|
18 | 18 | use std::sync::Arc; |
19 | 19 |
|
20 | 20 | use arrow::array::{ |
21 | | - ArrayRef, Int8Array, Int16Array, Int64Array, UInt8Array, UInt16Array, |
| 21 | + Array, ArrayRef, Int8Array, Int16Array, Int32Array, Int64Array, UInt8Array, |
| 22 | + UInt16Array, UInt32Array, |
22 | 23 | }; |
23 | 24 | use arrow::datatypes::{DataType, Field, Schema}; |
24 | 25 | use criterion::{Criterion, criterion_group, criterion_main}; |
25 | 26 | use datafusion_expr::function::AccumulatorArgs; |
26 | | -use datafusion_expr::{Accumulator, AggregateUDFImpl}; |
| 27 | +use datafusion_expr::{Accumulator, AggregateUDFImpl, EmitTo}; |
27 | 28 | use datafusion_functions_aggregate::count::Count; |
28 | 29 | use datafusion_physical_expr::expressions::col; |
29 | 30 | use rand::rngs::StdRng; |
@@ -87,6 +88,44 @@ fn create_i16_array(n_distinct: usize) -> Int16Array { |
87 | 88 | .collect() |
88 | 89 | } |
89 | 90 |
|
| 91 | +fn create_u32_array(n_distinct: usize) -> UInt32Array { |
| 92 | + let mut rng = StdRng::seed_from_u64(42); |
| 93 | + (0..BATCH_SIZE) |
| 94 | + .map(|_| Some(rng.random_range(0..n_distinct as u32))) |
| 95 | + .collect() |
| 96 | +} |
| 97 | + |
| 98 | +fn create_i32_array(n_distinct: usize) -> Int32Array { |
| 99 | + let mut rng = StdRng::seed_from_u64(42); |
| 100 | + (0..BATCH_SIZE) |
| 101 | + .map(|_| Some(rng.random_range(0..n_distinct as i32))) |
| 102 | + .collect() |
| 103 | +} |
| 104 | + |
| 105 | +fn prepare_args(data_type: DataType) -> (Arc<Schema>, AccumulatorArgs<'static>) { |
| 106 | + let schema = Arc::new(Schema::new(vec![Field::new("f", data_type, true)])); |
| 107 | + let schema_leaked: &'static Schema = Box::leak(Box::new((*schema).clone())); |
| 108 | + let expr = col("f", schema_leaked).unwrap(); |
| 109 | + let expr_leaked: &'static _ = Box::leak(Box::new(expr)); |
| 110 | + let return_field: Arc<Field> = Field::new("f", DataType::Int64, true).into(); |
| 111 | + let return_field_leaked: &'static _ = Box::leak(Box::new(return_field.clone())); |
| 112 | + let expr_field = expr_leaked.return_field(schema_leaked).unwrap(); |
| 113 | + let expr_field_leaked: &'static _ = Box::leak(Box::new(expr_field)); |
| 114 | + |
| 115 | + let accumulator_args = AccumulatorArgs { |
| 116 | + return_field: return_field_leaked.clone(), |
| 117 | + schema: schema_leaked, |
| 118 | + expr_fields: std::slice::from_ref(expr_field_leaked), |
| 119 | + ignore_nulls: false, |
| 120 | + order_bys: &[], |
| 121 | + is_reversed: false, |
| 122 | + name: "count(distinct f)", |
| 123 | + is_distinct: true, |
| 124 | + exprs: std::slice::from_ref(expr_leaked), |
| 125 | + }; |
| 126 | + (schema, accumulator_args) |
| 127 | +} |
| 128 | + |
90 | 129 | fn count_distinct_benchmark(c: &mut Criterion) { |
91 | 130 | for pct in [80, 99] { |
92 | 131 | let n_distinct = BATCH_SIZE * pct / 100; |
@@ -148,7 +187,273 @@ fn count_distinct_benchmark(c: &mut Criterion) { |
148 | 187 | .unwrap() |
149 | 188 | }) |
150 | 189 | }); |
| 190 | + |
| 191 | + // 32-bit integer types |
| 192 | + for pct in [80, 99] { |
| 193 | + let n_distinct = BATCH_SIZE * pct / 100; |
| 194 | + |
| 195 | + // UInt32 |
| 196 | + let values = Arc::new(create_u32_array(n_distinct)) as ArrayRef; |
