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John Sheffield commented on ARROW-11067: ---------------------------------------- (Sorry for the fragmented report here, but figured out a way to really isolate the issue.) The string read failures are deterministic and predictable, and the content of the strings doesn't seem to matter – only length. There's a switch between success/failure at every integer multiple of *N * (32 * 1024) characters*. * For N in [0,1), string length between 0 and 32767 characters, all reads succeed. * For N in [1,2], string length 32768 and 65535, all of the reads fail. * The same pattern repeats until we hit LongString limits: if floor(nchar/(32 * 1024) is 0 or even, the read succeeds. If floor(nchar/(32 * 1024) is odd, it fails. Code: {code:java} library(tidyverse) library(arrow) generate_string <- function(n){ paste0(sample(c(LETTERS, letters), size = n, replace = TRUE), collapse = "") } sample_breaks <- (1:60L * 16L * 1024L) sample_lengths <- sample_breaks - 1 set.seed(1234) test_strings <- purrr::map_chr(sample_lengths, generate_string) readr::write_csv(data.frame(str = test_strings, strlen = sample_lengths), "arrow_sample_data.csv") arrow::read_csv_arrow("arrow_sample_data.csv") %>% dplyr::mutate(failed_case = ifelse(is.na(str), "failed", "succeeded")) %>% dplyr::select(-str) %>% ggplot(data = ., aes(x = (strlen / (32 * 1024)), y = failed_case)) + geom_point(aes(color = ifelse(floor(strlen / (32 * 1024)) %% 2 == 0, "even", "odd")), size = 3) + scale_x_continuous(breaks = seq(0, 30)) + labs(x = "string length / (32 * 1024) : integer multiple of 32kb", y = "string read success/failure", color = "even/odd multiple of 32kb") {code} !arrow_explanation.png! > [R] read_csv_arrow silently fails to read some strings and returns nulls > ------------------------------------------------------------------------ > > Key: ARROW-11067 > URL: https://issues.apache.org/jira/browse/ARROW-11067 > Project: Apache Arrow > Issue Type: Bug > Components: R > Reporter: John Sheffield > Priority: Major > Fix For: 3.0.0 > > Attachments: arrow_explanation.png, arrow_failure_cases.csv, > arrow_failure_cases.csv, arrowbug1.png, arrowbug1.png, demo_data.csv > > > A sample file is attached, showing 10 rows each of strings with consistent > failures (false_na = TRUE) and consistent successes (false_na = FALSE). The > strings are in the column `json_string` – if relevant, they are geojsons with > min nchar of 33,229 and max nchar of 202,515. > When I read this sample file with other R CSV readers (readr and data.table > shown), the files are imported correctly and there are no NAs in the > json_string column. > When I read with arrow::read_csv_arrow, 50% of the sample json_string column > end up as NAs. as_data_frame TRUE or FALSE does not change the behavior, so > this might not be limited to the R interface, but I can't help debug much > further upstream. > > > {code:java} > aaa1 <- arrow::read_csv_arrow("demo_data.csv", as_data_frame = TRUE) > aaa2 <- arrow::read_csv_arrow("demo_data.csv", as_data_frame = FALSE) > bbb <- data.table::fread("demo_data.csv") > ccc <- readr::read_csv("demo_data.csv") > mean(is.na(aaa1$json_string)) # 0.5 > mean(is.na(aaa2$column(1))) # Scalar 0.5 > mean(is.na(bbb$json_string)) # 0 > mean(is.na(ccc$json_string)) # 0{code} > > > * arrow 2.0 (latest CRAN) > * readr 1.4.0 > * data.table 1.13.2 > * R version 4.0.1 (2020-06-06) > * MacOS Catalina 10.15.7 / x86_64-apple-darwin17.0 > > -- This message was sent by Atlassian Jira (v8.3.4#803005)