sparklyr v1.0.1

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R Interface to Apache Spark

R interface to Apache Spark, a fast and general engine for big data processing, see <http://spark.apache.org>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.

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sparklyr: R interface for Apache Spark

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  • Install and connect to Spark using YARN, Mesos, Livy or Kubernetes.
  • Use dplyr to filter and aggregate Spark datasets and streams then bring them into R for analysis and visualization.
  • Use MLlib, H2O, XGBoost and GraphFrames to train models at scale in Spark.
  • Create interoperable machine learning pipelines and productionize them with MLeap.
  • Create extensions that call the full Spark API or run distributed R code to support new functionality.

Installation

You can install the sparklyr package from CRAN as follows:

install.packages("sparklyr")

You should also install a local version of Spark for development purposes:

library(sparklyr)
spark_install()

To upgrade to the latest version of sparklyr, run the following command and restart your r session:

install.packages("devtools")
devtools::install_github("rstudio/sparklyr")

If you use the RStudio IDE, you should also download the latest preview release of the IDE which includes several enhancements for interacting with Spark (see the RStudio IDE section below for more details).

Connecting to Spark

You can connect to both local instances of Spark as well as remote Spark clusters. Here we’ll connect to a local instance of Spark via the spark_connect function:

library(sparklyr)
sc <- spark_connect(master = "local")

The returned Spark connection (sc) provides a remote dplyr data source to the Spark cluster.

For more information on connecting to remote Spark clusters see the Deployment section of the sparklyr website.

Using dplyr

We can now use all of the available dplyr verbs against the tables within the cluster.

We’ll start by copying some datasets from R into the Spark cluster (note that you may need to install the nycflights13 and Lahman packages in order to execute this code):

install.packages(c("nycflights13", "Lahman"))
library(dplyr)
iris_tbl <- copy_to(sc, iris)
flights_tbl <- copy_to(sc, nycflights13::flights, "flights")
batting_tbl <- copy_to(sc, Lahman::Batting, "batting")
src_tbls(sc)
## [1] "batting" "flights" "iris"

To start with here’s a simple filtering example:

# filter by departure delay and print the first few records
flights_tbl %>% filter(dep_delay == 2)
## # Source: spark<?> [?? x 19]
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
##  1  2013     1     1      517            515         2      830
##  2  2013     1     1      542            540         2      923
##  3  2013     1     1      702            700         2     1058
##  4  2013     1     1      715            713         2      911
##  5  2013     1     1      752            750         2     1025
##  6  2013     1     1      917            915         2     1206
##  7  2013     1     1      932            930         2     1219
##  8  2013     1     1     1028           1026         2     1350
##  9  2013     1     1     1042           1040         2     1325
## 10  2013     1     1     1231           1229         2     1523
## # … with more rows, and 12 more variables: sched_arr_time <int>,
## #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>

Introduction to dplyr provides additional dplyr examples you can try. For example, consider the last example from the tutorial which plots data on flight delays:

delay <- flights_tbl %>% 
  group_by(tailnum) %>%
  summarise(count = n(), dist = mean(distance), delay = mean(arr_delay)) %>%
  filter(count > 20, dist < 2000, !is.na(delay)) %>%
  collect

# plot delays
library(ggplot2)
ggplot(delay, aes(dist, delay)) +
  geom_point(aes(size = count), alpha = 1/2) +
  geom_smooth() +
  scale_size_area(max_size = 2)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Window Functions

dplyr window functions are also supported, for example:

batting_tbl %>%
  select(playerID, yearID, teamID, G, AB:H) %>%
  arrange(playerID, yearID, teamID) %>%
  group_by(playerID) %>%
  filter(min_rank(desc(H)) <= 2 & H > 0)
## # Source:     spark<?> [?? x 7]
## # Groups:     playerID
## # Ordered by: playerID, yearID, teamID
##    playerID  yearID teamID     G    AB     R     H
##    <chr>      <int> <chr>  <int> <int> <int> <int>
##  1 aaronha01   1959 ML1      154   629   116   223
##  2 aaronha01   1963 ML1      161   631   121   201
##  3 abadfe01    2012 HOU       37     7     0     1
##  4 abbated01   1905 BSN      153   610    70   170
##  5 abbated01   1904 BSN      154   579    76   148
##  6 abbeych01   1894 WAS      129   523    95   164
##  7 abbeych01   1895 WAS      132   511   102   141
##  8 abbotji01   1999 MIL       20    21     0     2
##  9 abnersh01   1992 CHA       97   208    21    58
## 10 abnersh01   1990 SDN       91   184    17    45
## # … with more rows

For additional documentation on using dplyr with Spark see the dplyr section of the sparklyr website.

