Benchmark: Add micro-benchmark for Nested Loop Join operator

benchmarks/bench.sh

@@ -115,6 +115,7 @@ imdb:                   Join Order Benchmark (JOB) using the IMDB dataset conver
 
 # Micro-Benchmarks (specific operators and features)
 cancellation:           How long cancelling a query takes
+nlj:                    Benchmark for simple nested loop joins, testing various join scenarios
 
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 Supported Configuration (Environment Variables)
@@ -187,6 +188,7 @@ main() {
                     data_clickbench_1
                     data_clickbench_partitioned
                     data_imdb
+                    # nlj uses range() function, no data generation needed
                     ;;
                 tpch)
                     data_tpch "1"
@@ -261,6 +263,10 @@ main() {
                     # same data as for tpch
                     data_tpch "1"
                     ;;
+                nlj)
+                    # nlj uses range() function, no data generation needed
+                    echo "NLJ benchmark does not require data generation"
+                    ;;
                 *)
                     echo "Error: unknown benchmark '$BENCHMARK' for data generation"
                     usage
@@ -317,6 +323,7 @@ main() {
                     run_h2o_join "BIG" "PARQUET" "join"
                     run_imdb
                     run_external_aggr
+                    run_nlj
                     ;;
                 tpch)
                     run_tpch "1" "parquet"
@@ -393,6 +400,9 @@ main() {
                 topk_tpch)
                     run_topk_tpch
                     ;;
+                nlj)
+                    run_nlj
+                    ;;
                 *)
                     echo "Error: unknown benchmark '$BENCHMARK' for run"
                     usage
@@ -1020,6 +1030,14 @@ run_topk_tpch() {
     $CARGO_COMMAND --bin dfbench -- sort-tpch --iterations 5 --path "${TPCH_DIR}" -o "${RESULTS_FILE}" --limit 100 ${QUERY_ARG}
 }
 
+# Runs the nlj benchmark
+run_nlj() {
+    RESULTS_FILE="${RESULTS_DIR}/nlj.json"
+    echo "RESULTS_FILE: ${RESULTS_FILE}"
+    echo "Running nlj benchmark..."
+    debug_run $CARGO_COMMAND --bin dfbench -- nlj --iterations 5 -o "${RESULTS_FILE}" ${QUERY_ARG}
+}
+
 
 compare_benchmarks() {
     BASE_RESULTS_DIR="${SCRIPT_DIR}/results"

benchmarks/src/bin/dfbench.rs

@@ -33,7 +33,7 @@ static ALLOC: snmalloc_rs::SnMalloc = snmalloc_rs::SnMalloc;
 #[global_allocator]
 static ALLOC: mimalloc::MiMalloc = mimalloc::MiMalloc;
 
-use datafusion_benchmarks::{cancellation, clickbench, h2o, imdb, sort_tpch, tpch};
+use datafusion_benchmarks::{cancellation, clickbench, h2o, imdb, nlj, sort_tpch, tpch};
 
 #[derive(Debug, StructOpt)]
 #[structopt(about = "benchmark command")]
@@ -42,6 +42,7 @@ enum Options {
     Clickbench(clickbench::RunOpt),
     H2o(h2o::RunOpt),
     Imdb(imdb::RunOpt),
+    Nlj(nlj::RunOpt),
     SortTpch(sort_tpch::RunOpt),
     Tpch(tpch::RunOpt),
     TpchConvert(tpch::ConvertOpt),
@@ -57,6 +58,7 @@ pub async fn main() -> Result<()> {
         Options::Clickbench(opt) => opt.run().await,
         Options::H2o(opt) => opt.run().await,
         Options::Imdb(opt) => Box::pin(opt.run()).await,
+        Options::Nlj(opt) => opt.run().await,
         Options::SortTpch(opt) => opt.run().await,
         Options::Tpch(opt) => Box::pin(opt.run()).await,
         Options::TpchConvert(opt) => opt.run().await,

