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Timings of a Grouped Rank Filter Task


This note shares an experiment comparing the performance of a number of data processing systems available in R. Our notional or example problem is finding the top ranking item per group (group defined by three string columns, and order defined by a single numeric column). This is a common and often needed task.


First let’s compare three methods on the same grouped ranking problem.

  • "Base-R" (term defined as R plus just core packages, earlier results here). We are using base::order() with the option "method = "auto"" (as described here).
  • dplyr.
  • The seemingly silly idea of using reticulate to ship the data to Python, and then using Pandas to do the work, and finally bring the result back to R.

We will plot the run-times (in seconds) of these three solutions to the same task as a function of the number of rows in the problem. For all tasks shorter run-times (being lower on the graph) is better. Since we are plotting a large range of values (1 through 100,000,000 rows) we will present the data as a "log-log" plot.

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Notice dplyr is slower (higher up on the graph) than base R for all problem scales tested (1 row through 100,000,000 rows). Height differences on a log-y scaled graph such as this represent ratios of run-times and we can see the ratio of dplyr to base-R runtime is large (often over 40 to 1).

Also notice by the time we get the problem size up to 5,000 rows even sending the data to Python and back for Pandas processing is faster than dplyr.

Note: in this article "pandas timing" means the time it would take an R process to use Pandas for data manipulation. This includes the extra overhead of moving the data from R to Python/Pandas and back. This is always going to be slower than Pandas itself as it includes extra overhead. We are not saying R users should round trip their data through Python and (as we will discuss later) these performance numbers alone are not a reason for R users to switch to Python. It does indicate that clients may not always be well-served by a pure-dplyr or pure-tidyverse approach. As an R advocate, I like R to have its best fair chance in the market, regardless of loyalty or dis-loyalty to any one set of packages.

All runs were performed on an Amazon EC2 r4.8xlarge (244 GiB RAM) 64-bit Ubuntu Server 16.04 LTS (HVM), SSD Volume Type – ami-ba602bc2. We used R 3.4.4, with all packages current as of 8-20-2018 (the date of the experiment).

We are not testing dtplyr for the simple reason it did not work with the dplyr pipeline as written. We demonstrate this issue below.

ds <- mk_data(3)

dplyr_pipeline <- . %>%
  group_by(col_a, col_b, col_c) %>% 
  arrange(col_x) %>% 
  filter(row_number() == 1) %>%
  ungroup() %>%
  arrange(col_a, col_b, col_c, col_x)

ds %>% 
## # A tibble: 3 x 4
##   col_a col_b col_c col_x
##   <chr> <chr> <chr> <dbl>
## 1 sym_1 sym_1 sym_1 0.751
## 2 sym_2 sym_1 sym_1 0.743
## 3 sym_2 sym_2 sym_1 0.542
ds %>% %>% 
## Error in argument "x" is missing, with no default

It is important to note the reason base-R is in the running is that Matt Dowle and Arun Srinivasan of the data.table team generously ported their radix sorting code into base-R. Please see help(sort) for details. This sharing of one of data.table‘s more important features (fast radix sorting) back into R itself is a very big deal.

For our example we used what I consider a natural or idiomatic dplyr solution to the problem. We saw that code or pipeline just above. That code may not be preferred, as dplyr has known (unfixed) issues with filtering in the presence of grouping. Let’s try to work around that with the following code (pivoting as many operations out of the grouped data section of the pipeline as practical).

d %>% 
  arrange(col_x) %>% 
  group_by(col_a, col_b, col_c) %>% 
  mutate(rn = row_number()) %>%
  ungroup() %>%
  filter(rn == 1) %>%
  select(col_a, col_b, col_c, col_x) %>%
  arrange(col_a, col_b, col_c, col_x)

We will call the above solution "dplyr_b". A new comparison including "dplyr_b" is given below.

Present3 1

In the above graph we added data.table results and left out the earlier Pandas results. It is already known that working with data.table in R is typically competitive with (and sometimes faster than) working with Pandas in python (some results are given here, here); so R users should not be seriously considering round-tripping their data through Python to get access to Pandas, and (at least with data.table) R users should not have data manipulation performance as a reason to abandon R for Python.

There are at least 2 ways to think about the relation of the dplyr and dplyr_b solutions. One interpretation is we found a way to speed up our dplyr code by a factor of 5. The other interpretation is that small variations in dplyr pipeline specification can easily affect your run-times by a factor of 5. At no scale tested does either of the dplyr solutions match the performance of either of base-R or data.table. The ratio of the runtime of the first (or more natural) dplyr solution over the data.table runtime (data.table being by far the best solution) is routinely over 80 to 1.

