We’re thrilled to announce sparklyr 1.5 is now
accessible on CRAN!

To put in sparklyr 1.5 from CRAN, run

On this weblog put up, we are going to spotlight the next features of sparklyr 1.5:

Higher dplyr interface

A big fraction of pull requests that went into the sparklyr 1.5 launch had been centered on making
Spark dataframes work with varied dplyr verbs in the identical method that R dataframes do.
The complete listing of dplyr-related bugs and have requests that had been resolved in
sparklyr 1.5 might be present in right here.

On this part, we are going to showcase three new dplyr functionalities that had been shipped with sparklyr 1.5.

Stratified sampling

Stratified sampling on an R dataframe might be completed with a mixture of dplyr::group_by() adopted by
dplyr::sample_n() or dplyr::sample_frac(), the place the grouping variables specified within the dplyr::group_by()
step are those that outline every stratum. As an illustration, the next question will group mtcars by quantity
of cylinders and return a weighted random pattern of dimension two from every group, with out substitute, and weighted by
the mpg column:

## # A tibble: 6 x 11
## # Teams:   cyl [3]
##     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1  33.9     4  71.1    65  4.22  1.84  19.9     1     1     4     1
## 2  22.8     4 108      93  3.85  2.32  18.6     1     1     4     1
## 3  21.4     6 258     110  3.08  3.22  19.4     1     0     3     1
## 4  21       6 160     110  3.9   2.62  16.5     0     1     4     4
## 5  15.5     8 318     150  2.76  3.52  16.9     0     0     3     2
## 6  19.2     8 400     175  3.08  3.84  17.0     0     0     3     2

Ranging from sparklyr 1.5, the identical may also be accomplished for Spark dataframes with Spark 3.0 or above, e.g.,:

library(sparklyr)

sc <- spark_connect(grasp = "native", model = "3.0.0")
mtcars_sdf <- copy_to(sc, mtcars, exchange = TRUE, repartition = 3)

mtcars_sdf %>%
  dplyr::group_by(cyl) %>%
  dplyr::sample_n(dimension = 2, weight = mpg, exchange = FALSE) %>%
  print()

# Supply: spark<?> [?? x 11]
# Teams: cyl
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  21       6 160     110  3.9   2.62  16.5     0     1     4     4
2  21.4     6 258     110  3.08  3.22  19.4     1     0     3     1
3  27.3     4  79      66  4.08  1.94  18.9     1     1     4     1
4  32.4     4  78.7    66  4.08  2.2   19.5     1     1     4     1
5  16.4     8 276.    180  3.07  4.07  17.4     0     0     3     3
6  18.7     8 360     175  3.15  3.44  17.0     0     0     3     2

or

## # Supply: spark<?> [?? x 11]
## # Teams: cyl
##     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1  21       6 160     110  3.9   2.62  16.5     0     1     4     4
## 2  21.4     6 258     110  3.08  3.22  19.4     1     0     3     1
## 3  22.8     4 141.     95  3.92  3.15  22.9     1     0     4     2
## 4  33.9     4  71.1    65  4.22  1.84  19.9     1     1     4     1
## 5  30.4     4  95.1   113  3.77  1.51  16.9     1     1     5     2
## 6  15.5     8 318     150  2.76  3.52  16.9     0     0     3     2
## 7  18.7     8 360     175  3.15  3.44  17.0     0     0     3     2
## 8  16.4     8 276.    180  3.07  4.07  17.4     0     0     3     3

Row sums

The rowSums() performance provided by dplyr is useful when one must sum up
numerous columns inside an R dataframe which can be impractical to be enumerated
individually.
For instance, right here we’ve a six-column dataframe of random actual numbers, the place the
partial_sum column within the consequence accommodates the sum of columns b by way of d inside
every row:

## # A tibble: 5 x 7
##         a     b     c      d     e      f partial_sum
##     <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>       <dbl>
## 1 0.781   0.801 0.157 0.0293 0.169 0.0978        1.16
## 2 0.696   0.412 0.221 0.941  0.697 0.675         2.27
## 3 0.802   0.410 0.516 0.923  0.190 0.904         2.04
## 4 0.200   0.590 0.755 0.494  0.273 0.807         2.11
## 5 0.00149 0.711 0.286 0.297  0.107 0.425         1.40

Starting with sparklyr 1.5, the identical operation might be carried out with Spark dataframes:

## # Supply: spark<?> [?? x 7]
##         a     b     c      d     e      f partial_sum
##     <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>       <dbl>
## 1 0.781   0.801 0.157 0.0293 0.169 0.0978        1.16
## 2 0.696   0.412 0.221 0.941  0.697 0.675         2.27
## 3 0.802   0.410 0.516 0.923  0.190 0.904         2.04
## 4 0.200   0.590 0.755 0.494  0.273 0.807         2.11
## 5 0.00149 0.711 0.286 0.297  0.107 0.425         1.40

As a bonus from implementing the rowSums characteristic for Spark dataframes,
sparklyr 1.5 now additionally presents restricted assist for the column-subsetting
operator on Spark dataframes.
For instance, all code snippets beneath will return some subset of columns from
the dataframe named sdf:

