Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Add benchmarks to window function queries #564

Merged
merged 1 commit into from
Jun 22, 2021
Merged

Add benchmarks to window function queries #564

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 4 additions & 0 deletions datafusion/Cargo.toml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -88,6 +88,10 @@ harness = false
name = "filter_query_sql"
harness = false

[[bench]]
name = "window_query_sql"
harness = false

[[bench]]
name = "scalar"
harness = false
126 changes: 8 additions & 118 deletions datafusion/benches/aggregate_query_sql.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,141 +17,31 @@

#[macro_use]
extern crate criterion;
use criterion::Criterion;

use rand::{rngs::StdRng, seq::SliceRandom, Rng, SeedableRng};
use std::sync::{Arc, Mutex};
use tokio::runtime::Runtime;

extern crate arrow;
extern crate datafusion;

use arrow::{
array::Float32Array,
array::Float64Array,
array::StringArray,
array::UInt64Array,
datatypes::{DataType, Field, Schema},
record_batch::RecordBatch,
};

use datafusion::datasource::MemTable;
mod data_utils;
use crate::criterion::Criterion;
use data_utils::create_table_provider;
use datafusion::error::Result;
use datafusion::execution::context::ExecutionContext;

pub fn seedable_rng() -> StdRng {
StdRng::seed_from_u64(42)
}
use std::sync::{Arc, Mutex};
use tokio::runtime::Runtime;

fn query(ctx: Arc<Mutex<ExecutionContext>>, sql: &str) {
let rt = Runtime::new().unwrap();

// execute the query
let df = ctx.lock().unwrap().sql(sql).unwrap();
rt.block_on(df.collect()).unwrap();
}

fn create_data(size: usize, null_density: f64) -> Vec<Option<f64>> {
// use random numbers to avoid spurious compiler optimizations wrt to branching
let mut rng = seedable_rng();

(0..size)
.map(|_| {
if rng.gen::<f64>() > null_density {
None
} else {
Some(rng.gen::<f64>())
}
})
.collect()
}

fn create_integer_data(size: usize, value_density: f64) -> Vec<Option<u64>> {
// use random numbers to avoid spurious compiler optimizations wrt to branching
let mut rng = seedable_rng();

(0..size)
.map(|_| {
if rng.gen::<f64>() > value_density {
None
} else {
Some(rng.gen::<u64>())
}
})
.collect()
criterion::black_box(rt.block_on(df.collect()).unwrap());
}

fn create_context(
partitions_len: usize,
array_len: usize,
batch_size: usize,
) -> Result<Arc<Mutex<ExecutionContext>>> {
// define a schema.
let schema = Arc::new(Schema::new(vec![
Field::new("utf8", DataType::Utf8, false),
Field::new("f32", DataType::Float32, false),
Field::new("f64", DataType::Float64, false),
// This field will contain integers randomly selected from a large
// range of values, i.e. [0, u64::MAX], such that there are none (or
// very few) repeated values.
Field::new("u64_wide", DataType::UInt64, false),
// This field will contain integers randomly selected from a narrow
// range of values such that there are a few distinct values, but they
// are repeated often.
Field::new("u64_narrow", DataType::UInt64, false),
]));

let mut rng = seedable_rng();

// define data.
let partitions = (0..partitions_len)
.map(|_| {
(0..array_len / batch_size / partitions_len)
.map(|i| {
// the 4 here is the number of different keys.
// a higher number increase sparseness
let vs = vec![0, 1, 2, 3];
let keys: Vec<String> = (0..batch_size)
.map(
// use random numbers to avoid spurious compiler optimizations wrt to branching
|_| format!("hi{:?}", vs.choose(&mut rng)),
)
.collect();
let keys: Vec<&str> = keys.iter().map(|e| &**e).collect();

let values = create_data(batch_size, 0.5);

// Integer values between [0, u64::MAX].
let integer_values_wide = create_integer_data(batch_size, 9.0);

// Integer values between [0, 9].
let integer_values_narrow_choices = (0..10).collect::<Vec<u64>>();
let integer_values_narrow = (0..batch_size)
.map(|_| *integer_values_narrow_choices.choose(&mut rng).unwrap())
.collect::<Vec<u64>>();

RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(StringArray::from(keys)),
Arc::new(Float32Array::from(vec![i as f32; batch_size])),
Arc::new(Float64Array::from(values)),
Arc::new(UInt64Array::from(integer_values_wide)),
Arc::new(UInt64Array::from(integer_values_narrow)),
],
)
.unwrap()
})
.collect::<Vec<_>>()
})
.collect::<Vec<_>>();

let mut ctx = ExecutionContext::new();

// declare a table in memory. In spark API, this corresponds to createDataFrame(...).
let provider = MemTable::try_new(schema, partitions)?;
ctx.register_table("t", Arc::new(provider))?;

let provider = create_table_provider(partitions_len, array_len, batch_size)?;
ctx.register_table("t", provider)?;
Ok(Arc::new(Mutex::new(ctx)))
}

Expand Down
154 changes: 154 additions & 0 deletions datafusion/benches/data_utils/mod.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,154 @@
// 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.

