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refactor: extract the data generate out of aggregate_topk benchmark #19523

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refactor: extract the data generate out of aggregate_topk benchmark #19523

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c1ba62a
refactor: extract the data generate out of benchmark
haohuaijin Dec 28, 2025
2cde46d
fmt
haohuaijin Dec 28, 2025
9b3a9db
Merge branch 'main' into refactor-topk-benchmark
haohuaijin Dec 28, 2025
4a8e01f
Merge branch 'main' into refactor-topk-benchmark
haohuaijin Dec 29, 2025
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124 changes: 43 additions & 81 deletions datafusion/core/benches/topk_aggregate.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -20,23 +20,21 @@ mod data_utils;
use arrow::util::pretty::pretty_format_batches;
use criterion::{Criterion, criterion_group, criterion_main};
use data_utils::make_data;
use datafusion::physical_plan::{ExecutionPlan, collect, displayable};
use datafusion::physical_plan::{collect, displayable};
use datafusion::prelude::SessionContext;
use datafusion::{datasource::MemTable, error::Result};
use datafusion_execution::TaskContext;
use datafusion_execution::config::SessionConfig;
use std::hint::black_box;
use std::sync::Arc;
use tokio::runtime::Runtime;

async fn create_context(
limit: usize,
partition_cnt: i32,
sample_cnt: i32,
asc: bool,
use_topk: bool,
use_view: bool,
) -> Result<(Arc<dyn ExecutionPlan>, Arc<TaskContext>)> {
) -> Result<SessionContext> {
let (schema, parts) = make_data(partition_cnt, sample_cnt, asc, use_view).unwrap();
let mem_table = Arc::new(MemTable::try_new(schema, parts).unwrap());

Expand All @@ -46,32 +44,32 @@ async fn create_context(
opts.optimizer.enable_topk_aggregation = use_topk;
let ctx = SessionContext::new_with_config(cfg);
let _ = ctx.register_table("traces", mem_table)?;

Ok(ctx)
}

fn run(rt: &Runtime, ctx: SessionContext, limit: usize, use_topk: bool, asc: bool) {
black_box(rt.block_on(async { aggregate(ctx, limit, use_topk, asc).await })).unwrap();
}

async fn aggregate(
ctx: SessionContext,
limit: usize,
use_topk: bool,
asc: bool,
) -> Result<()> {
let sql = format!(
"select max(timestamp_ms) from traces group by trace_id order by max(timestamp_ms) desc limit {limit};"
);
let df = ctx.sql(sql.as_str()).await?;
let physical_plan = df.create_physical_plan().await?;
let actual_phys_plan = displayable(physical_plan.as_ref()).indent(true).to_string();
let plan = df.create_physical_plan().await?;
let actual_phys_plan = displayable(plan.as_ref()).indent(true).to_string();
assert_eq!(
actual_phys_plan.contains(&format!("lim=[{limit}]")),
use_topk
);

Ok((physical_plan, ctx.task_ctx()))
}

#[expect(clippy::needless_pass_by_value)]
fn run(rt: &Runtime, plan: Arc<dyn ExecutionPlan>, ctx: Arc<TaskContext>, asc: bool) {
black_box(rt.block_on(async { aggregate(plan.clone(), ctx.clone(), asc).await }))
.unwrap();
}

async fn aggregate(
plan: Arc<dyn ExecutionPlan>,
ctx: Arc<TaskContext>,
asc: bool,
) -> Result<()> {
let batches = collect(plan, ctx).await?;
let batches = collect(plan, ctx.task_ctx()).await?;
assert_eq!(batches.len(), 1);
let batch = batches.first().unwrap();
assert_eq!(batch.num_rows(), 10);
Expand Down Expand Up @@ -107,106 +105,70 @@ fn criterion_benchmark(c: &mut Criterion) {
let partitions = 10;
let samples = 1_000_000;

let ctx = rt
.block_on(create_context(partitions, samples, false, false, false))
.unwrap();
c.bench_function(
format!("aggregate {} time-series rows", partitions * samples).as_str(),
|b| {
b.iter(|| {
let real = rt.block_on(async {
create_context(limit, partitions, samples, false, false, false)
.await
.unwrap()
});
run(&rt, real.0.clone(), real.1.clone(), false)
})
},
|b| b.iter(|| run(&rt, ctx.clone(), limit, false, false)),
);

let ctx = rt
.block_on(create_context(partitions, samples, true, false, false))
.unwrap();
c.bench_function(
format!("aggregate {} worst-case rows", partitions * samples).as_str(),
|b| {
b.iter(|| {
let asc = rt.block_on(async {
create_context(limit, partitions, samples, true, false, false)
.await
.unwrap()
});
run(&rt, asc.0.clone(), asc.1.clone(), true)
})
},
|b| b.iter(|| run(&rt, ctx.clone(), limit, false, true)),
);

let ctx = rt
.block_on(create_context(partitions, samples, false, true, false))
.unwrap();
c.bench_function(
format!(
"top k={limit} aggregate {} time-series rows",
partitions * samples
)
.as_str(),
|b| {
b.iter(|| {
let topk_real = rt.block_on(async {
create_context(limit, partitions, samples, false, true, false)
.await
.unwrap()
});
run(&rt, topk_real.0.clone(), topk_real.1.clone(), false)
})
},
|b| b.iter(|| run(&rt, ctx.clone(), limit, true, false)),
);

let ctx = rt
.block_on(create_context(partitions, samples, true, true, false))
.unwrap();
c.bench_function(
format!(
"top k={limit} aggregate {} worst-case rows",
partitions * samples
)
.as_str(),
|b| {
b.iter(|| {
let topk_asc = rt.block_on(async {
create_context(limit, partitions, samples, true, true, false)
.await
.unwrap()
});
run(&rt, topk_asc.0.clone(), topk_asc.1.clone(), true)
})
},
|b| b.iter(|| run(&rt, ctx.clone(), limit, true, true)),
);

// Utf8View schema，time-series rows
let ctx = rt
.block_on(create_context(partitions, samples, false, true, true))
.unwrap();
c.bench_function(
format!(
"top k={limit} aggregate {} time-series rows [Utf8View]",
partitions * samples
)
.as_str(),
|b| {
b.iter(|| {
let topk_real = rt.block_on(async {
create_context(limit, partitions, samples, false, true, true)
.await
.unwrap()
});
run(&rt, topk_real.0.clone(), topk_real.1.clone(), false)
})
},
|b| b.iter(|| run(&rt, ctx.clone(), limit, true, false)),
);

// Utf8View schema，worst-case rows
let ctx = rt
.block_on(create_context(partitions, samples, true, true, true))
.unwrap();
c.bench_function(
format!(
"top k={limit} aggregate {} worst-case rows [Utf8View]",
partitions * samples
)
.as_str(),
|b| {
b.iter(|| {
let topk_asc = rt.block_on(async {
create_context(limit, partitions, samples, true, true, true)
.await
.unwrap()
});
run(&rt, topk_asc.0.clone(), topk_asc.1.clone(), true)
})
},
|b| b.iter(|| run(&rt, ctx.clone(), limit, true, true)),
);
}

Expand Down