From af5d6c51570baf01a204bddf8d6e95db141f1e99 Mon Sep 17 00:00:00 2001
From: "google-labs-jules[bot]"
 <161369871+google-labs-jules[bot]@users.noreply.github.com>
Date: Sun, 25 Jan 2026 13:08:47 +0000
Subject: [PATCH] feat(datafusion): Optimize HashJoinExec for single-batch
 build side

Adds a fast path to the `collect_left_input` function to avoid the expensive `concat_batches` operation when the build side consists of a single `RecordBatch`.

This optimization reduces memory allocation and CPU usage for this common scenario.
---
 .../physical-plan/src/joins/hash_join/exec.rs | 20 +++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/datafusion/physical-plan/src/joins/hash_join/exec.rs b/datafusion/physical-plan/src/joins/hash_join/exec.rs
index 131b07461ebe5..f3eb1299e7447 100644
--- a/datafusion/physical-plan/src/joins/hash_join/exec.rs
+++ b/datafusion/physical-plan/src/joins/hash_join/exec.rs
@@ -172,7 +172,12 @@ fn try_create_array_map(
     let mem_size = ArrayMap::estimate_memory_size(min_val, max_val, num_row);
     reservation.try_grow(mem_size)?;
 
-    let batch = concat_batches(schema, batches)?;
+    // Fast path for single batch, avoiding `concat_batches` which is expensive.
+    let batch = if batches.len() == 1 {
+        batches[0].clone()
+    } else {
+        concat_batches(schema, batches)?
+    };
     let left_values = evaluate_expressions_to_arrays(on_left, &batch)?;
 
     let array_map = ArrayMap::try_new(&left_values[0], min_val, max_val)?;
@@ -1656,10 +1661,8 @@ async fn collect_left_input(
             let mut hashes_buffer = Vec::new();
             let mut offset = 0;
 
-            let batches_iter = batches.iter().rev();
-
             // Updating hashmap starting from the last batch
-            for batch in batches_iter.clone() {
+            for batch in batches.iter().rev() {
                 hashes_buffer.clear();
                 hashes_buffer.resize(batch.num_rows(), 0);
                 update_hash(
@@ -1675,8 +1678,13 @@ async fn collect_left_input(
                 offset += batch.num_rows();
             }
 
-            // Merge all batches into a single batch, so we can directly index into the arrays
-            let batch = concat_batches(&schema, batches_iter.clone())?;
+            // Merge all batches into a single batch, so we can directly index into the arrays.
+            // Fast path for single batch, avoiding `concat_batches` which is expensive.
+            let batch = if batches.len() == 1 {
+                batches[0].clone()
+            } else {
+                concat_batches(&schema, batches.iter().rev())?
+            };
 
             let left_values = evaluate_expressions_to_arrays(&on_left, &batch)?;