ARROW-15260: [R] open_dataset - add file_name as column #12826

thisisnic · 2022-04-07T14:22:12Z

No description provided.

github-actions · 2022-04-07T14:24:10Z

https://issues.apache.org/jira/browse/ARROW-15260

github-actions · 2022-04-07T14:24:14Z

⚠️ Ticket has not been started in JIRA, please click 'Start Progress'.

thisisnic · 2022-04-07T14:28:31Z

Currently this fails with this error:

Error in `handle_csv_read_error()` at r/R/dplyr-collect.R:33:6:
! Invalid: No match for FieldRef.Name(__filename) in int: int32
dbl: double
lgl: bool
chr: string
fct: dictionary<values=string, indices=int32, ordered=0>
ts: timestamp[us, tz=UTC]
group: int32
other: string

I think it's something to do with the fact that the new column is not in the schema; if I try to print the arrow_dplyr_query object before I collect, I get:

Error in schm$GetFieldByName(name)$type$ToString() : 
  attempt to apply non-function

Which appears to come from here:

arrow/r/R/dplyr.R

Line 97 in dd42155

schm$GetFieldByName(name)$type$ToString()

I can successfully run:

ds %>%
  mutate(x = Expression$field_ref("chr")) %>%
  collect()

and when I run

ds %>%
  mutate(x = Expression$field_ref("made_up_name")) %>%
  collect()

I get the same error (No match for FieldRef.Name) , which makes me think that we need to do something higher up somewhere as we're not even picking up the augmented field.

nealrichardson · 2022-04-07T18:06:42Z

Currently this fails with this error:

If you haven't already, can you build arrow with -DARROW_EXTRA_ERROR_CONTEXT=ON and include the C++ traceback from the error? From the error message it sounds like it's coming from C++ not R.

Re: the printing error, we'll have to handle that somehow. I wonder how many other places we assume that the schema contains all possible valid field refs; I also think that the C++ Dataset layer should have a way of handling this better than having us special case and sniff for these "augmented columns".

(Side note: Error in 'handle_csv_read_error()' is misleading and should be solvable; we're catching errors and inspecting for a CSV read error message and we rethrow the error if it's not that, but then shows the error coming from the error handler and not the original function. Surely there is a way to handle this, in rlang or otherwise.)

thisisnic · 2022-04-08T12:50:51Z

@nealrichardson I'd just trimmed it as I didn't realise it was relevant; here's the rest of it:

/home/nic2/arrow/cpp/src/arrow/type.h:1717  CheckNonEmpty(matches, root)
/home/nic2/arrow/cpp/src/arrow/compute/exec/expression.cc:397  ref->FindOne(in)
/home/nic2/arrow/cpp/src/arrow/compute/exec/expression.cc:410  BindImpl(std::move(argument), in, shape, exec_context)

thisisnic · 2022-04-08T15:54:05Z

@nealrichardson Have submitted a PR which handles the issue described in your "side note" there in #12839

nealrichardson · 2022-04-09T12:28:00Z

@nealrichardson I'd just trimmed it as I didn't realise it was relevant; here's the rest of it:

/home/nic2/arrow/cpp/src/arrow/type.h:1717  CheckNonEmpty(matches, root)
/home/nic2/arrow/cpp/src/arrow/compute/exec/expression.cc:397  ref->FindOne(in)
/home/nic2/arrow/cpp/src/arrow/compute/exec/expression.cc:410  BindImpl(std::move(argument), in, shape, exec_context)

Great. So that points to where in C++ the validation needs to change. Can you see if this is the same issue as Weston identified, or if it needs to be a separate one? (IIRC the issue he created was about Scanner and this is not using Scanner, but maybe it boils down to the same thing.)