| 197 | + c.bench_function(&format!("count_distinct u32 {pct}% distinct"), |b| { |
| 198 | + b.iter(|| { |
| 199 | + let mut accumulator = prepare_accumulator(DataType::UInt32); |
| 200 | + accumulator |
| 201 | + .update_batch(std::slice::from_ref(&values)) |
| 202 | + .unwrap() |
| 203 | + }) |
| 204 | + }); |
| 205 | + |
| 206 | + // Int32 |
| 207 | + let values = Arc::new(create_i32_array(n_distinct)) as ArrayRef; |
| 208 | + c.bench_function(&format!("count_distinct i32 {pct}% distinct"), |b| { |
| 209 | + b.iter(|| { |
| 210 | + let mut accumulator = prepare_accumulator(DataType::Int32); |
| 211 | + accumulator |
| 212 | + .update_batch(std::slice::from_ref(&values)) |
| 213 | + .unwrap() |
| 214 | + }) |
| 215 | + }); |
| 216 | + } |
| 217 | +} |
| 218 | + |
| 219 | +/// Create group indices with uniform distribution |
| 220 | +fn create_uniform_groups(num_groups: usize) -> Vec<usize> { |
| 221 | + let mut rng = StdRng::seed_from_u64(42); |
| 222 | + (0..BATCH_SIZE) |
| 223 | + .map(|_| rng.random_range(0..num_groups)) |
| 224 | + .collect() |
| 225 | +} |
| 226 | + |
| 227 | +/// Create group indices with skewed distribution (80% in 20% of groups) |
| 228 | +fn create_skewed_groups(num_groups: usize) -> Vec<usize> { |
| 229 | + let mut rng = StdRng::seed_from_u64(42); |
| 230 | + let hot_groups = (num_groups / 5).max(1); |
| 231 | + (0..BATCH_SIZE) |
| 232 | + .map(|_| { |
| 233 | + if rng.random_range(0..100) < 80 { |
| 234 | + rng.random_range(0..hot_groups) |
| 235 | + } else { |
| 236 | + rng.random_range(0..num_groups) |
| 237 | + } |
| 238 | + }) |
| 239 | + .collect() |
| 240 | +} |
| 241 | + |
| 242 | +fn count_distinct_groups_benchmark(c: &mut Criterion) { |
| 243 | + let count_fn = Count::new(); |
| 244 | + |
| 245 | + let group_counts = [100, 1000, 10000]; |
| 246 | + let cardinalities = [("low", 20), ("mid", 80), ("high", 99)]; |
| 247 | + let distributions = ["uniform", "skewed"]; |
| 248 | + |
| 249 | + // i64 benchmarks |
| 250 | + for num_groups in group_counts { |
| 251 | + for (card_name, distinct_pct) in cardinalities { |
| 252 | + for dist in distributions { |
| 253 | + let name = format!("i64_g{num_groups}_{card_name}_{dist}"); |
| 254 | + let n_distinct = BATCH_SIZE * distinct_pct / 100; |
| 255 | + let values = Arc::new(create_i64_array(n_distinct)) as ArrayRef; |
| 256 | + let group_indices = if dist == "uniform" { |
| 257 | + create_uniform_groups(num_groups) |
| 258 | + } else { |
| 259 | + create_skewed_groups(num_groups) |
| 260 | + }; |
| 261 | + |
| 262 | + let (_schema, args) = prepare_args(DataType::Int64); |
| 263 | + |
| 264 | + if count_fn.groups_accumulator_supported(args.clone()) { |
| 265 | + c.bench_function(&format!("count_distinct_groups {name}"), |b| { |
| 266 | + b.iter(|| { |
| 267 | + let mut acc = |
| 268 | + count_fn.create_groups_accumulator(args.clone()).unwrap(); |
| 269 | + acc.update_batch( |
| 270 | + std::slice::from_ref(&values), |
| 271 | + &group_indices, |
| 272 | + None, |
| 273 | + num_groups, |
| 274 | + ) |
| 275 | + .unwrap(); |
| 276 | + acc.evaluate(EmitTo::All).unwrap() |
| 277 | + }) |
| 278 | + }); |
| 279 | + } else { |
| 280 | + let arr = values.as_any().downcast_ref::<Int64Array>().unwrap(); |
| 281 | + let mut group_rows: Vec<Vec<i64>> = vec![Vec::new(); num_groups]; |