Using SQL

It’s also possible to execute SQL queries directly against tables within a Spark cluster. The spark_connection object implements a DBI interface for Spark, so you can use dbGetQuery to execute SQL and return the result as an R data frame:

library(DBI)
iris_preview <- dbGetQuery(sc, "SELECT * FROM iris LIMIT 10")
iris_preview
##    Sepal_Length Sepal_Width Petal_Length Petal_Width Species
## 1           5.1         3.5          1.4         0.2  setosa
## 2           4.9         3.0          1.4         0.2  setosa
## 3           4.7         3.2          1.3         0.2  setosa
## 4           4.6         3.1          1.5         0.2  setosa
## 5           5.0         3.6          1.4         0.2  setosa
## 6           5.4         3.9          1.7         0.4  setosa
## 7           4.6         3.4          1.4         0.3  setosa
## 8           5.0         3.4          1.5         0.2  setosa
## 9           4.4         2.9          1.4         0.2  setosa
## 10          4.9         3.1          1.5         0.1  setosa

Machine Learning

You can orchestrate machine learning algorithms in a Spark cluster via the machine learning functions within sparklyr. These functions connect to a set of high-level APIs built on top of DataFrames that help you create and tune machine learning workflows.

Here’s an example where we use ml_linear_regression to fit a linear regression model. We’ll use the built-in mtcars dataset, and see if we can predict a car’s fuel consumption (mpg) based on its weight (wt), and the number of cylinders the engine contains (cyl). We’ll assume in each case that the relationship between mpg and each of our features is linear.

# copy mtcars into spark
mtcars_tbl <- copy_to(sc, mtcars)

# transform our data set, and then partition into 'training', 'test'
partitions <- mtcars_tbl %>%
  filter(hp >= 100) %>%
  mutate(cyl8 = cyl == 8) %>%
  sdf_partition(training = 0.5, test = 0.5, seed = 1099)

# fit a linear model to the training dataset
fit <- partitions$training %>%
  ml_linear_regression(response = "mpg", features = c("wt", "cyl"))
fit
## Formula: mpg ~ wt + cyl
## 
## Coefficients:
## (Intercept)          wt         cyl 
##   33.499452   -2.818463   -0.923187

For linear regression models produced by Spark, we can use summary() to learn a bit more about the quality of our fit, and the statistical significance of each of our predictors.

summary(fit)
## Deviance Residuals:
##    Min     1Q Median     3Q    Max 
## -1.752 -1.134 -0.499  1.296  2.282 
## 
## Coefficients:
## (Intercept)          wt         cyl 
##   33.499452   -2.818463   -0.923187 
## 
## R-Squared: 0.8274
## Root Mean Squared Error: 1.422

Spark machine learning supports a wide array of algorithms and feature transformations and as illustrated above it’s easy to chain these functions together with dplyr pipelines. To learn more see the machine learning section.

Reading and Writing Data

You can read and write data in CSV, JSON, and Parquet formats. Data can be stored in HDFS, S3, or on the local filesystem of cluster nodes.

temp_csv <- tempfile(fileext = ".csv")
temp_parquet <- tempfile(fileext = ".parquet")
temp_json <- tempfile(fileext = ".json")

spark_write_csv(iris_tbl, temp_csv)
iris_csv_tbl <- spark_read_csv(sc, "iris_csv", temp_csv)

spark_write_parquet(iris_tbl, temp_parquet)
iris_parquet_tbl <- spark_read_parquet(sc, "iris_parquet", temp_parquet)

spark_write_json(iris_tbl, temp_json)
iris_json_tbl <- spark_read_json(sc, "iris_json", temp_json)

src_tbls(sc)
## [1] "batting"      "flights"      "iris"         "iris_csv"    
## [5] "iris_json"    "iris_parquet" "mtcars"

