Create a Priority Queue based Aggregation with limit (#7192)

Create a Priority Queue based Aggregation with `limit` (#7192)

datafusion/common/src/config.rs

@@ -380,6 +380,10 @@ config_namespace! {
         /// repartitioning to increase parallelism to leverage more CPU cores
         pub enable_round_robin_repartition: bool, default = true
 
+        /// When set to true, the optimizer will attempt to perform limit operations
+        /// during aggregations, if possible
+        pub enable_topk_aggregation: bool, default = true
+
         /// When set to true, the optimizer will insert filters before a join between
         /// a nullable and non-nullable column to filter out nulls on the nullable side. This
         /// filter can add additional overhead when the file format does not fully support

datafusion/core/Cargo.toml

@@ -105,6 +105,9 @@ env_logger = "0.10"
 half = "2.2.1"
 postgres-protocol = "0.6.4"
 postgres-types = { version = "0.2.4", features = ["derive", "with-chrono-0_4"] }
+ptree = "0.4.0"
+rand = { version = "0.8", features = ["small_rng"] }
+rand_distr = "0.4.3"
 regex = "1.5.4"
 rstest = "0.18.0"
 rust_decimal = { version = "1.27.0", features = ["tokio-pg"] }
@@ -157,3 +160,7 @@ name = "sql_query_with_io"
 [[bench]]
 harness = false
 name = "sort"
+
+[[bench]]
+harness = false
+name = "topk_aggregate"

datafusion/core/benches/topk_aggregate.rs

@@ -0,0 +1,239 @@
+// 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.
+
+use arrow::util::pretty::pretty_format_batches;
+use arrow::{datatypes::Schema, record_batch::RecordBatch};
+use arrow_array::builder::{Int64Builder, StringBuilder};
+use arrow_schema::{DataType, Field, SchemaRef};
+use criterion::{criterion_group, criterion_main, Criterion};
+use datafusion::physical_plan::{collect, displayable, ExecutionPlan};
+use datafusion::prelude::SessionContext;
+use datafusion::{datasource::MemTable, error::Result};
+use datafusion_common::DataFusionError;
+use datafusion_execution::config::SessionConfig;
+use datafusion_execution::TaskContext;
+use rand_distr::Distribution;
+use rand_distr::{Normal, Pareto};
+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,
+) -> Result<(Arc<dyn ExecutionPlan>, Arc<TaskContext>)> {
+    let (schema, parts) = make_data(partition_cnt, sample_cnt, asc).unwrap();
+    let mem_table = Arc::new(MemTable::try_new(schema, parts).unwrap());
+
+    // Create the DataFrame
+    let mut cfg = SessionConfig::new();
+    let opts = cfg.options_mut();
+    opts.optimizer.enable_topk_aggregation = use_topk;
+    let ctx = SessionContext::with_config(cfg);
+    let _ = ctx.register_table("traces", mem_table)?;
+    let sql = format!("select trace_id, 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();
+    assert_eq!(
+        actual_phys_plan.contains(&format!("lim=[{limit}]")),
+        use_topk
+    );
+
+    Ok((physical_plan, ctx.task_ctx()))
+}
+
+fn run(plan: Arc<dyn ExecutionPlan>, ctx: Arc<TaskContext>, asc: bool) {
+    let rt = Runtime::new().unwrap();
+    criterion::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?;
+    assert_eq!(batches.len(), 1);
+    let batch = batches.first().unwrap();
+    assert_eq!(batch.num_rows(), 10);
+
+    let actual = format!("{}", pretty_format_batches(&batches)?);
+    let expected_asc = r#"
++----------------------------------+--------------------------+
+| trace_id                         | MAX(traces.timestamp_ms) |
++----------------------------------+--------------------------+
+| 5868861a23ed31355efc5200eb80fe74 | 16909009999999           |
+| 4040e64656804c3d77320d7a0e7eb1f0 | 16909009999998           |
+| 02801bbe533190a9f8713d75222f445d | 16909009999997           |
+| 9e31b3b5a620de32b68fefa5aeea57f1 | 16909009999996           |
+| 2d88a860e9bd1cfaa632d8e7caeaa934 | 16909009999995           |
+| a47edcef8364ab6f191dd9103e51c171 | 16909009999994           |
+| 36a3fa2ccfbf8e00337f0b1254384db6 | 16909009999993           |
+| 0756be84f57369012e10de18b57d8a2f | 16909009999992           |
+| d4d6bf9845fa5897710e3a8db81d5907 | 16909009999991           |
+| 3c2cc1abe728a66b61e14880b53482a0 | 16909009999990           |
++----------------------------------+--------------------------+
+        "#
+    .trim();
+    if asc {
+        assert_eq!(actual.trim(), expected_asc);
+    }
+
+    Ok(())
+}
+
+fn make_data(
+    partition_cnt: i32,
+    sample_cnt: i32,
+    asc: bool,
+) -> Result<(Arc<Schema>, Vec<Vec<RecordBatch>>), DataFusionError> {
+    use rand::Rng;
+    use rand::SeedableRng;
+
+    // constants observed from trace data
+    let simultaneous_group_cnt = 2000;
+    let fitted_shape = 12f64;
+    let fitted_scale = 5f64;
+    let mean = 0.1;
+    let stddev = 1.1;
+    let pareto = Pareto::new(fitted_scale, fitted_shape).unwrap();
+    let normal = Normal::new(mean, stddev).unwrap();
+    let mut rng = rand::rngs::SmallRng::from_seed([0; 32]);
+
+    // populate data
+    let schema = test_schema();
+    let mut partitions = vec![];
+    let mut cur_time = 16909000000000i64;
+    for _ in 0..partition_cnt {
+        let mut id_builder = StringBuilder::new();
+        let mut ts_builder = Int64Builder::new();
+        let gen_id = |rng: &mut rand::rngs::SmallRng| {
+            rng.gen::<[u8; 16]>()
+                .iter()
+                .map(|b| format!("{:02x}", b))
+                .collect::<String>()
+        };
+        let gen_sample_cnt =
+            |mut rng: &mut rand::rngs::SmallRng| pareto.sample(&mut rng).ceil() as u32;
+        let mut group_ids = (0..simultaneous_group_cnt)
+            .map(|_| gen_id(&mut rng))
+            .collect::<Vec<_>>();
+        let mut group_sample_cnts = (0..simultaneous_group_cnt)
+            .map(|_| gen_sample_cnt(&mut rng))
+            .collect::<Vec<_>>();
+        for _ in 0..sample_cnt {
+            let random_index = rng.gen_range(0..simultaneous_group_cnt);
+            let trace_id = &mut group_ids[random_index];
+            let sample_cnt = &mut group_sample_cnts[random_index];
+            *sample_cnt -= 1;
+            if *sample_cnt == 0 {
+                *trace_id = gen_id(&mut rng);
+                *sample_cnt = gen_sample_cnt(&mut rng);
+            }
+
+            id_builder.append_value(trace_id);
+            ts_builder.append_value(cur_time);
+
+            if asc {
+                cur_time += 1;
+            } else {
+                let samp: f64 = normal.sample(&mut rng);
+                let samp = samp.round();
+                cur_time += samp as i64;
+            }
+        }
+
+        // convert to MemTable
+        let id_col = Arc::new(id_builder.finish());
+        let ts_col = Arc::new(ts_builder.finish());
+        let batch = RecordBatch::try_new(schema.clone(), vec![id_col, ts_col])?;
+        partitions.push(vec![batch]);
+    }
+    Ok((schema, partitions))
+}
+
+fn test_schema() -> SchemaRef {
+    Arc::new(Schema::new(vec![
+        Field::new("trace_id", DataType::Utf8, false),
+        Field::new("timestamp_ms", DataType::Int64, false),
+    ]))
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    let limit = 10;
+    let partitions = 10;
+    let samples = 1_000_000;
+
+    let rt = Runtime::new().unwrap();
+    let topk_real = rt.block_on(async {
+        create_context(limit, partitions, samples, false, true)
+            .await
+            .unwrap()
+    });
+    let topk_asc = rt.block_on(async {
+        create_context(limit, partitions, samples, true, true)
+            .await
+            .unwrap()
+    });
+    let real = rt.block_on(async {
+        create_context(limit, partitions, samples, false, false)
+            .await
+            .unwrap()
+    });
+    let asc = rt.block_on(async {
+        create_context(limit, partitions, samples, true, false)
+            .await
+            .unwrap()
+    });
+
+    c.bench_function(
+        format!("aggregate {} time-series rows", partitions * samples).as_str(),
+        |b| b.iter(|| run(real.0.clone(), real.1.clone(), false)),
+    );
+
+    c.bench_function(
+        format!("aggregate {} worst-case rows", partitions * samples).as_str(),
+        |b| b.iter(|| run(asc.0.clone(), asc.1.clone(), true)),
+    );
+
+    c.bench_function(
+        format!(
+            "top k={limit} aggregate {} time-series rows",
+            partitions * samples
+        )
+        .as_str(),
+        |b| b.iter(|| run(topk_real.0.clone(), topk_real.1.clone(), false)),
+    );
+
+    c.bench_function(
+        format!(
+            "top k={limit} aggregate {} worst-case rows",
+            partitions * samples
+        )
+        .as_str(),
+        |b| b.iter(|| run(topk_asc.0.clone(), topk_asc.1.clone(), true)),
+    );
+}
+
+criterion_group!(benches, criterion_benchmark);
+criterion_main!(benches);

