neilconway commented on code in PR #24586: URL: https://github.com/apache/datafusion/pull/24586#discussion_r3844032954
########## datafusion/physical-expr/benches/scalar_function_dictionary.rs: ########## @@ -0,0 +1,279 @@ +// 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. + +//! Scalar functions over a dictionary-encoded column, against the flat column +//! carrying the same rows. `reverse` has had a hand-written dictionary arm +//! since #23930; `encode` has no dictionary handling, so only its flat and +//! cast-away shapes are measured. +//! +//! `cold` gives every batch its own dictionary, as a projection building one +//! per batch does. `warm` shares one across batches, as a Parquet scan does +//! within a column chunk. + +use std::cell::Cell; +use std::hint::black_box; +use std::sync::Arc; + +use arrow::array::{ + Array, ArrayRef, BinaryArray, DictionaryArray, Int32Array, StringArray, +}; +use arrow::datatypes::{DataType, Field, Int32Type, Schema}; +use arrow::record_batch::RecordBatch; +use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main}; +use datafusion_common::ScalarValue; +use datafusion_common::config::ConfigOptions; +use datafusion_expr::ScalarUDF; +use datafusion_physical_expr::ScalarFunctionExpr; +use datafusion_physical_expr::expressions::{CastExpr, Column, Literal}; +use datafusion_physical_expr_common::physical_expr::PhysicalExpr; + +const ROWS: usize = 8192; + +/// Values in the type the function receives once coercion has run: `encode` +/// takes binary, `reverse` takes strings. +fn values_of(distinct: usize, binary: bool) -> ArrayRef { + let values: Vec<String> = (0..distinct).map(|i| format!("value-{i:05}")).collect(); + if binary { + Arc::new(BinaryArray::from( + values.iter().map(|v| v.as_bytes()).collect::<Vec<_>>(), + )) + } else { + Arc::new(StringArray::from(values)) + } +} + +/// `ROWS` rows drawn from `distinct` values, dictionary-encoded. `shift` moves +/// which value each row lands on, so batches built separately differ in their +/// keys as well as in the memory their values occupy. +fn dictionary_batch( + distinct: usize, + shift: usize, + binary: bool, +) -> (Schema, RecordBatch) { + let keys = Int32Array::from( + (0..ROWS) + .map(|i| ((i + shift) % distinct) as i32) + .collect::<Vec<_>>(), + ); + let dict = DictionaryArray::<Int32Type>::try_new(keys, values_of(distinct, binary)) + .expect("dictionary array"); + let schema = Schema::new(vec![Field::new("c", dict.data_type().clone(), true)]); + let batch = RecordBatch::try_new(Arc::new(schema.clone()), vec![Arc::new(dict)]) + .expect("batch"); + (schema, batch) +} + +/// The same rows without the encoding — what the function receives when the +/// dictionary is materialized before evaluation. +fn flat_batch(distinct: usize, binary: bool) -> (Schema, RecordBatch) { + let keys: Vec<usize> = (0..ROWS).map(|i| i % distinct).collect(); + let values: Vec<String> = keys.iter().map(|i| format!("value-{i:05}")).collect(); + let array: ArrayRef = if binary { + Arc::new(BinaryArray::from( + values.iter().map(|v| v.as_bytes()).collect::<Vec<_>>(), + )) + } else { + Arc::new(StringArray::from(values)) + }; + let data_type = array.data_type().clone(); + let schema = Schema::new(vec![Field::new("c", data_type, true)]); + let batch = + RecordBatch::try_new(Arc::new(schema.clone()), vec![array]).expect("batch"); + (schema, batch) +} + +/// Consecutive batches of one column chunk: their own keys, one dictionary. +fn chunk(distinct: usize, batches: usize, binary: bool) -> (Schema, Vec<RecordBatch>) { + let values = values_of(distinct, binary); + let schema = Schema::new(vec![Field::new( + "c", + DataType::Dictionary( + Box::new(DataType::Int32), + Box::new(values.data_type().clone()), + ), + true, + )]); + let batches = (0..batches) + .map(|b| { + let keys = Int32Array::from( + (0..ROWS) + .map(|i| ((i + b * 7) % distinct) as i32) + .collect::<Vec<_>>(), + ); + let dict = DictionaryArray::<Int32Type>::try_new(keys, Arc::clone(&values)) + .expect("dictionary array"); + RecordBatch::try_new(Arc::new(schema.clone()), vec![Arc::new(dict)]) + .expect("batch") + }) + .collect(); + (schema, batches) +} + +/// `batches` batches, each with a dictionary of its own. +fn separate(distinct: usize, batches: usize, binary: bool) -> (Schema, Vec<RecordBatch>) { + let mut schema = None; + let batches = (0..batches) + .map(|b| { + let (built, batch) = dictionary_batch(distinct, b, binary); + schema.get_or_insert(built); + batch + }) + .collect(); + (schema.expect("at least one batch"), batches) +} + +fn expr_over(udf: Arc<ScalarUDF>, schema: &Schema, base64: bool) -> ScalarFunctionExpr { + let mut args: Vec<Arc<dyn PhysicalExpr>> = vec![Arc::new(Column::new("c", 0))]; + if base64 { + args.push(Arc::new(Literal::new(ScalarValue::from("base64")))); + } + ScalarFunctionExpr::try_new(udf, args, schema, Arc::new(ConfigOptions::new())) + .expect("scalar function expr") +} + +fn criterion_benchmark(c: &mut Criterion) { + // (name, function, takes a base64 argument, dictionary-typed calls reach + // the function today) + let functions: Vec<(&str, Arc<ScalarUDF>, bool, bool)> = vec![ + ( + "encode", + datafusion_functions::encoding::encode(), + true, + false, + ), + ( + "reverse", + datafusion_functions::unicode::reverse(), + false, + true, + ), + ]; + + for (name, udf, binary, dictionary_calls) in &functions { + let mut group = c.benchmark_group(format!("scalar_function_dictionary/{name}")); + + // A dictionary of its own per batch: nothing carries over. + // + // The cursor lives outside the routine, which criterion calls afresh + // for every sample: restarted per sample it would revisit the first + // batches often enough for a result to still be remembered, and the + // group would quietly measure a warm dictionary under a cold name. + for distinct in [8usize, 256, 512, ROWS] + .into_iter() + .filter(|_| *dictionary_calls) Review Comment: Seems a bit odd to write the conditional this way, since `*dictionary_calls` is invariant over the inner loop. Write `if *dictionary_calls { ... }` instead? Here and below. -- 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] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