Distributed R

You can execute arbitrary r code across your cluster using spark_apply. For example, we can apply rgamma over iris as follows:

spark_apply(iris_tbl, function(data) {
  data[1:4] + rgamma(1,2)
})
## # Source: spark<?> [?? x 4]
##    Sepal_Length Sepal_Width Petal_Length Petal_Width
##           <dbl>       <dbl>        <dbl>       <dbl>
##  1         6.90        5.30         3.20        2.00
##  2         6.70        4.80         3.20        2.00
##  3         6.50        5.00         3.10        2.00
##  4         6.40        4.90         3.30        2.00
##  5         6.80        5.40         3.20        2.00
##  6         7.20        5.70         3.50        2.20
##  7         6.40        5.20         3.20        2.10
##  8         6.80        5.20         3.30        2.00
##  9         6.20        4.70         3.20        2.00
## 10         6.70        4.90         3.30        1.90
## # … with more rows

You can also group by columns to perform an operation over each group of rows and make use of any package within the closure:

spark_apply(
  iris_tbl,
  function(e) broom::tidy(lm(Petal_Width ~ Petal_Length, e)),
  columns = 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

Extensions

The facilities used internally by sparklyr for its dplyr and machine learning interfaces are available to extension packages. Since Spark is a general purpose cluster computing system there are many potential applications for extensions (e.g. interfaces to custom machine learning pipelines, interfaces to 3rd party Spark packages, etc.).

Here’s a simple example that wraps a Spark text file line counting function with an R function:

# write a CSV 
tempfile <- tempfile(fileext = ".csv")
write.csv(nycflights13::flights, tempfile, row.names = FALSE, na = "")

# define an R interface to Spark line counting
count_lines <- function(sc, path) {
  spark_context(sc) %>% 
    invoke("textFile", path, 1L) %>% 
      invoke("count")
}

# call spark to count the lines of the CSV
count_lines(sc, tempfile)
## [1] 336777

To learn more about creating extensions see the Extensions section of the sparklyr website.

Table Utilities

You can cache a table into memory with:

tbl_cache(sc, "batting")

and unload from memory using:

tbl_uncache(sc, "batting")

Connection Utilities

You can view the Spark web console using the spark_web function:

spark_web(sc)

You can show the log using the spark_log function:

spark_log(sc, n = 10)
## 19/02/22 14:13:08 INFO ContextCleaner: Cleaned shuffle 18
## 19/02/22 14:13:08 INFO ContextCleaner: Cleaned accumulator 1860
## 19/02/22 14:13:08 INFO ContextCleaner: Cleaned accumulator 1907
## 19/02/22 14:13:08 INFO ContextCleaner: Cleaned accumulator 613
## 19/02/22 14:13:08 INFO ContextCleaner: Cleaned accumulator 1626
## 19/02/22 14:13:08 INFO Executor: Finished task 0.0 in stage 70.0 (TID 94). 875 bytes result sent to driver
## 19/02/22 14:13:08 INFO TaskSetManager: Finished task 0.0 in stage 70.0 (TID 94) in 209 ms on localhost (executor driver) (1/1)
## 19/02/22 14:13:08 INFO TaskSchedulerImpl: Removed TaskSet 70.0, whose tasks have all completed, from pool 
## 19/02/22 14:13:08 INFO DAGScheduler: ResultStage 70 (count at NativeMethodAccessorImpl.java:0) finished in 0.215 s
## 19/02/22 14:13:08 INFO DAGScheduler: Job 47 finished: count at NativeMethodAccessorImpl.java:0, took 0.220383 s

Finally, we disconnect from Spark:

  spark_disconnect(sc)
## NULL

RStudio IDE

The latest RStudio Preview Release of the RStudio IDE includes integrated support for Spark and the sparklyr package, including tools for:

  • Creating and managing Spark connections
  • Browsing the tables and columns of Spark DataFrames
  • Previewing the first 1,000 rows of Spark DataFrames

Once you’ve installed the sparklyr package, you should find a new Spark pane within the IDE. This pane includes a New Connection dialog which can be used to make connections to local or remote Spark instances:

Once you’ve connected to Spark you’ll be able to browse the tables contained within the Spark cluster and preview Spark DataFrames using the standard RStudio data viewer:

You can also connect to Spark through Livy through a new connection dialog:

The RStudio IDE features for sparklyr are available now as part of the RStudio Preview Release.