…and `handle_parquet_io_error()` need better error tracing As discussed on #12826 Not sure how (if) to write tests but tried running it locally using the CSV directory set up in `test-dataset-csv.R` with and without this change, and without it, we get, e.g. ``` open_dataset(csv_dir) # Error in `handle_parquet_io_error()` at r/R/dataset.R:221:6: # ! Invalid: Error creating dataset. Could not read schema from '/tmp/RtmpuTyOD8/file5049dcf581a5/5/file1.csv': Could not open Parquet input source '/tmp/RtmpuTyOD8/file5049dcf581a5/5/file1.csv': Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file. # /home/nic2/arrow/cpp/src/arrow/dataset/file_parquet.cc:323 GetReader(source, scan_options). Is this a 'parquet' file? # /home/nic2/arrow/cpp/src/arrow/dataset/discovery.cc:40 InspectSchemas(std::move(options)) # /home/nic2/arrow/cpp/src/arrow/dataset/discovery.cc:262 Inspect(options.inspect_options) # ℹ Did you mean to specify a 'format' other than the default (parquet)? ``` and then with it: ``` open_dataset(csv_dir) # Error in `open_dataset()`: # ! Invalid: Error creating dataset. Could not read schema from '/tmp/RtmpLbqZs6/file4e4ca14fb5795/5/file1.csv': Could not open Parquet input source '/tmp/RtmpLbqZs6/file4e4ca14fb5795/5/file1.csv': Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file. # /home/nic2/arrow/cpp/src/arrow/dataset/file_parquet.cc:323 GetReader(source, scan_options). Is this a 'parquet' file? # /home/nic2/arrow/cpp/src/arrow/dataset/discovery.cc:40 InspectSchemas(std::move(options)) # /home/nic2/arrow/cpp/src/arrow/dataset/discovery.cc:262 Inspect(options.inspect_options) # ℹ Did you mean to specify a 'format' other than the default (parquet)? ``` Closes #12839 from thisisnic/ARROW-16154_error_trace Authored-by: Nic Crane <thisisnic@gmail.com> Signed-off-by: Nic Crane <thisisnic@gmail.com>

thisisnic · 2022-07-04T10:16:27Z

Great. So that points to where in C++ the validation needs to change. Can you see if this is the same issue as Weston identified, or if it needs to be a separate one? (IIRC the issue he created was about Scanner and this is not using Scanner, but maybe it boils down to the same thing.)

@nealrichardson Sorry, old ticket that I'm just picking up now, but how would I know if this is the same issue that Weston identified? Had a skim through the C++ code that is causing the error but don't really understand it. @westonpace - do you know if it'll likely be the same thing?

nealrichardson · 2022-07-05T20:29:49Z

I'm missing something here and I can't see what it is. I see that the ScanNode gets the "augmented fields" added to its schema: https://github.com/apache/arrow/blob/master/cpp/src/arrow/dataset/scanner.cc#L924-L932 So subsequent nodes should see them in the output_schema and be able to use them.

If I comment out these lines and don't project them away: https://github.com/apache/arrow/blob/master/r/R/query-engine.R#L150-L151 and I just do collect(ds), I see the augmented fields in the result. But with those lines in place, if I do mutate(files = Expression$field_ref("__filename")) and then collect, I get the error that @thisisnic showed above.

I can't rule out that there's an extra Project happening in R that I'm not seeing somewhere that is dropping the columns. I think I need to implement the ExecPlan::ToString bindings in order to help solve this.

westonpace · 2022-07-06T02:28:36Z

I don't think it is an extra project. The failure is coming from the two calls to Bind in ExecNode_Scan. The code there is binding both the projection and the filter to the dataset schema. This schema does not include the augmented fields and thus the bind fails. If I remove those two calls to bind then everything works.

Inside the scanner, we check both expressions. If they are not bound we bind them to the augmented dataset schema (which isn't exactly available to the caller and does include the augmented fields).

So I think the right answer here is to just remove those calls to bind. They don't exist on the python equivalent path. Binding expressions seems like an implementation detail anyways and we can probably move away from anyone outside arrow-cpp having to know about the concept.

r/src/compute-exec.cpp

dragosmg

LGTM with 1 comment / question

r/tests/testthat/test-dataset.R

r/R/dataset.R

r/tests/testthat/test-dataset.R

thisisnic · 2022-07-19T13:48:53Z

OK, more still needed here than I thought. I've now added it as an NSE func, but get an error when trying to print things (likely as we're looking at the schema, which it's not been added to) - as @nealrichardson said above "we assume that the schema contains all possible valid field refs". Not sure if there's a better way to get this info that uses something else.