benchmarks/src/lib.rs

@@ -20,6 +20,7 @@ pub mod cancellation;
 pub mod clickbench;
 pub mod h2o;
 pub mod imdb;
+pub mod nlj;
 pub mod sort_tpch;
 pub mod tpch;
 pub mod util;

benchmarks/src/nlj.rs

@@ -0,0 +1,228 @@
+// 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 crate::util::{BenchmarkRun, CommonOpt, QueryResult};
+use datafusion::{error::Result, prelude::SessionContext};
+use datafusion_common::instant::Instant;
+use datafusion_common::{exec_datafusion_err, exec_err, DataFusionError};
+use structopt::StructOpt;
+
+/// Run the Nested Loop Join (NLJ) benchmark
+///
+/// This micro-benchmark focuses on the performance characteristics of NLJs.
+///
+/// It always tries to use fast scanners (without decoding overhead) and
+/// efficient predicate expressions to ensure it can reflect the performance
+/// of the NLJ operator itself.
+///
+/// In this micro-benchmark, the following workload characteristics will be
+/// varied:
+/// - Join type: Inner/Left/Right/Full (all for the NestedLoopJoin physical
+///   operator)
+///   TODO: Include special join types (Semi/Anti/Mark joins)
+/// - Input size: Different combinations of left (build) side and right (probe)
+///   side sizes
+/// - Selectivity of join filters
+#[derive(Debug, StructOpt, Clone)]
+#[structopt(verbatim_doc_comment)]
+pub struct RunOpt {
+    /// Query number (between 1 and 10). If not specified, runs all queries
+    #[structopt(short, long)]
+    query: Option<usize>,
+
+    /// Common options
+    #[structopt(flatten)]
+    common: CommonOpt,
+
+    /// If present, write results json here
+    #[structopt(parse(from_os_str), short = "o", long = "output")]
+    output_path: Option<std::path::PathBuf>,
+}
+
+/// Inline SQL queries for NLJ benchmarks
+///
+/// Each query's comment includes:
+///   - Left (build) side row count × Right (probe) side row count
+///   - Join predicate selectivity (1% means the output size is 1% * input size)
+const NLJ_QUERIES: &[&str] = &[
+    // Q1: INNER 10K x 10K | LOW 0.1%
+    r#"
+        SELECT *
+        FROM range(10000) AS t1
+        JOIN range(10000) AS t2
+        ON (t1.value + t2.value) % 1000 = 0;
+    "#,
+    // Q2: INNER 10K x 10K | Medium 20%
+    r#"
+        SELECT *
+        FROM range(10000) AS t1
+        JOIN range(10000) AS t2
+        ON (t1.value + t2.value) % 5 = 0;
+    "#,
+    // Q3: INNER 10K x 10K | High 90%
+    r#"
+        SELECT *
+        FROM range(10000) AS t1
+        JOIN range(10000) AS t2
+        ON (t1.value + t2.value) % 10 <> 0;
+    "#,
+    // Q4: INNER 30K x 30K | Medium 20%
+    r#"
+        SELECT *
+        FROM range(30000) AS t1
+        JOIN range(30000) AS t2
+        ON (t1.value + t2.value) % 5 = 0;
+    "#,
+    // Q5: INNER 10K x 200K | LOW 0.1% (small to large)
+    r#"
+        SELECT *
+        FROM range(10000) AS t1
+        JOIN range(200000) AS t2
+        ON (t1.value + t2.value) % 1000 = 0;
+    "#,
+    // Q6: INNER 200K x 10K | LOW 0.1% (large to small)
+    r#"
+        SELECT *
+        FROM range(200000) AS t1
+        JOIN range(10000) AS t2
+        ON (t1.value + t2.value) % 1000 = 0;
+    "#,
+    // Q7: RIGHT OUTER 10K x 200K | LOW 0.1%
+    r#"
+        SELECT *
+        FROM range(10000) AS t1
+        RIGHT JOIN range(200000) AS t2
+        ON (t1.value + t2.value) % 1000 = 0;
+    "#,
+    // Q8: LEFT OUTER 200K x 10K | LOW 0.1%
+    r#"
+        SELECT *
+        FROM range(200000) AS t1
+        LEFT JOIN range(10000) AS t2
+        ON (t1.value + t2.value) % 1000 = 0;
+    "#,
+    // Q9: FULL OUTER 30K x 30K | LOW 0.1%
+    r#"
+        SELECT *
+        FROM range(30000) AS t1
+        FULL JOIN range(30000) AS t2
+        ON (t1.value + t2.value) % 1000 = 0;
+    "#,
+    // Q10: FULL OUTER 30K x 30K | High 90%