//! This module provides the in-memory table for more realistic benchmarking.

use arrow::{
array::Float32Array,
array::Float64Array,
array::StringArray,
array::UInt64Array,
datatypes::{DataType, Field, Schema, SchemaRef},
record_batch::RecordBatch,
};
use datafusion::datasource::MemTable;
use datafusion::error::Result;
use rand::rngs::StdRng;
use rand::seq::SliceRandom;
use rand::{Rng, SeedableRng};
use std::sync::Arc;

/// create an in-memory table given the partition len, array len, and batch size,
/// and the result table will be of array_len in total, and then partitioned, and batched.
pub(crate) fn create_table_provider(
partitions_len: usize,
array_len: usize,
batch_size: usize,
) -> Result<Arc<MemTable>> {
let schema = Arc::new(create_schema());
let partitions =
create_record_batches(schema.clone(), array_len, partitions_len, batch_size);
// declare a table in memory. In spark API, this corresponds to createDataFrame(...).
MemTable::try_new(schema, partitions).map(Arc::new)
}

/// create a seedable [`StdRng`](rand::StdRng)
fn seedable_rng() -> StdRng {
StdRng::seed_from_u64(42)
}

fn create_schema() -> Schema {
Schema::new(vec![
Field::new("utf8", DataType::Utf8, false),
Field::new("f32", DataType::Float32, false),
Field::new("f64", DataType::Float64, false),
// This field will contain integers randomly selected from a large
// range of values, i.e. [0, u64::MAX], such that there are none (or
// very few) repeated values.
Field::new("u64_wide", DataType::UInt64, false),
// This field will contain integers randomly selected from a narrow
// range of values such that there are a few distinct values, but they
// are repeated often.
Field::new("u64_narrow", DataType::UInt64, false),
])
}

fn create_data(size: usize, null_density: f64) -> Vec<Option<f64>> {
// use random numbers to avoid spurious compiler optimizations wrt to branching
let mut rng = seedable_rng();

(0..size)
.map(|_| {
if rng.gen::<f64>() > null_density {
None
} else {
Some(rng.gen::<f64>())
}
})
.collect()
}

fn create_integer_data(size: usize, value_density: f64) -> Vec<Option<u64>> {
// use random numbers to avoid spurious compiler optimizations wrt to branching
let mut rng = seedable_rng();

(0..size)
.map(|_| {
if rng.gen::<f64>() > value_density {
None
} else {
Some(rng.gen::<u64>())
}
})
.collect()
}

fn create_record_batch(
schema: SchemaRef,
rng: &mut StdRng,
batch_size: usize,
i: usize,
) -> RecordBatch {
// the 4 here is the number of different keys.
// a higher number increase sparseness
let vs = vec![0, 1, 2, 3];
let keys: Vec<String> = (0..batch_size)
.map(
// use random numbers to avoid spurious compiler optimizations wrt to branching
|_| format!("hi{:?}", vs.choose(rng)),
)
.collect();
let keys: Vec<&str> = keys.iter().map(|e| &**e).collect();

let values = create_data(batch_size, 0.5);

// Integer values between [0, u64::MAX].
let integer_values_wide = create_integer_data(batch_size, 9.0);

// Integer values between [0, 9].
let integer_values_narrow = (0..batch_size)
.map(|_| rng.gen_range(0_u64..10))
.collect::<Vec<_>>();

RecordBatch::try_new(
schema,
vec![
Arc::new(StringArray::from(keys)),
Arc::new(Float32Array::from(vec![i as f32; batch_size])),
Arc::new(Float64Array::from(values)),
Arc::new(UInt64Array::from(integer_values_wide)),
Arc::new(UInt64Array::from(integer_values_narrow)),
],
)
.unwrap()
}

fn create_record_batches(
schema: SchemaRef,
array_len: usize,
partitions_len: usize,
batch_size: usize,
) -> Vec<Vec<RecordBatch>> {
let mut rng = seedable_rng();
(0..partitions_len)
.map(|_| {
(0..array_len / batch_size / partitions_len)
.map(|i| create_record_batch(schema.clone(), &mut rng, batch_size, i))
.collect::<Vec<_>>()
})
.collect::<Vec<_>>()
}
Loading