tustvold commented on code in PR #6154:
URL: https://github.com/apache/arrow-datafusion/pull/6154#discussion_r1180284294
##########
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:
[unfold](https://docs.rs/futures/latest/futures/stream/fn.unfold.html) is
incredibly useful. The only downside is you can't name the type, at least until
we get existential types
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