Greater-order Features, Avro and Customized Serializers

sparklyr 1.3 is now obtainable on CRAN, with the next main new options:

To put in sparklyr 1.3 from CRAN, run

On this put up, we will spotlight some main new options launched in sparklyr 1.3, and showcase eventualities the place such options turn out to be useful. Whereas numerous enhancements and bug fixes (particularly these associated to spark_apply(), Apache Arrow, and secondary Spark connections) have been additionally an necessary a part of this launch, they won’t be the subject of this put up, and it is going to be a simple train for the reader to search out out extra about them from the sparklyr NEWS file.

Greater-order Features

Greater-order features are built-in Spark SQL constructs that permit user-defined lambda expressions to be utilized effectively to advanced information varieties akin to arrays and structs. As a fast demo to see why higher-order features are helpful, let’s say in the future Scrooge McDuck dove into his enormous vault of cash and located massive portions of pennies, nickels, dimes, and quarters. Having an impeccable style in information constructions, he determined to retailer the portions and face values of every little thing into two Spark SQL array columns:


sc <- spark_connect(grasp = "native", model = "2.4.5")
coins_tbl <- copy_to(
    portions = listing(c(4000, 3000, 2000, 1000)),
    values = listing(c(1, 5, 10, 25))

Thus declaring his internet price of 4k pennies, 3k nickels, 2k dimes, and 1k quarters. To assist Scrooge McDuck calculate the entire worth of every sort of coin in sparklyr 1.3 or above, we will apply hof_zip_with(), the sparklyr equal of ZIP_WITH, to portions column and values column, combining pairs of parts from arrays in each columns. As you might need guessed, we additionally must specify find out how to mix these parts, and what higher strategy to accomplish that than a concise one-sided method   ~ .x * .y   in R, which says we would like (amount * worth) for every sort of coin? So, now we have the next:

result_tbl <- coins_tbl %>%
  hof_zip_with(~ .x * .y, dest_col = total_values) %>%

result_tbl %>% dplyr::pull(total_values)
[1]  4000 15000 20000 25000

With the outcome 4000 15000 20000 25000 telling us there are in whole $40 {dollars} price of pennies, $150 {dollars} price of nickels, $200 {dollars} price of dimes, and $250 {dollars} price of quarters, as anticipated.

Utilizing one other sparklyr operate named hof_aggregate(), which performs an AGGREGATE operation in Spark, we will then compute the web price of Scrooge McDuck based mostly on result_tbl, storing the end in a brand new column named whole. Discover for this combination operation to work, we have to make sure the beginning worth of aggregation has information sort (particularly, BIGINT) that’s per the info sort of total_values (which is ARRAY<BIGINT>), as proven under:

result_tbl %>%
  dplyr::mutate(zero = dplyr::sql("CAST (0 AS BIGINT)")) %>%
  hof_aggregate(begin = zero, ~ .x + .y, expr = total_values, dest_col = whole) %>%
  dplyr::choose(whole) %>%
[1] 64000

So Scrooge McDuck’s internet price is $640 {dollars}.

Different higher-order features supported by Spark SQL to this point embody rework, filter, and exists, as documented in right here, and much like the instance above, their counterparts (particularly, hof_transform(), hof_filter(), and hof_exists()) all exist in sparklyr 1.3, in order that they are often built-in with different dplyr verbs in an idiomatic method in R.


One other spotlight of the sparklyr 1.3 launch is its built-in assist for Avro information sources. Apache Avro is a extensively used information serialization protocol that mixes the effectivity of a binary information format with the pliability of JSON schema definitions. To make working with Avro information sources easier, in sparklyr 1.3, as quickly as a Spark connection is instantiated with spark_connect(..., bundle = "avro"), sparklyr will robotically determine which model of spark-avro bundle to make use of with that connection, saving lots of potential complications for sparklyr customers attempting to find out the proper model of spark-avro by themselves. Just like how spark_read_csv() and spark_write_csv() are in place to work with CSV information, spark_read_avro() and spark_write_avro() strategies have been carried out in sparklyr 1.3 to facilitate studying and writing Avro information via an Avro-capable Spark connection, as illustrated within the instance under:


# The `bundle = "avro"` possibility is simply supported in Spark 2.4 or greater
sc <- spark_connect(grasp = "native", model = "2.4.5", bundle = "avro")

sdf <- sdf_copy_to(
    a = c(1, NaN, 3, 4, NaN),
    b = c(-2L, 0L, 1L, 3L, 2L),
    c = c("a", "b", "c", "", "d")

