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benchmarks, fixes found while doing benchmarks
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,160 @@ | ||
| use std::sync::Arc; | ||
|
|
||
| use arrow_array::builder::{StringBuilder, UInt32Builder}; | ||
| use arrow_array::RecordBatch; | ||
| use arrow_schema::{DataType, Field, Schema}; | ||
| use criterion::{black_box, criterion_group, criterion_main, Criterion}; | ||
| use datafusion::datasource::MemTable; | ||
| use datafusion::functions_aggregate::expr_fn::sum; | ||
| use datafusion::logical_expr::lit; | ||
| use datafusion::prelude::{col, SessionContext}; | ||
| use lazy_static::lazy_static; | ||
| use vortex::compress::Compressor; | ||
| use vortex::encoding::EncodingRef; | ||
| use vortex::{Array, Context, IntoArray, ToArrayData}; | ||
| use vortex_datafusion::{VortexMemTable, VortexMemTableOptions}; | ||
| use vortex_fastlanes::{BitPackedEncoding, DeltaEncoding, FoREncoding}; | ||
|
|
||
| lazy_static! { | ||
| pub static ref CTX: Context = Context::default().with_encodings([ | ||
| &BitPackedEncoding as EncodingRef, | ||
| // &DictEncoding, | ||
| &FoREncoding, | ||
| &DeltaEncoding, | ||
| ]); | ||
| } | ||
|
|
||
| fn toy_dataset_arrow() -> RecordBatch { | ||
| // 64,000 rows of string and numeric data. | ||
| // 8,000 values of first string, second string, third string, etc. | ||
|
|
||
| let names = vec![ | ||
| "Alexander", | ||
| "Anastasia", | ||
| "Archibald", | ||
| "Bartholomew", | ||
| "Benjamin", | ||
| "Christopher", | ||
| "Elizabeth", | ||
| "Gabriella", | ||
| ]; | ||
|
|
||
| let mut col1 = StringBuilder::with_capacity(64_000, 64_000_000); | ||
| let mut col2 = UInt32Builder::with_capacity(64_000); | ||
| for i in 0..64_000 { | ||
| col1.append_value(names[i % 8]); | ||
| col2.append_value(u32::try_from(i).unwrap()); | ||
| } | ||
|
|
||
| let col1 = col1.finish(); | ||
| let col2 = col2.finish(); | ||
|
|
||
| RecordBatch::try_new( | ||
| Arc::new(Schema::new(vec![ | ||
| Field::new("names", DataType::Utf8, false), | ||
| Field::new("scores", DataType::UInt32, false), | ||
| ])), | ||
| vec![Arc::new(col1), Arc::new(col2)], | ||
| ) | ||
| .unwrap() | ||
| } | ||
|
|
||
| fn toy_dataset_vortex() -> Array { | ||
| let uncompressed = toy_dataset_arrow().to_array_data().into_array(); | ||
| println!("uncompressed vortex size: {}B", uncompressed.nbytes()); | ||
|
|
||
| let compressor = Compressor::new(&CTX); | ||
| let compressed = compressor.compress(&uncompressed, None).unwrap(); | ||
| println!("compressed vortex size: {} B", compressed.nbytes()); | ||
| compressed | ||
| } | ||
|
|
||
| fn bench_datafusion(c: &mut Criterion) { | ||
| let mut group = c.benchmark_group("datafusion"); | ||
|
|
||
| let session = SessionContext::new(); | ||
|
|
||
| let arrow_dataset = toy_dataset_arrow(); | ||
| let arrow_table = | ||
| Arc::new(MemTable::try_new(arrow_dataset.schema(), vec![vec![arrow_dataset]]).unwrap()); | ||
|
|
||
| group.bench_function("arrow", |b| { | ||
| let arrow_table = arrow_table.clone(); | ||
| b.to_async( | ||
| tokio::runtime::Builder::new_current_thread() | ||
| .enable_all() | ||
| .build() | ||
| .unwrap(), | ||
| ) | ||
| .iter(|| async { | ||
| black_box(session.read_table(arrow_table.clone()).unwrap()) | ||
| .filter(col("scores").gt_eq(lit(3_000))) | ||
| .unwrap() | ||
| .filter(col("scores").lt_eq(lit(4_000))) | ||
| .unwrap() | ||
| .aggregate(vec![], vec![sum(col("scores"))]) | ||
| .unwrap() | ||
| .collect() | ||
| .await | ||
| .unwrap(); | ||
| }) | ||
| }); | ||
|
|
||
| let vortex_dataset = toy_dataset_vortex(); | ||
| let vortex_table_pushdown = Arc::new( | ||
| VortexMemTable::try_new(vortex_dataset, VortexMemTableOptions::default()).unwrap(), | ||
| ); | ||
| group.bench_function("vortex_pushdown", |b| { | ||
| let vortex_table_pushdown = vortex_table_pushdown.clone(); | ||
| b.to_async(tokio::runtime::Runtime::new().unwrap()) | ||
| .iter(|| async { | ||
| black_box(session.read_table(vortex_table_pushdown.clone()).unwrap()) | ||
| .filter(col("scores").gt_eq(lit(3_000))) | ||
| .unwrap() | ||
| .filter(col("scores").lt_eq(lit(4_000))) | ||
| .unwrap() | ||
| .aggregate(vec![], vec![sum(col("scores"))]) | ||
| .unwrap() | ||
| .collect() | ||
| .await | ||
| .unwrap(); | ||
| }) | ||
| }); | ||
|
|
||
| let vortex_dataset = toy_dataset_vortex(); | ||
| let vortex_table_no_pushdown = Arc::new( | ||
| VortexMemTable::try_new( | ||
| vortex_dataset, | ||
| VortexMemTableOptions::default().with_disable_pushdown(true), | ||
| ) | ||
| .unwrap(), | ||
| ); | ||
| group.bench_function("vortex_no_pushdown", |b| { | ||
| let vortex_table_no_pushdown = vortex_table_no_pushdown.clone(); | ||
| b.to_async( | ||
| tokio::runtime::Builder::new_current_thread() | ||
| .enable_all() | ||
| .build() | ||
| .unwrap(), | ||
| ) | ||
| .iter(|| async { | ||
| black_box( | ||
| session | ||
| .read_table(vortex_table_no_pushdown.clone()) | ||
| .unwrap(), | ||
| ) | ||
| .filter(col("scores").gt_eq(lit(3_000))) | ||
| .unwrap() | ||
| .filter(col("scores").lt_eq(lit(4_000))) | ||
| .unwrap() | ||
| .aggregate(vec![], vec![sum(col("scores"))]) | ||
| .unwrap() | ||
| .collect() | ||
| .await | ||
| .unwrap(); | ||
| }) | ||
| }); | ||
| } | ||
|
|
||
| criterion_group!(benches, bench_datafusion); | ||
| criterion_main!(benches); |
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