alamb commented on code in PR #6987:
URL: https://github.com/apache/arrow-datafusion/pull/6987#discussion_r1270610963
##########
datafusion/core/src/datasource/physical_plan/csv.rs:
##########
@@ -566,30 +564,32 @@ pub async fn plan_to_csv(
path: impl AsRef<str>,
) -> Result<()> {
let path = path.as_ref();
- // create directory to contain the CSV files (one per partition)
- let fs_path = Path::new(path);
- if let Err(e) = fs::create_dir(fs_path) {
- return Err(DataFusionError::Execution(format!(
- "Could not create directory {path}: {e:?}"
- )));
- }
-
+ let parsed = ListingTableUrl::parse(path)?;
+ let object_store_url = parsed.object_store();
+ let store = task_ctx.runtime_env().object_store(&object_store_url)?;
+ let mut buffer;
let mut join_set = JoinSet::new();
for i in 0..plan.output_partitioning().partition_count() {
- let plan = plan.clone();
- let filename = format!("part-{i}.csv");
- let path = fs_path.join(filename);
- let file = fs::File::create(path)?;
- let mut writer = csv::Writer::new(file);
- let stream = plan.execute(i, task_ctx.clone())?;
+ let storeref = store.clone();
+ let plan: Arc<dyn ExecutionPlan> = plan.clone();
+ let filename = format!("{}/part-{i}.csv", parsed.prefix());
+ let file = object_store::path::Path::parse(filename)?;
+ buffer = Vec::new();
Review Comment:
> If we must choose only one or the other, I would also favor multipart
upload, since large files could fail in the current implementation, whereas
small files would at worst be slower in a multipart implementation. I will work
on a multipart implementation of this!
Thank you -- I think if the overhead of doing a multi-part upload is too
big, it would make sense to add (optional ) buffering in the object_store crate
(if it doesn't have one already) such that multiple small multi-part writes are
coalesced into a smaller number of larger writes
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