# This instance Avro schema is a JSON string that basically says all columns
# ("a", "b", "c") of `sdf` are nullable.
avro_schema <- jsonlite::toJSON(listing(
  sort = "document",
  identify = "topLevelRecord",
  fields = listing(
    listing(identify = "a", sort = listing("double", "null")),
    listing(identify = "b", sort = listing("int", "null")),
    listing(identify = "c", sort = listing("string", "null"))
), auto_unbox = TRUE)

# persist the Spark information body from above in Avro format
spark_write_avro(sdf, "/tmp/information.avro", as.character(avro_schema))

# after which learn the identical information body again
spark_read_avro(sc, "/tmp/information.avro")
# Supply: spark<information> [?? x 3]
      a     b c
  <dbl> <int> <chr>
  1     1    -2 "a"
  2   NaN     0 "b"
  3     3     1 "c"
  4     4     3 ""
  5   NaN     2 "d"

Customized Serialization

Along with generally used information serialization codecs akin to CSV, JSON, Parquet, and Avro, ranging from sparklyr 1.3, custom-made information body serialization and deserialization procedures carried out in R can be run on Spark staff through the newly carried out spark_read() and spark_write() strategies. We will see each of them in motion via a fast instance under, the place saveRDS() known as from a user-defined author operate to save lots of all rows inside a Spark information body into 2 RDS information on disk, and readRDS() known as from a user-defined reader operate to learn the info from the RDS information again to Spark:


sc <- spark_connect(grasp = "native")
sdf <- sdf_len(sc, 7)
paths <- c("/tmp/file1.RDS", "/tmp/file2.RDS")

spark_write(sdf, author = operate(df, path) saveRDS(df, path), paths = paths)
spark_read(sc, paths, reader = operate(path) readRDS(path), columns = c(id = "integer"))
# Supply: spark<?> [?? x 1]
1     1
2     2
3     3
4     4
5     5
6     6
7     7

Different Enhancements


Sparklyr.flint is a sparklyr extension that goals to make functionalities from the Flint time-series library simply accessible from R. It’s presently beneath lively growth. One piece of excellent information is that, whereas the unique Flint library was designed to work with Spark 2.x, a barely modified fork of it’s going to work properly with Spark 3.0, and inside the present sparklyr extension framework. sparklyr.flint can robotically decide which model of the Flint library to load based mostly on the model of Spark it’s linked to. One other bit of excellent information is, as beforehand talked about, sparklyr.flint doesn’t know an excessive amount of about its personal future but. Perhaps you possibly can play an lively half in shaping its future!

EMR 6.0

This launch additionally encompasses a small however necessary change that permits sparklyr to appropriately connect with the model of Spark 2.4 that’s included in Amazon EMR 6.0.

Beforehand, sparklyr robotically assumed any Spark 2.x it was connecting to was constructed with Scala 2.11 and tried to load any required Scala artifacts constructed with Scala 2.11 as properly. This grew to become problematic when connecting to Spark 2.4 from Amazon EMR 6.0, which is constructed with Scala 2.12. Ranging from sparklyr 1.3, such downside could be mounted by merely specifying scala_version = "2.12" when calling spark_connect() (e.g., spark_connect(grasp = "yarn-client", scala_version = "2.12")).

Spark 3.0

Final however not least, it’s worthwhile to say sparklyr 1.3.0 is understood to be absolutely suitable with the not too long ago launched Spark 3.0. We extremely advocate upgrading your copy of sparklyr to 1.3.0 in case you plan to have Spark 3.0 as a part of your information workflow in future.


In chronological order, we wish to thank the next people for submitting pull requests in direction of sparklyr 1.3:

We’re additionally grateful for precious enter on the sparklyr 1.3 roadmap, #2434, and #2551 from [@javierluraschi](, and nice non secular recommendation on #1773 and #2514 from @mattpollock and @benmwhite.

Please word in case you imagine you might be lacking from the acknowledgement above, it might be as a result of your contribution has been thought of a part of the subsequent sparklyr launch fairly than half of the present launch. We do make each effort to make sure all contributors are talked about on this part. In case you imagine there’s a mistake, please be happy to contact the creator of this weblog put up through e-mail (yitao at rstudio dot com) and request a correction.

Should you want to study extra about sparklyr, we advocate visiting,, and a number of the earlier launch posts akin to sparklyr 1.2 and sparklyr 1.1.

Thanks for studying!