Dandandan commented on code in PR #7721: URL: https://github.com/apache/arrow-datafusion/pull/7721#discussion_r1342764263
########## datafusion/physical-plan/src/topk/mod.rs: ########## @@ -0,0 +1,754 @@ +// 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. + +//! TopK: Combination of Sort / LIMIT + +use arrow::row::{RowConverter, Rows, SortField}; +use std::{cmp::Ordering, collections::BinaryHeap, sync::Arc}; + +use arrow_array::{ + builder::StringBuilder, cast::AsArray, downcast_dictionary_array, Array, ArrayRef, + DictionaryArray, Int16Array, Int32Array, Int64Array, Int8Array, RecordBatch, + StringArray, UInt16Array, UInt32Array, UInt64Array, UInt8Array, +}; +use arrow_schema::{DataType, SchemaRef}; +use datafusion_common::Result; +use datafusion_execution::{ + memory_pool::{MemoryConsumer, MemoryReservation}, + runtime_env::RuntimeEnv, +}; +use datafusion_physical_expr::PhysicalSortExpr; +use hashbrown::HashMap; + +use crate::{stream::RecordBatchStreamAdapter, SendableRecordBatchStream}; + +use super::metrics::{BaselineMetrics, Count, ExecutionPlanMetricsSet, MetricBuilder}; + +/// Global TopK +/// +/// # Background +/// +/// "Top K" is a common query optimization used for queries such as +/// "find the top 3 customers by revenue". The (simplified) SQL for +/// such a query might be: +/// +/// ```sql +/// SELECT customer_id, revenue FROM 'sales.csv' ORDER BY revenue DESC limit 3; +/// ``` +/// +/// The simple plan would be: +/// +/// ``` +/// > explain SELECT customer_id, revenue FROM sales ORDER BY revenue DESC limit 3; +/// +--------------+----------------------------------------+ +/// | plan_type | plan | +/// +--------------+----------------------------------------+ +/// | logical_plan | Limit: 3 | +/// | | Sort: revenue DESC NULLS FIRST | +/// | | Projection: customer_id, revenue | +/// | | TableScan: sales | +/// +--------------+----------------------------------------+ +/// ``` +/// +/// While this plan produces the correct answer, it will fully sorts the +/// input before discarding everything other than the top 3 elements. +/// +/// The same answer can be produced by simply keeping track of the top +/// N elements, reducing the total amount of required buffer memory. +/// +/// # Structure +/// +/// This operator tracks the top K items using a `TopKHeap`. +pub struct TopK { + /// schema of the output (and the input) + schema: SchemaRef, + /// Runtime metrics + metrics: TopKMetrics, + /// Reservation + reservation: MemoryReservation, + /// The target number of rows for output batches + batch_size: usize, + /// sort expressions + expr: Arc<[PhysicalSortExpr]>, + /// row converter, for sort keys + row_converter: RowConverter, + /// scratch space for converting rows + scratch_rows: Rows, + /// stores the top k values and their sort key values, in order + heap: TopKHeap, +} + +impl TopK { + /// Create a new [`TopK`] that stores the top `k` values, as + /// defined by the sort expressions in `expr`. + // TOOD: make a builder or some other nicer API to avoid the + // clippy warning + #[allow(clippy::too_many_arguments)] + pub fn try_new( + partition_id: usize, + schema: SchemaRef, + expr: Vec<PhysicalSortExpr>, + k: usize, + batch_size: usize, + runtime: Arc<RuntimeEnv>, + metrics: &ExecutionPlanMetricsSet, + partition: usize, + ) -> Result<Self> { + let reservation = MemoryConsumer::new(format!