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https://issues.apache.org/jira/browse/ARROW-13611?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17423038#comment-17423038
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Carl Boettiger edited comment on ARROW-13611 at 9/30/21, 10:59 PM:
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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:
{code: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")
ds %>% filter(COUNTRY == "Mexico", `COMMON NAME`=="Wood thrush") %>% compute()
{code}
was (Author: cboettig):
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:
```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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