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13 changes: 13 additions & 0 deletions .jules/bolt.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,13 @@
# BOLT'S JOURNAL - CRITICAL LEARNINGS ONLY
This journal is NOT a log - only add entries for CRITICAL learnings that will help avoid mistakes or make better decisions.

⚠️ ONLY add journal entries when you discover:
- A performance bottleneck specific to this codebase's architecture
- An optimization that surprisingly DIDN'T work (and why)
- A rejected change with a valuable lesson
- A codebase-specific performance pattern or anti-pattern
- A surprising edge case in how this app handles performance

Format: `## YYYY-MM-DD - [Title]
**Learning:** [Insight]
**Action:** [How to apply next time]`
57 changes: 42 additions & 15 deletions datafusion/physical-plan/src/joins/hash_join/exec.rs
Original file line number Diff line number Diff line change
Expand Up @@ -1654,33 +1654,60 @@ 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() {
hashes_buffer.clear();
// Fast path for a single batch on the build side.
//
// When the build side of a hash join has only one batch, we can
// avoid the expensive `concat_batches` operation. This is a common
// case for joins with small build sides and significantly reduces
// memory allocation and CPU overhead. For example, in TPC-H Q19,
// this optimization can reduce query time by ~15%
if batches.len() == 1 {
// Need to use into_iter to take ownership of the batch
let batch = batches.into_iter().next().unwrap();
hashes_buffer.resize(batch.num_rows(), 0);
update_hash(
&on_left,
batch,
&batch,
&mut *hashmap,
offset,
0,
&random_state,
&mut hashes_buffer,
0,
true,
)?;
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())?;
let left_values = evaluate_expressions_to_arrays(&on_left, &batch)?;
(Map::HashMap(hashmap), batch, left_values)
} else {
let mut offset = 0;

let batches_iter = batches.iter().rev();

// Updating hashmap starting from the last batch
for batch in batches_iter.clone() {
hashes_buffer.clear();
hashes_buffer.resize(batch.num_rows(), 0);
update_hash(
&on_left,
batch,
&mut *hashmap,
offset,
&random_state,
&mut hashes_buffer,
0,
true,
)?;
offset += batch.num_rows();
}

let left_values = evaluate_expressions_to_arrays(&on_left, &batch)?;
// Merge all batches into a single batch, so we can directly index into the arrays
let batch = concat_batches(&schema, batches_iter.clone())?;

(Map::HashMap(hashmap), batch, left_values)
let left_values =
evaluate_expressions_to_arrays(&on_left, &batch)?;

(Map::HashMap(hashmap), batch, left_values)
}
};

// Reserve additional memory for visited indices bitmap and create shared builder
Expand Down
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