Using H2O

rsparkling is a CRAN package from H2O that extends sparklyr to provide an interface into Sparkling Water. For instance, the following example installs, configures and runs h2o.glm:

library(rsparkling)
library(sparklyr)
library(dplyr)
library(h2o)

sc <- spark_connect(master = "local", version = "2.3.2")
mtcars_tbl <- copy_to(sc, mtcars, "mtcars")

mtcars_h2o <- as_h2o_frame(sc, mtcars_tbl, strict_version_check = FALSE)

mtcars_glm <- h2o.glm(x = c("wt", "cyl"), 
                      y = "mpg",
                      training_frame = mtcars_h2o,
                      lambda_search = TRUE)
mtcars_glm
## Model Details:
## ==============
## 
## H2ORegressionModel: glm
## Model ID:  GLM_model_R_1527265202599_1 
## GLM Model: summary
##     family     link                              regularization
## 1 gaussian identity Elastic Net (alpha = 0.5, lambda = 0.1013 )
##                                                                lambda_search
## 1 nlambda = 100, lambda.max = 10.132, lambda.min = 0.1013, lambda.1se = -1.0
##   number_of_predictors_total number_of_active_predictors
## 1                          2                           2
##   number_of_iterations                                training_frame
## 1                  100 frame_rdd_31_ad5c4e88ec97eb8ccedae9475ad34e02
## 
## Coefficients: glm coefficients
##       names coefficients standardized_coefficients
## 1 Intercept    38.941654                 20.090625
## 2       cyl    -1.468783                 -2.623132
## 3        wt    -3.034558                 -2.969186
## 
## H2ORegressionMetrics: glm
## ** Reported on training data. **
## 
## MSE:  6.017684
## RMSE:  2.453097
## MAE:  1.940985
## RMSLE:  0.1114801
## Mean Residual Deviance :  6.017684
## R^2 :  0.8289895
## Null Deviance :1126.047
## Null D.o.F. :31
## Residual Deviance :192.5659
## Residual D.o.F. :29
## AIC :156.2425
spark_disconnect(sc)

Connecting through Livy

Livy enables remote connections to Apache Spark clusters. Connecting to Spark clusters through Livy is under experimental development in sparklyr. Please post any feedback or questions as a GitHub issue as needed.

Before connecting to Livy, you will need the connection information to an existing service running Livy. Otherwise, to test livy in your local environment, you can install it and run it locally as follows:

livy_install(version = "2.4.0")
livy_service_start()

To connect, use the Livy service address as master and method = "livy" in spark_connect. Once connection completes, use sparklyr as usual, for instance:

sc <- spark_connect(master = "http://localhost:8998", method = "livy", version = "2.4.0")
copy_to(sc, iris)
## # Source: spark<iris> [?? x 5]
##    Sepal_Length Sepal_Width Petal_Length Petal_Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa 
## # … with more rows
spark_disconnect(sc)
## NULL

Once you are done using livy locally, you should stop this service with:

livy_service_stop()

To connect to remote livy clusters that support basic authentication connect as:

config <- livy_config(username="<username>", password="<password>")
sc <- spark_connect(master = "<address>", method = "livy", config = config)
spark_disconnect(sc)