library(arrow)
library(dplyr)

tf <- tempfile()
dir.create(tf)
write_dataset(mtcars, tf, partitioning = "cyl")

# this works and returns the dataset with the augmented file correctly added
open_dataset(tf) %>%
  mutate(filename = add_filename()) %>%
  collect()
#>     mpg  disp  hp drat    wt  qsec vs am gear carb cyl
#> 1  18.7 360.0 175 3.15 3.440 17.02  0  0    3    2   8
#> 2  14.3 360.0 245 3.21 3.570 15.84  0  0    3    4   8
#> 3  16.4 275.8 180 3.07 4.070 17.40  0  0    3    3   8
#> 4  17.3 275.8 180 3.07 3.730 17.60  0  0    3    3   8
#> 5  15.2 275.8 180 3.07 3.780 18.00  0  0    3    3   8
#> 6  10.4 472.0 205 2.93 5.250 17.98  0  0    3    4   8
#> 7  10.4 460.0 215 3.00 5.424 17.82  0  0    3    4   8
#> 8  14.7 440.0 230 3.23 5.345 17.42  0  0    3    4   8
#> 9  15.5 318.0 150 2.76 3.520 16.87  0  0    3    2   8
#> 10 15.2 304.0 150 3.15 3.435 17.30  0  0    3    2   8
#> 11 13.3 350.0 245 3.73 3.840 15.41  0  0    3    4   8
#> 12 19.2 400.0 175 3.08 3.845 17.05  0  0    3    2   8
#> 13 15.8 351.0 264 4.22 3.170 14.50  0  1    5    4   8
#> 14 15.0 301.0 335 3.54 3.570 14.60  0  1    5    8   8
#> 15 22.8 108.0  93 3.85 2.320 18.61  1  1    4    1   4
#> 16 24.4 146.7  62 3.69 3.190 20.00  1  0    4    2   4
#> 17 22.8 140.8  95 3.92 3.150 22.90  1  0    4    2   4
#> 18 32.4  78.7  66 4.08 2.200 19.47  1  1    4    1   4
#> 19 30.4  75.7  52 4.93 1.615 18.52  1  1    4    2   4
#> 20 33.9  71.1  65 4.22 1.835 19.90  1  1    4    1   4
#> 21 21.5 120.1  97 3.70 2.465 20.01  1  0    3    1   4
#> 22 27.3  79.0  66 4.08 1.935 18.90  1  1    4    1   4
#> 23 26.0 120.3  91 4.43 2.140 16.70  0  1    5    2   4
#> 24 30.4  95.1 113 3.77 1.513 16.90  1  1    5    2   4
#> 25 21.4 121.0 109 4.11 2.780 18.60  1  1    4    2   4
#> 26 21.0 160.0 110 3.90 2.620 16.46  0  1    4    4   6
#> 27 21.0 160.0 110 3.90 2.875 17.02  0  1    4    4   6
#> 28 21.4 258.0 110 3.08 3.215 19.44  1  0    3    1   6
#> 29 18.1 225.0 105 2.76 3.460 20.22  1  0    3    1   6
#> 30 19.2 167.6 123 3.92 3.440 18.30  1  0    4    4   6
#> 31 17.8 167.6 123 3.92 3.440 18.90  1  0    4    4   6
#> 32 19.7 145.0 175 3.62 2.770 15.50  0  1    5    6   6
#>                                                  filename
#> 1  /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 2  /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 3  /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 4  /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 5  /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 6  /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 7  /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 8  /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 9  /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 10 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 11 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 12 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 13 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 14 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=8/part-0.parquet
#> 15 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=4/part-0.parquet
#> 16 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=4/part-0.parquet
#> 17 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=4/part-0.parquet
#> 18 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=4/part-0.parquet
#> 19 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=4/part-0.parquet
#> 20 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=4/part-0.parquet
#> 21 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=4/part-0.parquet
#> 22 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=4/part-0.parquet
#> 23 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=4/part-0.parquet
#> 24 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=4/part-0.parquet
#> 25 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=4/part-0.parquet
#> 26 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=6/part-0.parquet
#> 27 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=6/part-0.parquet
#> 28 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=6/part-0.parquet
#> 29 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=6/part-0.parquet
#> 30 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=6/part-0.parquet
#> 31 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=6/part-0.parquet
#> 32 /tmp/RtmpzzDK4v/file32c352040ed57/cyl=6/part-0.parquet

