lgaborini opened a new issue, #38804:
URL: https://github.com/apache/arrow/issues/38804
### Describe the bug, including details regarding any error messages,
version, and platform.
With {arrow} 14.0.0, I was able to import a large number of CSVs, merge the
schemas, establish a partitioning and write them with `arrow::write_dataset()`.
The datasets can be manipulated in-memory and queried without issues, but
most queries fail once the dataset is read from disk.
The number of rows is also different.
While building the reprex, I was able to pin down the error thanks to to a
base R error mentioning the presence of embedded nuls in the partitioning
variable.
This is not always reproducible, though.
Also, queries fail at random points: it sounds like a racing condition, but
threads are disabled according to `arrow.use_threads`.
Reprex [here](https://github.com/lgaborini/arrow-reprex)
If one has tips on how to further trim down the dataset, I can refine it.
## Example
### Reading
No issues here:
``` r
tbl_input <-
readr::read_rds(fs::path_expand("~/temp/reprex/data_reprex_small.rds"))
# It prints correctly
tbl_input
#> # A tibble: 245 × 2
#> var_1 var_2
#> <chr> <chr>
#> 1 a FIL
#> 2 a FIL
#> 3 a FIL
#> 4 a FIL
#> 5 a FIL
#> 6 a FIL
#> 7 a FIL
#> 8 a FIL
#> 9 a FIL
#> 10 a FIL
#> # ℹ 235 more rows
tbl_input$var_1
#> [1] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [19] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [37] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [55] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [73] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [91] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [109] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [127] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [145] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [163] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [181] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [199] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [217] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
"a"
#> [235] "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
tbl_input$var_2
#> [1] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [11] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [21] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [31] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [41] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [51] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [61] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [71] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [81] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [91] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [101] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [111] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FIL"
#> [121] "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL" "FIL"
"FILX"
#> [131] "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX"
"FILX"
#> [141] "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX"
"FILX"
#> [151] "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX"
"FILX"
#> [161] "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX"
"FILX"
#> [171] "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX"
"FILX"
#> [181] "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX"
"FILX"
#> [191] "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX"
"FILX"
#> [201] "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX"
"FILX"
#> [211] "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX"
"FILX"
#> [221] "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX"
"FILX"
#> [231] "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX" "FILX"
"FILX"
#> [241] "FILX" "FIL" "FIL" "FILx" "FILx"
# Input number of rows
nrow(tbl_input)
#> [1] 245
# Input queries: OK
tbl_input |> dplyr::filter(var_2 == "FIL") |> dplyr::collect()
#> # A tibble: 131 × 2
#> var_1 var_2
#> <chr> <chr>
#> 1 a FIL
#> 2 a FIL
#> 3 a FIL
#> 4 a FIL
#> 5 a FIL
#> 6 a FIL
#> 7 a FIL
#> 8 a FIL
#> 9 a FIL
#> 10 a FIL
#> # ℹ 121 more rows
tbl_input |> dplyr::count(var_1) |> dplyr::collect()
#> # A tibble: 1 × 2
#> var_1 n
#> <chr> <int>
#> 1 a 245
```
### Writing
```r
f_dataset_merged <- tempfile()
tbl_input |>
arrow::write_dataset(
path = f_dataset_merged,
partitioning = "var_2"
)
```
### Reading from disk
Usually `arrow::open_dataset()` is s silent (I used also to get errors
here).