Functions in sparklyr

Name Description
connection_config Read configuration values for a connection
checkpoint_directory Set/Get Spark checkpoint directory
DBISparkResult-class DBI Spark Result.
compile_package_jars Compile Scala sources into a Java Archive (jar)
connection_is_open Check whether the connection is open
ft_dct Feature Transformation -- Discrete Cosine Transform (DCT) (Transformer)
ft_hashing_tf Feature Transformation -- HashingTF (Transformer)
ft_ngram Feature Transformation -- NGram (Transformer)
ft_elementwise_product Feature Transformation -- ElementwiseProduct (Transformer)
ft_one_hot_encoder Feature Transformation -- OneHotEncoder (Transformer)
ft_normalizer Feature Transformation -- Normalizer (Transformer)
ft_lsh Feature Transformation -- LSH (Estimator)
ft_one_hot_encoder_estimator Feature Transformation -- OneHotEncoderEstimator (Estimator)
ft_regex_tokenizer Feature Transformation -- RegexTokenizer (Transformer)
ensure Enforce Specific Structure for R Objects
livy_service_start Start Livy
ft_lsh_utils Utility functions for LSH models
copy_to.spark_connection Copy an R Data Frame to Spark
jobj_class Superclasses of object
find_scalac Discover the Scala Compiler
jobj_set_param Parameter Setting for JVM Objects
download_scalac Downloads default Scala Compilers
ml-constructors Constructors for Pipeline Stages
connection_spark_shinyapp A Shiny app that can be used to construct a spark_connect statement
ml_call_constructor Wrap a Spark ML JVM object
ft_chisq_selector Feature Transformation -- ChiSqSelector (Estimator)
ml_feature_importances Spark ML - Feature Importance for Tree Models
ft_index_to_string Feature Transformation -- IndexToString (Transformer)
copy_to Copy To
ml_fpgrowth Frequent Pattern Mining -- FPGrowth
ft_interaction Feature Transformation -- Interaction (Transformer)
ft_standard_scaler Feature Transformation -- StandardScaler (Estimator)
ft_pca Feature Transformation -- PCA (Estimator)
ft_count_vectorizer Feature Transformation -- CountVectorizer (Estimator)
ft_idf Feature Transformation -- IDF (Estimator)
ft_imputer Feature Transformation -- Imputer (Estimator)
ft_stop_words_remover Feature Transformation -- StopWordsRemover (Transformer)
ft_polynomial_expansion Feature Transformation -- PolynomialExpansion (Transformer)
ft_tokenizer Feature Transformation -- Tokenizer (Transformer)
ft_word2vec Feature Transformation -- Word2Vec (Estimator)
ml_chisquare_test Chi-square hypothesis testing for categorical data.
hive_context_config Runtime configuration interface for Hive
ml_glm_tidiers Tidying methods for Spark ML linear models
ml_gbt_classifier Spark ML -- Gradient Boosted Trees
ft_string_indexer Feature Transformation -- StringIndexer (Estimator)
ft_vector_assembler Feature Transformation -- VectorAssembler (Transformer)
ft_vector_indexer Feature Transformation -- VectorIndexer (Estimator)
ml_model_data Extracts data associated with a Spark ML model
ml_lda_tidiers Tidying methods for Spark ML LDA models
ml-params Spark ML -- ML Params
ml-model-constructors Constructors for `ml_model` Objects
livy_config Create a Spark Configuration for Livy
ft_vector_slicer Feature Transformation -- VectorSlicer (Transformer)
ml_aft_survival_regression Spark ML -- Survival Regression
ml_multilayer_perceptron_classifier Spark ML -- Multilayer Perceptron
ft_binarizer Feature Transformation -- Binarizer (Transformer)
ft_bucketizer Feature Transformation -- Bucketizer (Transformer)
ft_max_abs_scaler Feature Transformation -- MaxAbsScaler (Estimator)
ml-persistence Spark ML -- Model Persistence
ml-transform-methods Spark ML -- Transform, fit, and predict methods (ml_ interface)
ml_linear_regression Spark ML -- Linear Regression
ft_min_max_scaler Feature Transformation -- MinMaxScaler (Estimator)
livy_install Install Livy
ml_als Spark ML -- ALS
ml_gaussian_mixture Spark ML -- Gaussian Mixture clustering.
ml_multilayer_perceptron_tidiers Tidying methods for Spark ML MLP
ft_quantile_discretizer Feature Transformation -- QuantileDiscretizer (Estimator)
ml_als_tidiers Tidying methods for Spark ML ALS
ml_bisecting_kmeans Spark ML -- Bisecting K-Means Clustering
