Dandandan commented on code in PR #1537:
URL:
https://github.com/apache/datafusion-ballista/pull/1537#discussion_r3032864818
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ballista/core/src/utils.rs:
##########
@@ -159,46 +160,83 @@ pub fn default_config_producer() -> SessionConfig {
SessionConfig::new_with_ballista()
}
-/// Stream data to disk in Arrow IPC format
+/// Stream data to disk in Arrow IPC format.
+///
+/// Batches are read from the async stream and forwarded through a bounded
+/// channel to a `spawn_blocking` task that performs all synchronous file I/O,
+/// keeping the tokio worker thread unblocked.
pub async fn write_stream_to_disk(
stream: &mut Pin<Box<dyn RecordBatchStream + Send>>,
- path: &str,
+ path: &Path,
disk_write_metric: &metrics::Time,
+ channel_capacity: usize,
) -> Result<PartitionStats> {
- let file = BufWriter::new(File::create(path).map_err(|e| {
- error!("Failed to create partition file at {path}: {e:?}");
- BallistaError::IoError(e)
- })?);
-
- let mut num_rows = 0;
- let mut num_batches = 0;
- let mut num_bytes = 0;
+ let schema = stream.schema();
+ let path_owned = path.to_owned();
+ let write_metric = disk_write_metric.clone();
- let options = IpcWriteOptions::default()
- .try_with_compression(Some(CompressionType::LZ4_FRAME))?;
+ let (tx, mut rx) =
tokio::sync::mpsc::channel::<RecordBatch>(channel_capacity);
- let mut writer =
- StreamWriter::try_new_with_options(file, stream.schema().as_ref(),
options)?;
+ let handle = tokio::task::spawn_blocking(move || -> Result<u64> {
+ let file = BufWriter::new(File::create(&path_owned).map_err(|e| {
+ error!(
+ "Failed to create partition file at {}: {e:?}",
+ path_owned.display()
+ );
+ BallistaError::IoError(e)
+ })?);
- while let Some(result) = stream.next().await {
- let batch = result?;
+ let options = IpcWriteOptions::default()
+ .try_with_compression(Some(CompressionType::LZ4_FRAME))?;
- let batch_size_bytes: usize = batch.get_array_memory_size();
- num_batches += 1;
- num_rows += batch.num_rows();
- num_bytes += batch_size_bytes;
+ let mut writer =
Review Comment:
I mean something slightly differently.
This adds extra parallelism by adding extra worker threads.
This helps when there is some skew, i.e. one partition is finishing faster
than the other (and this is likely the source of the speedup) but also
increases memory usage and decreases cache-friendliness.
With e.g. morsel-driven parallelism:
https://github.com/apache/datafusion/issues/20529 this would be not needed /
create overhead.
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