Jefffrey commented on code in PR #4661:
URL: https://github.com/apache/arrow-datafusion/pull/4661#discussion_r1051586622
##########
datafusion/core/src/datasource/file_format/csv.rs:
##########
@@ -120,27 +123,92 @@ impl FileFormat for CsvFormat {
let mut records_to_read =
self.schema_infer_max_rec.unwrap_or(usize::MAX);
- for object in objects {
- let data = store
+ 'iterating_objects: for object in objects {
+ // stream to only read as many rows as needed into memory
+ let stream = store
.get(&object.location)
- .and_then(|r| r.bytes())
- .await
- .map_err(|e| DataFusionError::External(Box::new(e)))?;
-
- let decoder =
self.file_compression_type.convert_read(data.reader())?;
- let (schema, records_read) =
arrow::csv::reader::infer_reader_schema(
- decoder,
- self.delimiter,
- Some(records_to_read),
- self.has_header,
- )?;
- schemas.push(schema.clone());
- if records_read == 0 {
- continue;
+ .await?
+ .into_stream()
+ .map_err(|e| DataFusionError::External(Box::new(e)));
+ let stream = newline_delimited_stream(stream);
+ pin_mut!(stream);
+
+ // first chunk may have header, initialize names & types vec
+ // as use header names, and types vec is used to record all
inferred types across chunks
+ let (column_names, mut column_types): (Vec<_>, Vec<_>) =
+ if let Some(data) = stream.next().await.transpose()? {
Review Comment:
Thanks for the feedback, I was initially back and forth on how to handle
this special case. I'll give a shot at refactoring to reduce the duplication
here
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