sparklyr 0.6: Distributed R and external sources
We’re excited to announce a new release of the sparklyr package, available in CRAN today! sparklyr 0.6 introduces new features to:
- Distribute R computations using
spark_apply()to execute arbitrary R code across your Spark cluster. You can now use all of your favorite R packages and functions in a distributed context. - Connect to External Data Sources using
spark_read_source(),spark_write_source(),spark_read_jdbc()andspark_write_jdbc(). - Use the Latest Frameworks including dplyr 0.7, DBI 0.7, RStudio 1.1 and Spark 2.2.
and several improvements across:
- Spark Connections add a new Databricks connection that enables using sparklyr in Databricks through
mode="databricks", add support for Yarn Cluster throughmaster="yarn-cluster"and connection speed was also improved. - Dataframes add support for
sdf_pivot(),sdf_broadcast(),cbind(),rbind(),sdf_separate_column(),sdf_bind_cols(),sdf_bind_rows(),sdf_repartition()andsdf_num_partitions(). - Machine Learning adds support for multinomial regression in
ml_logistic_regression(),weights.columnfor GLM,ml_model_data()and a newft_count_vectorizer()function forml_lda(). - Many other improvements, from initial support for broom over
ml_linear_regression()andml_generalized_linear_regression(), dplyr support for%like%,%rlike%and%regexp%, sparklyr extensions now supportdownload_scalac()to help you install the required Scala compilers while developing extensions, Hive database management got simplified withtbl_change_db()andsrc_databases()to query and switch between Hive databases. RStudio started a joint effort with Microsoft to support a cross-platform Spark installer under github.com/rstudio/spark-install.
Additional changes and improvements can be found in the sparklyr NEWS file.
Updated documentation and examples are available at spark.rstudio.com. For questions or feedback, please feel free to open a sparklyr github issue or a sparklyr stackoverflow question.
Distributed R##
sparklyr 0.6 provides support for executing distributed R code through spark_apply(). For instance, after connecting and copying some data:
library(sparklyr)
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris)
We can apply an arbitrary R function, say jitter(), to each column over each row as follows:
iris_tbl %>% spark_apply(function(e) sapply(e[,1:4], jitter))
# Source: spark<?> [?? x 4]
Sepal_Length Sepal_Width Petal_Length Petal_Width
<dbl> <dbl> <dbl> <dbl>
1 5.10 3.49 1.39 0.208
2 4.89 2.99 1.40 0.206
3 4.69 3.21 1.31 0.211
4 4.61 3.10 1.48 0.181
5 5.01 3.62 1.39 0.190
6 5.39 3.88 1.71 0.398
7 4.60 3.41 1.39 0.318
8 4.99 3.41 1.48 0.194
9 4.38 2.89 1.42 0.186
10 4.88 3.10 1.51 0.106
# … with more rows
One can also group by columns to apply an operation over each group of rows, say, to perform linear regression over each group as follows:
spark_apply(
iris_tbl,
function(e) broom::tidy(lm(Petal_Width ~ Petal_Length, e)),
names = c("term", "estimate", "std.error", "statistic", "p.value"),
group_by = "Species"
)
# Source: spark<?> [?? x 6]
Species term estimate std.error statistic p.value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 versicolor (Intercept) -0.0843 0.161 -0.525 6.02e- 1
2 versicolor Petal_Length 0.331 0.0375 8.83 1.27e-11
3 virginica (Intercept) 1.14 0.379 2.99 4.34e- 3
4 virginica Petal_Length 0.160 0.0680 2.36 2.25e- 2
5 setosa (Intercept) -0.0482 0.122 -0.396 6.94e- 1
6 setosa Petal_Length 0.201 0.0826 2.44 1.86e- 2
Packages can be used since they are automatically distributed to the worker nodes; however, using spark_apply() requires R to be installed over each worker node. Please refer to Distributing R Computations for additional information and examples.
External Data Sources
sparklyr 0.6 adds support for connecting Spark to databases. This feature is useful if your Spark environment is separate from your data environment, or if you use Spark to access multiple data sources. You can use spark_read_source(), spark_write_source with any data connector available in Spark Packages. Alternatively, you can use spark_read_jdbc() and spark_write_jdbc() and a JDBC driver with almost any data source.