datafusion/core/src/physical_optimizer/mod.rs

@@ -32,6 +32,7 @@ pub mod pipeline_checker;
 pub mod pruning;
 pub mod replace_with_order_preserving_variants;
 mod sort_pushdown;
+pub mod topk_aggregation;
 mod utils;
 
 #[cfg(test)]

datafusion/core/src/physical_optimizer/optimizer.rs

@@ -27,6 +27,7 @@ use crate::physical_optimizer::enforce_distribution::EnforceDistribution;
 use crate::physical_optimizer::enforce_sorting::EnforceSorting;
 use crate::physical_optimizer::join_selection::JoinSelection;
 use crate::physical_optimizer::pipeline_checker::PipelineChecker;
+use crate::physical_optimizer::topk_aggregation::TopKAggregation;
 use crate::{error::Result, physical_plan::ExecutionPlan};
 
 /// `PhysicalOptimizerRule` transforms one ['ExecutionPlan'] into another which
@@ -94,6 +95,11 @@ impl PhysicalOptimizer {
             // diagnostic error message when this happens. It makes no changes to the
             // given query plan; i.e. it only acts as a final gatekeeping rule.
             Arc::new(PipelineChecker::new()),
+            // The aggregation limiter will try to find situations where the accumulator count
+            // is not tied to the cardinality, i.e. when the output of the aggregation is passed
+            // into an `order by max(x) limit y`. In this case it will copy the limit value down
+            // to the aggregation, allowing it to use only y number of accumulators.
+            Arc::new(TopKAggregation::new()),
         ];
 
         Self::with_rules(rules)