("TopK[{partition_id}]")) + .register(&runtime.memory_pool); + + let expr: Arc<[PhysicalSortExpr]> = expr.into(); + + let sort_fields: Vec<_> = expr + .iter() + .map(|e| { + Ok(SortField::new_with_options( + e.expr.data_type(&schema)?, + e.options, + )) + }) + .collect::<Result<_>>()?; + + let row_converter = RowConverter::new(sort_fields)?; + let scratch_rows = row_converter.empty_rows( + batch_size, + 20 * batch_size, // guestimate 20 bytes per row + ); + + Ok(Self { + schema: schema.clone(), + metrics: TopKMetrics::new(metrics, partition), + reservation, + batch_size, + expr, + row_converter, + scratch_rows, + heap: TopKHeap::new(k, batch_size, schema), + }) + } + + /// Insert `batch`, remembering it if any of its values are among + /// the top k seen so far. + pub fn insert_batch(&mut self, batch: RecordBatch) -> Result<()> { + // Updates on drop + let _timer = self.metrics.baseline.elapsed_compute().timer(); + + let sort_keys: Vec<ArrayRef> = self + .expr + .iter() + .map(|expr| { + let value = expr.expr.evaluate(&batch)?; + Ok(value.into_array(batch.num_rows())) + }) + .collect::<Result<Vec<_>>>()?; + + // reuse existing `Rows` to avoid reallocations + let rows = &mut self.scratch_rows; + rows.clear(); + self.row_converter.append(rows, &sort_keys)?; + + // TODO make this algorithmically better?: + // 1. only check topk values in rows + // 2. only do one update through top_k + + let mut batch_entry = self.heap.register_batch(batch); + for (index, row) in rows.iter().enumerate() { + match self.heap.max() { + // heap has k items, and the new row is greater than the + // current max in the heap ==> it is not a new topk + Some(max_row) if row.as_ref() >= max_row.row() => {} + // don't yet have k items or new item is lower than the currently k low values + None | Some(_) => { + self.heap.add(&mut batch_entry, row, index); + self.metrics.row_replacements.add(1); + } + } + } + self.heap.insert_batch_entry(batch_entry); + + // conserve memory + self.heap.maybe_compact()?; + + // update memory reservation + self.reservation.try_resize(self.size())?; + Ok(()) + } + + /// Returns the top k results broken into `batch_size` [`RecordBatch`]es, consuming the heap + pub fn emit(self) -> Result<SendableRecordBatchStream> { + let Self { + schema, + metrics, + reservation: _, + batch_size, + expr: _, + row_converter: _, + scratch_rows: _, + mut heap, + } = self; + let _timer = metrics.baseline.elapsed_compute().timer(); // time updated on drop + + let mut batch = heap.emit()?; + metrics.baseline.output_rows().add(batch.num_rows()); + + // break into record batches as needed + let mut batches = vec![]; + loop { + if batch.num_rows() < batch_size { + batches.push(Ok(batch)); + break; + } else { + batches.push(Ok(batch.slice(0, batch_size))); + let remaining_length = batch.num_rows() - batch_size; + batch = batch.slice(batch_size, remaining_length); + } + } + Ok(Box::pin(RecordBatchStreamAdapter::new( + schema, + futures::stream::iter(batches), + ))) + } + + /// return the size of memory used by this operator, in bytes + fn size(&self) -> usize { + std::mem::size_of::<Self>() + + self.row_converter.size() + + self.scratch_rows.size() + + self.heap.size() + } +} + +struct TopKMetrics { + /// metrics + pub baseline: BaselineMetrics, + + /// count of how many rows were replaced in the heap + pub row_replacements: Count, +} + +impl TopKMetrics { + fn new(metrics: &ExecutionPlanMetricsSet, partition: usize) -> Self { + Self { + baseline: BaselineMetrics::new(metrics, partition), + row_replacements: MetricBuilder::new(metrics) + .counter("row_replacements", partition), + } + } +} + +/// This structure keeps at most the *smallest* k items, using the +/// [arrow::row] format for sort keys. While it is called "topK" for +/// values like `1, 2, 3, 4, 5` the "top 3" really means the +/// *smallest* 3 , `1, 2, 3`, not the *largest* 3 `3, 4, 5`. +/// +/// Using the `Row` format handles things such as ascending vs +/// descending and nulls first vs nulls last. +/// +/// It doesn't use `BinaryHeap` in the Rust standard library because +/// it is important to check the current minimum value in the heap +/// prior to creating a new value to insert. +struct TopKHeap { + /// The maximum number of elemenents to store in this heap. + k: usize, + /// The target number of rows for output batches + batch_size: usize, + /// Storage for up at most `k` items using a BinaryHeap. Reverserd + /// so that the smallest k so far is on the top + inner: BinaryHeap<TopKRow>, + /// Storage the original row values (TopKRow only has the sort key) + store: RecordBatchStore, + /// The size of all owned data held by this heap + owned_bytes: usize, +} + +impl TopKHeap { + fn new(k: usize, batch_size: usize, schema: SchemaRef) -> Self { + assert!