We can take a closer look at the ratio of run-times. In our next graph we present the ratio two dplyr solution run times to the data.table solution run-time. We will call the ratio of the runtime of the first dplyr solution over the data.table run time "ratio_a"; and call the ratio of the runtime of the second (improved) dplyr solution over the data.table run time "ratio_b".

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A practical lesson is to look at is what happens at 5 million rows (times in seconds).

rows 5000000
base_r 2.04
data.table 0.981
dplyr 89.8
dplyr_b 17.2
pandas_reticulate 27.2
rqdatatable 3.05
ratio_a 91.5
ratio_b 17.5

At this scale data.table takes about 1 second. Base-R takes about 2 seconds (longer, but tolerable). dplyr takes 90 to 17 seconds (depending on which variation you use). These are significantly different user experiences. We have also included the timing for rqdatatable, which relies on data.table as its implementation and has some data-copying overhead (in this case leading to a total runtime of 3 seconds).


In our simple example we have seen very large differences in performance driven by seemingly small code changes. This emphasizes the need to benchmark one’s own tasks and workflows. Choosing tools based on mere preference or anecdote may not be safe. Also, even if one does not perform such tests, clients often do see and experience overall run times when scheduling jobs and provisioning infrastructure. Even if you do not measure, somebody else may be measuring later.

We must emphasize that performance of these systems will vary from example to example. However, the above results are consistent with what we have seen (informally) in production systems. In comparing performance one should look to primary sources (experiments actually run, such as this) over repeating indirect and unsupported (in the sense of no shared code or data) claims (or at least run such claims down to their primary sources).

All Results

Full results are below (and all code and results are here and here). Times below are reported in seconds.

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rows base_r data.table dplyr dplyr_b pandas_reticulate rqdatatable
1e+00 0.0003267 0.0009635 0.0032435 0.0056354 0.0515041 0.0068414
2e+00 0.0003528 0.0011762 0.0039913 0.0056410 0.0502828 0.0073060
5e+00 0.0003385 0.0010677 0.0034936 0.0045818 0.0525998 0.0068656
1e+01 0.0002953 0.0011747 0.0033224 0.0046752 0.0528985 0.0061786
2e+01 0.0003701 0.0011826 0.0033672 0.0051183 0.0515722 0.0072840
5e+01 0.0003502 0.0011314 0.0049174 0.0058086 0.0501480 0.0079664
1e+02 0.0004028 0.0011917 0.0050619 0.0058480 0.0516048 0.0063728
2e+02 0.0003551 0.0011431 0.0061629 0.0054713 0.0521349 0.0069566
5e+02 0.0005584 0.0011976 0.0091727 0.0059109 0.0536094 0.0075940
1e+03 0.0006866 0.0013731 0.0175628 0.0071574 0.0525276 0.0083290
2e+03 0.0009508 0.0014346 0.0306059 0.0085873 0.0587110 0.0085833
5e+03 0.0017535 0.0016617 0.1062600 0.0134023 0.0633362 0.0103101
1e+04 0.0032618 0.0023880 0.1585770 0.0215488 0.0783897 0.0117951
2e+04 0.0068442 0.0036975 0.2773583 0.0388676 0.1047436 0.0781040
5e+04 0.0220546 0.0076919 0.8126197 0.1302733 0.1919448 0.0287966
1e+05 0.0542680 0.0150029 1.6392750 0.2519953 0.3619354 0.0539985
2e+05 0.0984879 0.0297835 3.8033730 0.4226258 0.7554858 0.1376937
5e+05 0.1888721 0.0750671 8.6772974 1.6534837 2.0085191 0.3987250
1e+06 0.3081734 0.1597136 19.0250048 3.1239851 4.5261344 0.6841254
2e+06 0.6314713 0.4500708 37.5676553 6.6708313 9.9416448 1.2899695
5e+06 2.0422985 0.9812918 89.7617447 17.1999063 27.1812446 3.0494435
1e+07 4.1903058 2.9464046 185.6145993 38.0824507 55.4756717 8.5571020
2e+07 10.0234737 4.5928288 371.9769376 87.0174476 121.7105373 14.2129314
5e+07 30.3027149 10.7915545 978.9227351 227.6743024 313.1767425 33.9917370
1e+08 96.0148219 27.9374640 2075.8621812 573.3556189 683.8245597 66.8635493

