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https://issues.apache.org/jira/browse/ARROW-16010?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17511627#comment-17511627
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Riaz Arbi commented on ARROW-16010:
-----------------------------------
Thank you for looking in to this [~dragosmg] .
That's a nice trick with the coercion into an arrow Table - I'll use it to
coerce my new data to get around my failing tests.
If I can use this as an opportunity to deepen my understanding - below I've
printed each value. I would expect a truncation to be a value that is identical
until a certain decimal point, with zeros thereafter. The first five decimal
points are identical between the two values (IIRC 6 decimal points is
microseconds). But why are there non-zero decimal values after that, and why
are they different between the two values?
I appreciate that this might be out of the issue; in which case you can feel
free to close the issue.
{code:java}
R value : [1]
"1631494810.376999855041503906250000000000000000000000000000000000"
Arrow Value:[1]
"1631494810.376998901367187500000000000000000000000000000000000000" {code}
> [R] write_parquet alters <dttm> value
> -------------------------------------
>
> Key: ARROW-16010
> URL: https://issues.apache.org/jira/browse/ARROW-16010
> Project: Apache Arrow
> Issue Type: Bug
> Components: R
> Affects Versions: 6.0.0
> Environment: Ubuntu focal
> R 4.1.1
> RStudio 1.4.1772
> Reporter: Riaz Arbi
> Priority: Minor
>
> When we write a dataframe column of type `<dttm>` to parquet using the arrow
> package, subsequent reading in of the parquet file to dataframe returns a
> slightly different value.
> This behaviour does not replicate with columns of type `<double>`
>
> Reprex:
>
> {code:java}
>
> #Create sample dataframe
> n <- 1631494810.376999855041503906250000000000000000000000000000000000
> df <- data.frame(x = "a",
> n = n,
> t = as.POSIXct(n, origin = "1970-01-01"))
> #Write to disk
> df %>% write_parquet("/tmp/tmp.parquet")
> #Extract time-based cols
> dft <- df %>%
> filter(x == "a") %>%
> pull(t) %>%
> as.numeric
> pqt <- read_parquet("/tmp/tmp.parquet") %>%
> filter(x == "a") %>%
> pull(t) %>%
> as.numeric
> dft == pqt
> sprintf("%.54f",dft)
> sprintf("%.54f",pqt)
> #Extract numeric cols
> dfn <- df %>%
> filter(x == "a") %>%
> pull(n) %>%
> as.numeric
> pqn <- read_parquet("/tmp/tmp.parquet") %>%
> filter(x == "a") %>%
> pull(n) %>%
> as.numeric
> dfn == pqn
> sprintf("%.54f",dfn)
> sprintf("%.54f",pqn) {code}
>
> The critical issue is that `dft == pqt` returns `FALSE` while `dfn == pqn`
> returns TRUE.
>
> Why is this a problem? We use `arrow` to store dataframes to disk. When we
> want to update these parquet files, we first check whether any data has
> actually changed and put in place tripwires to ensure that if a significant
> proportion of the data has changed the pipeline fails and is flagged for
> manual review.
>
> With the current behaviour, above, all of the dataframes that contain
> `<dttm>` type columns are failing.
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