| 282 | + for (idx, &group_idx) in group_indices.iter().enumerate() { |
| 283 | + if arr.is_valid(idx) { |
| 284 | + group_rows[group_idx].push(arr.value(idx)); |
| 285 | + } |
| 286 | + } |
| 287 | + let group_arrays: Vec<ArrayRef> = group_rows |
| 288 | + .iter() |
| 289 | + .map(|rows| Arc::new(Int64Array::from(rows.clone())) as ArrayRef) |
| 290 | + .collect(); |
| 291 | + |
| 292 | + c.bench_function(&format!("count_distinct_groups {name}"), |b| { |
| 293 | + b.iter(|| { |
| 294 | + let mut accumulators: Vec<_> = (0..num_groups) |
| 295 | + .map(|_| prepare_accumulator(DataType::Int64)) |
| 296 | + .collect(); |
| 297 | + |
| 298 | + for (group_idx, batch) in group_arrays.iter().enumerate() { |
| 299 | + if !batch.is_empty() { |
| 300 | + accumulators[group_idx] |
| 301 | + .update_batch(std::slice::from_ref(batch)) |
| 302 | + .unwrap(); |
| 303 | + } |
| 304 | + } |
| 305 | + |
| 306 | + let _results: Vec<_> = accumulators |
| 307 | + .iter_mut() |
| 308 | + .map(|acc| acc.evaluate().unwrap()) |
| 309 | + .collect(); |
| 310 | + }) |
| 311 | + }); |
| 312 | + } |
| 313 | + } |
| 314 | + } |
| 315 | + } |
| 316 | + |
| 317 | + // i32 benchmarks |
| 318 | + for num_groups in group_counts { |
| 319 | + for (card_name, distinct_pct) in cardinalities { |
| 320 | + for dist in distributions { |
| 321 | + let name = format!("i32_g{num_groups}_{card_name}_{dist}"); |
| 322 | + let n_distinct = BATCH_SIZE * distinct_pct / 100; |
| 323 | + let values = Arc::new(create_i32_array(n_distinct)) as ArrayRef; |
| 324 | + let group_indices = if dist == "uniform" { |
| 325 | + create_uniform_groups(num_groups) |
| 326 | + } else { |
| 327 | + create_skewed_groups(num_groups) |
| 328 | + }; |
| 329 | + |
| 330 | + let (_schema, args) = prepare_args(DataType::Int32); |
| 331 | + |
| 332 | + if count_fn.groups_accumulator_supported(args.clone()) { |
| 333 | + c.bench_function(&format!("count_distinct_groups {name}"), |b| { |
| 334 | + b.iter(|| { |
| 335 | + let mut acc = |
| 336 | + count_fn.create_groups_accumulator(args.clone()).unwrap(); |
| 337 | + acc.update_batch( |
| 338 | + std::slice::from_ref(&values), |
| 339 | + &group_indices, |
| 340 | + None, |
| 341 | + num_groups, |
| 342 | + ) |
| 343 | + .unwrap(); |
| 344 | + acc.evaluate(EmitTo::All).unwrap() |
| 345 | + }) |
| 346 | + }); |
| 347 | + } else { |
| 348 | + let arr = values.as_any().downcast_ref::<Int32Array>().unwrap(); |
| 349 | + let mut group_rows: Vec<Vec<i32>> = vec![Vec::new(); num_groups]; |
| 350 | + for (idx, &group_idx) in group_indices.iter().enumerate() { |
| 351 | + if arr.is_valid(idx) { |
| 352 | + group_rows[group_idx].push(arr.value(idx)); |
| 353 | + } |
| 354 | + } |
| 355 | + let group_arrays: Vec<ArrayRef> = group_rows |
| 356 | + .iter() |
| 357 | + .map(|rows| Arc::new(Int32Array::from(rows.clone())) as ArrayRef) |
| 358 | + .collect(); |
| 359 | + |
| 360 | + c.bench_function(&format!("count_distinct_groups {name}"), |b| { |
| 361 | + b.iter(|| { |
| 362 | + let mut accumulators: Vec<_> = (0..num_groups) |
| 363 | + .map(|_| prepare_accumulator(DataType::Int32)) |
| 364 | + .collect(); |
| 365 | + |
| 366 | + for (group_idx, batch) in group_arrays.iter().enumerate() { |