ft_r_formula Feature Transformation -- RFormula (Estimator)
invoke Invoke a Method on a JVM Object
ml_clustering_evaluator Spark ML - Clustering Evaluator
ml_generalized_linear_regression Spark ML -- Generalized Linear Regression
ml_corr Compute correlation matrix
ml_isotonic_regression Spark ML -- Isotonic Regression
ml_naive_bayes Spark ML -- Naive-Bayes
ml_kmeans Spark ML -- K-Means Clustering
ml_isotonic_regression_tidiers Tidying methods for Spark ML Isotonic Regression
invoke_method Generic Call Interface
ml_pca_tidiers Tidying methods for Spark ML Principal Component Analysis
na.replace Replace Missing Values in Objects
ml_lda Spark ML -- Latent Dirichlet Allocation
ml_logistic_regression Spark ML -- Logistic Regression
%>% Pipe operator
ml_naive_bayes_tidiers Tidying methods for Spark ML Naive Bayes
ml-tuning Spark ML -- Tuning
ml_one_vs_rest Spark ML -- OneVsRest
sdf-transform-methods Spark ML -- Transform, fit, and predict methods (sdf_ interface)
ml_logistic_regression_tidiers Tidying methods for Spark ML Logistic Regression
ml_random_forest_classifier Spark ML -- Random Forest
ml_add_stage Add a Stage to a Pipeline
sdf_collect Collect a Spark DataFrame into R.
ml_standardize_formula Standardize Formula Input for `ml_model`
ml_stage Spark ML -- Pipeline stage extraction
sdf_copy_to Copy an Object into Spark
ml_uid Spark ML -- UID
ml_summary Spark ML -- Extraction of summary metrics
sdf_crosstab Cross Tabulation
ml_survival_regression_tidiers Tidying methods for Spark ML Survival Regression
ml_decision_tree_classifier Spark ML -- Decision Trees
ml_default_stop_words Default stop words
ml_pipeline Spark ML -- Pipelines
ml_tree_tidiers Tidying methods for Spark ML tree models
sdf_checkpoint Checkpoint a Spark DataFrame
sdf_debug_string Debug Info for Spark DataFrame
print_jobj Generic method for print jobj for a connection type
sdf_along Create DataFrame for along Object
ml_evaluate Evaluate the Model on a Validation Set
random_string Random string generation
ml_unsupervised_tidiers Tidying methods for Spark ML unsupervised models
sdf_bind Bind multiple Spark DataFrames by row and column
register_extension Register a Package that Implements a Spark Extension
sdf_coalesce Coalesces a Spark DataFrame
sdf_repartition Repartition a Spark DataFrame
sdf_last_index Returns the last index of a Spark DataFrame
ml_evaluator Spark ML - Evaluators
sdf_broadcast Broadcast hint
sdf_num_partitions Gets number of partitions of a Spark DataFrame
sdf_len Create DataFrame for Length
sdf_persist Persist a Spark DataFrame
spark_session_config Runtime configuration interface for the Spark Session
spark_connection-class spark_connection class
spark_extension Create Spark Extension
sdf_read_column Read a Column from a Spark DataFrame
sdf_register Register a Spark DataFrame
sdf-saveload Save / Load a Spark DataFrame
spark_get_java Find path to Java
sdf_describe Compute summary statistics for columns of a data frame
sdf_dim Support for Dimension Operations
sdf_sort Sort a Spark DataFrame
sdf_residuals.ml_model_generalized_linear_regression Model Residuals
ml_linear_svc Spark ML -- LinearSVC
sdf_sample Randomly Sample Rows from a Spark DataFrame
spark_read_table Reads from a Spark Table into a Spark DataFrame.
ml_linear_svc_tidiers Tidying methods for Spark ML linear svc
reactiveSpark Reactive spark reader
spark_read_text Read a Text file into a Spark DataFrame
sdf_quantile Compute (Approximate) Quantiles with a Spark DataFrame
sdf_sql Spark DataFrame from SQL
reexports Objects exported from other packages
sdf_random_split Partition a Spark Dataframe
sdf_schema Read the Schema of a Spark DataFrame
sdf_separate_column Separate a Vector Column into Scalar Columns
spark_compile Compile Scala sources into a Java Archive
spark_apply Apply an R Function in Spark
spark-api Access the Spark API
spark_write_parquet Write a Spark DataFrame to a Parquet file
spark_apply_bundle Create Bundle for Spark Apply
spark-connections Manage Spark Connections
spark_config Read Spark Configuration
spark_write_source Writes a Spark DataFrame into a generic source
spark_config_exists A helper function to check value exist under spark_config()