+    r#"
+        SELECT *
+        FROM range(30000) AS t1
+        FULL JOIN range(30000) AS t2
+        ON (t1.value + t2.value) % 10 <> 0;
+    "#,
+];
+
+impl RunOpt {
+    pub async fn run(self) -> Result<()> {
+        println!("Running NLJ benchmarks with the following options: {self:#?}\n");
+
+        // Define query range
+        let query_range = match self.query {
+            Some(query_id) => {
+                if query_id >= 1 && query_id <= NLJ_QUERIES.len() {
+                    query_id..=query_id
+                } else {
+                    return exec_err!(
+                        "Query {query_id} not found. Available queries: 1 to {}",
+                        NLJ_QUERIES.len()
+                    );
+                    // return Err(exec_datafusion_err!(
+                    //     "Query {} not found. Available queries: 1 to {}",
+                    //     query_id,
+                    //     NLJ_QUERIES.len()
+                    // ));
+                }
+            }
+            None => 1..=NLJ_QUERIES.len(),
+        };
+
+        let config = self.common.config()?;
+        let rt_builder = self.common.runtime_env_builder()?;
+        let ctx = SessionContext::new_with_config_rt(config, rt_builder.build_arc()?);
+
+        let mut benchmark_run = BenchmarkRun::new();
+        for query_id in query_range {
+            let query_index = query_id - 1; // Convert 1-based to 0-based index
+
+            let sql = NLJ_QUERIES[query_index];
+            benchmark_run.start_new_case(&format!("Query {query_id}"));
+            let query_run = self.benchmark_query(sql, &query_id.to_string(), &ctx).await;
+            match query_run {
+                Ok(query_results) => {
+                    for iter in query_results {
+                        benchmark_run.write_iter(iter.elapsed, iter.row_count);
+                    }
+                }
+                Err(e) => {
+                    return Err(DataFusionError::Context(
+                        "NLJ benchmark Q{query_id} failed with error:".to_string(),
+                        Box::new(e),
+                    ));
+                }
+            }
+        }
+
+        benchmark_run.maybe_write_json(self.output_path.as_ref())?;
+        Ok(())
+    }
+
+    /// Validates that the query's physical plan uses a NestedLoopJoin (NLJ),
+    /// then executes the query and collects execution times.
+    ///
+    /// TODO: ensure the optimizer won't change the join order (it's not at
+    /// v48.0.0).
+    async fn benchmark_query(
+        &self,
+        sql: &str,
+        query_name: &str,
+        ctx: &SessionContext,
+    ) -> Result<Vec<QueryResult>> {
+        let mut query_results = vec![];
+
+        // Validate that the query plan includes a Nested Loop Join
+        let df = ctx.sql(sql).await?;
+        let physical_plan = df.create_physical_plan().await?;
+        let plan_string = format!("{physical_plan:#?}");
+
+        if !plan_string.contains("NestedLoopJoinExec") {
+            return Err(exec_datafusion_err!(
+                "Query {query_name} does not use Nested Loop Join. Physical plan: {plan_string}"
+            ));
+        }
+
+        for i in 0..self.common.iterations {
+            let start = Instant::now();
+            let df = ctx.sql(sql).await?;
+            let batches = df.collect().await?;
+            let elapsed = start.elapsed();
+
+            let row_count = batches.iter().map(|b| b.num_rows()).sum();
+            println!(
+                    "Query {query_name} iteration {i} returned {row_count} rows in {elapsed:?}"
+                );
+
+            query_results.push(QueryResult { elapsed, row_count });
+        }
+
+        Ok(query_results)
+    }
+}