For example, you can connect to Cassandra using spark_read_source(). Notice that the Cassandra connector version needs to match the Spark version as defined in their version compatibility section.
config <- spark_config()
config[["sparklyr.defaultPackages"]] <- c(
"datastax:spark-cassandra-connector:2.0.0-RC1-s_2.11")
sc <- spark_connect(master = "local", config = config)
spark_read_source(sc, "emp",
"org.apache.spark.sql.cassandra",
list(keyspace = "dev", table = "emp"))
To connect to MySQL, one can download the MySQL connector and use spark_read_jdbc() as follows:
config <- spark_config()
config$`sparklyr.shell.driver-class-path` <-
"~/Downloads/mysql-connector-java-5.1.41/mysql-connector-java-5.1.41-bin.jar"
sc <- spark_connect(master = "local", config = config)
spark_read_jdbc(sc, "person_jdbc", options = list(
url = "jdbc:mysql://localhost:3306/sparklyr",
user = "root", password = "<password>",
dbtable = "person"))
Notice that the Cassandra connector version needs to match the Spark version as defined in their version compatibility section. See also crassy, an sparklyr extension being developed to read data from Cassandra with ease.
Dataframe Functions
sparklyr 0.6 includes many improvements for working with DataFrames. Here are some additional highlights.
x_tbl <- sdf_copy_to(sc, data.frame(a = c(1,2,3), b = c(2,3,4)))
y_tbl <- sdf_copy_to(sc, data.frame(b = c(3,4,5), c = c("A","B","C")))
Pivoting DataFrames
It is now possible to pivot (i.e. cross tabulate) one or more columns using sdf_pivot().
sdf_pivot(y_tbl, b ~ c, list(b = "count"))
# Source: spark<?> [?? x 4]
b A B C
<dbl> <dbl> <dbl> <dbl>
1 4 NaN 1 NaN
2 3 1 NaN NaN
3 5 NaN NaN 1
Binding Rows and Columns
Binding DataFrames by rows and columns is supported through sdf_bind_rows() and sdf_bind_cols():
sdf_bind_rows(x_tbl, y_tbl)
# Source: spark<?> [?? x 3]
a b c
<dbl> <dbl> <chr>
1 1 2 NA
2 2 3 NA
3 3 4 NA
4 NaN 3 A
5 NaN 4 B
6 NaN 5 C
sdf_bind_cols(x_tbl, y_tbl)
# Source: spark<?> [?? x 4]
a b.x b.y c
<dbl> <dbl> <dbl> <chr>
1 1 2 3 A
2 3 4 5 C
3 2 3 4 B
Separating Columns
Separate lists into columns with ease. This is especially useful when working with model predictions that are returned as lists instead of scalars. In this example, each element in the probability column contains two items. We can use sdf_separate_column() to isolate the item that corresponds to the probability that vs equals one.
cars <- copy_to(sc, mtcars)
ml_logistic_regression(cars, vs ~ mpg) %>%
ml_predict(cars) %>%
sdf_separate_column("probability", list("P[vs=1]" = 2)) %>%
dplyr::select(`P[vs=1]`)
# Source: spark<?> [?? x 1]
`P[vs=1]`
<dbl>
1 0.551
2 0.551
3 0.727
4 0.593
5 0.313
6 0.261
7 0.0643
8 0.841
9 0.727
10 0.361
# … with more rows
Machine Learning
Multinomial Regression
sparklyr 0.6 adds support for multinomial regression for Spark 2.1.0 or higher:
iris_tbl %>%
ml_logistic_regression(Species ~ Sepal_Length + Sepal_Width)
Formula: Species ~ Sepal_Length + Sepal_Width
Coefficients:
(Intercept) Sepal_Length Sepal_Width
versicolor -201.6 73.19 -59.84
virginica -214.6 75.10 -59.43
setosa 416.2 -148.29 119.27
Improved Text Mining with LDA
ft_tokenizer() was introduced in sparklyr 0.5 but sparklyr 0.6 introduces ft_count_vectorizer() to simplify LDA:
library(janeaustenr)
lines_tbl <- sdf_copy_to(sc,austen_books()[c(1,3),])
lines_tbl %>%
ft_tokenizer("text", "tokens") %>%
ft_count_vectorizer("tokens", "features") %>%
ml_lda(features_col = "features", k = 4)
The vocabulary can be printed with:
ml_lda(lines_tbl, ~text, k = 4)$vocabulary
[1] "jane" "sense" "austen" "sensibility"
That’s all for now, disconnecting:
spark_disconnect(sc)
