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https://issues.apache.org/jira/browse/ARROW-11433?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17278440#comment-17278440
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Jonathan Keane commented on ARROW-11433:
----------------------------------------
And to prove that the problem from R is resolved:
{code:r}
> system.time({
+ for (i in 1:10) {
+ print(system.time(tab <-
read_csv_arrow("source_data/nyctaxi_2010-01.csv", as_data_frame = FALSE)))
+ tab <- NULL
+ }
+ })
user system elapsed
23.336 13.718 5.810
user system elapsed
24.825 10.481 6.110
user system elapsed
24.874 11.088 4.798
user system elapsed
24.906 12.385 5.700
user system elapsed
24.821 12.834 5.671
user system elapsed
24.827 12.462 5.489
user system elapsed
24.835 12.567 5.687
user system elapsed
24.850 12.672 5.658
user system elapsed
24.996 12.802 5.502
user system elapsed
24.992 11.940 5.248
user system elapsed
249.552 132.579 67.680
{code}
> [R] Unexpectedly slow results reading csv
> -----------------------------------------
>
> Key: ARROW-11433
> URL: https://issues.apache.org/jira/browse/ARROW-11433
> Project: Apache Arrow
> Issue Type: Bug
> Components: R
> Reporter: Jonathan Keane
> Priority: Minor
>
> This came up working on benchmarking Arrow's CSV reading. As far as I can
> tell this only impacts R, and only when reading the csv into arrow (but not
> pulling it in to R). It appears that most arrow interactions after the csv is
> read will result in this behavior not happening.
> What I'm seeing is that on subsequent reads, the time to read gets longer and
> longer (frequently in a stair step pattern where every other iteration takes
> longer).
> {code:r}
> > system.time({
> + for (i in 1:10) {
> + print(system.time(tab <-
> read_csv_arrow("source_data/nyctaxi_2010-01.csv", as_data_frame = FALSE)))
> + tab <- NULL
> + }
> + })
> user system elapsed
> 24.788 19.485 7.216
> user system elapsed
> 24.952 21.786 9.225
> user system elapsed
> 25.150 23.039 10.332
> user system elapsed
> 25.382 31.012 17.995
> user system elapsed
> 25.309 25.140 12.356
> user system elapsed
> 25.302 26.975 13.938
> user system elapsed
> 25.509 34.390 21.134
> user system elapsed
> 25.674 28.195 15.048
> user system elapsed
> 25.031 28.094 16.449
> user system elapsed
> 25.825 37.165 23.379
> # total time:
> user system elapsed
> 256.178 299.671 175.119
> {code}
> Interestingly, doing something as unrelated as
> {{arrow:::default_memory_pool()}} which is [only getting the default memory
> pool|https://github.com/apache/arrow/blob/f291cd7b96463a2efd40a976123c64fad5c01058/r/src/memorypool.cpp#L68-L70].
> Other interactions totally unrelated to the table also similarly alleviate
> this behavior (e.g. {{empty_tab <- Table$create(data.frame())}}) or
> proactively invalidating with {{tab$invalidate()}}
> {code:r}
> > system.time({
> + for (i in 1:10) {
> + print(system.time(tab <-
> read_csv_arrow("source_data/nyctaxi_2010-01.csv", as_data_frame = FALSE)))
> + pool <- arrow:::default_memory_pool()
> + tab <- NULL
> + }
> + })
> user system elapsed
> 25.257 19.475 6.785
> user system elapsed
> 25.271 19.838 6.821
> user system elapsed
> 25.288 20.103 6.861
> user system elapsed
> 25.188 20.290 7.217
> user system elapsed
> 25.283 20.043 6.832
> user system elapsed
> 25.194 19.947 6.906
> user system elapsed
> 25.278 19.993 6.834
> user system elapsed
> 25.355 20.018 6.833
> user system elapsed
> 24.986 19.869 6.865
> user system elapsed
> 25.130 19.878 6.798
> # total time:
> user system elapsed
> 255.381 210.598 83.109
> >
> {code}
> I've tested this against Arrow 3.0.0, 2.0.0, and 1.0.0 and all experience the
> same behavior.
> I checked against pyarrow, and do not see the same:
> {code:python}
> from pyarrow import csv
> import time
> for i in range(1, 10):
> start = time.time()
> table = csv.read_csv("r/source_data/nyctaxi_2010-01.csv")
> print(time.time() - start)
> del table
> {code}
> results:
> {code}
> 7.586184978485107
> 7.542470932006836
> 7.92852783203125
> 7.647372007369995
> 7.742412805557251
> 8.101378917694092
> 7.7359960079193115
> 7.843957901000977
> 7.6457719802856445
> {code}
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