# this doesn't - as it tries to print it
open_dataset(tf) %>%
  mutate(filename = add_filename())
#> Error in schm$GetFieldByName(name)$type$ToString(): attempt to apply non-function

# if we try it on a table, we get an error message - 
# I can look to catch this and raise an error with more context
arrow_table(mtcars) %>% mutate(filename = add_filename()) %>% collect()
#> Error in `collect()`:
#> ! Invalid: No match for FieldRef.Name(__filename) in mpg: double
#> cyl: double
#> disp: double
#> hp: double
#> drat: double
#> wt: double
#> qsec: double
#> vs: double
#> am: double
#> gear: double
#> carb: double
#> /home/nic2/arrow/cpp/src/arrow/type.h:1800  CheckNonEmpty(matches, root)
#> /home/nic2/arrow/cpp/src/arrow/compute/exec/expression.cc:429  ref->FindOne(in)
#> /home/nic2/arrow/cpp/src/arrow/compute/exec/project_node.cc:67  expr.Bind(*inputs[0]->output_schema(), plan->exec_context())

# same error as with the dataset - "attempt to apply non-function"
arrow_table(mtcars) %>% mutate(filename = add_filename())
#> Error in schm$GetFieldByName(name)$type$ToString(): attempt to apply non-function

nealrichardson · 2022-07-19T16:45:33Z

Re: the print method, you probably need more special casing here. The other place that may make assumptions about FieldRefs is implicit_schema(), which is called when a query is collapsed. So you may want to add some tests that do aggregation, or joins, or head/tail, etc., as these are all cases that would involve implicit_schema (iirc).

Re: handling for non-Datasets, ideally you'd catch that when add_filename() is called. You should be able to find .data somewhere in the env stack and inspect it.

thisisnic · 2022-07-20T22:56:09Z

Re: handling for non-Datasets, ideally you'd catch that when add_filename() is called. You should be able to find .data somewhere in the env stack and inspect it.

@nealrichardson I'm a bit lost as to how I'd be expecting .data to look different between a Table and a Dataset. I've managed to access it via caller_env()$.data but the contents look the same either way - a named list of Expressions.

nealrichardson · 2022-07-21T22:50:28Z

Re: handling for non-Datasets, ideally you'd catch that when add_filename() is called. You should be able to find .data somewhere in the env stack and inspect it.

@nealrichardson I'm a bit lost as to how I'd be expecting .data to look different between a Table and a Dataset. I've managed to access it via caller_env()$.data but the contents look the same either way - a named list of Expressions.

Not that .data, the .data that that is generated from in arrow_mask() I was thinking along the lines of what I proposed in https://issues.apache.org/jira/browse/ARROW-13186, which is (as you can see) not implemented. One idea would be to poke a magic variable into the data mask that says "I am a dataset", or attach an attribute to that .data to that effect, and you could find it here. But we can defer that--maybe make a TODO JIRA for that?

thisisnic · 2022-07-26T20:20:56Z

This still isn't done as the tests I added don't hit the implicit_schema() path.

…ndlers to match style

thisisnic · 2022-08-09T13:14:03Z

Re: handling for non-Datasets, ideally you'd catch that when add_filename() is called. You should be able to find .data somewhere in the env stack and

I've opened ARROW-17356 as a follow-up.

nealrichardson

A suggestion about the error message but otherwise LGTM, thanks for persisting with this!

nealrichardson · 2022-08-09T16:48:34Z

r/tests/testthat/test-dataset.R

+
+  # this hits the implicit_schema path by joining afterwards
+  join_after <- ds %>%
+      mutate(file = add_filename()) %>%


Indentation here and on the next example are off

nealrichardson · 2022-08-09T16:51:41Z

r/R/util.R

+        "Augmented fields such as 'filename' must",
+        "only be used with with Dataset objects which have",
+        "not been aggregated or joined."