spark_config_kubernetes Kubernetes Configuration
sdf_fast_bind_cols Fast cbind for Spark DataFrames
spark_connection Retrieve the Spark Connection Associated with an R Object
spark_load_table Reads from a Spark Table into a Spark DataFrame.
spark_connection_find Find Spark Connection
spark_home_dir Find the SPARK_HOME directory for a version of Spark
stream_id Spark Stream's Identifier
sdf_is_streaming Spark DataFrame is Streaming
spark_log View Entries in the Spark Log
spark_default_compilation_spec Default Compilation Specification for Spark Extensions
spark_home_set Set the SPARK_HOME environment variable
spark_save_table Saves a Spark DataFrame as a Spark table
stream_name Spark Stream's Name
spark_default_version determine the version that will be used by default if version is NULL
sdf_pivot Pivot a Spark DataFrame
spark_install_find Find a given Spark installation by version.
sdf_project Project features onto principal components
spark_table_name Generate a Table Name from Expression
sdf_seq Create DataFrame for Range
stream_stats Stream Statistics
stream_read_csv Read CSV Stream
spark_config_settings Retrieve Available Settings
sdf_with_sequential_id Add a Sequential ID Column to a Spark DataFrame
spark_read_libsvm Read libsvm file into a Spark DataFrame.
spark_read_orc Read a ORC file into a Spark DataFrame
spark_install_sync helper function to sync sparkinstall project to sparklyr
spark_config_value A helper function to retrieve values from spark_config()
spark_read_parquet Read a Parquet file into a Spark DataFrame
spark_write_json Write a Spark DataFrame to a JSON file
spark_read_source Read from a generic source into a Spark DataFrame.
stream_read_json Read JSON Stream
spark_write_csv Write a Spark DataFrame to a CSV
spark_write_jdbc Writes a Spark DataFrame into a JDBC table
spark_write_orc Write a Spark DataFrame to a ORC file
spark_context_config Runtime configuration interface for the Spark Context.
stream_read_text Read Text Stream
stream_render Render Stream
sdf_with_unique_id Add a Unique ID Column to a Spark DataFrame
tbl_uncache Uncache a Spark Table
spark_dataframe Retrieve a Spark DataFrame
spark_write_table Writes a Spark DataFrame into a Spark table
ft_sql_transformer Feature Transformation -- SQLTransformer
spark_apply_log Log Writer for Spark Apply
spark_pipeline_stage Create a Pipeline Stage Object
spark_compilation_spec Define a Spark Compilation Specification
worker_spark_apply_unbundle Extracts a bundle of dependencies required by spark_apply()
spark_dependency Define a Spark dependency
stream_write_json Write JSON Stream
src_databases Show database list
stream_write_kafka Write Kafka Stream
spark_dependency_fallback Fallback to Spark Dependency
stream_stop Stops a Spark Stream
spark_write_text Write a Spark DataFrame to a Text file
spark_read_csv Read a CSV file into a Spark DataFrame
spark_read_jdbc Read from JDBC connection into a Spark DataFrame.
spark_read_json Read a JSON file into a Spark DataFrame
spark_jobj-class spark_jobj class
spark_versions Retrieves a dataframe available Spark versions that van be installed.
stream_write_console Write Console Stream
stream_write_csv Write CSV Stream
spark_jobj Retrieve a Spark JVM Object Reference
spark_version Get the Spark Version Associated with a Spark Connection
spark_web Open the Spark web interface
stream_trigger_continuous Spark Stream Continuous Trigger
stream_trigger_interval Spark Stream Interval Trigger
stream_write_parquet Write Parquet Stream
stream_find Find Stream
spark_version_from_home Get the Spark Version Associated with a Spark Installation
stream_read_kafka Read Kafka Stream
stream_read_orc Read ORC Stream
stream_write_text Write Text Stream
stream_generate_test Generate Test Stream
stream_view View Stream
stream_watermark Watermark Stream
stream_read_parquet Read Parquet Stream
stream_read_scoket Read Socket Stream
stream_write_memory Write Memory Stream
stream_write_orc Write a ORC Stream
tbl_cache Cache a Spark Table
tbl_change_db Use specific database
collect Collect
ft_feature_hasher Feature Transformation -- FeatureHasher (Transformer)
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