# choose columns `b` by way of `e`
sdf[2:5]

# choose columns `b` and `c`
sdf[c("b", "c")]

# drop the primary and third columns and return the remainder
sdf[c(-1, -3)]

Weighted-mean summarizer

Much like the 2 dplyr features talked about above, the weighted.imply() summarizer is one other
helpful perform that has develop into a part of the dplyr interface for Spark dataframes in sparklyr 1.5.
One can see it in motion by, for instance, evaluating the output from the next

with output from the equal operation on mtcars in R:

each of them ought to consider to the next:

##     cyl mpg_wm
##   <dbl>  <dbl>
## 1     4   25.9
## 2     6   19.6
## 3     8   14.8

New additions to the sdf_* household of features

sparklyr supplies numerous comfort features for working with Spark dataframes,
and all of them have names beginning with the sdf_ prefix.

On this part we are going to briefly point out 4 new additions
and present some instance situations through which these features are helpful.

sdf_expand_grid()

Because the identify suggests, sdf_expand_grid() is solely the Spark equal of develop.grid().
Quite than working develop.grid() in R and importing the ensuing R dataframe to Spark, one
can now run sdf_expand_grid(), which accepts each R vectors and Spark dataframes and helps
hints for broadcast hash joins. The instance beneath reveals sdf_expand_grid() making a
100-by-100-by-10-by-10 grid in Spark over 1000 Spark partitions, with broadcast hash be part of hints
on variables with small cardinalities:

library(sparklyr)

sc <- spark_connect(grasp = "native")

grid_sdf <- sdf_expand_grid(
  sc,
  var1 = seq(100),
  var2 = seq(100),
  var3 = seq(10),
  var4 = seq(10),
  broadcast_vars = c(var3, var4),
  repartition = 1000
)

grid_sdf %>% sdf_nrow() %>% print()

## [1] 1e+06

sdf_partition_sizes()

As sparklyr consumer @sbottelli advised right here,
one factor that may be nice to have in sparklyr is an environment friendly strategy to question partition sizes of a Spark dataframe.
In sparklyr 1.5, sdf_partition_sizes() does precisely that:

library(sparklyr)

sc <- spark_connect(grasp = "native")

sdf_len(sc, 1000, repartition = 5) %>%
  sdf_partition_sizes() %>%
  print(row.names = FALSE)

##  partition_index partition_size
##                0            200
##                1            200
##                2            200
##                3            200
##                4            200

sdf_unnest_longer() and sdf_unnest_wider()

sdf_unnest_longer() and sdf_unnest_wider() are the equivalents of
tidyr::unnest_longer() and tidyr::unnest_wider() for Spark dataframes.
sdf_unnest_longer() expands all components in a struct column into a number of rows, and
sdf_unnest_wider() expands them into a number of columns. As illustrated with an instance
dataframe beneath,

library(sparklyr)

sc <- spark_connect(grasp = "native")
sdf <- copy_to(
  sc,
  tibble::tibble(
    id = seq(3),
    attribute = listing(
      listing(identify = "Alice", grade = "A"),
      listing(identify = "Bob", grade = "B"),
      listing(identify = "Carol", grade = "C")
    )
  )
)

sdf %>%
  sdf_unnest_longer(col = file, indices_to = "key", values_to = "worth") %>%
  print()

evaluates to

## # Supply: spark<?> [?? x 3]
##      id worth key
##   <int> <chr> <chr>
## 1     1 A     grade
## 2     1 Alice identify
## 3     2 B     grade
## 4     2 Bob   identify
## 5     3 C     grade
## 6     3 Carol identify

whereas

sdf %>%
  sdf_unnest_wider(col = file) %>%
  print()

evaluates to

## # Supply: spark<?> [?? x 3]
##      id grade identify
##   <int> <chr> <chr>
## 1     1 A     Alice
## 2     2 B     Bob
## 3     3 C     Carol

RDS-based serialization routines

Some readers have to be questioning why a model new serialization format would should be carried out in sparklyr in any respect.
Lengthy story quick, the reason being that RDS serialization is a strictly higher substitute for its CSV predecessor.
It possesses all fascinating attributes the CSV format has,
whereas avoiding quite a lot of disadvantages which can be widespread amongst text-based knowledge codecs.

On this part, we are going to briefly define why sparklyr ought to assist at the least one serialization format aside from arrow,
deep-dive into points with CSV-based serialization,
after which present how the brand new RDS-based serialization is free from these points.

Why arrow shouldn’t be for everybody?

To switch knowledge between Spark and R accurately and effectively, sparklyr should depend on some knowledge serialization
format that’s well-supported by each Spark and R.
Sadly, not many serialization codecs fulfill this requirement,
and among the many ones that do are text-based codecs comparable to CSV and JSON,
and binary codecs comparable to Apache Arrow, Protobuf, and as of latest, a small subset of RDS model 2.
Additional complicating the matter is the extra consideration that
sparklyr ought to assist at the least one serialization format whose implementation might be totally self-contained inside the sparklyr code base,
i.e., such serialization mustn’t rely on any exterior R bundle or system library,
in order that it could possibly accommodate customers who wish to use sparklyr however who don’t essentially have the required C++ compiler device chain and
different system dependencies for establishing R packages comparable to arrow or
protolite.
Previous to sparklyr 1.5, CSV-based serialization was the default different to fallback to when customers should not have the arrow bundle put in or
when the kind of knowledge being transported from R to Spark is unsupported by the model of arrow accessible.