(k > 0); + Self { + k, + batch_size, + inner: BinaryHeap::new(), + store: RecordBatchStore::new(schema), + owned_bytes: 0, + } + } + + /// Register a [`RecordBatch`] with the heap, returning the + /// appropriate entry + pub fn register_batch(&mut self, batch: RecordBatch) -> RecordBatchEntry { + self.store.register(batch) + } + + /// Insert a [`RecordBatchEntry`] created by a previous call to + /// [`Self::register_batch`] into storage. + pub fn insert_batch_entry(&mut self, entry: RecordBatchEntry) { + self.store.insert(entry) + } + + /// Returns the largest value stored by the heap if there are k + /// items, otherwise returns None. Remember this structure is + /// keeping the "smallest" k values + fn max(&self) -> Option<&TopKRow> { + if self.inner.len() < self.k { + None + } else { + self.inner.peek() + } + } + + /// Adds `row` to this heap. If inserting this new item would + /// increase the size past `k`, removes the previously smallest + /// item. + fn add( + &mut self, + batch_entry: &mut RecordBatchEntry, + row: impl AsRef<[u8]>, + index: usize, + ) { + let batch_id = batch_entry.id; + batch_entry.uses += 1; + + assert!(self.inner.len() <= self.k); + let row = row.as_ref(); + + // Reuse storage for evicted item if possible + let new_top_k = if self.inner.len() == self.k { + let prev_min = self.inner.pop().unwrap(); + + // Update batch use + if prev_min.batch_id == batch_entry.id { + batch_entry.uses -= 1; + } else { + self.store.unuse(prev_min.batch_id); + } + + // update memory accounting + self.owned_bytes -= prev_min.owned_size(); + prev_min.with_new_row(row, batch_id, index) + } else { + TopKRow::new(row, batch_id, index) + }; + + self.owned_bytes += new_top_k.owned_size(); + + // put the new row into the heap + self.inner.push(new_top_k) + } + + /// Returns the values stored in this heap, from values low to + /// high, as a single [`RecordBatch`], resetting the inner heap + pub fn emit(&mut self) -> Result<RecordBatch> { + Ok(self.emit_with_state()?.0) + } + + /// Returns the values stored in this heap, from values low to + /// high, as a single [`RecordBatch`], and a sorted vec of the + /// current heap's contents + pub fn emit_with_state(&mut self) -> Result<(RecordBatch, Vec<TopKRow>)> { + let schema = self.store.schema().clone(); + + let mut topk_rows = std::mem::take(&mut self.inner).into_vec(); + + // sort low to high (reverse the reverse) + topk_rows.sort(); + + if self.store.is_empty() { + return Ok((RecordBatch::new_empty(schema), topk_rows)); + } + + // Indices for each row within its respective RecordBatch + let indices: Vec<_> = topk_rows + .iter() + .enumerate() + .map(|(i, k)| (i, k.index)) + .collect(); + + let num_columns = schema.fields().len(); + + // build the output columns one at time, using the + // `interleave` kernel to pick rows from different arrays + let output_columns: Vec<_> = (0..num_columns) + .map(|col| { + let input_arrays: Vec<_> = topk_rows + .iter() + .map(|k| { + let entry = + self.store.get(k.batch_id).expect("invalid stored batch id"); + entry.batch.column(col) as &dyn Array + }) + .collect(); + + // at this point `indices` contains indexes within the + // rows and `input_arrays` contains a reference to the + // relevant Array for that index. `interleave` pulls + // them together into a single new array + Ok(interleave(&input_arrays, &indices)?) + }) + .collect::<Result<_>>()?; + + let new_batch = RecordBatch::try_new(schema, output_columns)?; + Ok((new_batch, topk_rows)) + } + + /// Compact this heap, rewriting all stored batches into a single + /// input batch + pub fn maybe_compact(&mut self) -> Result<()> { + // we compact if the number of "unused" rows in the store is + // past some pre-defined threshold. Target holding up to + // around 20 batches, but handle cases of large k where some + // batches might be partially full + let max_unused_rows = (20 * self.batch_size) + self.k; + let unused_rows = self.store.unused_rows(); + + // don't compact if the store has only one batch or + if self.store.len() <= 2 || unused_rows < max_unused_rows { + //if self.store.len() <= 2 { + return Ok(()); + } + // at first, compact the entire thing always into a new batch + // (maybe we can get fancier in the future about ignoring + // batches that have a high usage ratio already + + // Note: new batch is in the same order as inner + let num_rows = self.inner.len(); + let (new_batch, mut topk_rows) = self.emit_with_state()?; + + // clear all old entires in store (this invalidates all + // store_ids in `inner`) + self.store.clear(); + + let mut batch_entry = self.register_batch(new_batch); + batch_entry.uses = num_rows; + + // rewrite all existing entries to use the new batch, and + // remove old entries. The sortedness and their relative + // position do not change + for (i, topk_row) in topk_rows.iter_mut().enumerate() { + topk_row.batch_id = batch_entry.id; + topk_row.index = i; + } + self.insert_batch_entry(batch_entry); + // restore the heap + self.inner = BinaryHeap::from(topk_rows); + + Ok(()) + } + + /// return the size of memory used by this heap, in bytes + fn size(&self) -> usize { + std::mem::size_of::<Self>() + + (self.inner.capacity() * std::mem::size_of::<TopKRow>()) + + self.store.size() + + self.owned_bytes + } +} + +/// Represents one of the top K rows held in this heap. Orders +/// according to memcmp of row (e.g. the arrow Row format, but could +/// also be primtive values) +/// +/// Reuses allocations to minimize runtime overhead of creating new Vecs +#[derive(Debug, PartialEq)] +struct TopKRow { + /// the value of the sort key for this row. This contains the + /// bytes that could be stored in `OwnedRow` but uses `Vec<u8>` to + /// reuse allocations. + row: Vec<u8>, + /// the RecordBatch this row came from: an id into a [`RecordBatchStore`] + batch_id: u32, + /// the index in this record batch the row came from + index: usize, +} + +impl TopKRow { + /// Create a new TopKRow with new allocation + fn new(row: impl AsRef<[u8]>, batch_id: u32, index: usize) -> Self { + Self { + row: row.as_ref().to_vec(), + batch_id, + index, + } + } + + /// Create a new TopKRow reusing the existing allocation + fn with_new_row( + self, + new_row: impl AsRef<[u8]>, + batch_id: u32, + index: usize, + ) -> Self { + let Self { + mut row, + batch_id: _, + index: _, + } = self; + row.clear(); + row.extend_from_slice(new_row.as_ref()); + + Self { + row, + batch_id, + index, + } + } + + /// Returns the number of bytes owned by this row in the heap (not + /// including itself) + fn owned_size(&self) -> usize { + self.row.capacity() + } + + /// Returns a slice to the owned row value + fn row(&self) -> &[u8] { + self.row.as_slice() + } +} + +impl Eq for TopKRow {} + +impl PartialOrd for TopKRow { + fn partial_cmp(&self, other: &Self) -> Option<Ordering> { + Some(self.cmp(other)) + } +} + +impl Ord for TopKRow { + fn cmp(&self, other: &Self) -> Ordering { + self.row.cmp(&other.row) + } +} + +#[derive(Debug)] +struct RecordBatchEntry { + id: u32, + batch: RecordBatch, + // for this batch, how many times has it been used + uses: usize, +} + +/// This structure tracks [`RecordBatch`] by an id so that: +/// +/// 