Subsequent queries might fail:
```r
tbl_written <- arrow::open_dataset(
sources = f_dataset_merged,
partitioning = arrow::hive_partition()
)
# Queries that might or may not fail
# Different number of rows
nrow(tbl_written)
#> Error: IOError: Could not open Parquet input source
'/Temp/RtmpaKq6Pd/file3598288b2763/var_2=FILX/part-0.parquet': Couldn't
deserialize thrift: TProtocolException: Invalid data
tbl_written |> dplyr::filter(var_2 == "FIL") |> dplyr::collect()
#> # A tibble: 131 × 2
#> var_1 var_2
#> <chr> <chr>
#> 1 a FIL
#> 2 a FIL
#> 3 a FIL
#> 4 a FIL
#> 5 a FIL
#> 6 a FIL
#> 7 a FIL
#> 8 a FIL
#> 9 a FIL
#> 10 a FIL
#> # ℹ 121 more rows
tbl_written |> dplyr::count(var_1) |> dplyr::collect()
#> Error in `compute.arrow_dplyr_query()`:
#> ! IOError: Could not open Parquet input source
'/Temp/RtmpaKq6Pd/file3598288b2763/var_2=FILX/part-0.parquet': Couldn't
deserialize thrift: TProtocolException: Invalid data
#> Backtrace:
#> ▆
#> 1. ├─dplyr::collect(dplyr::count(tbl_written, var_1))
#> 2. └─arrow:::collect.arrow_dplyr_query(dplyr::count(tbl_written, var_1))
#> 3. └─arrow:::compute.arrow_dplyr_query(x)
#> 4. └─base::tryCatch(...)
#> 5. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
#> 6. └─base (local) tryCatchOne(expr, names, parentenv,
handlers[[1L]])
#> 7. └─value[[3L]](cond)
#> 8. └─arrow:::augment_io_error_msg(e, call, schema = schema())
#> 9. └─rlang::abort(msg, call = call)
```
<sup>Created on 2023-11-20 with [reprex
v2.0.2](https://reprex.tidyverse.org)</sup>
### Another failure mode
Just by relaunching the reprex, the reading parts fails with another path.
The row number can be computed (but it's different):
```r
# Queries that might or may not fail
# Different number of rows
nrow(tbl_written)
#> [1] 243
tbl_written |> dplyr::filter(var_2 == "FIL") |> dplyr::collect()
#> # A tibble: 131 × 2
#> var_1 var_2
#> <chr> <chr>
#> 1 a FIL
#> 2 a FIL
#> 3 a FIL
#> 4 a FIL
#> 5 a FIL
#> 6 a FIL
#> 7 a FIL
#> 8 a FIL
#> 9 a FIL
#> 10 a FIL
#> # ℹ 121 more rows
tbl_written |> dplyr::count(var_1) |> dplyr::collect()
#> Error in `compute.arrow_dplyr_query()`:
#> ! IOError: Couldn't deserialize thrift: No more data to read.
#> Deserializing page header failed.
#> Backtrace:
#> ▆
#> 1. ├─dplyr::collect(dplyr::count(tbl_written, var_1))
#> 2. └─arrow:::collect.arrow_dplyr_query(dplyr::count(tbl_written, var_1))
#> 3. └─arrow:::compute.arrow_dplyr_query(x)
#> 4. └─base::tryCatch(...)
#> 5. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
#> 6. └─base (local) tryCatchOne(expr, names, parentenv,
handlers[[1L]])
#> 7. └─value[[3L]](cond)
#> 8. └─arrow:::augment_io_error_msg(e, call, schema = schema())
#> 9. └─rlang::abort(msg, call = call)
```
This is `arrow::arrow_info()`:
``` r
arrow::arrow_info()
#> Arrow package version: 14.0.0
#>
#> Capabilities:
#>
#> acero TRUE
#> dataset TRUE
#> substrait FALSE
#> parquet TRUE
#> json TRUE
#> s3 TRUE
#> gcs TRUE
#> utf8proc TRUE
#> re2 TRUE
#> snappy TRUE
#> gzip TRUE
#> brotli TRUE
#> zstd TRUE
#> lz4 TRUE
#> lz4_frame TRUE
#> lzo FALSE
#> bz2 TRUE
#> jemalloc FALSE
#> mimalloc TRUE
#>
#> Arrow options():
#>
#> arrow.use_threads FALSE
#>
#> Memory:
#>
#> Allocator mimalloc
#> Current 0 bytes
#> Max 0 bytes
#>
#> Runtime:
#>
#> SIMD Level avx2
#> Detected SIMD Level avx2
#>
#> Build:
#>
#> C++ Library Version 14.0.0
#> C++ Compiler GNU
#> C++ Compiler Version 10.3.0
#> Git ID 2dcee3f82c6cf54b53a64729fd81840efa583244
```
### Component(s)
R
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