[jira] [Commented] (ARROW-13611) [C++] Scanning datasets does not enforce back pressure

["Carl Boettiger (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-13611?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17423038#comment-17423038 ] 

Carl Boettiger commented on ARROW-13611:
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Any news on this?  I believe this bug is still the cause of the system crashes I see when trying to access large parquet files in arrow, e.g. in R:


library(arrow)
library(dplyr)
file <- "part-0.parquet"download.file("https://minio.cirrus.carlboettiger.info/shared-data/birddb/parquet/part-0.parquet", file)
ds <- open_dataset(file, format = "parquet")
# OOM after consuming ~ 100 GB of RAM, crashes R
ds %>% filter(COUNTRY == "Mexico", `COMMON NAME`=="Wood thrush") %>% compute()

> [C++] Scanning datasets does not enforce back pressure
> ------------------------------------------------------
>
>                 Key: ARROW-13611
>                 URL: https://issues.apache.org/jira/browse/ARROW-13611
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: C++
>    Affects Versions: 4.0.0, 5.0.0, 4.0.1
>            Reporter: Weston Pace
>            Priority: Major
>             Fix For: 6.0.0
>
>
> I have a simple test case where I scan the batches of a 4GB dataset and print out the currently used memory:
> {code:python}
> import pyarrow as pa
> import pyarrow.dataset as ds
> dataset = ds.dataset('/home/pace/dev/data/dataset/csv/5_big', format='csv')
> num_rows = 0
> for batch in dataset.to_batches():
>     print(pa.total_allocated_bytes())
>     num_rows += batch.num_rows
> print(num_rows)
> {code}
> In pyarrow 3.0.0 this consumes just over 5MB.  In pyarrow 4.0.0 and 5.0.0 this consumes multiple GB of RAM.



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