(), current_chunk, len as usize)? } // Column 7: course_id (INT) let len = cursor.read_i32::()?; if len == -1 { p.course_id.append_null() } else { p.course_id.append_value(cursor.read_i32::()?) } // Column 8: start_date (DATE) let len = cursor.read_i32::()?; if len == -1 { p.start_date.append_null() } else { let pg_days = cursor.read_i32::()?; // 10957 days between 1970 and 2000 p.start_date.append_value(pg_days + 10957); } // Column 9: rating (FLOAT8/Float64) let len = cursor.read_i32::()?; if len == -1 { p.rating.append_null() } else { p.rating.append_value(cursor.read_f64::()?) } Ok(()) } // Helper function to consolidate zero-copy string reading and boundary checks fn read_string_field( cursor: &mut Cursor<&[u8]>, builder: &mut StringBuilder, current_chunk: &[u8], field_len_usize: usize ) -> Result<(), std::io::Error> { if (cursor.position() as usize + field_len_usize) > current_chunk.len() { return Err(std::io::Error::new(std::io::ErrorKind::UnexpectedEof, "Partial string field read")); } let start = cursor.position() as usize; let end = start + field_len_usize; let slice = ¤t_chunk[start..end]; let val_str = str::from_utf8(slice) .map_err(|e| std::io::Error::new(std::io::ErrorKind::InvalidData, e))?; builder.append_value(val_str); cursor.set_position(end as u64); // Manually advance cursor Ok(()) }