1. The baches can be tracked via an id that can be copied cheaply +/// 2. The total memory held by all batches is tracked +#[derive(Debug)] +struct RecordBatchStore { + /// id generator + next_id: u32, + /// storage + batches: HashMap<u32, RecordBatchEntry>, + /// total size of all record batches tracked by this store + batches_size: usize, + /// schema of the batches + schema: SchemaRef, +} + +impl RecordBatchStore { + fn new(schema: SchemaRef) -> Self { + Self { + next_id: 0, + batches: HashMap::new(), + batches_size: 0, + schema, + } + } + + /// Register this batch with the store and assign an ID. No + /// attempt is made to compare this batch to other batches + pub fn register(&mut self, batch: RecordBatch) -> RecordBatchEntry { + let id = self.next_id; + self.next_id += 1; + RecordBatchEntry { id, batch, uses: 0 } + } + + /// Insert a record batch entry into this store, tracking its + /// memory use, if it has any uses + pub fn insert(&mut self, entry: RecordBatchEntry) { + // uses of 0 means that none of the rows in the batch were stored in the topk + if entry.uses > 0 { + self.batches_size += entry.batch.get_array_memory_size(); + self.batches.insert(entry.id, entry); + } + } + + /// Clear all values in this store, invalidating all previous batch ids + fn clear(&mut self) { + self.batches.clear(); + self.batches_size = 0; + } + + fn get(&self, id: u32) -> Option<&RecordBatchEntry> { + self.batches.get(&id) + } + + /// returns the total number of batches stored in this store + fn len(&self) -> usize { + self.batches.len() + } + + /// Returns the total number of rows in batches minus the number + /// which are in use + fn unused_rows(&self) -> usize { + self.batches + .values() + .map(|batch_entry| batch_entry.batch.num_rows() - batch_entry.uses) + .sum() + } + + /// returns true if the store has nothing stored + fn is_empty(&self) -> bool { + self.batches.is_empty() + } + + /// return the schema of batches stored + fn schema(&self) -> &SchemaRef { + &self.schema + } + + /// remove a use from the specified batch id. If the use count + /// reaches zero the batch entry is removed from the store + /// + /// panics if there were no remaining uses of id + pub fn unuse(&mut self, id: u32) { + let remove = if let Some(batch_entry) = self.batches.get_mut(&id) { + batch_entry.uses = batch_entry.uses.checked_sub(1).expect("underflow"); + batch_entry.uses == 0 + } else { + panic!("No entry for id {id}"); + }; + + if remove { + let old_entry = self.batches.remove(&id).unwrap(); + self.batches_size = self + .batches_size + .checked_sub(old_entry.batch.get_array_memory_size()) + .unwrap(); + } + } + + /// returns the size of memory used by this store, including all + /// referenced `RecordBatch`es, in bytes + pub fn size(&self) -> usize { + std::mem::size_of::<Self>() + + self.batches.capacity() + * (std::mem::size_of::<u32>() + std::mem::size_of::<RecordBatchEntry>()) + + self.batches_size + } +} + +/// wrapper over [`arrow::compute::interleave`] that re-encodes +/// dictionaries that have a low usage (values referenced) +fn interleave(values: &[&dyn Array], indices: &[(usize, usize)]) -> Result<ArrayRef> { + // for now, always re-encode only string dictionaries + if !values.is_empty() { + match values[0].data_type() { + DataType::Dictionary(_key_type, value_type) + if value_type.as_ref() == &DataType::Utf8 => + { + return interleave_and_repack_dictionary(values, indices); + } + _ => {} + } + } + // fallback to arrow + Ok(arrow::compute::interleave(values, indices)?) +} + +/// Special interleave kernel that re-creates the dictionary values, +/// ensuring no unused space +fn interleave_and_repack_dictionary( Review Comment: Thanks - I made the change -- This is an automated message from the Apache Git Service. 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