Spaarsh commented on code in PR #1041:
URL:
https://github.com/apache/datafusion-python/pull/1041#discussion_r1978835022
##########
src/dataframe.rs:
##########
@@ -90,59 +90,108 @@ impl PyDataFrame {
}
fn __repr__(&self, py: Python) -> PyDataFusionResult<String> {
- let df = self.df.as_ref().clone().limit(0, Some(10))?;
+ // Get 11 rows to check if there are more than 10
+ let df = self.df.as_ref().clone().limit(0, Some(11))?;
let batches = wait_for_future(py, df.collect())?;
- let batches_as_string = pretty::pretty_format_batches(&batches);
+ let num_rows = batches.iter().map(|batch|
batch.num_rows()).sum::<usize>();
+
+ // Flatten batches into a single batch for the first 10 rows
+ let mut all_rows = Vec::new();
+ let mut total_rows = 0;
+
+ for batch in &batches {
+ let num_rows_to_take = if total_rows + batch.num_rows() > 10 {
+ 10 - total_rows
+ } else {
+ batch.num_rows()
+ };
+
+ if num_rows_to_take > 0 {
+ let sliced_batch = batch.slice(0, num_rows_to_take);
+ all_rows.push(sliced_batch);
+ total_rows += num_rows_to_take;
+ }
+
+ if total_rows >= 10 {
+ break;
+ }
+ }
+
+ let batches_as_string = pretty::pretty_format_batches(&all_rows);
+
Review Comment:
I did try this initiatially but calling ```collect``` twice led to a severe
performance degradation. It used to take ```50ms```. With the manual slicing,
it dropped to ```5ms```.
You can check my initial suggestion for the same
[here](https://github.com/apache/datafusion-python/issues/1026#issuecomment-2689730430)
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