[GitHub] [arrow-datafusion] metesynnada commented on a diff in pull request #6154: Simplify MemoryWriteExec

["metesynnada (via GitHub)" <gi...@apache.org>]

metesynnada commented on code in PR #6154:
URL: https://github.com/apache/arrow-datafusion/pull/6154#discussion_r1180493624


##########
datafusion/core/src/physical_plan/memory.rs:
##########
@@ -327,24 +326,20 @@ impl ExecutionPlan for MemoryWriteExec {
     ) -> Result<SendableRecordBatchStream> {
         let batch_count = self.batches.len();
         let data = self.input.execute(partition, context)?;
-        if batch_count >= self.input.output_partitioning().partition_count() {
-            // If the number of input partitions matches the number of MemTable partitions,
-            // use a lightweight implementation that doesn't utilize as many locks.
-            let table_partition = self.batches[partition].clone();
-            Ok(Box::pin(MemorySinkOneToOneStream::try_new(
-                table_partition,
-                data,
-                self.schema.clone(),
-            )?))
-        } else {
-            // Otherwise, use the locked implementation.
-            let table_partition = self.batches[partition % batch_count].clone();
-            Ok(Box::pin(MemorySinkStream::try_new(
-                table_partition,
-                data,
-                self.schema.clone(),
-            )?))
-        }
+        let schema = self.schema.clone();
+        let state = (data, self.batches[partition % batch_count].clone());
+
+        let stream = futures::stream::unfold(state, |mut state| async move {

Review Comment:
   Now I get what you mean, if we do not hold the async lock in the state as acquired, the folding becomes possible. This substantially shrinks the code size, cool pattern.



##########
datafusion/core/src/physical_plan/memory.rs:
##########
@@ -327,24 +326,20 @@ impl ExecutionPlan for MemoryWriteExec {
     ) -> Result<SendableRecordBatchStream> {
         let batch_count = self.batches.len();
         let data = self.input.execute(partition, context)?;
-        if batch_count >= self.input.output_partitioning().partition_count() {
-            // If the number of input partitions matches the number of MemTable partitions,
-            // use a lightweight implementation that doesn't utilize as many locks.
-            let table_partition = self.batches[partition].clone();
-            Ok(Box::pin(MemorySinkOneToOneStream::try_new(
-                table_partition,
-                data,
-                self.schema.clone(),
-            )?))
-        } else {
-            // Otherwise, use the locked implementation.
-            let table_partition = self.batches[partition % batch_count].clone();
-            Ok(Box::pin(MemorySinkStream::try_new(
-                table_partition,
-                data,
-                self.schema.clone(),
-            )?))
-        }
+        let schema = self.schema.clone();
+        let state = (data, self.batches[partition % batch_count].clone());

Review Comment:
   There is a performance difference of batch_count * acquire_lock, if this is OK, we can move on to this. Do you think it would affect the benchmarks? I am not quite familiar with that part.



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