Wordsmithing here, how about something like "'filename' can only be used with Dataset objects, and it can only be added before doing an aggregation or a join"?

That's a lot clearer, thank you! I've updated the error to incorporate both that, and the two different ways of referring to the __filename variable.

ursabot · 2022-08-10T05:32:08Z

Benchmark runs are scheduled for baseline = b3116fa and contender = 8386871. 8386871 is a master commit associated with this PR. Results will be available as each benchmark for each run completes.
Conbench compare runs links:
[Finished ⬇️0.0% ⬆️0.0%] ec2-t3-xlarge-us-east-2
[Failed ⬇️2.28% ⬆️0.03%] test-mac-arm
[Failed ⬇️25.54% ⬆️0.0%] ursa-i9-9960x
[Finished ⬇️0.21% ⬆️0.11%] ursa-thinkcentre-m75q
Buildkite builds:
[Finished] 83868717 ec2-t3-xlarge-us-east-2
[Failed] 83868717 test-mac-arm
[Failed] 83868717 ursa-i9-9960x
[Finished] 83868717 ursa-thinkcentre-m75q
[Finished] b3116fa3 ec2-t3-xlarge-us-east-2
[Finished] b3116fa3 test-mac-arm
[Finished] b3116fa3 ursa-i9-9960x
[Finished] b3116fa3 ursa-thinkcentre-m75q
Supported benchmarks:
ec2-t3-xlarge-us-east-2: Supported benchmark langs: Python, R. Runs only benchmarks with cloud = True
test-mac-arm: Supported benchmark langs: C++, Python, R
ursa-i9-9960x: Supported benchmark langs: Python, R, JavaScript
ursa-thinkcentre-m75q: Supported benchmark langs: C++, Java

ursabot · 2022-08-10T05:32:27Z

['Python', 'R'] benchmarks have high level of regressions.
test-mac-arm
ursa-i9-9960x

vspinu · 2022-08-29T19:16:28Z

After this commit I see a 10X memory usage increase and 5x slower compute times in my application. Instead of 400MB I am hitting 4GB now. 🙄

Shouldn't this feature have no effect whatsoever unless add_filename() is explicitly requested?

nealrichardson · 2022-08-29T22:15:04Z

The change triggered ARROW-17556, will get fixed there.

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thisisnic requested a review from nealrichardson April 7, 2022 14:37

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ARROW-16154: [R] Errors which pass through handle_csv_read_error() and handle_parquet_io_error() need better error tracing #12839

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thisisnic force-pushed the ARROW-15260_filenames branch 3 times, most recently from c3b5774 to df817b2 Compare July 26, 2022 20:04

thisisnic added 13 commits August 9, 2022 13:39

Add add_filename to NEWS

a3e6c66

Move to an NSE func

c5d42c2

Add condition to return string type

5d09a61

Register the binding

ca96473

Remove unnecessary .Rd file

5a7532e

Add handling of augmented fields with table and update other error ha…

23e476e

…ndlers to match style

Correct typo

92411e4

Add more tests

20f1c57

Add test which hits implicit_schema

765d834

Add field ref manually to schema

51ae21c

Add filename manually to the schema, and add more tests

b44e1e2

Run styler

b734085

Add __filename to schema manually

7d9d356

thisisnic force-pushed the ARROW-15260_filenames branch from c0190c2 to 7d9d356 Compare August 9, 2022 12:39

thisisnic added 4 commits August 9, 2022 13:44

Fix bad merge

3966f9c

Remove from pkgdown

671edf5

Add comment on augmented field

6ac5e8a

Add comments to refactored functions

4c16978

thisisnic requested a review from nealrichardson August 9, 2022 15:40

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thisisnic added 2 commits August 9, 2022 21:54

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Update error message for clarity

2521a43

nealrichardson merged commit 8386871 into apache:master Aug 10, 2022

eitsupi mentioned this pull request Aug 19, 2022

ARROW-17429: [R] Error messages are not helpful of read_csv_arrow with col_types option #13922

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[R] open_dataset - add file_name as column #30754

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ARROW-15260: [R] open_dataset - add file_name as column #12826

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