| 367 | + if !batch.is_empty() { |
| 368 | + accumulators[group_idx] |
| 369 | + .update_batch(std::slice::from_ref(batch)) |
| 370 | + .unwrap(); |
| 371 | + } |
| 372 | + } |
| 373 | + |
| 374 | + let _results: Vec<_> = accumulators |
| 375 | + .iter_mut() |
| 376 | + .map(|acc| acc.evaluate().unwrap()) |
| 377 | + .collect(); |
| 378 | + }) |
| 379 | + }); |
| 380 | + } |
| 381 | + } |
| 382 | + } |
| 383 | + } |
| 384 | + |
| 385 | + // u32 benchmarks |
| 386 | + for num_groups in group_counts { |
| 387 | + for (card_name, distinct_pct) in cardinalities { |
| 388 | + for dist in distributions { |
| 389 | + let name = format!("u32_g{num_groups}_{card_name}_{dist}"); |
| 390 | + let n_distinct = BATCH_SIZE * distinct_pct / 100; |
| 391 | + let values = Arc::new(create_u32_array(n_distinct)) as ArrayRef; |
| 392 | + let group_indices = if dist == "uniform" { |
| 393 | + create_uniform_groups(num_groups) |
| 394 | + } else { |
| 395 | + create_skewed_groups(num_groups) |
| 396 | + }; |
| 397 | + |
| 398 | + let (_schema, args) = prepare_args(DataType::UInt32); |
| 399 | + |
| 400 | + if count_fn.groups_accumulator_supported(args.clone()) { |
| 401 | + c.bench_function(&format!("count_distinct_groups {name}"), |b| { |
| 402 | + b.iter(|| { |
| 403 | + let mut acc = |
| 404 | + count_fn.create_groups_accumulator(args.clone()).unwrap(); |
| 405 | + acc.update_batch( |
| 406 | + std::slice::from_ref(&values), |
| 407 | + &group_indices, |
| 408 | + None, |
| 409 | + num_groups, |
| 410 | + ) |
| 411 | + .unwrap(); |
| 412 | + acc.evaluate(EmitTo::All).unwrap() |
| 413 | + }) |
| 414 | + }); |
| 415 | + } else { |
| 416 | + let arr = values.as_any().downcast_ref::<UInt32Array>().unwrap(); |
| 417 | + let mut group_rows: Vec<Vec<u32>> = vec![Vec::new(); num_groups]; |
| 418 | + for (idx, &group_idx) in group_indices.iter().enumerate() { |
| 419 | + if arr.is_valid(idx) { |
| 420 | + group_rows[group_idx].push(arr.value(idx)); |
| 421 | + } |
| 422 | + } |
| 423 | + let group_arrays: Vec<ArrayRef> = group_rows |
| 424 | + .iter() |
| 425 | + .map(|rows| Arc::new(UInt32Array::from(rows.clone())) as ArrayRef) |
| 426 | + .collect(); |
| 427 | + |
| 428 | + c.bench_function(&format!("count_distinct_groups {name}"), |b| { |
| 429 | + b.iter(|| { |
| 430 | + let mut accumulators: Vec<_> = (0..num_groups) |
| 431 | + .map(|_| prepare_accumulator(DataType::UInt32)) |
| 432 | + .collect(); |
| 433 | + |
| 434 | + for (group_idx, batch) in group_arrays.iter().enumerate() { |
| 435 | + if !batch.is_empty() { |
| 436 | + accumulators[group_idx] |
| 437 | + .update_batch(std::slice::from_ref(batch)) |
| 438 | + .unwrap(); |
| 439 | + } |
| 440 | + } |
| 441 | + |
| 442 | + let _results: Vec<_> = accumulators |
| 443 | + .iter_mut() |
| 444 | + .map(|acc| acc.evaluate().unwrap()) |
| 445 | + .collect(); |
| 446 | + }) |
| 447 | + }); |
| 448 | + } |
| 449 | + } |
| 450 | + } |
| 451 | + } |
151 | 452 | } |
152 | 453 |
|
153 | | -criterion_group!(benches, count_distinct_benchmark); |
| 454 | +criterion_group!( |
| 455 | + benches, |
| 456 | + count_distinct_benchmark, |
| 457 | + count_distinct_groups_benchmark |
| 458 | +); |
154 | 459 | criterion_main!(benches); |
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