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Nicola Crane commented on ARROW-14583: -------------------------------------- I've since been playing around in R and found that even without filtering, I get a crash when doing group_by + summarise on partitioned data, e.g. I get a segfault from the below code {code:java} library(arrow) library(dplyr) write_dataset(group_by(iris, Species), "iris_data") open_dataset("iris_data") %>% group_by(Species) %>% summarise(mean(Sepal.Length)) %>% collect() {code} > [R][C++] Crash when summarizing after filtering to no rows on partitioned data > ------------------------------------------------------------------------------ > > Key: ARROW-14583 > URL: https://issues.apache.org/jira/browse/ARROW-14583 > Project: Apache Arrow > Issue Type: Bug > Components: C++, R > Affects Versions: 6.0.0 > Environment: I am using a windows 10 machine, R 4.1.0, up to date R > packages, and latest RStudio IDE. > Reporter: Zsolt Kegyes-Brassai > Assignee: David Li > Priority: Major > Labels: pull-request-available, query-engine > Time Spent: 1h > Remaining Estimate: 0h > > Original issue report is below; here's an even more minimal example: > {code:r} > library(arrow) > library(dplyr) > td <- tempfile() > dir.create(td) > # if there is no partitioning in data data, this won't segfault > # write_dataset(iris, td) - swap this in and won't segfault > write_dataset(group_by(iris, Species), td) > open_dataset(td) %>% > filter(Species == "tulip") %>% > group_by(Sepal.Length) %>% > summarise(n = n()) %>% > collect() > {code} > ---- > I was trying the new features introduced in latest {{arrow (6.0.2)}} package > based on examples from the “New Directions for Apache Arrow” talk. > The RStudio IDE was crashing and the R session was aborted. > Looking closely I found that I downloaded only 2 years of data (2018 & 2019) > and after the first filter ({{year == 2015}}) no data remains to be processed > further. > After some debugging, by replacing the collect() function, it turns out that > the {{summarize()}} is the one which function is causing the crash. > > {code:java} > as_dataset <- open_dataset("c:/Rproj_learn/nyc-taxi/", > partitioning = c("year", "month")) %>% > filter(total_amount > 100 & year == 2015) %>% > select(tip_amount, total_amount, passenger_count) %>% > mutate(tip_pct = tip_amount / total_amount * 100) %>% > group_by(passenger_count) %>% > summarize(avg_tip_pct = mean(tip_pct), n = n()) %>% > filter(n > 5000) %>% > arrange(desc(avg_tip_pct)) %>% > collect(){code} > > I would expect to get an error message (without crashing the IDE), which can > be handled in code. > Another alternative result would be an empty data.frame, like in case when > the parquet file was read in as a data.frame. I simulated this situation by > setting a high {{total_amount}} value when filtering. Note: when using an > Arrow table an error message is generated. > > {code:java} > library(tidyverse) > #> Warning: package 'tibble' was built under R version 4.1.1 > #> Warning: package 'tidyr' was built under R version 4.1.1 > #> Warning: package 'readr' was built under R version 4.1.1 > library(arrow) > #> Warning: package 'arrow' was built under R version 4.1.1 > #> > #> Attaching package: 'arrow' > #> The following object is masked from 'package:utils': > #> > #> timestamp > read_parquet("c:/Rproj_learn/nyc-taxi/2018/01/data.parquet", > as_data_frame = FALSE) %>% > # filter(total_amount > 100) %>% > filter(total_amount > 1e10) %>% > select(tip_amount, total_amount, passenger_count) %>% > mutate(tip_pct = tip_amount / total_amount * 100) %>% > group_by(passenger_count) %>% > summarize(avg_tip_pct = mean(tip_pct), n = n()) %>% > filter(n > 500) %>% > arrange(desc(avg_tip_pct)) %>% > collect() > #> Error: Invalid: Must pass at least one array > read_parquet("c:/Rproj_learn/nyc-taxi/2018/01/data.parquet", > as_data_frame = TRUE) %>% > # filter(total_amount > 100) %>% > filter(total_amount > 1e10) %>% > select(tip_amount, total_amount, passenger_count) %>% > mutate(tip_pct = tip_amount / total_amount * 100) %>% > group_by(passenger_count) %>% > summarize(avg_tip_pct = mean(tip_pct), n = n()) %>% > filter(n > 500) %>% > arrange(desc(avg_tip_pct)) %>% > collect() > #> # A tibble: 0 x 3 > #> # ... with 3 variables: passenger_count <int>, avg_tip_pct <dbl>, n <int> > {code} -- This message was sent by Atlassian Jira (v8.20.1#820001)