rows data.table dplyr dplyr_b ratio_a ratio_b
1e+00 0.0009635 0.0032435 0.0056354 3.366340 5.848884
2e+00 0.0011762 0.0039913 0.0056410 3.393389 4.795870
5e+00 0.0010677 0.0034936 0.0045818 3.272137 4.291283
1e+01 0.0011747 0.0033224 0.0046752 2.828368 3.980007
2e+01 0.0011826 0.0033672 0.0051183 2.847248 4.327989
5e+01 0.0011314 0.0049174 0.0058086 4.346260 5.133901
1e+02 0.0011917 0.0050619 0.0058480 4.247761 4.907425
2e+02 0.0011431 0.0061629 0.0054713 5.391524 4.786483
5e+02 0.0011976 0.0091727 0.0059109 7.659537 4.935831
1e+03 0.0013731 0.0175628 0.0071574 12.790704 5.212602
2e+03 0.0014346 0.0306059 0.0085873 21.333661 5.985711
5e+03 0.0016617 0.1062600 0.0134023 63.946449 8.065404
1e+04 0.0023880 0.1585770 0.0215488 66.404714 9.023651
2e+04 0.0036975 0.2773583 0.0388676 75.011801 10.511779
5e+04 0.0076919 0.8126197 0.1302733 105.646123 16.936417
1e+05 0.0150029 1.6392750 0.2519953 109.263959 16.796454
2e+05 0.0297835 3.8033730 0.4226258 127.700648 14.189929
5e+05 0.0750671 8.6772974 1.6534837 115.593808 22.026728
1e+06 0.1597136 19.0250048 3.1239851 119.119507 19.559920
2e+06 0.4500708 37.5676553 6.6708313 83.470539 14.821737
5e+06 0.9812918 89.7617447 17.1999063 91.473038 17.527820
1e+07 2.9464046 185.6145993 38.0824507 62.996982 12.925058
2e+07 4.5928288 371.9769376 87.0174476 80.990813 18.946373
5e+07 10.7915545 978.9227351 227.6743024 90.711929 21.097452
1e+08 27.9374640 2075.8621812 573.3556189 74.303887 20.522823

Categories: Pragmatic Data Science Tutorials

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Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.

2 replies

  1. A couple of comments about the code used. First, your dplyr pipelines arrange two times. If you want everything sorted at the end, I think that it’s more efficienty to arrange just once at the beginning. Second, for data frames, I find more natural to use “slice” instead of filtering by row number.

    Also, there is a third way not considered here that is 1) even more idiomatic and 2) faster: summarising by the minimum of the grouped data frame! Here are my pipeline versions (trying a “pre” HTML block, not sure if it will work…):

    dplyr.a %>%
      arrange(col_a, col_b, col_c, col_x) %>%
      group_by(col_a, col_b, col_c) %>%
      slice(1) %>%
    dplyr.b %>%
      arrange(col_a, col_b, col_c, col_x) %>% 
      group_by(col_a, col_b, col_c) %>% 
      mutate(rn = row_number()) %>%
      ungroup() %>%
      filter(rn == 1) %>%
    dplyr.c %>%
      group_by(col_a, col_b, col_c) %>% 
      summarise(col_x = min(col_x)) %>%
      ungroup() %>%
      arrange(col_a, col_b, col_c, col_x)
    1. Iñaki,

      Thanks for the pipelines. These are variations one can try, they may or may not help enough to close the huge runtime ratios.

      I hadn’t thought of using slice() for the simple reason I do a lot of dbplyr work (where slice() is not available, one usually gets the error message: Error in UseMethod("slice_") : no applicable method for 'slice_' applied to an object of class "c('tbl_dbi', 'tbl_sql', 'tbl_lazy', 'tbl')"). That may or may not represent a significant speedup for in-memory work.

      I think at best the second pipeline would speed us up by at most a factor of 2 (that would be under the untested assumption most of the time is in sorting and that having one sorting stage instead of 2 is indeed half that work). So that one is good, but not enough to fix the large run time ratios we are seeing (around 50 times).

      The third summarize() works the exact problem at hand, but won’t fit my (unfortunately unstated application needs) of bringing over other columns from the winning rows (there were to such columns in the minimized example, but there ofter are in such applications). So it is a good idea, but not as powerful as picking by row-number.

      Of course the above is all just speculation. When tested, none of these variations produced a dplyr timing that was competitive with base-R or data.table (not repeating the reticulate timing, as that is not a serious alternative). Running them all to small scale gives the following results.

      Notice all the new dplyr variations form a pack (or group) on the graph and do not approach the performance of base R or data.table. The problem was not fixed by more dplyr variations.

      Details here and here.