Why is the CSV format not best?

There are at the least three causes to imagine CSV format shouldn’t be your best option in relation to exporting knowledge from R to Spark.

One purpose is effectivity. For instance, a double-precision floating level quantity comparable to .Machine$double.eps must
be expressed as "2.22044604925031e-16" in CSV format in an effort to not incur any lack of precision, thus taking on 20 bytes
reasonably than 8 bytes.

However extra essential than effectivity are correctness issues. In a R dataframe, one can retailer each NA_real_ and
NaN in a column of floating level numbers. NA_real_ ought to ideally translate to null inside a Spark dataframe, whereas
NaN ought to proceed to be NaN when transported from R to Spark. Sadly, NA_real_ in R turns into indistinguishable
from NaN as soon as serialized in CSV format, as evident from a fast demo proven beneath:

##     x is_nan
## 1  NA  FALSE
## 2 NaN   TRUE

csv_file <- "/tmp/knowledge.csv"
write.csv(original_df, file = csv_file, row.names = FALSE)
deserialized_df <- learn.csv(csv_file)
deserialized_df %>% dplyr::mutate(is_nan = is.nan(x)) %>% print()

##    x is_nan
## 1 NA  FALSE
## 2 NA  FALSE

One other correctness situation very a lot just like the one above was the truth that
"NA" and NA inside a string column of an R dataframe develop into indistinguishable
as soon as serialized in CSV format, as accurately identified in
this Github situation
by @caewok and others.

RDS to the rescue!

RDS format is likely one of the most generally used binary codecs for serializing R objects.
It’s described in some element in chapter 1, part 8 of
this doc.
Amongst benefits of the RDS format are effectivity and accuracy: it has a fairly
environment friendly implementation in base R, and helps all R knowledge varieties.

Additionally value noticing is the truth that when an R dataframe containing solely knowledge varieties
with wise equivalents in Apache Spark (e.g., RAWSXP, LGLSXP, CHARSXP, REALSXP, and many others)
is saved utilizing RDS model 2,
(e.g., serialize(mtcars, connection = NULL, model = 2L, xdr = TRUE)),
solely a tiny subset of the RDS format will likely be concerned within the serialization course of,
and implementing deserialization routines in Scala able to decoding such a restricted
subset of RDS constructs is in actual fact a fairly easy and simple job
(as proven in
right here
).

Final however not least, as a result of RDS is a binary format, it permits NA_character_, "NA",
NA_real_, and NaN to all be encoded in an unambiguous method, therefore permitting sparklyr
1.5 to keep away from all correctness points detailed above in non-arrow serialization use instances.

Different advantages of RDS serialization

Along with correctness ensures, RDS format additionally presents fairly just a few different benefits.

One benefit is in fact efficiency: for instance, importing a non-trivially-sized dataset
comparable to nycflights13::flights from R to Spark utilizing the RDS format in sparklyr 1.5 is
roughly 40%-50% quicker in comparison with CSV-based serialization in sparklyr 1.4. The
present RDS-based implementation remains to be nowhere as quick as arrow-based serialization
although (arrow is about 3-4x quicker), so for performance-sensitive duties involving
heavy serialization, arrow ought to nonetheless be the best choice.

One other benefit is that with RDS serialization, sparklyr can import R dataframes containing
uncooked columns straight into binary columns in Spark. Thus, use instances such because the one beneath
will work in sparklyr 1.5

Whereas most sparklyr customers in all probability received’t discover this functionality of importing binary columns
to Spark instantly helpful of their typical sparklyr::copy_to() or sparklyr::acquire()
usages, it does play a vital function in decreasing serialization overheads within the Spark-based
foreach parallel backend that
was first launched in sparklyr 1.2.
It’s because Spark staff can straight fetch the serialized R closures to be computed
from a binary Spark column as an alternative of extracting these serialized bytes from intermediate
representations comparable to base64-encoded strings.
Equally, the R outcomes from executing employee closures will likely be straight accessible in RDS
format which might be effectively deserialized in R, reasonably than being delivered in different
much less environment friendly codecs.

Acknowledgement

In chronological order, we wish to thank the next contributors for making their pull
requests a part of sparklyr 1.5:

We might additionally like to precise our gratitude in the direction of quite a few bug reviews and have requests for
sparklyr from a unbelievable open-source neighborhood.

Lastly, the writer of this weblog put up is indebted to
@javierluraschi,
@batpigandme,
and @skeydan for his or her beneficial editorial inputs.

Should you want to study extra about sparklyr, try sparklyr.ai,
spark.rstudio.com, and among the earlier launch posts comparable to
sparklyr 1.4 and
sparklyr 1.3.

Thanks for studying!